Method for generating a biodegradation curve associated with a chemical substance

Abstract

The disclosure refers to a method for generating a biodegradation curve associated with a chemical substance, wherein a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time. Property data associated with one or more chemical and / or physical properties of the chemical substance is received. A biodegradation model is provided based on the property data. The biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process. The biodegradation model is applied to the property data of the chemical substance as input and the biodegradation curve is received as biodegradation model output. The biodegradation curve is provided for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

Description

[0001] 231051W001

[0002] 1

[0003] METHOD FOR GENERATING A BIODEGRADATION CURVE ASSOCIATED WITH A CHEMICAL

[0004] SUBSTANCE

[0005] TECHNICAL FIELD

[0006] The disclosure relates to a method, an apparatus and a computer program product for generating a biodegradation curve associated with a chemical substance, wherein a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time. Further, the disclosure refers to control data generated utilizing the method for generating a biodegradation curve associated with a chemical substance. Moreover, the disclosure refers to the use of control data generated utilizing the method for generating a biodegradation curve associated with a chemical substance.

[0007] TECHNICAL BACKGROUND

[0008] Nowadays, one of the major tasks in modern chemical industries is the development of new chemical products that are tailored to specific application requirements and at the same time have a small environmental impact. Chemical products that are made from biodegradable materials offer a way to reduce the environmental impact. However, the current methods for assessing the overall biodegradability of a new product are costly, time-consuming and resource intensive.

[0009] SUMMARY OF THE INVENTION

[0010] The development of new chemical products that are tailored to application requirements is a predominant problem in modern chemical industries and additionally needs to take into account the environmental impact of the chemical product along its lifecycle. One important aspect of the environmental impact is prevention of waste contamination and accumulation in the environment. This increasing problem can be avoided if the chemical substance is biodegradable. For the evaluation of the biodegradation, a series of standardized tests is currently used. The variety of tests with specified conditions, for example, ISO13432, ISO14852, ISO14855, ISO17556 and OECD 301 , often strike a balance between a time-efficient testing and real-life conditions. For example, higher temperatures than real conditions are often used to speed up the testing time. The testing may still require a considerable amount of time, typically between 14 days and 24 months. Companies developing new chemical substances need to invest significant resources in selfassessing product sustainability and in certification. The overall biodegradability assessment including laboratory spaces and equipment becomes costly, time-consuming and resource intensive. Thus, there is a need to early in the development process identify the biodegradability of a new chemical substance to reduce the development cost and time. It would be advantageous to provide a possibility that is accurate, fast and consumes less resources to generate information regarding the biodegradability of a new chemical substance during an early stage of the development process.

[0011] Furthermore, in some application cases, the overall biodegradability is not sufficient to determine if the chemical substance is suited for the application. An example may be mulch films made from polymers for covering agricultural fields that are supposed to biodegrade in the field after use. Such films may be used to cover strawberry fields or asparagus 231051W001

[0012] 2 fields. The objective then is that the biodegradable mulch film stays stable while it is still used for covering the field. After the intended time of use, however, a fast biodegradation is desirable as well as a high degree of biodegradability. Such application requirements make it necessary to perform extensive measurements that extend over a long time period, for example, several months. Thus, it would be advantageous to provide a possibility to generate, during an early stage of the development process, information regarding the biodegradation of a new chemical substance with time that is accurate, fast and consumes less resources. Moreover, it would be advantageous to provide a possibility to generate, during an early stage of the development process, information regarding the biodegradation of a new chemical substance that allows for experimentally measuring the biodegradation of the new chemical substance with time in an accurate, fast and inexpensive manner.

[0013] It is an object of the present disclosure to provide a method, an apparatus and a computer program product that allow generating a biodegradation curve associated with a chemical substance, wherein a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time, in an accurate, fast and inexpensive manner. It is further an object of the disclosure to provide control data generated utilizing the method for generating a biodegradation curve associated with a chemical substance and the use of control data generated utilizing the method for generating a biodegradation curve associated with a chemical substance for controlling and / or monitoring a biodegradation experiment for determining the biodegradation of a chemical substance in an accurate, fast and inexpensive manner.

[0014] In a first aspect is disclosed a method, in particular, a computer implemented method, is presented for generating a biodegradation curve associated with a chemical substance, wherein a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time, wherein the method comprises A) receiving, at a computer interface, property data associated with one or more chemical and / or physical properties of the chemical substance, B) providing a biodegradation model based on the property data, wherein the biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process and wherein one or more trainable parameters of the biodegradation model are parameterized based on a historical training dataset, wherein the historical training dataset comprises a) measured degradation curves of at least one chemical substance, and b) respective property data of the at least one chemical substance, wherein the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based on property data of the chemical substance as input, C) generating the biodegradation curve based on the biodegradation model and the property data, and D) providing the generated biodegradation curve for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

[0015] In a further aspect is disclosed an apparatus for generating a biodegradation curve associated with a chemical substance, wherein the apparatus comprises A) an input interface configured to i) receive property data associated with one or more chemical and / or physical properties of the chemical substance, ii) provide a biodegradation model based on the property data, wherein the biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual 231051W001

[0016] 3 substances during the biodegradation process and wherein one or more trainable parameters of the biodegradation model are parameterized based on a historical training dataset, wherein the historical training dataset comprises a) measured degradation curves of at least one chemical substance, and b) respective property data of the at least one chemical substance, wherein the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based on property data of the chemical substance as input, B) a processor configured to generate the biodegradation curve based on the biodegradation model and the property data, and C) an output interface configured to provide the generated biodegradation curve for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

[0017] In a further aspect is disclosed a computer program product for generating a biodegradation curve associated with a chemical substance, wherein the computer program product comprises program code means for causing a computing system to execute the method for generating a biodegradation curve associated with a chemical substance as disclosed herein.

[0018] In a further aspect is disclosed control data generated utilizing the method for generating a biodegradation curve associated with a chemical substance as disclosed.

[0019] In a further aspect is disclosed use of control data generated utilizing the method for generating a biodegradation curve associated with a chemical substance as disclosed herein for controlling and / or monitoring a biodegradation experiment for determining the biodegradation of a chemical substance.

[0020] In a further aspect is disclosed a method, in particular, a computer implemented method, for testing chemical substances with respect to a biodegradation, wherein the method comprises a) receiving, at a computer interface, property data associated with one or more chemical and / or physical properties of one or more candidate chemical substances, b) providing a biodegradation model based on the property data, wherein the biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process and wherein one or more trainable parameters of the biodegradation model are parameterized based on a historical training dataset, wherein the historical training dataset comprises a) measured degradation curves of at least one chemical substance, and b) respective property data of the at least one chemical substance, wherein the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based on property data of the chemical substance as input, c) generating a biodegradation curve for the one or more candidate chemical substances based on the biodegradation model and the property data, respectively, and providing, based on the generated biodegradation curves, digital representations associated with one or more chemical substances for further testing with respect to one or more application goals.

[0021] Since the biodegradation model is specifically adapted to generate a biodegradation curve associated with a chemical substance in a habitat based on property data associated with one or more chemical and / or physical properties of the 231051W001

[0022] 4 chemical substance, the biodegradation curve associated with the chemical substance can be determined very accurately. Furthermore, since the biodegradation model has specifically been parameterized based on a historical training dataset comprising measured degradation curves of at least one chemical substance and respective property data of the at least one chemical substance, less training data becomes necessary for the training and the biodegradation model becomes more flexible with respect to determining the biodegradation curves associated with new chemical substances not being part of the training data set. Thus, an accurate generation of the biodegradation curve associated with a chemical substance that is computationally inexpensive is provided.

[0023] Since the biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat, and since the biodegradation curve can be generated accurately and in a computationally inexpensive manner, also the biodegradation of the chemical substance can be determined accurately and in a computationally inexpensive manner. Currently utilized test methods for testing the biodegradation of a chemical substance are extremely time consuming and can take months or years to get results, whereas the above-described method allows providing results essentially immediately. Thus, not only the technical requirements for assessing biodegradability can be reduced, but also the time required for designing a new biodegradable product can be considerably shortened.

[0024] Since the biodegradation model is based on a kinetic model associated with a mass transfer from the chemical substance to biomass and one or more residual substances, the experiments necessary to generate the historic training dataset can be carried out with well-established and straightforward experimental methods that allow saving tests and resources for providing the historic training dataset. Furthermore, the biodegradation model is parameterized based on property data associated with one or more chemical and / or physical properties of the chemical substance. Utilizing the chemical and / or physical characteristics allows training of a biodegradation model with less training data, because some of the correlation information that needs to be learned is already presented to the model by using the chemical and / or physical characteristics. This allows to further save tests and experiments necessary for providing the historical training dataset.

[0025] Moreover, providing an easy possibility for generating information regarding the biodegradability of a new chemical substance already during the design process of the new chemical substance allows to design the chemical substance such that waste contamination and accumulation in the environment can be avoided. In particular, it can be ensured that a new chemical substance used in a product will biodegrade in a respectively expected environment, for example, in a marine habitat. Since the biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time, the biodegradation model further allows to design a new chemical substance specifically tailored for solving a particular technical task, for example, resisting biodegradation in a given habitat for a predetermined duration.

[0026] Furthermore, since the biodegradation model allows to provide a biodegradation curve in an accurate, fast and inexpensive manner, the biodegradation curve can easily be adapted to changes in the property data associated with one or more chemical and / or physical properties of the chemical substance and can thus be easily utilized for controlling and / or monitoring chemical processes in an accurate, fast and inexpensive manner. 231051W001

[0027] 5

[0028] The method refers in particular to a computer-implemented method and can thus be performed by a general or dedicated computer adapted to perform the method, for instance, by executing a respective computer program. The method can also be performed in a distributed computing environment, like a cloud computing environment. The method is adapted to generate a biodegradation curve associated with a chemical substance. In general, a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time.

[0029] In general, the biodegradation of a chemical substance in a habitat refers to the process of the substance being biodegraded, wherein the process of the substance being degraded takes place in the habitat. In general, biodegradation refers to the process of degradation or decomposition of a chemical substance caused by biological processes, i.e. processes that include biological material, in particular, microorganisms, taking part in the degradation process. Thus, biodegradation does not refer to purely chemical or purely physical degradation processes that do not include microbial activity. In particular, biodegradation can refer to a degradation process in which microbes break the chemical substance into smaller fragments and take up such fragments to then metabolize and convert the fragments, for instance, under aerobic conditions, into biomass and one or more residual substances, for instance, carbon dioxide, water and / or inorganic salts. For example, biodegradation of a biodegradable polymer can comprise depolymerization of the biodegradable polymer by microbes into smaller fragments such as monomers and oligomers which can then be taken up through the microbial cell membrane and be metabolized and converted into biomass, carbon dioxide, water and inorganic salts. In general, the factors influencing the biodegradation process of a chemical substance in a habitat comprise chemical and / or physical properties of the chemical substance and the respective habitat conditions as will be further described below. Factors influencing the biodegradation process of a chemical substance in a habitat can influence, for instance, a rate at which the biodegradation proceeds, a change in the rate at which the biodegradation proceeds, the type and amount of intermediate products of the biodegradation process, and / or the type and amount of final products of the biodegradation process. It can generally be assumed that, if reliable information indicative of the biodegradation of one particular chemical substance in one particular habitat is available, for instance, from an experiment, these information are also indicative of the biodegradation of the same or a very similar chemical substance in the same or a very similar habitat.

[0030] A biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time. Thus, the biodegradation curve is indicative of parameters that quantify the biodegradation of a chemical substance. Generally, a biodegradation curve is indicative of at least two parameters quantifying the biodegradation of a chemical substance, wherein each parameter is associated with a respective time point and wherein the two respective time points are not the same. The biodegradation curve can comprise information that allow deriving and / or accessing two or more parameters that quantify the biodegradation of a chemical substance associated with respective time points. The biodegradation curve can also comprise two or more parameters that quantify the biodegradation of a chemical substance associated with respective time points. For example, the biodegradation curve can comprise two or more parameters quantifying the biodegradation of a chemical substance, wherein each of the two or more parameters quantifying the biodegradation of the chemical substance is associated with one time point, wherein not all of the parameters quanti- 231051W001

[0031] 6 tying the biodegradation of the chemical substance are associated with the same time point. Preferably, the biodegradation curve comprises parameters that quantify the biodegradation of a chemical substance associated with a plurality of different time points. One or more parameters associated with the same time point may also be regarded a data point. In an embodiment, the biodegradation curve comprises two or more data points, wherein each data point comprises one or more parameters indicative of the biodegradation of the chemical substance associated with one time point, wherein the time point with which the one or more parameters indicative of the biodegradation of the chemical substance of a first data point are associated is different from the time point with which the one or more parameters indicative of the biodegradation of the chemical substance of a second data point are associated. In a further embodiment, the biodegradation curve comprises more than two data points, wherein the one or more parameters indicative of the biodegradation of the chemical substance of the first data point are associated with a first time point, wherein the one or more parameters indicative of the biodegradation of the chemical substance of the second data point are associated with a second time point, wherein the first time point is different from the second time point, and wherein the one or more parameters indicative of the biodegradation of the chemical substance of data points which are not the first data point and not the second data point are associated with time points distributed between the first time point and the second time point according to a predetermined pattern. In a variation of this embodiment, the one or more parameters indicative of the biodegradation of the chemical substance of data points which are not the first data point and not the second data point are associated with time points distributed according to uniform time intervals between the first time point and the second time point. In an example, the biodegradation curve can comprise a first data point comprising one or more parameters quantifying the biodegradation of the chemical substance at a start time point and a second data point comprising parameters quantifying the biodegradation of the chemical substance at an end time point. The biodegradation curve can comprise further data points, which are different from the first and the second data point and which comprise one or more parameters quantifying the biodegradation of the chemical substance at time points between the start time point and the end time point. For example, the further data points can be associated with time points which are distributed equally between the start time point and the end time point. The biodegradation curve of this example could be regarded as a collection of data points describing the biodegradation of the chemical substance in equally distributed time steps. The further data points can also be associated with time points which are not distributed equally between the start time point and the end time point, but according to a predetermined pattern. However, the further data points can also be associated with time points which are not distributed according to a predetermined pattern, but rather are distributed according to a pattern associated with other parameters. For instance, further data points can be associated with time points which are spaced inversely proportional to a rate at which the biodegradation occurs. It shall be noted that one or more parameters being "associated with” a time point can refer, for instance, to determining the parameters at the time point or generating the parameters, for instance as part of generating a biodegradation curve, for the time point, but it can also refer, for instance, to determining the parameter before or after the time point or within a predetermined time window around the time point or to generating the parameter for a time before or after the time point or for a predetermined time window around the time point. 231051W001

[0032] 7

[0033] Parameters quantifying the biodegradation of a chemical substance can refer to any parameters that contain quantitative information about the biodegradation process. The biodegradation curve can be indicative of, in particular comprise, parameters quantifying the mass transfer from the chemical substance to biomass and / or one or more residual substances during the biodegradation process with time. Parameters quantifying the mass transfer from the chemical substance to biomass and / or one or more residual substances during the biodegradation process with time can refer to, for instance, a rate at which the mass transfer from the chemical substance to biomass and / or one or more residual substances proceeds, a change in the rate at which the mass transfer from the chemical substance to biomass and / or one or more residual substances proceeds, an amount and / or concentration of intermediate products of the mass transfer from the chemical substance to biomass and / or one or more residual substances, an amount and / or concentration of final products of the mass transfer from the chemical substance to biomass and / or one or more residual substances, and / or chemical and / or physical properties of the biomass and / or the one or more residual substances. In general, residual substances are the products of the biodegradation of the chemical substance which are not biomass, for instance, carbon dioxide, water and / or inorganic salts. The biodegradation curve can be indicative of, in particular comprise, one or more parameters quantifying the biodegradation of a chemical substance by quantifying the amount of the residual substances produced during the biodegradation process. The biodegradation curve can be indicative of, in particular comprise, one or more parameters quantifying the biodegradation of a chemical substance by quantifying the amount of consumed substances, wherein the consumed substances may comprise the chemical substance that is biodegraded or one or more substances different from the chemical substance that is biodegraded. A consumed substance that is different from the chemical substance that is biodegraded can be, for instance, oxygen. The biodegradation curve can also be indicative of, in particular comprise, parameters quantifying statistical characteristics of the chemical substance that is biodegraded, statistical characteristics of intermediate products or statistical characteristics of the final products. A statistical property of a chemical substance, intermediate products or final products can be, for instance, the distribution of components making up the chemical substance. For example, the chemical substance may be a polymer and a statistical characteristic of the chemical substance can be a distribution of polymer chain lengths. In the same example, biodegradation may include breaking the polymer into smaller fragments, so that a distribution of polymer chain lengths can be a statistical characteristic of intermediate products or final products. In some examples, the biodegradation of a chemical substance in a habitat may cause a change in habitat conditions. For example, when a chemical substance is introduced into a habitat in such large amounts that it becomes one of the major nutrients for a particular kind of microorganism, the concentration of this particular kind of microorganism may increase. If this particular kind of microorganism also consumes or produces other nutrients, an increase in the concentration of the microorganism may cause a change in the concentration of nutrients. In particular, the biodegradation of a chemical substance may change habitat conditions when the biodegradation occurs in a spatially limited habitat. A spatially limited habitat can be, for instance, a habitat provided for a biodegradation experiment in a laboratory or a habitat provided for biodegradation of biodegradable waste in a waste management facility, but not, for instance, the open sea. Especially in spatially limited habitats, the biodegradation of a chemical substance may cause changes in habitat conditions that are measurable and, in turn, indicative of the biodegradation of the chemical substance. The biodegradation curve can thus be indicative of, in particular comprise, parameters quantifying habitat conditions of the habitat in which the biodegradation of the chemical substance proceeds, for instance, an amount and / or concentration of 231051W001

[0034] 8 substances and / or microorganisms in the habitat. The biodegradation curve can be indicative of, in particular comprise, one or more parameters quantifying the biodegradation of a chemical substance by quantifying the disintegration of the chemical substance, in particular, disintegration of the chemical substance on a macroscopic level. For example, the biodegradation curve can be indicative of, in particular comprise, one or more parameters quantifying one or more of a loss of mechanical properties, a weight, an integrity and visual disappearance of a macroscopic object comprising the chemical substance.

[0035] Generally, the chemical substance can be any chemical substance. The chemical substance in general can refer to a chemical substance comprising only a single element or to a substance comprising chemical compounds. The chemical substance can refer to a substance comprising only one type of chemical entities or to a substance comprising a blend or mixture of different chemical entities. The chemical substance can refer to a chemical substance in different physical states or phases, for instance, the chemical substance can be solid or liquid. Even without changing the chemical properties of the chemical substance, these different physical states or phases can give rise to a plurality of different physical properties of the chemical substance. These different physical properties of the substance can give rise to differences in how the chemical substance interacts with the environment, in particular, how the chemical substance can be biodegraded in the environment. Preferably, the chemical substance refers to a chemical substance in the solid phase. A chemical substance in the solid phase can refer to a chemical substance in different physical states and shapes. For example, a chemical substance can refer to a chemical substance provided in the form of bulk material having a substantially three-dimensional shape, a film having a substantially two-dimensional shape, or a powder composed of many fine particles. In general, the chemical substance can have any ratio of surface area to volume. Different ratios of surface area to volume can give rise to differences in how the chemical substance interacts with the environment, in particular, how the chemical substance can be biodegraded in the environment. In general, the chemical and / or physical properties of the chemical substance can change during the biodegradation process. Preferably, the chemical substance is a biodegradable chemical substance. A biodegradable chemical substance refers to a chemical substance that can be degraded by biological processes, in particular, a biodegradable chemical substance can refer to a chemical substance that can be assimilated by bacteria and / or fungi to give environmentally friendly products, i.e., to decompose into non-polluting residuals, for example, by producing mineralized carbon and / or biomass.

[0036] In an example, the chemical substance can refer to a polymer. Generally, the polymer can be any polymer, for instance, a synthetic polymer or a natural polymer. A synthetic polymer can be a chemical compound which is produced by a chemical production from one or more starting materials such as monomers and which comprises at least two monomer units. The monomer units may be regarded as subunits of the polymer. The polymer may be prepared from the monomers by commonly known polymerization techniques. The polymer may be produced from a single type of monomer or from different monomers. The polymer may be produced by a single polymerization technique or by a combination of different ones. The monomer units may be distributed randomly or may be present as blocks within the polymer. The polymer may be a linear polymer. The polymer may be a branched polymer. The polymer can be a cross-linked polymer. The polymer can be chemically modified after polymerization. A natural polymer can be a chemical compound which is produced by a biological organism. The polymer may be extracted from the biological organism. In particular, 231051W001

[0037] 9 a chemical substance can refer to a biodegradable polymer. Preferably, the chemical substance is a biodegradable synthetic polymer. The chemical substance can also refer to a blend or mixture of different polymers. In another example, the chemical substance can refer to a small molecule, wherein a small molecule is herein defined as a molecule that is present in the environment in a form that allows to completely describe the molecule using simple structural formulas that contain the relevant information. A simple molecular formula refers to molecules that can be described by covalent bonds between the atoms of the molecule. However, molecules could also consist out of isomers and could be partly protonated. Examples, where this is not the case, are, for instance, systems with dynamic equilibria between several forms like monomers and oligomers as in the case of several inorganic acids, or ionic species with very localized charge that strongly interacts with a solvent, for instance, via hydrogen bonding. Preferably, a small molecule is defined by a molecular weight of less than 600 g / mol, more preferably of less than 300 g / mol.

[0038] In general, a habitat in which the chemical substance is biodegraded can refer to any habitat. The biodegradation of a chemical substance in a habitat can be influenced by a plurality of different habitat conditions that are indicative of environmental characteristics of the habitat. In particular, the environmental characteristics of a habitat can influence the biological activity in the respective habitat, for example, can influence the presence, growth or absence of specific bacteria. Thus, the environmental characteristics defined by the habitat conditions can indirectly also influence the biodegradation of a chemical substance in a respective habitat. For example, if a polymer is biodegradable by a specific bacterium that needs a specific salt concentration, the polymer will biodegrade fast in a habitat providing such a salt concentration, like a marine habitat, but will biodegrade much slower in a habitat with not the right salt concentration, like wastewater. The habitat can refer to any one of an aqueous habitat, in particular, a marine habitat, a wastewater habitat or a limnic habitat, a compost habitat or a soil habitat. Generally, a habitat can also refer to a particular habitat compartment or a particular habitat found in a particular geographic region. For example, a marine habitat can refer to different marine compartments, for instance, eulittoral, benthic and pelagic compartments. The habitat can refer to an aqueous habitat, wherein the habitat conditions refer to at least one of a salt concentration, a sedimentation type, a sludge concentration, a solid content, oxygen level, location, sample depth, a water temperature, a nutrient concentration, for example, a nitrogen, phosphate, potassium, and / or dissolved organic carbon concentration, a pH value, an environmental type, oxygen content, a microbial community, a concentration of microbes and an enzyme environment. The habitat can refer to a marine habitat, wherein the habitat conditions refer to at least one of a salt concentration, a sedimentation type, oxygen level, location, sample depth, a water temperature, a nutrient concentration, for example, a nitrogen, phosphate, potassium, and / or dissolved organic carbon concentration, a pH value, an environmental type, oxygen content, a microbial community and a concentration of microbes. The habitat can refer to a limnic habitat, wherein the habitat conditions can refer to at least one of a salt concentration, a sedimentation type, oxygen level, location, sample depth, a water temperature, a nutrient concentration, a pH value, an environmental type, a microbial community and a concentration of microbes. The habitat can refer to wastewater, wherein the habitat conditions refer to at least one of a water temperature, a microbial community, a sludge concentration, a nutrient concentration, a pH value, a test duration, a solid content, and an enzyme environment. Moreover, in this habitat the sludge can also be a separate habitat. Thus, the habitat can also be a sludge habitat, for example, as the aerobic part of a wastewater treatment plant and the habitat conditions refer to at least one of a solid content, pH, nutrient content, heavy metal 231051W001

[0039] 10 content, microbial community and concentration of microbes. The habitat can refer to soil, wherein the habitat conditions refer to at least one of a temperature, composition, for example, a sand and / or clay content, a pH value, a moisture content, a nutrient concentration, a microbial community, a concentration of microbes, a nitrogen content, a water holding capacity and an enzyme environment. The biodegradation habitat can refer to compost, wherein the habitat conditions refer to at least one of a temperature, compost activity, a pH value, a moisture content, humidity, compost maturity, compost composition, compost origin, a nutrient concentration, a microbial community, a concentration of microbes, a solid content, a water holding capacity, and an enzyme environment. Generally, a habitat can also refer to a habitat of a standardized test utilized for determining the biodegradation properties of a chemical substance. For example, standardized tests as defined by ISO13432, ISO14852, ISO14855, ISO17556, ISO 18830, ISO 19679, ISO 20200, ISO 22404, ISO 23977-1 , OECD 301 , OECD 302 B and OECD 306 also define a habitat with specific habitat conditions in which the biodegradation takes place.

[0040] In a preferred embodiment, the biodegradation model is associated with a specific biodegradation habitat, and the method comprises receiving habitat data associated with one or more habitat conditions of a specific habitat, wherein the biodegradation model is provided based on the habitat data. Since the habitat conditions can have a pronounced effect on the biodegradation of a chemical substance in a habitat, receiving habitat data associated with one or more habitat conditions of a specific habitat allows for an even more accurate generation of a biodegradation curve. Furthermore, since the biodegradation model is provided based on the habitat data in this embodiment, the biodegradation model can be adapted more specifically to the modeled situation and less training data is required, thereby further reducing the resources required to set up and parameterize the biodegradation model. For example, the biodegradation model can be selected based on the habitat data from a plurality of models that have been trained with respect to specific habitats. However, although taking the habitat into account improves the accuracy and reliability of the generated biodegradation curve, a biodegradation model can also be trained without taking the habitat into account. For example, the model can be trained based on a plurality of habitats to generate a biodegradation curve that averages over these habitats. This allows, for instance, to provide a measure of the general biodegradation behavior of a chemical substance. Alternatively, the model can be trained with respect to a predetermined habitat, preferably, a habitat of a predetermined standard test and / or a habitat that refers to the most likely habitat for biodegradation, for example compost.

[0041] In a first step, the method comprises receiving at a computer interface property data associated with one or more chemical and / or physical properties of the chemical substance. In particular, the receiving can refer to receiving the property data from an input of a user using, for instance, a respective input unit. Moreover, the receiving can also refer to accessing information stored in a storage unit in which one or more chemical and / or physical properties of the chemical substance are already stored. Further, the receiving can also comprise receiving one or more chemical and / or physical properties of the chemical substance, for instance, via a network connection from other sources. Property data associated with one or more chemical and / or physical properties of the chemical substance can refer to any property data which allows to deduce information about chemical and / or physical properties of the chemical substance. The property data can comprise one or more chemical and / or physical properties of the chemical substance, for example, 231051W001

[0042] 11 in form of values for respective quantities. One or more chemical and / or physical properties of the chemical substance can be derived from the property data using one or more known relations. For example, property data can refer to a synthesis specification or a structural formula of a chemical substance that can be used to derive, using known chemical and physical laws and relations, respective chemical and / or physical properties of the chemical substance. Preferably, receiving property data associated with one or more chemical and / or physical properties of the chemical substance refers to receiving property data that comprises parameters or characteristics indicative of the chemical and / or physical properties of the chemical substance. Generally, throughout the following description referring to a parameter or a characteristic comprises referring both to the respective quantity and also to a specific value of the quantity if not explicitly defined otherwise. For example, a parameter being a "molecular weight” always refers to the quantity being a molecular weight and also to a value of the molecular weight being set for the quantity. Since in most cases the explicit value of the parameter can be different for different embodiments and application cases the value is generally not mentioned. However, providing a parameter or characteristic generally means providing the quantity, e.g. the information that a value is a molecular weight, and also the value of the quantity or characteristic itself.

[0043] Preferably, the property data comprises parameters indicative of chemical and / or physical parameters of the chemical substance comprising one or more of a molar mass, an average molar mass, a molar mass distribution, a density, a total mass, a concentration, a bond type and a composition from elemental analysis. Preferably, the property data comprises parameters indicative of a specific surface area of the chemical substance, in particular, if the habitat is a compost habitat or soil habitat. Preferably, the property data comprises parameters indicative of a solubility of the chemical substance, in particular, if the habitat is an aqueous habitat, a marine habitat, a wastewater habitat or a limnic habitat. If the chemical substance is a polymer, the property data preferably comprises parameters indicative of the type of polymer and / or a bond type between monomer units. If the chemical substance is a blend or mixture of different chemical entities, the property data preferably comprises parameters indicative of the type of chemical entities in the blend or mixture.

[0044] Generally, the property data can comprise parameters specifying at least one of constitutional descriptors, count descriptors, list of structural fragments, fingerprints, graph invariants, 3D-descriptors and / or higher dimensional descriptors that are indicative of the chemical and / or physical properties of the chemical substance. In an example, the chemical substance can refer to a polymer and the chemical and / or physical properties of the chemical substance can further be derived from chemical and / or physical properties of subgroups of the chemical substance. Thus, also the parameters indicative of chemical and / or physical properties of subgroups of a polymer can refer to the same parameters indicative of chemical and / or physical properties as stated above. However, the parameters indicative of chemical and / or physical properties can generally also be derived, for instance, by quantum chemical simulations of the chemical substance. In the following, the possible parameters indicative of chemical and / or physical properties are defined in more detail. Also in these cases the defined parameters indicative of chemical and / or physical properties can refer directly to parameters indicative of chemical and / or physical properties of the chemical substance or, optionally, to the parameters indicative of chemical and / or physical properties of subgroups of the chemical substance in an example where the chemical substance refers to a polymer. 231051W001

[0045] 12

[0046] A constitutional descriptor can refer to any of a potential, a molecular weight, an average molecular weight, a molecular weight distribution, polydispersity, charge, spin, boiling point, melting point, enthalpy of fusion, dissociation constant, Hansen parameter, protic, polar and dispersive contributions, Abraham parameter, retention index, TPSA, receptor binding constant, Michaelis-Menten constant, Inhibitor constant, Mutagenicity, LD50, bioconcentration, toxicity, biodegradation profile and viscosity.

[0047] A count descriptor can refer to any of a sum of atomic electro negativities, a sum of atomic polarizabilities, an amount of ingredients, a ratio of amounts of ingredients, a number of atoms and non H-atoms, a number of H, B, C, N, 0, P, S, Hal and heavy atoms, a number of H-donor and H-acceptor atoms, a number of bonds, non-H or multiple bonds, a number of double, triple and aromatic bonds, a number of functional groups, a ratio of functional groups, a sum of bond orders, an aromatic ratio, a number of rings or circuits, a number of unpaired electrons, a number of rotatable bonds, rotatable bond fractions, and a number of conformers.

[0048] Parameters specifying a list of structural fragment descriptors can refer to at least one of a list of molecular fractions, a list of functional groups, a list of bonds, and a list of atoms. Fingerprint descriptors comprise, preferably, at least one of MACCS keys, preferably, in bit format or total amount format, Morgan and other circular fingerprints, preferably, in bit format or total amount format, topological torsion, atom pairs, infrared and related spectra, fingerprint count, Pub- Chem fingerprint, substructure fingerprint, and Klekota-Roth fingerprint. Graph invariants / topological indices descriptors comprise preferably at least one of topostructural indices and topochemical indices. 3D descriptors comprise preferably at least one of a volume as sum overall atoms, a mean volume per atom, an area as sum overall atoms, an area as mean per atom, an area over all atoms, an area as mean per atom, a solvent accessible surface, a dispersion energy, a dielectric energy, a H-donor, H-acceptor, polar and non-polar surface area, an atom resolved H-donor, H- acceptor, polar and non-polar surface area, a shape, a sphericity, dipole and higher electric moments, polarizability, dielectric energy, protic, polar and non-polar surface area, orbital energies and orbital gaps, ionization energy, electron affinity, hardness, electronegativity, electrophilicity, excitation energies and intensities, infrared and ultraviolet absorption bands, reactivity measurements, redox potential, bond criterial points, partial charges, charge surface areas, atomic orbital contributions, bond orders, atom radius. A higher dimensional descriptor comprises preferably at least one of a conformational partition function, solubility, vapor pressure, activity coefficient, diffusion coefficient, partition coefficient, interfacial activity, rotational constant, moment of inertia, radius of gyration, compositional drift of a polymer, density, viscosity, conformer weighted volume and area, conformer weighted H-donor, H-acceptor, protic, polar and / or non-polar surface area, charge distribution, conformational dipole moment and molecular refraction.

[0049] Preferably, the property data comprise parameters specifying one or more descriptors that are indicative of the chemical and / or physical properties of the chemical substance and that refer to one or more of a molecular weight, an average molecular weight, a molecular weight distribution, polydispersity, viscosity, Michaelis-Menten constant, Inhibitor constant, density and surface area. 231051W001

[0050] 13

[0051] The chemical substance can comprise a polymer. The chemical substance can be a polymer. The property data can comprise chemical and / or physical properties of the polymer. The chemical and / or physical properties of the polymer can refer to chemical and / or physical properties of subgroups of the polymer. In this embodiment, the property data can also be provided such that it allows to derive chemical and / or physical properties of the polymer by determining subgroups of the polymer and to determine the chemical and / or physical properties of the polymer based on chemical and / or physical properties of the determined subgroups. Generally, a subgroup refers to a part of the polymer, wherein all subgroups of a polymer together form the polymer. For example, a subgroup can refer to a part of the polymer, wherein the subgroups are linked together successively along a chain or network to form the polymer. Preferably, the subgroups of the polymer refer to repeating units that describe a part of the polymer which when repeated produces the complete polymer chain. However, in some cases, a subgroup can also refer to a single part of the polymer that is not repeated. Moreover, it is preferred that the subgroups comprise parts that are repeated, for example, a subgroup of a polymer can comprise a repeating core also present in other subgroups and further additional parts that are not present in other subgroups. Preferably, the subgroups refer to at least one of polymerized monomers or oligomer fragments. More preferably, the subgroups refer to polymerized monomers. In this context, polymerized monomers refer to monomers after their polymerization sometimes also called "mer unit” or "mer”. In particular, polymerized monomers do not refer to monomers, i.e. raw materials, as present in a reaction mixture before polymerization, but refer to repeating units derived from monomers that have been changed during or after the polymerization. Thus, chemical and / or physical properties for polymerized monomers can be different from chemical and / or physical properties determined for unreacted monomers before polymerization. In particular, the polymerized monomers can allow determining chemical and / or physical properties of the polymerized monomers that allow the biodegradation model to accurately generate the biodegradation curve for the polymer. The digital representation of the polymer can comprise subgroups provided as molecular model which is indicative of a chemical structure of the subgroup after its polymerization. In particular, the molecular model of a subgroup can be determined in a way that is suited for quantum chemical computations regarding a number of atoms and their connectivity that is representative of the properties of the subgroup within the polymer. Moreover, additionally and alternatively to a molecular model of a subgroup treating the subgroup as a monomer structure, also a molecular model referring to an oligomer model can be utilized that takes into account effects of neighboring molecular structures of the subgroup in the polymer.

[0052] Generally, if the property data of the polymer does not directly comprise chemical and / or physical properties of the polymer, it is preferred that chemical and / or physical properties of the polymer are determined by determining the subgroups of the polymer. For example, respective subgroups of the polymer can be determined utilizing known methods. For example, the subgroups can be determined such that between atoms of different subgroups in the polymer the bond is at least as polarized as possible and, preferably, with a bond order as small as possible (e.g. a CC single bond). Additionally, the subgroups representing a polymer can comprise the same number of active non-hydrogen- atoms as the polymer. Besides the active atoms, a subgroup can also contain further atoms, which can be ignored during computing the parameters indicative of chemical and / or physical properties of the subgroup. Further, subgroups can be determined in a way that polymers comprising parts, which were built up with different polymerization tech- 231051W001

[0053] 14 niques, are well covered and fulfill the foresaid conditions. An example is a polyether used as ingredient for a polyurethane. Generally, a database or archive with a plurality of reactions between polymer parts can be generated and the subgroups can be derived from the respective structure of the reactions. For example, specific chemical languages like SMILES and SMARTS can be utilized to easily derive the subgroup of a polymer. For example, a database of reaction SMARTS can be generated and then based on the polymerization of the respective polymer a corresponding reaction SMARTS can be selected. From the selected reaction SMARTS then the SMILES of monomers of the polymer are directly derivable and, for example, RDkit can be used to determine from the SMILES of the monomers the SMILES, i.e. the number and connectivity of the atoms, of the subgroups.

[0054] The determined subgroups of the polymer are associated with chemical and / or physical properties of the subgroups in the polymer. In particular, it is preferred that if the chemical and / or physical properties of the polymer are not directly provided by the property data, chemical and / or physical properties of the polymer are determined by determining chemical and / or physical properties for each of the subgroups and to determine the chemical and / or physical properties of the polymer based on the chemical and / or physical properties of the subgroups, for instance, by averaging. Thus, the method can comprise first providing or determining for the polymer the subgroups of the polymer, then to determine or provide the chemical and / or physical properties of the subgroups, for instance values of parameters quantifying the chemical and / or physical properties of the subgroups, and then to determine the chemical and / or physical properties of the polymer based on the chemical and / or physical properties of the subgroup of each polymer.

[0055] Preferably, the property data associated with one or more chemical and / or physical properties of a chemical substance that is a polymer comprise parameters specifying at least one of constitutional descriptors, count descriptors, list of structural fragments, fingerprints, graph invariants, 3D-descriptors and / or higher dimensional descriptors that are indicative of the chemical and / or physical properties of the chemical substance, as described in more detail above.

[0056] The method further comprises providing a biodegradation model based on property data. In particular, it is preferred that the providing of the biodegradation model refers to a selecting of a biodegradation model based on the property data associated with one or more chemical and / or physical properties of the chemical substance. For example, a plurality of biodegradation models can be stored on a biodegradation storage, wherein the biodegradation models have been developed for particular property data. For example, each biodegradation model can be developed for a particular set of property data. Based on the received property data associated with one or more chemical and / or physical properties of the chemical substance, a respective suitable biodegradation model can be selected from the plurality of biodegradation models. For example, a biodegradation model is suitable if the received property data fall within the ranges of the property data for which the biodegradation model has been developed. For example, a respective lookup table can be provided that allows for an easy comparison between the indicated property data and the property data for which the biodegradation models stored on the storage have been developed such that directly a suitable biodegradation model can be selected. Additionally and / or alternatively, a similarity measure describing the similarity between the provided property data associated with one or more chemical and / or physical properties of the chemical substance and the particular set of property data for which the biodegradation model has been developed can be utilized. Based 231051W001

[0057] 15 on the similarity measure, a suitable biodegradation model can be selected. However, the providing of a biodegradation model based on the received property data can also refer to a user selection of the biodegradation model. For instance, the user can be provided with a preselection of biodegradation models that refer to the received property data and then be allowed to select the respective biodegradation model that should be utilized. Generally, the stored biodegradation models can refer to biodegradation models that have already been parameterized based on a respective historical training data set for one or more chemical substances. Since the training data sets utilized for parameterizing a biodegradation model are historical training data, the biodegradation models can be trained and thus generated at any time for the determination of a specific biodegradation for a specific chemical substance, and after the training be stored in a respective database. However, the training and the generation of a biodegradation model can also be performed at the time when it is determined that a specific biodegradation model is needed.

[0058] The biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process. Generally, a kinetic model is a model of a process converting incoming starting materials to outgoing materials that is based on tracing the mass transfer from the incoming starting materials to the outgoing materials and that is based on rates at which the process occurs. In particular, a kinetic model can refer to a model of a chemical reaction that is based on tracing a mass transfer from the one or more reactants to the one or more products of the chemical reaction and that is based on reaction rates. Generally, reactants refer to the one or more chemical entities that take part in the chemical reaction and products refer to the one or more chemical entities that are formed in the chemical reaction. The products can comprise one or more chemical entities that can be the same as or different from the one or more chemical entities that belong to the reactants. In a biodegradation process reactants can refer to, for instance, a chemical substance that is biodegraded, a carbon source, oxygen and water, and products can refer to, for instance, biomass and residual substances such as carbon dioxide, other carbon-containing products, water and inorganic salts. However, in a kinetic model of biodegradation, also microbes involved in the biodegradation can be considered as reactants and / or products. In general, a kinetic model is associated with the rates of the mass transfer from the reactants to the products of the chemical reaction. In particular, a kinetic model can describe a process comprising a chemical reaction based on the reaction rate that is indicative of the rate at which the chemical reaction takes place. Further, a kinetic model can describe a process comprising several chemical reactions based on one or more reaction rates indicative of the rates at which the respective individual chemical reactions take place. In general, a kinetic model can be used to determine the one or more rates at which one or more products are generated in one or more chemical reactions based on information about the initial amount of the one or more reactants taking part in the respective one or more chemical reactions and the one or more rates at which the respective one or more chemical reactions take place.

[0059] It should be noted here that a kinetic model as used in the present disclosure can also refer to a kinetic model of a process involving multiple chemical reactions or a combination of one or more chemical reactions and sub-processes which do not involve a chemical modification of reactants or intermediate products. In general, the kinetic model can be a model developed to describe a highly complex process. A kinetic model can be a model developed to describe a 231051W001

[0060] 16 process which is not fully understood in all details. For example, a kinetic model may refer to a model developed to describe a process of which only some of the reactants, but not all reactants, are known. In another example, a kinetic model may refer to a model developed to describe a process of which only some of the products, but not all products, are known. In yet another example, a kinetic model may refer to a model developed to describe a process of which only one of the reactants, but not all reactants, are known. An example of such a situation can be biodegradation of a chemical substance, in which only one of the reactants, for instance the chemical substance, is well-known and only one of the products, for instance carbon dioxide, is well-known, while the other reactants may include reactants that are not well-known, for instance a plurality of microbes participating in the biodegradation process, and the other products may include products that are not well-known, for instance further microbes and inorganic material. In such an exemplary case of biodegradation of a chemical substance the process itself may comprise a plurality of individual subprocesses related to the microbes breaking the chemical substance into smaller fragments, taking up such fragments, and metabolizing and converting the fragments. In the present disclosure, a kinetic model can thus also refer to a kinetic model describing a highly complex process which includes both sub-processes that involve one or more chemical reactions and sub-processes which do not involve a chemical reaction. In particular, a kinetic model can refer to a model of a process which comprises sub-processes that involve a physical degradation process. In a case where a kinetic model describes a process comprising several chemical reactions and / or other sub-processes, the chemical reactions and / or other sub-processes can occur simultaneously, consecutively or in any desired staggered and / or overlapping manner. A kinetic model may thus also be considered an effective model of a complex process. In the present disclosure, reference to "the reaction” or "a reaction” can thus refer to an effective description of a biodegradation process comprising a combination of one or more chemical reactions and one or more other sub-processes. Further, reference to "the reactants” or "reactants” and "the products” or "products” can thus refer to incoming starting materials and outgoing materials of a biodegradation process.

[0061] The rate at which a process occurs generally refers to the amount of incoming starting materials that is converted to outgoing materials in the process per time. In particular, the rate of a reaction, or reaction rate, generally refers to the amount of reactants that is converted to products in the reaction per time. The amount of reactants and the amount of products can be quantified, for instance, as a concentration of the reactants and the products, respectively, or in terms of an absolute amount of reactants and products, respectively, or as a ratio of individual reactants and products, respectively. A concentration of reactants and products can be provided in terms of a molar concentration, a mass concentration, a volume concentration or a number concentration. A molar concentration of a substance in a mixture generally refers to a concentration of the substance, in particular to a concentration of a solute in a solution, provided as the amount of the substance, expressed in units of mol, per amount of mixture, expressed in terms of a volume of the mixture or in terms of a mass of the mixture. A mass concentration of a substance in a mixture generally refers to a concentration of the substance, in particular to a concentration of a solute in a solution, provided as the amount of the substance, expressed in terms of a mass of the substance, per amount of mixture, expressed in terms of a volume of the mixture or in terms of a mass of the mixture. A volume concentration of a substance in a mixture generally refers to a concentration of the substance, in particular to a concentration of a solute in a solution, provided as the amount of the substance, expressed in terms of a volume of the substance, per amount of mixture, expressed in terms of a volume 231051W001

[0062] 17 of the mixture or in terms of a mass of the mixture. A number concentration of a substance in a mixture generally refers to a concentration of the substance, in particular to a concentration of a solute in a solution, provided as the amount of the substance, expressed in terms of a number of distinct particles of the substance, per amount of mixture, expressed in terms of a volume of the mixture, in terms of a mass of the mixture or in terms of a number of distinct particles of the mixture. An absolute amount of reactants and products, respectively, can refer to a mass of the respective reactants and products, to a volume of the respective reactants and products, or to a number of distinct particles making up the reactants and products, for instance provided in units of mol. A ratio of the respective reactants and products can refer to, for instance, a ratio of the concentration of one or more reactants to the concentration of one or more reactants, a ratio of the concentration of one or more reactants to the concentration of one or more products or a ratio of the concentration of one or more products to the concentration of one or more products. Moreover, a ratio of the respective reactants and products can refer to, for instance, a ratio of the absolute amount of one or more reactants to the absolute amount of one or more reactants or a ratio of the absolute amount of one or more reactants to the absolute amount of one or more products, a ratio of the absolute amount of one or more products to the absolute amount of one or more products.

[0063] A reaction rate can be substantially constant or vary in dependence of other parameters relevant for the chemical reaction. For example, the reaction rate can be well approximated by a constant, if the rate of the chemical reaction is substantially independent of other parameters such as the concentration of one or more reactants or the concentration of one or more products. However, the reaction rate for a respective chemical reaction may also depend on the surrounding conditions, for instance, temperature, pressure or excitation by electromagnetic radiation or particle radiation. The reaction rate can thus also depend on habitat conditions, for instance, habitat conditions as described above, that are indicative of environmental characteristics of the habitat. In general, the reaction rate utilized in a kinetic model can be described as a function of the amount of reactants and products in combination with further variables which depend on habitat conditions. For example, the reaction rate utilized in a kinetic model can be proportional to the amount of one or more reactants, for instance a concentration of one or more reactants. The reaction rate utilized in a kinetic model can also be proportional to the amount of one or more products, for instance a concentration of one or more products. The reaction rate utilized in a kinetic model can also be proportional to any mathematical combination, for instance, a sum, a difference, a ratio, or an exponentiation, of the amounts of one or more reactants and / or products. The proportionality constant can, in general, be a constant and / or can depend on habitat conditions in this case. For example, a reaction rate utilized in a kinetic model describing a chemical reaction involving two reactants, A and B, to form two products, C and D, can be proportional to the concentration of reactant A. The proportionality constant can be a constant in this case or it can depend on habitat conditions, for example, temperature. This would be an example of a first order kinetic model. In another example, a reaction rate utilized in a kinetic model describing a chemical reaction involving two reactants, A and B, to form two products, C and D, can be proportional to the product of the concentration of reactant A and the concentration of reactant B. The proportionality constant can be a constant in this case or it can depend on habitat conditions, for example, temperature. This would be an example of a second order kinetic model, as will be described in more details below. 231051W001

[0064] 18

[0065] The kinetic model of the present disclosure is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process. Biomass can refer to any kind of living or dead biological matter that is produced in the biodegradation process. For example, the biodegradation of a chemical substance can involve the action of microbes, which break the chemical substance into smaller fragments and take up such fragments to then metabolize the fragments. Metabolization of the fragments by the microbes generally comprises incorporation of at least a portion of the fragments into the microbes, for example to enable the microbes to grow and / or multiply. In this example, the biomass can refer to the microbes and a mass transfer from the chemical substance to biomass can refer to a transfer of material that was originally part of the chemical substance to material that is part of the biomass in the form of microbes. Residual substances can refer to all substances that are produced during the biodegradation process and are not biomass. In particular, residual substances can refer to carbon dioxide, water and / or inorganic salts.

[0066] The biodegradation model is in particular based on a machine learning based kinetic model. Generally, the machine learning based kinetic model contains trainable parameters that are parameterized based on a historical training data set such that it can generate biodegradation curves indicative of the biodegradation of a chemical substance in a habitat. In particular, the historical training data set comprises a) measured degradation curves of at least one chemical substance and b) respective property data of at least one chemical substance. The machine learning based kinetic model can thus be parameterized using the measured degradation curves of at least one chemical substance and the respective property data of at least one chemical substance. In an example, the providing of a biodegradation model based on property data can refer to providing a biodegradation model that has been parameterized based on historical training data comprising the same property data of a chemical substance. In another example, the providing of a biodegradation model based on property data can refer to providing a biodegradation model that has been parameterized based on historical training data comprising similar property data, associated with similar chemical and / or physical properties of the same or a similar chemical substance. "Similar property data” can refer to property data which is similar to the received property data in the sense that the biodegradation curve which is based on the similar property data is expected to be also indicative of the to be generated biodegradation curve. In another example, several biodegradation models can be parameterized based on a historical training dataset comprising several measured biodegradation curves associated with several sets of property data. In this example, providing a biodegradation model based on the received property data can refer to providing one of the several parameterized biodegradation models based on, for example, evaluating the similarity between the received property data and the set of property data that was used to parameterize the biodegradation models.

[0067] Parameterization of the biodegradation model generally refers to the process of adjusting the one or more trainable parameters of the biodegradation model such that the model can generate a biodegradation curve based on received property data. Adjusting the one or more trainable parameters can be achieved in different ways, in particular utilizing known methods of machine learning to obtain a machine learning based biodegradation model. "Machine learning” as used here refers to a data-driven parameterization of the biodegradation model. The term "data driven” is used here to emphasize that the model is at least partially based on historical training data input and not, for instance, solely on 231051W001

[0068] 19 intuition, personal experience or knowledge. However, also in a machine learning based biodegradation model, one or more parameters of the model may be determined not based on historical training data, but, for instance, based on intuition, personal experience or knowledge, or based on results of a simulation of the biodegradation. In a preferred embodiment, some or all of the respective trainable parameters of the machine learning based kinetic model are determined based on historical training data that is associated with respective property data of at least one chemical substance utilizing known training methods for machine learning based models. In particular, the machine learning based kinetic model can be trained such that the biodegradation model is also able to determine a biodegradation curve of a chemical substance that is not part of the historical training data set. The parameterized biodegradation model is configured to generate as output a biodegradation curve of the chemical substance based on property data of the chemical substance as input.

[0069] Trainable parameters can generally refer to all parameters of a biodegradation model and, in particular, to parameters which can be adjusted to achieve a more accurate biodegradation curve as model output. For example, trainable parameters can refer to parameters associated with a kinetic model as described above. In particular, trainable parameters can refer to rate constants and / or proportionality constants relating to other quantities, for example, the amounts of incoming starting materials and outgoing materials, but also parameters associated with habitat conditions, for instance, temperature or pH value, to rate constants or to process rates at which processes described by the kinetic model occur. Proportionality constants relating other quantities to rate constants or process rates are also referred to as degradation coefficients. Additionally or alternatively, trainable parameters associated with a kinetic model can refer to stoichiometric coefficients and / or amounts of incoming starting materials and outgoing materials, for instance, specified as concentrations, absolute amounts or ratios of individual incoming starting materials and outgoing materials. In particular, if the kinetic model describes a biodegradation process comprising chemical reactions, the trainable parameters can refer to reaction rates, parameters relating reaction rates to other parameters, stoichiometric coefficients and / or amounts of reactants and products. It shall be noted here that some types of parameters of the kinetic model may be defined as trainable parameters or as parameters specified by providing property data and habitat data, depending, for example, on the amount of knowledge about the biodegradation process and the amount of knowledge about the parameters. For example, for a biodegradation process of a chemical substance performed under well- controlled conditions in a laboratory, many parameters like, for instance, an amount and type of microorganisms present in the habitat or the amount and type of nutrients different from the chemical substance available in the habitat, may be well-known. Such well-known parameters can enter a respective biodegradation model as input parameter specified in the property data and / or habitat data provided. However, for a very similar biodegradation process of the same chemical substance performed under less well-controlled conditions, the same kinds of parameters, for instance, an amount and type of microorganisms or the amount and type of nutrients present in the habitat, may be trainable parameters and not specified as input to the biodegradation model. Less well-controlled conditions may occur, for instance, when performing the biodegradation process in the open sea or in a waste management facility. It shall further be noted that trainable parameters may not only refer to parameters directly entering the kinetic model. Rather, train- able parameters can also refer to parameters based on which the parameters entering the kinetic model may be determined. One already mentioned example would be degradation coefficients which relate a rate constant or a process 231051W001

[0070] 20 rate to other parameters. Another example would be a likelihood of a microorganism to consume a chemical substance, based on which process rates and stoichiometric coefficients may be determined. In general, determining parameters entering the kinetic model can also refer to more complex scenarios. For example, and as described in more detail below, a machine learning based model may be used to select and parameterize a particular biodegradation model. In this example, trainable parameters may refer also to trainable parameters of the machine learning based model used for selecting a particular biodegradation model and determining the adjustable parameters of this particular biodegradation model. Preferably, the one or more trainable parameters of the biodegradation model refer to at least one of an initial microbe concentration, a likelihood of a microorganism to consume the chemical substance, and a degradation coefficient. The inventors found that such trainable parameters are especially suited to parameterize a biodegradation model.

[0071] Generally, a machine learning based model refers to a model that is based on known machine learning algorithms like, for example, neural networks, regression models and classification algorithms. Regression models can be based on linear regression, random forests, boosted trees, lasso, ridge regression and MARS algorithms. Machine learning based classification models can be based on random forests, logistic regression and SVM algorithms.

[0072] Moreover, in a preferred embodiment, the kinetic model is a second order kinetic model. A second order kinetic model generally refers to a kinetic model of a process in which at least one reaction rate is expressed as a function of the amounts of chemical substances involved in the process, wherein the sum of the exponents of the amounts of chemical substances involved in the process is equal to two. In particular, a second order kinetic model can refer to a kinetic model of a process in which at least one reaction rate is expressed as a constant multiplied by the product of the amounts of two reactants, a constant multiplied by the product of the amounts of one reactant and one product, or a constant multiplied by the product of the amounts of two products. In particular, the amounts can refer to concentrations. The two reactants, the one reactant and one product, or the two products, respectively, can each refer to the same or different chemical substances. In an example, a reaction rate kA+B^G+Dutilized in a second order kinetic model describing a chemical reaction involving two reactants, A and B, to form two products, C and D, can be expressed as a constant const, multiplied by the product of the concentration of reactant A, [A], and of reactant B, [B], to read kA+B^G+D= const. [A] x [B], In another example, a reaction rate kE+F^G+Hutilized in a second order kinetic model describing a chemical reaction involving two reactants, E and F, to form two products, G and H, can be expressed as a constant const, multiplied by the product of the concentration of reactant E, [E] squared to read kE+F^G+H= const.* [E] x [E], In yet another example, a reaction rate kJ+K^L+Mutilized in a second order kinetic model describing a chemical reaction involving two reactants, J and K, to form two products, L and M, can be expressed as a constant const, multiplied by the product of the concentration of reactant K, [K], and of product L, [L], to read kJ+K^L+M= const.* [E] x [L], The inventors found that a second order kinetic model allows generating biodegradation curves in a particularly accurate, fast and computationally inexpensive manner. In particular, the inventors found that a second order kinetic model allows generating biodegradation curves that are sufficiently accurate for a wide range of technical applications. 231051W001

[0073] 21

[0074] In general, the kinetic model is a model based on tracing mass transfer from the chemical substance to biomass and residual substances and is further based on the reaction rates describing the rate at which the mass transfer occurs. Tracing mass transfer and taking into account the corresponding rate equations makes it possible to determine the development of parameters that describe biodegradation over time, e.g. the amount of reactants or products as a function of time. The output of the kinetic model can be a biodegradation curve comprising, for instance, the amount or concentration of one or more reactants as a function of time or the amount or concentration of one or more products over time.

[0075] In a preferred embodiment, the kinetic model is based on a carbon mass balance associated with a mass transfer of carbon from the chemical substance to the microorganisms and one or more residual substances. The inventors found that a kinetic model based on a carbon mass balance allows for an even more accurate generation of the biodegradability curve. In a preferred embodiment, the biodegradation model is a second order kinetic model that describes the biodegradation of a chemical substance containing carbon by microorganisms, wherein the second order kinetic model can be based on tracing the mass transfer of carbon from the chemical substance to microorganisms and carbon dioxide according to fc(M)

[0076] C - > (1 — a) CM+ aCO2, and (1) k(M)

[0077] M - ymM, (2) wherein C is an amount of carbon in the chemical substance, (1 - a)CMis the amount of carbon incorporated into the microorganisms, aC02is an amount of carbon incorporated in carbon dioxide, M is an amount of microbes, ymis a stoichiometric coefficient accounting for an increase in microorganisms due to the biodegradation of the chemical substance, wherein a < 1 and ym> 1, wherein a reaction rate / c(M) can be provided by k(M = kdeg[C][M], wherein kdegis a rate constant, [C] is a concentration of carbon in the chemical substance and [M] is a concentration of microorganisms, wherein a biodegradation curve indicative of the biodegradation of the chemical substance can be generated by solving the following set of differential equations: wherein initial conditions IC at time t = 0 can be wherein [CO2] is a concentration of carbon dioxide, [Co] is an initial concentration of carbon in the chemical substance, [Mo] is the initial concentration of microorganisms, and wherein ym, a, kdegand [Mo] are trainable param- eters of the biodegradation model. In general, however, also initial conditions different from the initial conditions defined 231051W001

[0078] 22 in equation (7) can be utilized in this embodiment. In an example, the initial concentration of carbon dioxide, [CO2], can be non-zero, if carbon dioxide is already present in the biodegradation habitat at the beginning of the biodegradation, or the initial amount of carbon incorporated into the microorganisms may be non-zero if the microorganisms had already biodegraded a portion of the chemical substance before the start of the biodegradation. Thus, the initial conditions may generally depend on habitat conditions. The trainable parameters of the biodegradation model, for instance ym, a, kdegand [Mo] in the embodiment described above, can be parameterized, or adjusted, based on historical training data using any of the known methods for machine learning. In an example, the trainable parameters, for instance the rate constant kdeg, can be adjusted based on historical training data comprising a measured biodegradation curve of a chemical substance in a habitat and respective property data of the chemical substance, but possibly also habitat data specifying respective habitat conditions. Adjusting the trainable parameters in this example can refer to performing a regression to find the parameters providing the best fit between the biodegradation curve generated based on equations (1) to (7) and the respective measured biodegradation curve. In this example, it is also possible that a respective adjustment of the trainable parameters is carried out for historical training data comprising a set of several measured biodegradation curves associated with a set of property data and / or habitat data. The resulting biodegradation model or the resulting set of biodegradation models with the adjusted parameters may then be stored in a database and be associated with the respective property data and habitat conditions. The providing of a biodegradation model based on property data and habitat data can then comprise selecting a biodegradation model for which the trainable parameters were adjusted based on a measured biodegradation curve associated with the same or similar property data and habitat data.

[0079] In an example, the biodegradation model is based on a second order kinetic model that describes the biodegradation of a polymer by microorganisms to form more microorganisms, further breakdown products, CO2, H2O, and other residual substances according to microorganisms / more \ / breakdown productsx (polymer) - > (microorganisms)+( CO2, H2O, . . . )(8)

[0080] The inventors found that it is beneficial to focus on CO2 as breakdown product and assume the carbon atoms (C) of the polymer are degraded by the microorganisms (M). In this example, one balance on the carbon, and one balance on the microbes can be described as

[0081] Here, CMis the carbon remaining within the microorganisms. Per 1 mole of C converted, 1 - a moles of carbon are incorporated in the microorganisms, and a moles become CO2 (hence a < 1). ymis a stoichiometric coefficient accounting for the increase in microorganism concentration due to the biodegradation of the polymer, i.e. ym> 1. The inventors found it is beneficial to assume the rate of this reaction to be a function of the microorganism concentration and the concentration of carbon. The rate of the reaction can be expressed as: 231051W001

[0082] 23 where kdegis a second order rate constant, [C] is the current concentration of available carbon, and [M] the microorganism concentration. The inventors found that differential equations according to equations (3) to (6) arising from the kinetic scheme, together with a reaction rate according to equation (11) and initial conditions according to equations (7) are suitable to generate an accurate biodegradation curve of a polymer. For example, a biodegradation curve indicative of the biodegradation process of a chemical substance can be generated by solving the above equations for the amount of carbon converted to CO2 over time to calculate a time-dependent biodegradation percentage as

[0083] Biodegradation percentage = x 100, (12) where [CO2] is the concentration of carbon dioxide and [Co] is the amount of initially available carbon. In this embodiment, the biodegradation model can be utilized to generate several parameters that are indicative of the biodegradation of the chemical substance, in particular as a function of time t after t=0. The biodegradation model can thus be utilized to generate a biodegradation curve indicative of the biodegradation of the chemical substance. For example, the biodegradation model in this embodiment can be utilized to calculate an amount or concentration of carbon in the chemical substance, the amount or concentration of carbon incorporated into CO2, a biodegradation percentage, etc. These parameters are indicative for the biodegradation of the chemical substance and can be calculated for different time points. The set of parameter values with time can be regarded a biodegradation curve. The trainable parameters of the biodegradation model of this embodiment may then be adjusted based on a measured biodegradation curve, for instance, a measured amount of carbon incorporated into CO2 with time using, for instance, regression algorithms.

[0084] However, parameterizing, or adjusting, the trainable parameters of a biodegradation model does not always refer to obtaining the parameter values which provide a best fit to one particular measured biodegradation curve. It can be beneficial to provide a biodegradation model in which the trainable parameters were adjusted based on historical training data comprising several measured biodegradation curves. In an example, trainable parameters may have been found for a set of biodegradation models based on performing a regression in relation to a set of measured biodegradation curves for a set of particular chemical substances and a particular habitat. Each of the parameterized biodegradation models in this example is thus associated with particular property data associated with properties of a chemical substance from the set of chemical substances. If a biodegradation model shall be provided for generating a biodegradation curve in the same habitat, but associated with a chemical substance which has not been part of the historical training dataset, it may be more accurate to generate a new biodegradation model or to generate a biodegradation model with adapted parameters for this task than to use a biodegradation model from the already existing set of biodegradation models. In an example, it is preferred that a biodegradation model is determined that can generate a biodegradation curve of polymer PA in a particular habitat. For a set of two biodegradation models the trainable parameters can be parameterized based on the measured biodegradation curves of polymers PBI and PB2. Further the polymers PA, PBI and PB2 can be regarded as chemically similar in the sense that they are obtained by a polymerization reaction of the same building blocks. The main difference can be their different molecular mass, i.e., the number of subunits in the polymers. For example, the molecular mass of polymer PBimay be smaller than the molecular mass of polymer PAand the molecular mass of polymer PAmay be smaller than the molecular mass of polymer PB2. The trainable parameters of the biodegradation models associated with polymer PBI and PB2, respectively, may be similar, except for a trainable parameter x, which may be set to a value XBI for polymer PBI and XB2 for polymer PB2. When 231051W001

[0085] 24 providing a biodegradation model for generating a biodegradation curve associated with polymer PA, it may lead to more accurate results to set up a new biodegradation model with a parameter xAobtained, for instance, by interpolation between the values XBI and XB2 for trainable parameter xA, as compared to using either one of the already existing models. In general, providing a biodegradation model associated with biodegradation of a chemical substance in a habitat can thus refer to providing a biodegradation model in which trainable parameters are parameterized based on a historical training dataset, which does not comprise this particular chemical substance and / or this particular biodegradation habitat. In an embodiment, providing the biodegradation model comprises selecting a biodegradation model with trainable parameters and parameterizing the trainable parameters based on the received property data and habitat data based on a machine learning based selection and parameterization model. It shall be understood that the machine learning based selection and parameterization model can be part of the machine learning based kinetic model and can be trained based on the historical training dataset.

[0086] In an embodiment, the method described in the present disclosure further comprises receiving an initial biodegradation curve indicative of a measured biodegradation in a predetermined biodegradation phase, wherein the method further comprises adjusting the trained one or more trainable parameters of the biodegradation model further based on the initial biodegradation curve and utilizing the adjusted one or more trainable parameters of the biodegradation model to generate the biodegradation curve for other biodegradation phases based on the property data. Since the biodegradation model is further adjusted based on an initial biodegradation curve in this embodiment, the biodegradation curves for other biodegradation phases can be generated even more accurately. At the same time, less training data is needed to initially parameterize the biodegradation model since the initial biodegradation curve provides additional information for adjusting the biodegradation model. An initial biodegradation curve indicative of a measured biodegradation in a predetermined biodegradation phase generally refers to a biodegradation curve which covers a limited amount of time. In an example of a biodegradation of a chemical substance, the initial biodegradation curve can refer to the biodegradation curve measured during the first part of the biodegradation, for example during the first 10 % of the total determined time for biodegradation or the first 20 % of the total determined time for biodegradation. In another example, an initial biodegradation curve can refer to the biodegradation curve measured up to the time required for a condition to be met, for instance, up to the time required for 25% of the chemical substance to biodegrade, or up to the time required for 50% of the chemical substance to biodegrade. However, an initial biodegradation curve does not always refer to a biodegradation curve measured from the beginning of the biodegradation, but it can also refer to a biodegradation curve measured during a predetermined biodegradation phase which does not include the start time. For example, the measurement of an initial biodegradation curve can be started when the first 5 % of the total determined biodegradation time has been completed and continue until the first 10 % of the total determined biodegradation time has been completed, or it can be started when the first 10 % of the total determined biodegradation time has been completed and continue until the first 20 % of the total determined biodegradation time has been completed. In another example, the measurement of an initial biodegradation curve can be started when 5 % of the chemical substance have been biodegraded and continue until 10 % of the chemical substance have been biodegraded, or it can be started when 10 % of the chemical substance have been biodegraded and continue until 20 % of the chemical substance have been biode- 231051W001

[0087] 25 graded. In a preferred embodiment, the initial biodegradation curve indicative of a measured biodegradation in a predetermined biodegradation phase can be compared with a biodegradation curve generated by the method of the present disclosure using an initial biodegradation model with initial values for the trainable parameters and the trained one or more trainable parameters of the biodegradation model can be adjusted based on the comparison. In an example of a biodegradation experiment to determine the time required to biodegrade 95 % of a chemical substance, a biodegradation curve may first be generated using a biodegradation model as disclosed herein at the time the biodegradation experiment is initiated. Based on this generated biodegradation curve, the total time required to biodegrade 95 % of the chemical substance can be determined. The actual biodegradation can be measured during the first 10 % of the determined total time required for 95 % of the chemical substance to biodegrade to obtain an initial biodegradation curve indicative of a measured biodegradation in a predetermined biodegradation phase, wherein the predetermined biodegradation phase in this example refers to the first biodegradation phase. The initial biodegradation curve can be compared to the generated biodegradation curve, the trainable parameters of the biodegradation model used can be adjusted based on the comparison, and a new biodegradation curve can be generated that is expected to more accurately describe the actual biodegradation. In this example, the total time required to biodegrade 95% of the chemical substance can then be determined more accurately from the new biodegradation curve and it may not even be necessary to continue the biodegradation experiment until this condition is met. In this example it is possible to obtain a highly accurate biodegradation curve describing the full biodegradation process by combining a biodegradation model with an experimental determination of the biodegradation in an initial biodegradation phase which, however, requires much less time as compared to measuring the full biodegradation curve.

[0088] The method further comprises generating the biodegradation curve based on the biodegradation model and the property data. Generating the biodegradation curve based on the biodegradation model and the property data can refer to applying the biodegradation model to the property data and receiving the biodegradation curve as biodegradation model output. Furthermore, generating the biodegradation curve based on the biodegradation model and the property data can be regarded as a virtual measurement of the biodegradation curve. In particular, the biodegradation model is based on measured data, for example, measured degradation curves of chemical substances used for the training of the biodegradation model and / or an initial biodegradation curve. Thus, the biodegradation model comprises information provided by these previous measurements. Moreover, the respective property data can in some cases also refer to measured chemical and / or physical properties of the chemical substance. Accordingly, also the generated biodegradation curve of a new chemical substance determined utilizing the biodegradation model can be regarded as being based at least partly on measurement results.

[0089] In a further step, the generated biodegradation curve is provided for further processing for monitoring and / or controlling chemical processes associated with the chemical substance, the generated biodegradation curve is provided for further processing for monitoring and / or controlling chemical processes referring to a technical application of the biodegradation curve. For example, the providing can comprise providing the generated biodegradation curve to the user as an output. The generated biodegradation curve can, for example, be provided to an output unit or to a computing unit for further processing. Preferably, the providing of the generated biodegradation curve leads to further processing utilizing 231051W001

[0090] 26 the received biodegradation curve. Controlling and / or monitoring chemical processes associated with the chemical substance can refer to controlling and / or monitoring chemical processes comprising, for instance, a synthesis of the chemical substance or parts thereof, a technical application of the chemical substance or the biodegradation of the chemical substance, in particular the biodegradation of the chemical substance in a biodegradation experiment or in a waste management facility. For example, further processing the generated biodegradation curve for controlling a chemical process related to the synthesis of a chemical substance can comprise deriving parameters of the chemical process to obtain a chemical substance with predetermined chemical and / or physical properties. In another example, further processing the generated biodegradation curve for controlling a chemical process related to the technical application of the chemical substance can comprise deriving the properties of the chemical substance and the habitat conditions required for the chemical substance to meet technical requirements and deriving a monitoring scheme for determining whether the chemical substance actually meets the technical requirements. In yet another example, further processing the generated biodegradation curve for controlling a chemical process related to the biodegradation of the chemical substance can comprise deriving parameters associated with habitat conditions and / or defining a monitoring scheme comprising, for instance, time points for measuring the biodegradation. However, further processing for monitoring and / or controlling chemical processes associated with the chemical substance does not always refer to deriving further parameters from the biodegradation curve. In one example, the generated biodegradation curve may be processed so as to be provided as output to a user, for example on the screen of a computer or handheld device, as a printout, or as otherwise perceptible information. For example, the user may be an operator of a chemical process associated with the chemical substance and may use the generated biodegradation curve for informational purposes or to make decisions about how to control and / or monitor the chemical process.

[0091] In an embodiment, the processing of the biodegradation curve comprises generating control data for controlling and / or monitoring a biodegradation experiment for determining the biodegradation of the chemical substance based on the generated biodegradation curve. A biodegradation experiment for determining the biodegradation in general refers to an experiment that is conducted with the aim to determine by measurement the biodegradation of the chemical substance. For example, a biodegradation experiment can measure the biodegradation of a chemical substance over time. In particular, a biodegradation experiment can determine the biodegradation of a chemical substance based on measuring the amount of consumed reactants and / or by measuring the amount of evolved products during the biodegradation process. In particular, a biodegradation experiment can be based on measuring the amount of consumed oxygen or the evolved CO2 during the biodegradation process using, for instance, respirometric methods. A biodegradation experiment can additionally or alternatively determine the biodegradation of a chemical substance based on measuring the disintegration of the chemical substance. In particular, a biodegradation experiment can be based on measuring the amount of the chemical substance that has disintegrated during the biodegradation process. In this embodiment, processing of the biodegradation curve comprises generating control data for controlling and / or monitoring a biodegradation experiment. Control data generally refers to any data that can be utilized for controlling and / or monitoring a biodegradation experiment. Controlling and / or monitoring a biodegradation experiment for determining the biodegradation of the chemical substance can comprise planning the biodegradation experiment, carrying out the biodegradation experiment, or analyzing the biodegradation experiment. Planning the biodegradation experiment can comprise, 231051W001

[0092] 27 for instance, finding a chemical substance to perform the biodegradation experiment, identifying a habitat for performing the biodegradation experiment, setting specific parameters of the biodegradation experiment, and / or identifying habitat conditions for the biodegradation experiment. In particular, planning the biodegradation experiment can comprise generating control data associated with a predetermined biodegradation characteristic to be met and / or generating control data associated with a monitoring scheme for the biodegradation experiment. For example, identifying a chemical substance for the biodegradation experiment can comprise identifying any of the substances mentioned above, in particular, identifying a polymer for the biodegradation experiment. Identifying a habitat for the biodegradation experiment can comprise identifying any of the above-mentioned habitats as habitat for the biodegradation experiment, for instance, an aqueous habitat, a marine habitat, a wastewater habitat, a limnic habitat, a compost habitat or a soil habitat. Identifying habitat conditions for the biodegradation experiment that are indicative of environmental characteristics of the habitat can comprise identifying habitat conditions associated with any of the above-mentioned habitats. Planning and carrying out the biodegradation experiment can also comprise determining the relevant time scales for the biodegradation experiment.

[0093] In a preferred embodiment, the generating of the control data comprises determining an end measurement time for the biodegradation experiment based on the biodegradation curve and generating the control data such that the final biodegradation measurement is performed in the biodegradation experiment at the determined end measurement time. By enabling the determination of an end measurement time for the biodegradation experiment based on the biodegradation curve, a biodegradation measurement can be terminated at the appropriate time. The duration of the biodegradation experiment can therefore be planned accurately, and time and resources are not wasted on biodegradation experiments that are terminated prematurely, making the data unreliable, or that run longer than necessary. An end measurement time can be determined, for instance, as a time point at which a predetermined biodegradation condition is expected to be met based on the generated biodegradation curve or based on a comparison of the generated biodegradation curve and a measured biodegradation. For example, an end measurement time can be determined as a specific time point at which the final biodegradation measurement is to be performed. If the biodegradation curve has a certain characteristic, for instance a pronounced increase in biodegradation which indicates the onset of a high biodegradation rate or a plateau which indicates a final drop in the biodegradation rate, a final measurement time can be determined as the time at which the characteristic is expected to have occured. In another example, an end measurement time can be determined based on comparing the measured biodegradation with the generated biodegradation curve to determine a time point at which an expected characteristic of the biodegradation curve, for instance a pronounced increase in biodegradation or a plateau, occurs in the experiment. For example, the generated biodegradation curve may comprise a plateau which indicates that the biodegradation is substantially finished once this plateau is reached, i.e. , that the remaining portion of the chemical substance is not expected to further be biodegraded in significant amounts. In this example it may be determined, based on the generated biodegradation curve, that continuing a biodegradation experiment does not make technical sense once the plateau is reached, even if a portion of the chemical substance is not completely biodegraded at this point. 231051W001

[0094] 28

[0095] In a further embodiment, the method described above comprises generating control data for controlling and / or monitoring an end of a biodegradation measurement based on the generated biodegradation curve. Determining an end of a biodegradation measurement based on the generated biodegradation curve is of course not limited to determining an end measurement time for a biodegradation experiment. Also in general, it may be beneficial to terminate a biodegradation measurement at the appropriate time. The duration of the biodegradation measurement can be planned accurately, and time and resources are not wasted on biodegradation measurements that are terminated prematurely, making the data unreliable, or that run longer than necessary. In particular, an end of a biodegradation measurement can refer to a time point at which a predetermined biodegradation condition is expected to be met based on the generated biodegradation curve or based on a comparison of the generated biodegradation curve and the measured biodegradation. However, an end of a biodegradation measurement can also be determined based on comparing the measured biodegradation with the generated biodegradation curve to determine a time point at which an expected characteristic of the biodegradation curve, for instance a pronounced increase in biodegradation or a plateau, occurs in the biodegradation curve. For example, when performing a biodegradation measurement, for instance as a series of measurements of the biodegradation, in a waste management facility for biodegradable waste an end of the biodegradation measurement may be determined based on the biodegradation curve generated during initialization of the biodegradation. The end of the biodegradation measurement may be determined according to the expected occurrence of a final plateau in the biodegradation percentage, which may be associated with a final drop-in biodegradation rate. In this example, based on the final measurement of the biodegradation it may be decided on the next processing steps of the remaining substances. For example, if the final biodegradation percentage does not meet predetermined threshold, the remaining substances may be subjected to further recycling steps.

[0096] In an embodiment, the processing of the biodegradation curve comprises validating, based on the biodegradation curve, whether the chemical substance fulfils a predetermined biodegradation goal, and generating control data for monitoring and / or controlling chemical processes associated with the chemical substance based on the validation. Generally, a predetermined biodegradation goal can specify a target value related to any characteristic of the biodegradation process or a target characteristic of the biodegradation process. Validating as part of the processing of the biodegradation curve refers to any measures that can determine whether the chemical substance fulfils a predetermined biodegradation goal. For instance, validating can refer to calculating one or more values of one or more characteristics of the biodegradation process based on the biodegradation curve associated with a chemical substance and comparing these values with a predetermined biodegradation goal to determine whether the chemical substance fulfils the biodegradation goal. Generating control data for monitoring and / or controlling chemical processes associated with the chemical substance based on the validation can refer to, for instance, generating control data for controlling a synthetization of the chemical substance, for controlling a biodegradation process of the chemical substance or for monitoring a biodegradation experiment with the chemical substance. Preferably, the predetermined biodegradation goal is related to one or more of a biodegradation rate constant, a maximum biodegradation percentage and a biodegradation lag phase. A biodegradation rate constant generally is indicative of the rate at which a biodegradation occurs. For instance, a biodegradation rate constant associated with the biodegradation of a chemical substance can be indicative of the amount of the chemical substance that is biodegraded per time. "Indicative of” can refer, for instance, to 231051W001

[0097] 29 the biodegradation rate constant directly comprising a rate at which the chemical substance is biodegraded, or to the biodegradation rate constant comprising a parameter which, when used in a respective biodegradation model, determines a rate at which the chemical substance is biodegraded. The biodegradation rate constant is not necessarily constant over time but can vary in the course of the biodegradation. A predetermined biodegradation goal related to a biodegradation rate constant can thus refer to a value of a biodegradation rate constant at a predetermined point in time. A predetermined biodegradation goal related to a biodegradation rate constant can also refer to a value of a biodegradation rate constant which, when used in a respective biodegradation model, determines a rate at which the chemical substance is biodegraded. A maximum biodegradation percentage is generally indicative of the maximum amount of a chemical substance that is biodegraded during biodegradation of the chemical substance relative to the initial amount of the chemical substance. "Indicative of” can refer, for instance, to the maximum biodegradation percentage directly comprising a maximum relative amount of the chemical substance that can be degraded in a biodegradation, or to the maximum biodegradation percentage comprising a parameter which, when used in a respective biodegradation model, determines a maximum relative amount of the chemical substance that can be degraded in a biodegradation.

[0098] In an embodiment it is preferred that one or more candidate chemical substances are provided and the biodegradation curve generated for the one or more candidate chemical substance as described above. The biodegradation curve can then be provided for selecting one or more candidate chemical substances for further testing. The testing can include producing one or more candidate chemical substances. Preferably, digital representations of one or more of the candidate chemical substances are selected based on the biodegradation curve and are provided for further testing with respect to one or more application goals. The provided digital representations associated with one or more candidate chemicals substances can include machine executable instructions for producing the respective one or more candidate chemical substance. The providing of the digital representations associated with the one or more candidate chemical substances based on the determined biodegradation curve can comprise selecting one or more of the candidate chemical substances as test chemical substances based on the determined biodegradation curve and providing the digital representation associated with the test chemical substances for further testing including producing the test chemical substances. Further the provided digital representations associated with the test chemical substances can be configured for further testing including producing the test chemical substances, for example, can include respective production instructions and optionally testing instructions. The selecting can be based on the determined biodegradation curve and a biodegradation curve or target biodegradation property associated with the curve. For example, based on a comparison between the determined biodegradation curve and a target biodegradation curve a candidate chemical substance can be selected as test chemical substance if the associated biodegradation curve meets the respective target. The providing can further include providing production data comprising instructions with respect to the production of one or more of the candidate chemical substances that are selected. The production data can be configured for controlling and / or monitoring a production of the respective selected candidate chemical substance, for instance, the production data can comprise respective control data. The production data can refer to any data that allows to derive 231051W001

[0099] 30 respective controlling and / or monitoring signals from the production data. Such produced candidate chemical substance can then be subjected to further testing with respect to the one or more predetermined application goals of the chemical substance.

[0100] In general, biodegradation can be a nonlinear process and can proceed at different rates over the course of the biodegradation. In particular, the biodegradation of a chemical substance can proceed at a smaller rate when most of the chemical substance has been already biodegraded. A reason for a decrease of the biodegradation rate when most of the chemical substance is biodegraded could be that the remaining portion of the chemical substance contains material that takes more time to biodegrade, while the material that takes less time to biodegrade has been biodegraded already. Thus, the maximum biodegradation percentage can also refer to a biodegradation percentage obtained at a predetermined point in time which is indicative of the maximum amount of a chemical substance that could be biodegraded at a later point in time. Furthermore, the biodegradation of a chemical substance can proceed at a smaller rate during an initial starting phase of the biodegradation process. Reasons for a smaller rate of biodegradation during an initial starting phase can be, for instance, that the initial amount of relevant microorganisms is small, that the relevant microorganisms initially need to adapt to biodegrading the given chemical substance or that the initial steps of biodegradation, such as breaking the chemical substance into smaller fragments, requires more time than the following steps of biodegradation. A biodegradation lag phase generally is indicative of the length of an initial starting phase of the biodegradation process, during which the biodegradation may proceed at a smaller rate. "Indicative of' can refer, for instance, to the biodegradation lag phase directly comprising an initial duration during which the biodegradation proceeds at a smaller rate, or to the biodegradation lag phase comprising a parameter which, when used in a respective biodegradation model, determines an initial duration during which the biodegradation proceeds at a smaller rate.

[0101] In a further embodiment, the predetermined biodegradation goal is defined by a biodegradation norm. Validating, based on the biodegradation curve, whether the chemical substance fulfils a predetermined biodegradation goal, and generating control data for monitoring and / or controlling chemical processes associated with the chemical substance based on the validation allows for more accurately determining whether a new chemical substance is suited for a particular technical task. The inventors found that a particularly accurate determination is possible if the predetermined biodegradation goal is related to one or more of a biodegradation rate constant, a maximum biodegradation percentage and a biodegradation lag phase. Using a predetermined biodegradation goal as defined by a biodegradation norm further allows for assessing the biodegradation of a chemical substance in a more standardized and thus comparable manner. For example, biodegradation norms as defined by ISO 14855-1, ISO 17556, ISO 18830, ISO 19679, ISO 20200, ISO 22404, ISO 23977-1 , ISO 23977-2, OECD 301 , OECD 302 and OECD 306 specify biodegradation goals. For instance, some of these biodegradation norms define as biodegradation goal that a specified amount of the chemical substance be biodegraded within a specified time window. The specified time window may be defined in relation to the biodegradation process itself. For instance, the specified time window can begin after an initial lag phase, during which a specified amount of the chemical substance is biodegraded. 231051W001

[0102] 31

[0103] If it is determined that the chemical substance does not fulfil the predetermined biodegradation goal, in an embodiment processing of the biodegradation curve comprises generating, based on the biodegradation curve, measures for adjusting the chemical substance such that the adjusted chemical substance does fulfil the predetermined biodegradation goal or measures for specifying an adjusted biodegradation habitat such that the chemical substance does fulfil the predetermined biodegradation goal in the adjusted biodegradation habitat and providing the measures for further processing for monitoring and / or controlling chemical processes associated with the chemical substance. Since in this embodiment the method comprises adjusting the chemical substance and / or the habitat data in order to fulfil the predetermined biodegradation goal, new chemical substance and / or habitat conditions which are expected to meet the predetermined biodegradation goal can be found in an accurate and fast manner, requiring less resources for tests and experiments. For instance, measures for adjusting the chemical substance and / or measures for specifying an adjusted biodegradation habitat can be generated by adapting property data and / or habitat data based on which an adjusted biodegradation model is provided, generating a new biodegradation curve based on the adjusted biodegradation model and determining based on the new biodegradation curve whether the adjusted chemical substance and / or the adjusted biodegradation habitat allow for meeting the predetermined biodegradation goal. In an example, it may be determined that a chemical substance in a habitat does not fulfil a predetermined biodegradation goal, for instance a biodegradation rate constant or a maximum biodegradation percentage. In this example, determining that the chemical substance in the habitat does not fulfil the predetermined biodegradation goal can refer to processing of a generated biodegradation curve, to processing a measured biodegradation curve, or to a combined processing of a generated and a measured biodegradation curve, for instance, through comparison, wherein a measured biodegradation curve can also refer to an initial biodegradation curve. In this example, new biodegradation curves may be generated based on adapted property data and / or adapted habitat conditions and processed to determine whether the adapted property data and / or adapted habitat conditions allow for meeting the biodegradation goal. It this example it may be found, for instance, that changing the temperature in the habitat or changing the amount or type of microorganisms in the habitat will allow for meeting the biodegradation goal. In the same example it may be found that changing other habitat conditions, for instance, a salt concentration or a pH value, may not allow for meeting the biodegradation goal. Still in the same example it may be found that adapting property data, for instance, increasing the molecular weight may also allow for meeting the biodegradation goal, but only when adapting the habitat conditions at the same time. Measures such as changing the temperature in the habitat, changing the amount or type of microorganisms or adjusting the molecular weight are then provided for further processing for monitoring and / or controlling chemical processes, for instance for adjusting a synthesis process or for changing habitat conditions accordingly.

[0104] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to a polymer type being a least one of a poly al koxy I ate, polycondensate, addition polymer, vinylic polymer, natural polymer, polymer dispersion, polymer film, biopolymer, polysilicone, resin, rubber and polyketone, wherein the biodegradation model is specifically trained for the respective polymer type to which the polymer belongs. In particular, the historical training data for parameterizing the biodegradation model comprises polymers of the respective polymer type. However, the biodegradation model can also be parameterized with training data of polymers from more than one polymer type. 231051W001

[0105] 32

[0106] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to polyalkoxylates and the habitat is a wastewater habitat, in particular, a sludge habitat. Moreover, it is preferred in this embodiment that the property data comprise at least one of a molar mass, an ingredient, a chemical moiety, solubility in water and a partition coefficient, more preferably, the property data comprise a molar mass, even more preferably, comprise a molar mass, an ingredient, even more preferably, comprise a molar mass, an ingredient, and a partition coefficient.

[0107] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to polycondensates, preferably, polyesters, polyamides and phenoplasts, and the habitat is a wastewater habitat, in particular, a sludge habitat, or soil habitat. Moreover, it is preferred in this embodiment that the property data comprise at least one of a molar mass, an ingredient, chemical moiety, solubility in water, a partition coefficient, a measure for stability against hydrolysis, and degree of crystallinity, more preferably, the property data comprise a molar mass and an ingredient, even more preferably, comprise a molar mass, an ingredient and a chemical moiety, even more preferably, comprise a molar mass, an ingredient, chemical moiety, and a degree of crystallinity.

[0108] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to addition polymers, preferably, polyurethanes and polyureas, and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that property data comprise at least one of an ingredient, a chemical moiety, a ratio of chemical moieties, a ratio of ingredients, and a degree of crystallinity, more preferably, the property data comprise an ingredient, even more preferably, comprise an ingredient and a ratio of chemical moieties, even more preferably, comprise an ingredient, a ratio of chemical moieties, a ratio of ingredients.

[0109] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to the vinylic polymers, preferably polyvinyls, polyacrylates, polystryrenes polyvinylethers, and polyvinylalcohols and the habitat is a wastewater habitat, in particular, a sludge habitat, or a soil habitat. Moreover, it is preferred in this embodiment that the property data comprise at least one of a molar mass, an ingredient, a chemical moiety, solubility in water, a partition coefficient, more preferably, the property data comprise a molar mass, even more preferably, comprise a molar mass, and an ingredient, even more preferably, comprise a molar mass, an ingredient and a chemical moiety.

[0110] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to natural polymers, preferably, polysaccharides, polynucleotides, lignin, suberin, cutin, cutan, melanin, natural rubber and polypeptides, and the habitat is a wastewater habitat, in particular, a sludge habitat, or a soil habitat. Moreover, it is preferred in this embodiment that the property data comprises at least one of a molar mass, a chemical moiety, and solubility in water and a partition coefficient, more preferably, the property data comprises a chemical moiety, even more preferably, comprise a chemical moiety, and a molar mass.

[0111] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to polymer dispersions, and the habitat is a marine habitat, a compost habitat, a sludge habitat or soil habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a chemical moiety, a 231051W001

[0112] 33 solubility in water and a particle size, more preferably, the property data comprises an ingredient and a particle size, even more preferably, comprise an ingredient, a chemical moiety, and a particle size.

[0113] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to polymer films, and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a molar mass, a chemical moiety, solubility in water, degree of crystallinity, and surface / volume ratio, more preferably, the property data comprise an ingredient and surface / volume ratio, even more preferably, comprise an ingredient, a chemical moiety and a surface / volume ratio, even more preferably, comprise an ingredient, a chemical moiety, a degree of crystallinity, and surface / volume ratio.

[0114] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to polysilicones and the habitat is a soil habitat, wastewater habitat, in particular, sludge habitat, or marine habitat. Moreover, it is preferred in this embodiment that the property data comprise at least one of an ingredient, a molar mass, a chemical moiety, solubility in water, a partition coefficient, and surface / volume ratio, more preferably, the property data comprise a molar mass, even more preferably, comprise a molar mass and an ingredient, even more preferably, comprise a molar mass, an ingredient and a partition coefficient.

[0115] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to the resins, and the habitat is a soil habitat, a marine habitat or a limnic habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a molar mass, a chemical moiety, solubility in water, degree of crystallinity, and surface / volume ratio, more preferably, the property data comprise a molar mass, even more preferably, comprise a molar mass and an ingredient, even more preferably, comprise a molar mass, an ingredient, and surface / volume ratio.

[0116] In a preferred embodiment, the chemical substance is a polymer, wherein the polymer belongs to the rubbers, and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that the property data comprise at least one of an ingredient, a chemical moiety, solubility in water, degree of crystallinity, and surface / volume ratio, more preferably, the property data comprise an ingredient, even more preferably, comprise an ingredient and a surface / volume ratio, even more preferably, comprise an ingredient, a chemical moiety, degree of crystallinity, and surface / volume ratio.

[0117] In a preferred embodiment, the property data comprise at least one of a molar mass, a chemical moiety, solubility in water and / or in octanol, a degree of crystallinity, and a surface / volume ratio. More preferably, the property data comprise a chemical moiety, even more preferably, comprises a molar mass, a chemical moiety, and a solubility in water. For a chemical substance being a polymer belonging to polyalkoxylates, polycondensates, vinylic polymers, or pol- yslicones it is preferred that the property data comprise further at least one of a partition coefficient and an ingredient. For a chemical substance being a polymer belonging to polycondensates, addition polymers, polymer films, resins, rubbers, or polyketones it is preferred that the property data comprise further at least one of a degree of crystallinity 231051W001

[0118] 34 and a measure for stability against hydrolysis. For a chemical substance being a polymer belonging to resins, rubbers, addition polymer or polysilicones, it is preferred that the physicochemical characteristics comprise further a surface / vol- ume ratio. It is preferred that the machine learning based kinetic biodegradation model is based on a second order kinetic model.

[0119] It shall be understood that the methods as described above, the apparatuses as described above and the computer program products as described above have similar and / or identical preferred embodiments, in particular, as defined in the dependent claims. Moreover, also the control data and the use of control data as described above have similar and / or preferred embodiments, in particular, as defined in the dependent claims.

[0120] It shall be understood that a preferred embodiment of the present disclosure can also be any combination of the dependent claims or above embodiments with the respective independent claim.

[0121] These and other aspects of the present disclosure will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0122] BRIEF DESCRIPTION OF THE DRAWINGS

[0123] In the following drawings, shows schematically and exemplarily an embodiment of a system comprising an apparatus for generating a biodegradation curve associated with a chemical substance, shows schematically and exemplarily a flow chart of a method for generating a biodegradation curve associated with a chemical substance, show schematically and exemplary a flow chart of preferred more detailed embodiments of a method for generating a biodegradation curve associated with a chemical substance, show exemplarily measured biodegradation curves and corresponding models fit with biodegradation models, shows exemplary measured biodegradation curves and corresponding model fits with biodegradation models together with exemplary depictions of biodegradation habitats and partially biodegraded chemical substances, and shows exemplary measured biodegradation curves and corresponding model fits with biodegradation models of Fig. 8 grouped differently as compared to Fig. 8, show exemplary of biodegradation curves in wastewater for polymers of different molecular weights of biodegradation kinetics as determined by the biodegradation model and experimental data, show exemplary of biodegradation curves in wastewater for a polymer in a habitat at different temperatures of biodegradation kinetics as determined by the biodegradation model and experimental data. 231051W001

[0124] 35

[0125] Fig. 1 shows schematically and exemplary an embodiment of a system 100 comprising an apparatus 110 for generating a biodegradation curve associated with a chemical substance. Further, the system 100 comprises a database of 130, on which generated biodegradation curves can be stored, and a biodegradation system 120 configured for performing a biodegradation of the chemical substance, for example for experimentally testing the biodegradation of the chemical substance, wherein the biodegradation system 120 can be monitored and / or controlled using the generated biodegradation curves.

[0126] Biodegradation refers to the process of degradation or decomposition of a chemical substance caused by biological processes. Biodegradation thus refers to processes that include biological material, in particular, microorganisms, taking part in the degradation process. For example, biodegradation can refer to a degradation process in which microbes break the chemical substance into smaller fragments and take up such fragments to then metabolize and convert the fragments, for instance, under aerobic conditions, into biomass and one or more residual substances. In particular, biodegradation of a biodegradable polymer can comprise depolymerization of the biodegradable polymer by microbes into smaller fragments such as monomers and oligomers which can then be taken up through the microbial cell membrane and be metabolized and converted into biomass, and residual substances. The biodegradation system is configured for performing biodegradation of a chemical substance, for instance, by providing the necessary habitat conditions such as a suitable amount and the right type of microorganisms, a suitable temperature, salt and nutrient concentration, etc., for the biodegradation to occur. Additionally, the biodegradation system may comprise measures to collect data indicative of the biodegradation process, for instance, sensors configured for collecting data indicative of the amounts or concentrations of materials taking part in the biodegradation, for instance, microorganisms, the to be degraded chemical substance, oxygen, etc., or of the amounts and concentration of material being produced in the biodegradation process as intermediate or final products, for instance, biomass and residual substances such as CO2, water and / or inorganic salts. A biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time. Thus, the biodegradation curve is indicative of two or more parameters that quantify the biodegradation of a chemical substance, wherein each of the two or more parameters is associated with a respective time point. In particular, the biodegradation curve can comprise two or more parameters quantifying the biodegradation of a chemical substance, wherein each of the two or more parameters quantifying the biodegradation of the chemical substance is determined at one respective time point, and wherein not all parameters quantifying the biodegradation of the chemical substance are determined at the same time. The biodegradation curve can be indicative of, in particular comprise, parameters quantifying the mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process with time. For example, the biodegradation curve can be indicative of, in particular comprise, two or more parameters quantifying the biodegradation of a chemical substance by quantifying the amount of substances consumed during the biodegradation process, for instance oxygen, or the amount of the residual substances produced during the biodegradation process, for instance carbon dioxide. Generally, the chemical substance can be any chemical substance and can refer, for instance, to a single element, to a substance comprising chemical compounds, to a substance comprising only one type of chemical entity or to a substance comprising a mixture of different chemical entities. The chemical substance can refer to a chemical substance in any physical state or phase, for instance, the chemical substance can be solid, liquid. In general, the chemical and / or physical properties 231051W001

[0127] 36 of the chemical substance can substantially influence the biodegradation process and the properties themselves can change during the biodegradation process. Preferably, the chemical substance is a biodegradable chemical substance that can be degraded by biological processes. The chemical substance can refer to a polymer, for instance, a synthetic polymer, in particular biodegradable synthetic polymer, or a natural polymer. The chemical substance can also refer to a small molecule.

[0128] In the embodiment the apparatus 110 comprises an input interface 111 , a processor 112 and an output interface 113.

[0129] The input interface 111 is configured to i) receive property data associated with one or more chemical and / or physical properties of the chemical substance and ii) provide a biodegradation model based on the property data. The input interface 111 can refer, for instance, to an input unit into which a user can input respective property data associated with one or more chemical and / or physical properties of the chemical substance. Additionally, the input interface 111 can refer, for instance, to an input unit which allows a user to select or specify a biodegradation model to be provided. Moreover, the input interface 111 can refer to or can be a part of a user interface that allows the user to interact with the apparatus 110 for generating a biodegradation curve. However, the input interface 111 can also refer to or be communicatively coupled with a storage unit on which property data associated with one or more chemical and / or physical properties of the chemical substance and / or respective biodegradation models are already stored. For example, the input interface 111 can be communicatively coupled with the database 130. Generally, property data associated with one or more chemical and / or physical properties of the chemical substance can refer to any property data which allow deducing information about chemical and / or physical properties of the chemical substance. In particular, the property data can directly comprise chemical and / or physical properties of the chemical substance, for example, in form of values for respective quantities. In another example, property data can refer to a synthesis specification or a structural formula of a chemical substance that can be used to derive, using known chemical and physical laws and relations, the respective chemical and / or physical properties of the chemical substance. Preferably, the property data comprises parameters specifying at least one of constitutional descriptors, count descriptors, list of structural fragments, fingerprints, graph invariants, 3D-descriptors and / or higher dimensional descriptors that are indicative of the chemical and / or physical properties of the chemical substance. For instance, property data could be indicative of an average molecular weight, a number and type of atoms, a number and type of bonds between atoms, a number or type of functional groups, etc. In an exemplary embodiment, the chemical substance can be a polymer and the received property data can refer to chemical and / or physical properties of subgroups of the polymer, for instance, polymerized monomers or oligomer fragments. In an exemplary embodiment, the input interface may also receive habitat conditions that are indicative of environmental characteristics of the habitat and can influence the biological activity in the respective habitat, for example, the presence, growth or absence of specific bacteria. Environmental characteristics of a habitat can, for instance, refer to a salt concentration, an oxygen level, a location, a temperature, a nutrient concentration, for example, a nitrogen, phosphate, potassium, and / or dissolved organic carbon concentration, a pH value, or a microbial community and a concentration of microbes, etc. Habitat conditions may be specified by specifying the habitat itself, for example, whether the habitat is a marine habitat, a wastewater habitat, a limnic habitat, a compost habitat, a soil habitat, etc. 231051W001

[0130] 37

[0131] The biodegradation model provided by the input interface 111 is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process. Generally, a kinetic model is a model of a process, for instance a chemical reaction, converting incoming starting materials, for example reactants, to outgoing materials, for example products, that is based on tracing the mass transfer from the incoming starting materials to the outgoing materials and that is based on rates, for example reaction rates, at which the process occurs. For setting up a kinetic model of a particular process it can be beneficial to have detailed knowledge of the different sub-processes, for instance, reactions, that are part of this particular process. However, a kinetic model can also refer to an effective model of a complex process including any number or combination of sub-processes and chemical reactions. The kinetic model may thus be set up without detailed knowledge of sub-processes and chemical reactions, but rather describe the complex process based on knowledge of some or all of the incoming starting materials and outgoing materials and one or more effective rates at which the complex process occurs. A biodegradation process such as the biodegradation process occurring in the biodegradation system 120 usually comprises a plurality of individual sub-processes and chemical reactions and can thus be considered a complex process. In a biodegradation process of a chemical substance, incoming starting materials can refer to, for instance, the chemical substance that is biodegraded, oxygen and water, but also microorganisms involved in the biodegradation process. In particular, incoming starting materials can refer to materials present as part of the biodegradation habitat. In a biodegradation process of a chemical substance, outgoing materials can refer to, for instance, biomass, including microorganisms involved in the biodegradation process, and residual substances, for instance, carbon dioxide, water and inorganic salts. The rate at which a process occurs generally refers to the amount of incoming starting materials that is converted to outgoing materials in the process per time. In particular, the rate of a reaction, or reaction rate, generally refers to the amount of reactants that is converted to products in the reaction per time.

[0132] Moreover, the biodegradation model provided by the input interface 111 is based on a machine learning based kinetic model wherein one or more trainable parameters of the biodegradation model are parameterized based on a historical training dataset. The historical training dataset comprises a) measured degradation curves of at least one chemical substance, and b) respective property data of the at least one chemical substance. The biodegradation model provided by the input interface 111 is parameterized, or trained, such that the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based on property data of the chemical substance as input. In an embodiment, the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based further on habitat data indicative of habitat conditions. Generally, a machine learning based kinetic model contains trainable parameters that are parameterized based on a historical training data set such that it can generate biodegradation curves indicative of the biodegradation of a chemical substance in a habitat. The machine learning based kinetic model can thus be parameterized using the measured degradation curves of at least one chemical substance and the respective property data of at least one chemical substance. "Machine learning” as used here refers to a data-driven parameterization of the biodegradation model, which can also be considered training the biodegradation model. For instance, the machine learning based model can be a model based on known machine learning algorithms like, for example, neural networks, regression models, classification algorithms, 231051W001

[0133] 38 and can be trained using any of the known training algorithms suitable for the particular task. The trainable parameters that are parameterized based on the historical training data set can generally refer to all parameters of a biodegradation model and, in particular, to parameters which can be adjusted to achieve a more accurate biodegradation curve as model output. For example, trainable parameters can refer to parameters associated with the kinetic model, for instance rate constants and / or proportionality constants, for instance degradation coefficients, relating other quantities to rate constants or to process rates, stoichiometric coefficients and / or amounts of incoming starting materials and outgoing materials. T rainable parameters can also refer to parameters based on which the parameters entering the kinetic model may be determined, for instance, the already mentioned degradation coefficients, a likelihood of a microorganism to consume a chemical substance or trainable parameters of machine learning based models used for selecting a particular biodegradation model and determining the adjustable parameters of this particular biodegradation model.

[0134] The processor 112 is configured to generate the biodegradation curve based on the biodegradation model and the property.

[0135] The output interface 113 is configured to provide the generated biodegradation curve for further processing for monitoring and / or controlling chemical processes associated with the chemical substance. The output interface 113 can refer, for instance, to an output unit configured to provide the generated biodegradation curve to a user. The output interface 113 can also refer to or can be a part of a user interface that allows the user to interact with the apparatus 110 for generating a biodegradation curve. The output interface 113 can, for example, comprise a display configured to display the generated biodegradation curve to a user. The user can further monitor and / or control chemical processes associated with the chemical substance. However, the output interface 113 can also refer to or be communicatively coupled with a storage unit on which received biodegradation curves can be stored. For example, the output interface 113 can be communicatively coupled to the database 130. Furthermore, the output interface 113 can provide the generated biodegradation curve to another unit that is configured for further processing the generated biodegradation curve for monitoring and / or controlling chemical processes associated with the chemical substance.

[0136] Optionally, the apparatus 110 can comprise a control unit 114 that is adapted to generate control data and provide control signals based on the generated biodegradation curve for controlling a unit for conducting chemical processes associated with the chemical substance, for instance as part of the biodegradation system 120 configured for performing biodegradation of the chemical substance. In particular, it is preferred that the provided control signals are used for monitoring and / or controlling a biodegradation experiment for determining the biodegradation of a chemical substance. For instance, the control data can comprise time intervals between successive biodegradation measurements and / or the control data can comprise an end measurement time for the final biodegradation measurement, after which the biodegradation experiment is finished. The provided control signals can then be used to monitor and / or control the biodegradation experiment accordingly. The biodegradation system 120 can also be part of or comprise a waste management facility adapted for biodegradation of waste materials. The provided control signals can then be used for monitoring and / or controlling the biodegradation of waste materials. For instance, the control data can comprise information on which chemical substances are suitable for biodegradation in the waste management facility, the control 231051W001

[0137] 39 data can comprise information on which habitat conditions within the waste management facility are suitable for biodegradation of the waste material and / or the control data can comprise a duration required for the biodegradation of the waste material. The provided control signals can then be used to monitor and / or control the waste management facility accordingly.

[0138] Fig. 2 shows schematically and exemplarily a flow chart of a method for generating a biodegradation curve associated with a chemical substance. The method 200 comprises a first step 210 of receiving, at a computer interface, property data associated with one or more chemical and / or physical properties of the chemical substance. The method 200 further comprises a step 220 of providing a biodegradation model based on the property data. The steps 210 and 220 can be performed in accordance with the principles described above with respect to the input interface 111. In particular, the providing of the biodegradation model can also refer to a selection of the biodegradation model based on the received property data. Moreover, the biodegradation model is based on a machine learning based kinetic model parameterized based on historic training data as discussed above in more detail. The method 200 further comprises a step 230 of applying the biodegradation model to the property data of the chemical substance as input and receiving the biodegradation curve as biodegradation model output. Moreover, the method 200 comprises a step 240 of providing the generated biodegradation curve for further processing for monitoring and / or controlling chemical processes associated with the chemical substance. Also the steps 230 and 240 can be performed in accordance with the principles described above with respect to the processor 112 and the output interface 113, respectively. In particular, providing the generated biodegradation curve can refer to providing the biodegradation curve to a user interface to be displayed on a display. Providing the biodegradation curve can also refer to providing the biodegradation curve to another unit that is configured for further processing the generated biodegradation curve for monitoring and / or controlling chemical processes associated with the chemical substance. For instance, in an optional step 250, the method can additionally comprise generating control data for monitoring and / or controlling a chemical process associated with the chemical substance.

[0139] In the following, more detailed preferred examples of the above-described method and the corresponding apparatus will be described. An exemplary embodiment of the method can consist of steps described in the following. A schematic and exemplary flow chart of an exemplary embodiment of the method, in particular for monitoring and controlling a biodegradation experiment, is provided by Fig. 3. In this exemplary embodiment, the method starts with the user entering, for instance via a user interface, property data of a chemical substance for which a biodegradation experiment is to be performed. Further, a condition, for instance a desired biodegradation percentage, a biodegradation rate or a characteristic of the biodegradation curve, for an end measurement time can be requested, for instance, also via the user interface. In the next step, the biodegradation model is provided based on the property data. This can refer to the user selecting an appropriate biodegradation model from a list of available biodegradation models, for instance a list of available biodegradation models containing biodegradation models suitable for the entered property data, or it can refer to an automated selection of an appropriate biodegradation model based on the property data. Optionally, further preselected conditions indicated by the provided biodegradation model can be requested. For example, the biodegra- 231051W001

[0140] 40 dation model can be adapted to utilize further information, for instance, optional habitat conditions, application constraints, etc. that can be requested if necessary and that might allow the biodegradation model to determine the biodegradation curve with higher accuracy. In an example, the available biodegradation models can comprise trainable parameters such as an initial microbe concentration, a likelihood of a microorganism to consume the chemical substance, and a degradation coefficient. The provided biodegradation model and corresponding values for the trainable parameters may then be selected based on comparing the provided property parameters with the property data of the chemical substances based on which the biodegradation models were parameterized. In the next step, the provided biodegradation model is applied to the received property data and, optionally, to further information like habitat conditions, to generate as a model output the biodegradation curve. The biodegradation curve can then be processed to determine characteristics of the biodegradation process, in particular to determine an expected end measurement time based on the received property data and optional further information. Based on the determined characteristics of the biodegradation process, it can be evaluated whether performing a biodegradation experiment to measure the biodegradation of the chemical substance is warranted. In particular, it can be evaluated whether the end measurement time is determined to be within an acceptable time frame, for instance, whether the determined duration of the biodegradation experiment is acceptable in terms of development time, and required resources. If the determined end measurement time is not within an acceptable time frame, the biodegradation experiment is aborted and, for instance, another biodegradation experiment can be started based on a different chemical substance and / or modified optional information such as habitat conditions. If the determined end measurement time is within an acceptable time frame, the biodegradation of the chemical substance can be performed experimentally, which in particular can comprise monitoring the biodegradation and evaluating whether the condition for the end measurement time is met. Monitoring the biodegradation can refer to, for instance, measuring the amount of the chemical substance that is not yet biodegraded, measuring the amount of consumed substances like oxygen, measuring the amount of produced substances like CO2 or other residual substances, etc. In particular, based on monitoring data an experimental biodegradation curve can be determined. For example, the measured amounts of consumed substances or produced substances can be evaluated as a function of time and can, optionally, be displayed to a user or stored in a database. The monitoring data is also used to assess whether the condition for the final measurement time has been met and whether the biodegradation process can either be terminated or resumed accordingly. However, monitoring the biodegradation can also comprise recording the time that has already elapsed in the biodegradation process. A condition for an end measurement time can thus also refer to the end of a certain period of time, for instance a period of time predetermined by processing the generated biodegradation curve in a previous step. Optionally, the experimentally determined biodegradation curve can be compared with the computationally determined biodegradation curve. Furthermore, the provided biodegradation model can optionally be adjusted based on the measured biodegradation curve. For example, the provided biodegradation model may be based on second order kinetic model and further based on a carbon mass balance associated with a mass transfer of carbon from the chemical substance to microorganisms and one or more residual substances, in particular, CO2. If the measured rate of this biodegradation process is different from the computationally determined rate associated with the provided biodegradation model, it may be beneficial to adjust one of the trainable parameters. For example, the degradation coefficient relating the rate of the biodegradation process to the product of the concentrations of, for instance, the microorganisms and the carbon available for biodegradation, may be adjusted accordingly. 231051W001

[0141] 41

[0142] The adjusted biodegradation model can be used to again computationally determine a biodegradation curve, evaluate the characteristics of the biodegradation, and determine if a continuation of the biodegradation experiment is warranted.

[0143] Fig. 4 shows a schematic and exemplary flow chart of a further exemplary embodiment of the method, in particular for monitoring and controlling a biodegradation process. In this exemplary embodiment, the method starts with experimentally determining biodegradation parameters and the user entering, for instance via a user interface, property data of a chemical substance which is supposed to be biodegraded in the biodegradation process. Further, a biodegradation goal, for instance a desired biodegradation rate constant, a maximum biodegradation percentage or a biodegradation lag phase, can be requested, for instance, also via the user interface. Optionally, a biodegradation goal and / or required biodegradation parameters can also be defined by a biodegradation norm. Experimentally determining biodegradation parameters can, in particular, refer to experimentally determining habitat conditions. For example, the biodegradation process can be carried out in any one of an aqueous habitat, a marine habitat, a wastewater habitat, a limnic habitat, a compost habitat or a soil habitat and the habitat conditions can refer to any of the respective habitat conditions described above. In particular, the habitat conditions can refer to combinations of the respective habitat conditions. The determined biodegradation parameters, in particular experimentally determined habitat conditions, are then provided and, together with the received property data, used to select a corresponding biodegradation model. The provided biodegradation model is then used to generate a biodegradation curve based on the property data and biodegradation parameters. Based on processing the generated biodegradation curve, it can be assessed whether the biodegradation process currently meets the defined biodegradation goal or whether the biodegradation process can be expected to meet the defined biodegradation goal in the future. If the biodegradation goal is not met or not expected to be met in the future, the biodegradation parameters may be adjusted accordingly. In an example, one of the biodegradation parameters may be a temperature at which the biodegradation process is performed, and a defined biodegradation goal could be a final biodegradation percentage. If it is found that with the initially determined temperature the desired final biodegradation percentage will not be met, the temperature can be adjusted accordingly. The biodegradation process can be resumed, for example with adjusted parameters, and can be further monitored, for instance by measuring the biodegradation and by determining the biodegradation parameters. If the biodegradation process is not finished yet, the monitoring data and the updated biodegradation parameters can be used again as input to the provided biodegradation model to obtain an updated biodegradation curve, which can again be used to assess whether the biodegradation goal is met or whether it is expected to be matched in the future, etc. Optionally, the experimentally determined biodegradation curve can be compared with the computationally determined biodegradation curve, and the provided biodegradation model can be adjusted based on the measured biodegradation curve. This exemplary embodiment in particular demonstrates how a biodegradation process can be continuously monitored and controlled based on a computationally determined biodegradation curve in accordance with the disclosed method.

[0144] Fig. 5 shows schematically another exemplary embodiment of the method, in particular for determining a chemical substance suitable to fulfil a specific biodegradation goal in a specific habitat. In this exemplary embodiment, the method starts with the user entering, for instance via a user interface, a biodegradation goal, for instance, a desired biodegradation rate constant, a maximum biodegradation percentage or a biodegradation lag phase, and expected 231051W001

[0145] 42 habitat data, for instance expected habitat conditions for the respective habitat in which the chemical substance is supposed to be biodegraded. Optionally, further conditions, for example further application constraints, can be requested. Based on the provided information, a candidate chemical substance can be defined in the next step. Defining a candidate chemical substance can refer to, for instance, the user selecting a chemical substance from a list of available chemical substances, the user entering a chemical substance, an automated selection of a chemical substance, a random selection of a chemical substance from a list of chemical substances, etc. A biodegradation curve associated with the candidate chemical substance is then generated in accordance with the method disclosed herein and based on the biodegradation curve it is assessed whether the candidate chemical substance fulfils the specified biodegradation goal for the specified habitat. If these conditions are not met, the chemical substance is adjusted, for instance by selecting another chemical substance as candidate chemical substance, and the steps of generating and processing the biodegradation curve are performed again. Once a candidate chemical substance is found that is determined to meet the specified biodegradation goal for the specified habitat, the candidate chemical substance can be synthesized and a respective biodegradation experiment performed and monitored, in particular by measuring the biodegradation and determining an experimental biodegradation curve. As long as the end condition for the biodegradation experiment is not met and the measured biodegradation curve remains in reasonable agreement with the generated biodegradation curve, the biodegradation experiment can continue. If this is not the case, the biodegradation model is adjusted, and another generated biodegradation curve is computed and processed. If based on the processing result of the new generated biodegradation curve it is to be expected that the biodegradation goal is currently met or can be met in the future, the biodegradation experiment is continued. However, if it is to be expected that the biodegradation goal is currently not met and / or cannot be met in the future, the biodegradation experiment is aborted and the chemical substance is adjusted, for instance by selecting another chemical substance as candidate chemical substance, and the steps of generating and processing the biodegradation curve are performed again. In this embodiment, the measured biodegradation curves and / or the adjustments made to the currently utilized biodegradation model can optionally be used to update the database with training data and the parameterization of the biodegradation models.

[0146] In the following, a more detailed description will be provided of how biodegradation models according to the present disclosure may be set up based on biodegradation measurements. Moreover, the examples show the accuracy and reliability of the model for different application cases. The following description is partly based on comparing the model to experimental results which are presented as well. In order to show the possible accuracy of the model, the utilized examples are specific biodegradation cases for a specific polymer in different environments, and the biodegradation model has been trained for these specific cases. However, the principles can also be applied to training more generic models that can be applied to different substances and different environments. Although the below examples are based on polymers, as one of the most important application cases, respective principals can also be applied to other chemical substances.

[0147] The rapid advance of our modern lifestyles would not have been possible without the development of plastic with their broad applicability and availability. The durability of these materials in their use phase however turns into persistence once they enter the environment, visible in the growing plastic pollution in nature. Of late, an improvement in retrieving 231051W001

[0148] 43 materials ending up in the environment, as well as better collection and recycling processes all over the world is being seen. However, substantial steps still need to be taken by all actors in the value chain to tackle this pressing issue. Biodegradable plastics are a valuable alternative to conventional plastic materials for environmental applications or for products that cannot be kept from ending into the environment.

[0149] Biodegradable materials are designed to be degraded by natural microorganisms in end-of-life scenarios, e.g., certified compostable bags and packaging items in industrial composting plants, and certified soil biodegradable mulch films in agricultural fields. In order to be biodegraded by microbes, polymeric materials are a) first depolymerized, e.g. by enzymes, into smaller fragments such as monomers and oligomers, then b) such fragments are taken up through the microbial cell membrane, to then be c) metabolized and converted, under aerobic conditions, into biomass, carbon dioxide, water and inorganic salts. Biodegradability, be it of a natural or synthetic polymer, can be a function of environmental and polymer-related factors. Environmental factors can include habitat, temperature, nutrient availability, and microbial communities that can be taken into account as part of the model described in the following. Polymer- related factors can comprise chemical structure and composition, molecular weight distribution, surface to volume ratio, solubility, and crystallinity, that can be provided as part of property data. Given the possible influence of the environmental factors, the end-of-life scenario, e.g. the habitat, is preferably provided when discussing the biodegradability of a material, ideally referring to methods and standards used to prove biodegradability.

[0150] Currently, the assessment of biodegradation of solid plastic products is performed with two complementary tests: 1) biodegradation, measured using respirometric methods which either track the consumed oxygen or the evolved CO2 during the microbial metabolization of the micronized test material and 2) disintegration, measured by assessing the loss of mechanical properties, weight, integrity, and visual disappearance of a macroscopic object done usually on the final product. Biodegradation is the ultimate proof that a material is metabolized by the microbes present in the tested inoculum. Disintegration assesses how the final product degrades on a macroscopic level, hence is not a sufficient proof for biodegradability. Nevertheless, if biodegradability is proven in laboratory experiments, disintegration can be used as a tool to assess biodegradation under real life conditions.

[0151] The discussion on environmental accumulation of plastic and polymeric materials calls for the creation of a framework able to assess the biodegradability of materials entering the environment. Biodegradability of chemicals is normally assessed using OECD guidelines for the testing of small molecules. In recent years, a variety of other standard methods have been developed in ISO and other standardization boards to investigate and determine biodegradability of plastic materials in different end-of-life scenarios, e.g. habitats. Some of these standards have recently been proposed as suitable methods to determine environmental biodegradability in the restriction proposal for intentionally added microplastic. However, the environmental relevance of the methods developed within ISO is still questioned and studies across different environments comparing laboratory and field data are rare. A systematic assessment on the environmental relevance of laboratory methods as well as the link between different compartments is therefore needed. In this context, modelling tools, inter alia, could further improve the understanding and quantification of the biodegradation process and help design standards. 231051W001

[0152] 44

[0153] In the following some results with respect to the relevance of ISO laboratory test methods and their transferability to field test methods and real-life conditions are provided. Further, test results of biodegradation in different environments are presented to provide preferred factors affecting biodegradation kinetics that can be utilized in a respective kinetic model. Moreover, a kinetic model is presented that and compared to the measurements results to show its general applicability and further usability for providing additional insights into to respective biodegradation process. As example, a certified compostable polyester compound, hereafter named as BP, is used as model material. At first, the biodegradability of BP is determined under industrial and home composting conditions. Successively environmental biodegradability under soil and marine conditions is determined, including different marine compartments, e.g.eulittoral, benthic and pelagic and two geographic regions, e.g. one subtropical, Elba, Italy and one tropical, Banka, Indonesia. Test result of biodegradation under laboratory conditions using ISO standard methods tracking CO2 evolution is provided. For each laboratory test a corresponding disintegration under real life conditions, tracking surface area disintegration, is presented to assess the environmental relevance of the laboratory methods. Along with these experiments, an examination of the data using a self-developed kinetic model is provided. ecovio® FT 2341 , a commercial blend based on poly(butylene adipate-co-terephthalate) (PBAT), poly(butylene seba- cate-co-terephthalate) (PBSeT), poly lactic acid (PLA) and a small portion of mineral filler is provided as example for a compostable biodegradable compound (BP). Microcrystalline cellulose powder is used as positive control in the biodegradation experiments. Ashless filter paper made of cellulose with thickness of 170 m, is used as positive control for biodegradation tests in marine samples under laboratory conditions.

[0154] The biodegradable polymer PHBH (polyhydroxybutyrate co-hydroxyhexanoate) with a thickness of 30 pm can be used as positive control for marine field tests in tropical Southeast Asia, and PHB (polyhydroxybutyrate) grade Mirel P5001 (Metabolix, USA) with a thickness of 85 pm for marine field tests in the Mediterranean Sea. Low density polyethylene (LDPE) (thickness: 25 pm) can be used as negative control for both marine lab and field experiments.

[0155] Mature compost derived from the organic fraction of municipal solid waste (MSW) can be used for the composting experiments. Environmental samples used as inoculum for laboratory biodegradation experiments can be collected at the same site in which the disintegration tests are performed. None of the environmental samples is pre-exposed to the tested materials. Samples may be colleted from a one or more locations, for example, soil samples may be collected from a lysimeter, water samples from beach and seafloor and sediment from water column and seafloor may be sampled from a respective marine zones and from, for example, two different sites, such as islands of Elba and Pianosa (Italy) and Island of Bangka (Sulawesi, Indonesia).

[0156] Test were performed according to ISO 14855-1. The controlled composting biodegradation test is an optimized simulation of an intensive aerobic composting process where the biodegradability of a test item under dry, aerobic conditions is determined. Positive control, e.g. microcrystalline cellulose and BP were mixed with the inoculum and introduced into a static reactor vessel where it is intensively composted under optimal oxygen, temperature, and moisture conditions. The reactors are incubated at 58 ± 2°C for industrial composting experiments and at 28 ± 2°C to simulate home 231051W001

[0157] 45 composting conditions. During the biodegradation experiments, the carbon dioxide production is monitored at regular intervals and integrated to determine the extent of biodegradation, according to the calculations reported in the standard ISO.

[0158] All biodegradation experiments with environmental samples presented here were performed using respirometric methods. For all experiments 250 mL Duran® bottles were used. All experiments were performed in triplicates, when logistically possible, or in duplicates. For each of the experiments a set of blanks, positive controls, and test material were investigated. Negative controls were included exclusively in the marine experiments with a single replicate, since the development of the methods was still ongoing at the time of the experiments. After the addition of the inoculum and the test material, a 15 mL Falcon tube containing 4 mL of a 1 M sodium hydroxide solution was added and the bottles were capped with OxiTop® devices to monitor pressure changes. The incubation was performed at constant temperature in the dark. Whenever the internal pressure reached -100 hPa, all the bottles were ventilated and the Falcon tubes replaced with new ones containing fresh sodium hydroxide solution. Carbon dioxide evolved during mineralization and trapped in the sodium hydroxide solution was measured after dilution using a Shimadzu Total Organic Carbon Analyzer. Mineralization was calculated according to the equations reported in the supporting information.

[0159] Soil biodegradation was assessed in the following examples using a downscaled version of the standard method ISO 17556. Bottles were filled with 50 g of LIHof soil (dry mass). For each of the materials under investigation, a mass corresponding to 45 mg organic carbon were added to the soil. While the powder of microcrystalline cellulose was incorporated into the soil by mixing, the plastic films of 1 cm2in size were positioned horizontally between layers of soil to ensure complete coverage of the material and contact with the microorganisms. For each of the bottles, Millipore water was added to reach 50 % of the soil water holding capacity and weighted. The experiments were then started and incubated at 25 °C.

[0160] Marine sediment and water were used in the following examples to test polymer biodegradation under laboratory conditions simulating three different scenarios: eulittoral (sediment test), benthal (water / sediment test) and pelagic (water test). For each of the tests a total of 12.5 mg organic carbon of the investigated materials were added. The amount of carbon, inoculum, as well as amount and type of nutrients were chosen based on optimization work.

[0161] The marine sediment test was performed in the here presented example according to ISO 22404. For each test, 75 g sediment samples were added to the bottles after dripping for 15 min using a 260 pm sieve (final water content around 21.9%). The bottles were preincubated at 23.5°C (Elba) or 28°C (Bangka) for 1 week. Afterwards, 0.1 ml of 0.089 M NaNOa solution was added to each test in order to amend C:N ratio of 100:1. Polymer powder or film was buried in the sediment. The testing bottles were incubated at 23.5°C (Elba) or 28°C (Banka).

[0162] A marine water test was performed in the here presented examples according to ISO 23977: 75 g seawater supplied with 0.1 g / L KH2PO4 and 0.05g / L NH4CI. While cellulose powder was directly added to the test, polyester films were clamped in a teflon-coated fiber network (mesh size: 4x4 mm) folded to give a circular shape to avoid mechanical degradation. In the middle of the net, a sterile magnetic stir stick (1.2 cm length) was places stirring was kept at 100 231051W001

[0163] 46 rpm170 U / min. The testing bottles were incubated at 25°C or 28°C and were opened periodically to replenish fresh air and exchange the CO2 absorber.

[0164] The laboratory Seawater / sandy sediment interface degradation test was performed in the here presented examples according to adapted ISO 18830 and ISO 19679 with modifications. The freshly sampled sediment from sublittoral area was dripped for 15 min using a 260 m sieve. For each test, 30 g of the dripped sediment together with 70 g seawater sample were pre-incubated at 25°C or 28°C for 1 week. Cellulose powder was directly added to the test, while polymer films were covered by a Kevlar net (4 x 4 cm, mesh size) and placed at the water-sediment interface under a teflon- coated fiber net (mesh size: 4 x 4 mm) to simulate the sunken plastic film in the ocean. The testing bottles were incubated at 23°C or 28.5°C.

[0165] The disintegration tests in the examples presented here were performed at a lysimeter. The test material BP and the positive control (PHBH) were cut and placed into standards slide frames. The samples were then positioned vertically in a perforated plastic basket filled with LIHof soil. The net-type perforation of the baskets with a 2 mm mesh size allowed water to flow through the soil without clogging. The basket was placed at the lysimeter and left open on top. No samples were lost or came to the surface during the time of the experiment. The basket was retrieved at different time points and one set of triplicates extracted and scanned on both sides. The experiment was concluded after 344 days.

[0166] The disintegration behavior of BP under marine conditions was investigated for the examples presented here in two climate zones: the Mediterranean Sea at the islands of Elba and Pianosa (Italy) was selected as warm-temperate subtropical zone, while in Southeast Asia the island of Bangka (Sulawesi, Indonesia) between the Celebes and Molucca Sea was chosen as a tropical zone. Three environments were targeted in both tropical and sub-tropical scenarios: samples were buried in beach sand (eulittoral), exposed to seawater (pelagic, at 20 m water depth) and laid on the seafloor-water interface (benthic, at a water depth of 30 m in Indonesia, 40 m in the Mediterranean). The field experiments were performed according to the methods previously described. In Indonesia the eulittoral tests were conducted by burying the samples directly in the beach sand whereas on Elba the samples were positioned in the mid-water line in bins filled with sand. The experiments were performed according to procedures described above and according to the standard ISO 22766:2020. Some of the data for the positive controls (PHB) and negative controls (LDPE) were generated in parallel. The first set of field data was generated under warm-temperate conditions with water temperatures ranging from -1 °C to 38 °C for the eulittoral, from 14 °C to 25 °C for the pelagic and from 14 °C to 20 °C for the benthic tests in the Mediterranean Sea (Elba / Pianosa, Italy), while the second set of experiments was performed under tropical conditions with 28 °C in all habitats all year in Southeast Asia (Bangka Island, Sulawesi, Indonesia). The materials were tested in the form of a film in a dimension of 200 x 160 mm, protected from physical deterioration by a 2 x 2 mm non-degradable polyester mesh and mounted in a HYDRA® frame. At predefined sampling time points, the frames containing the polymer films were retrieved, carefully opened in the laboratory to be then photographed and 231051W001

[0167] 47 documented. Quantification of the disintegrated area of the films was determined by photogrammetry. While the experiments were designed to sample triplicates at each time point, for some of the latest time points, single replicates were extracted from the devices to cover longer time periods.

[0168] It has been found that for the kinetic model even simple approaches capture the key phenomena, namely polymer biodegradation, by means of microorganisms, resulting in the formation of breakdown products and microorganisms multiplication, commonly described as biomass formation. The following model approach can be utilized. microorganisms / more \ / breakdown products \

[0169] (polymer) - > (microorganisms)+( CO2, H2O, .. . )(13)

[0170] Focusing on CO2 as the only breakdown product, and assuming only the carbon atoms (C) of the biopolymer are degraded by the microorganisms (M), a balance on the carbon, and one on the microbes can be provided as basis for a kinetic model:

[0171] Here, CMis the carbon remaining within the microorganisms; per 1 mole of C converted, 1 - a moles are incorporated in the microorganisms, and a moles become CO2 (hence a < 1). ymis the stoichiometric coefficient accounting for the increase in microbe concentration due to the biopolymer degradation, i.e. ym> 1. The rate of this reaction can be assumed to be a function of the microbes concentration, / c(M). The empirical formulation can be simplified to a second order process:

[0172] / c(M) = kdeg[C] [M] (16)

[0173] Where kdegis a second order rate constant (L mol-1 s-1), [C] is the current concentration of available carbon, and [M] the microbes concentration. To determine the parameters that can be regarded as training parameters that take into account respective variables, like that habitat conditions, the model can be fitted to respective measured data. Based on the available data using Monod's equation, where / c(M) = , and Cxis Monod's constant, the inventors consistently noticed that Cx» [C], Thus, it can be approximated that [C] + Cx« Cxand it can be defined that kdeg= leading to the expression defined in equation (16). The differential equations that can be derived from the kinetic scheme read:

[0174] The corresponding initial conditions (IC) can be chosen as: 231051W001

[0175] 48

[0176] The biodegradation % can be calculated as the ratio of carbon that is converted to CO2 over the theoretically available carbon at the beginning of the degradation process [Co], Note that the initial carbon concentration can be obtained by multiplying the weight % of carbon in the polymer times the density of the polymer. These properties of the polymer can be provided as property data. Due to the difference in densities, different values of [Co] can be obtained, even though the same carbon mass is used in all exemplary experiments.

[0177] The full conversion of carbon to CO2 is limited by the parameter a, comprised between 0 and 1 (equation(22)), that accounts for the likelihood of the microbes to consume the polymer: 100 (22)

[0178] The trainable parameters for an exemplary model can be ym, a, kdegand [Mo], These can be reduced, by assigned ym= 2. This simplification can be removed if accessibility to the time-dependent microorganisms concentration is available, or by training the parameter with respective training data, a, the limiting value of the amount of carbon transformed to CO2, can be obtained by fitting the plateau of generated and measured biodegradation curves for respective polymers.

[0179] To appreciate the impact of the key parameters, a, kdegand [Mo], a sensitivity analysis has been performed. Changing a implies controlling the extent of biodegradation, larger values of this parameter lead to a larger % of mineralization. kdeghas the effect of changing the slope of the biodegradation evolution, whereas a larger initial microbes concentration, [Mo], reduces the lag-phase before degradation onset.

[0180] After fitting respectively measured data sets, the inventors have identified an average uncertainty of the fitted kinetic parameters (about 10% for kdegand about 20% for [Mo]). These uncertainties can be used to construct the model trustworthiness region that is showed along the fits in the next sections.

[0181] Further, a statistical analysis of the field disintegration data, measured as described above, was performed in R. The disintegrated area (in percent) was transformed to the interval (0, 1) and modelled with a Beta regression with exposure time (in days) as explanatory variable. The best link function was chosen according to the Root Mean Square Deviation after a ten-fold cross-validation. The 95% confidence intervals were calculated considering the 0.95 quantiles of a t-distribution scaled by the standard errors of the model parameters.

[0182] The prerequisite for market introduction and use of compostable plastics is a full biodegradation in their intended end- of-life. Biodegradation experiments are at the base of the certification schemes for compostable carrier bags and guarantee that the material will fully biodegrade under composting conditions. While the inoculum used for the laboratory composting tests has the same characteristics, the tests, as described above, were performed at different temperatures for home composting (28 °C) and industrial composting (58 °C). Cellulose quickly degraded in both experiments without any lag-phase and reached more than 90% mineralization at the end of the test, proving the activity of the inoculum as shown in Fig. 6. The mineralization of BP in industrial composting started with an extremely short lag-phase and reached 75% degradation after 14 days (left part of Fig. 6). As the test progressed, the biodegradation rate steadily 231051W001

[0183] 49 decreased and achieved an absolute value of 99 ± 1 % at the end of the test (45 days). The high mineralization value at plateau, above the normally expected 90% threshold, points towards a low biomass formation or could be the result of priming effects. While for industrial composting the mineralization of BP took place at high rates almost identical to those of cellulose, under home composting conditions (right part, of Fig. 6) a more pronounced lag-phase was observed. A plateau was reached with an absolute biodegradation of 90 ± 1 % after 105 days. The results fulfil the standards specifications according to ISO 17088 and EN 13434.

[0184] The trained kinetic model as described above well-describes the biodegradation of both positive control and BP in home and industrial compost capturing process with both short and long lag-phases (compare continuous lines and circles in left and right panel of Fig. 6). Further a simpler model with biodegradation rates that only depend on carbon concentration (with a first-order dependency), neglecting the role of microbes, i.e. / c(M) = kdeg[C] as trainable parameter, has been taken into account. To clarify when such assumption is justified, the available experimental data of home compost was also fitted (right panel of Fig. 6) using both a first-order model in carbon concentration ( / c(M) = kdeg[C], black dashed lines) and the kinetic model described above ( / c(M) = kdeg[C] [M], continuous lines). The positive control case can be indeed modelled with a first order kinetic model, as shown by the overlap of the dashed black line and the continuous red line. This points to the fact that microbes are present in such a large concentration with respect to the carbon that at least some processes can be simplified from a second order to a first order model: / c(M) = kdeg[M] [C] = kdeg[C], where kdis a pseudo-first order rate constant, as trainable parameter, incorporating the microbes concentration. The same does not hold when inspecting the BP fits. The fit with a first order process (dashed black line) is far off the one obtained with a second order process (green continuous line). This comparison highlights the improvement when utilizing a second order model, taken for example the microbe concentration into account.

[0185] Accounting for the microbes comes at the price of fitting a further trainable parameter quantifying their initial concentration ([Mo]), that can provide interesting insights. The ratio [M0] / [C0] turns out to be three orders of magnitude larger in the positive control case, than for BP. This does not prevent BP to reach 90% biodegradation, but it reflects that a lag phase of about 20 days is necessary for the microorganisms to colonize the polymer and initiate its depolymerization. A similar parameter comparison holds for the industrial compost case where [M0] / [C0] is about 2 orders of magnitude larger for the positive control, while its kdegis a factor 2 smaller than the one of BP.

[0186] It should be stressed that the fitted microbes concentration, [Mo], is not to be interpreted as an absolute number, but rather as a trainable parameter that includes many microbe-related phenomena, that go well-beyond concentration. For example, the following parameters can be part of the microbes concentration, rate of microbial colonization of the polymer, rate of enzyme production and diffusion towards the polymer matrix, as well as nutrients present in a given soil. Similarly the trainable parameter, kdegcan account for several variables that revolve around the rate coefficient of biodegradation, such as the enzyme-polymer interaction, the temperature, the polymer molecular weight distribution, and its crystallinity. 231051W001

[0187] 50

[0188] Under laboratory conditions, the biodegradation in soil of cellulose and of BP starts with short lag phases as shown in Fig. 7 and proceeds for the different replicates very similarly. While mineralization of cellulose proceeded more rapidly, after 400 days the absolute biodegradation of both materials exceeds 90 %. In line with the laboratory experiments, BP reached almost complete disintegration of the samples after one year, as can be seen in Fig. 7 (right panel). Despite the difference in thickness between the BP films used for the lab (25 m) and the field study (12 pm), biodegradation proceeded more rapidly than disintegration under field conditions, most probably due to the higher and constant temperature (25 °C) and humidity in laboratory experiments. Although the disintegration tests show a less extensive biodegradation then under laboratory conditions, the similar trends prove the environmental relevance of ISO 17556 to investigate soil biodegradability of materials. The above-described kinetic model well-describes the biodegradation data of both positive control and BP also in the soil environment (see continuous lines in the left panel of Fig. 7). The fitted model parameters indicate that the faster biodegradation of the positive control is due to a larger [Mo] and a larger kdeg. This suggests that microbes will colonize the polymer faster, and that the rate with which the enzymes (produced by the microorganisms) degrade the polymer is larger for the positive control when compared to the BP polymer.

[0189] Results generated for the different tests, environments and locations for the marine habitat are summarized in Fig. 8. For laboratory biodegradation experiments, mineralization reached plateau with values varying between 50 and 100%, for both positive control and BP. The only exception was observed for the test in pure water from Elba, where only about 15% mineralization was measured for BP. During the test substantial variations between replicates were observed, but in most cases the values converged at the plateau phase. This variation might be a consequence of an inhomogeneous distribution of the microbial communities present in the inoculum and especially in the sediment, despite the efforts invested in homogenizing the samples before the test. Common trends were observed in the lab tests with sub-tropical (Fig. 8 first row) and tropical inocula (Fig. 8 third row) with the highest activity and largest biodegradation values at plateau for both positive control and BP according to the order benthic>eulittoral>pelagic.

[0190] When compared to the laboratory results, similar trends could be observed for the field experiments. In the sub-tropical field tests (Fig. 8 second row), both the positive control and BP completely disintegrated within 1500 days in the eulit- toral and the benthic habitats, while in the pelagic habitat slow disintegration was observed, not reaching completion in the time frame investigated. Such behavior for both positive control and BP in the pelagic environment is most probably due to the limited availability of microbes and nutrients for microorganisms. Still, while lab experiments reached for BD a plateau after 400 days, the disintegration under real life conditions was still progressing after 1500 days, highlighting limitation of the current laboratory methods in capturing slow biodegradation processes. In the tropical environment, substantial disintegration was already measured at the first sampling time points (between 50 and 100 days), as highlighted in Fig. 8 last row. Similarly, to the sub-tropical environment, disintegration under pelagic conditions resulted to be the slowest and benthic slightly faster than the eulittoral for BP. According to the statistical model completion was reached between 300 and 400 days under benthic and eulittoral conditions. Trend and differences between the regions and the environments can be further captured by comparing the half-lives tso of BP (SI), calculated with the statistical model, according to methods previously reported. It must be mentioned that while tso is 231051W001

[0191] 51 an effective tool in comparing samples consistent in chemical compositions and shape, this must be used with care. Differently from small molecules, polymeric materials are intrinsically heterogenous and therefore o should be discussed only on the base of biodegradation data showing the full biodegradation of the material.

[0192] A different perspective on the data can be obtained thanks to the trained kinetic model, that well-describes all biodegradation trends in the sub-tropical and tropical environments, for both BP and positive controls (compare continuous lines and circles). Notably, the ratio [M0] / [C0] is typically larger (in 5 out of 6 cases) for the positive controls when compared to their BP counterpart in a given system. This implies that the microbes consistently tend to "recognize” the positive controls easier than the BP in all environments, starting the biodegradation process sooner. As a result, the lag-phases of the positive controls curves are typically much shorter than the ones of BP. The pelagic system is an outlier in this sense, where the BP data exhibits a longer lag-phase than the positive control despite a larger [M0] / [C0], This is probably due to the small plateau value reached, corresponding to the low a value imposed to the model, which in turn impacts the fitted parameter values. The fitted values of the kdeg, are either comparable (4 / 6 cases same order of magnitude) or larger (2 / 6 cases by 1 order of magnitude) for the positive controls, when compared to the corresponding BP system. The larger values of kdegare observed in the subtropical system in the pelagic and benthic systems. In the pelagic system, the much larger kdegvalue of the positive control system compensates for its smaller [M0] / [C0] ratio. In the benthic case, only a slightly larger value of [M0] / [C0] was observed for the positive control when compared to BP, and the biodegradation process starts similarly for both polymers, with almost no lagphase. The difference observed in biodegradation slope is related to the larger kdegvalue of the positive control. Overall, a similar trend was observed when discussing the home compost parameters, larger values of [M0] / [C0] characterize the positive controls, while the kdegare either comparable or even larger in the BP case. As a result, a longer lag-phase is observed for the BP, that however does not limit the extent of biodegradation (neglecting the outlier case of the pelagic subtropical system).

[0193] To better appreciate the impact of the marine environment (eulittoral, pelagic, benthic) on the biodegradation, the biodegradation experiments, along with the kinetic model predictions, are shown in Fig. 9 grouping the same polymers (BP or positive controls) in either the tropical or subtropical systems.

[0194] It appears evident that both BP (top panels in Fig. 9) and positive controls (bottom panels in Fig. 9) degrade differently in each environment. The difference is higher for BP, where in both tropical and subtropical zones different final values of biodegradation, slopes and lag phases are observed for the different environments (eulittoral, pelagic, benthic). The main difference for the positive controls emerges when comparing the couple eulittoral-pelagic (that evolve similarly) with the benthic curve, where biodegradation seems to proceed faster and to a larger extent than in the other two systems. Since the temperature and the carbon concentrations are constant in all compartments considered, it is clear that the environment, along with its features (e.g. microbes concentration and types, nutrients), can have an impact on the biodegradation process. Thus, fitting the kinetic model for specific habitat can improve an accuracy and reliability of the determined biodegradation curve. 231051W001

[0195] 52

[0196] The kinetic model provides, in addition to generating a prediction for the biodegradation curve, a way to quantify the difference between the curves. Starting with BP in the subtropical system (top left panel in Fig. 9), and neglecting the pelagic curve (an outlier, as discussed before), it can be seen how the [M0] / [C0] parameter is about 6 times larger in the benthic case, compared to the eulittoral one. This quantified the observed lag-phase difference between the two curves. The relatively similar kdegidentified (1.5x larger in the eulittoral case) reflect the similarity in biodegradation slopes.

[0197] When inspecting the fitted parameters for the positive controls in the subtropical system (bottom left panel in Fig. 9), it can be observed that the kdegorder is pelagic>benthic»eulittoral, that reflects the slopes of the biodegradation curves. The [M0] / [C0] is largest in the eulittoral case (the curve that indeed has the shortest lag phase), followed by the benthic case and then by the pelagic case, that has the longest lag-phase of the three.

[0198] A similar comparison holds for BP in the tropical zone (top right panel in Fig. 9), where the benthic curve has the largest [M0] / [C0] ratio (1 order of magnitude larger than the other two systems), and as a result has the shortest lag phase. The eulittoral and pelagic curves have a very comparable [M0] / [C0] ratio, that does not allow any discrimination of the curves, given the 20% uncertainty estimated for this parameter. In this context it can be seen that this type of comparison reaches its limits when the curves become "too similar”, and indeed the eulittoral and pelagic curves are almost overlapped at the beginning of the biodegradation rates.

[0199] In the positive control case in the tropical zone, we can observe how the benthic curve has the largest [M0] / [C0] ratio, followed by the eulittoral case and then by the pelagic one. While in the benthic environment the biodegradation starts indeed much earlier, pelagic and eulittoral proceed in a very similar manner. The larger [M0] / [C0] value observed for the eulittoral curve is offset by a lower kdegvalue.

[0200] As shown the kinetic biodegradation model, along with the parameters [M0] / [C0] and kdeg, is a powerful tool to quantitatively determine a biodegradation curve and also to compare experimental results. The biodegradation model provides a solid base for an environmental assessment of materials and products. The here presented results for the different environments and geographic areas and materials clearly demonstrate a correlation between the lab biodegradation and the field disintegration exists, as well as a good correlation with the kinetics model of the processes. These aspects prove the relevance of ISO laboratory biodegradation methods to assess environmental biodegradability of materials. The use of plastic thin films for the correlation of the lab and field conditions provided an effective way of proving environmental relevance of the laboratory ISO methods, without the need of more expensive techniques, such as13C or14C isotope labelling. These methods can be therefore considered a valuable alternative for the testing of biodegradability of polymeric materials (e.g. biodegradation of microplastic) to already existing OECD testing guidelines developed for testing of small molecules.

[0201] Positive controls provide insights into laboratory investigation about the activity of the inoculum and the tendency of the microbial communities to produce biomass during the specific biodegradation study. In fact similar mineralization 231051W001

[0202] 53 values at plateau in lab experiments were reached for positive control and BP, pointing towards a similar behavior under the investigated conditions, being the extent of biomass formation or for example limitations of the test system such as nutrient availability. For field investigations positive controls allow assessing the environmental variations especially deriving from climatic conditions. On the other hand, in all lab and field tests, the negative control, low density polyethylene (LDPE), did not show any sign of biodegradation or disintegration in neither Elba nor Bangka, indicating that even under real life conditions, non-biodegradable plastic will not be degraded in relevant time scales by abiotic factors.

[0203] The examples above demonstrate the presence of microbial communities capable of degrading BP across the compartments, from artificial, such as compost, to natural environments, e.g. soil and marine (pelagic, benthic and eulitto- ral). BP is a compound based mainly on PBAT and PBSeT, with smaller amounts of PLA. Since PBAT and PBSeT undergo biodegradation after enzymatic cleavage by esterases, e.g. cutinases are broadly available in the environment, biodegradation can be expected for these materials.

[0204] The examples above showed an influence of environmental parameters on the degradation. For example, biodegradation can proceed faster at higher temperatures, for instance, the temperature in the sub-tropical environment oscillates strongly throughout the year (between x and y °C), while it is higher and broadly constant in the investigated tropical area. As a result, the disintegration of BP in the tropical region is 3 times faster than in the sub-tropical zone with a kinetic similar to those of the laboratory experiments. Similarly, soil disintegration in a continental climate proceeds more slowly than in laboratory experiments at constant temperature. However other parameters besides temperature can play role such as the environmental medium (e.g. the dynamic of water vs the localization of soil), nutrients availability, but especially microbial communities. This can be seen when inspecting the evolution of the disintegration of the materials in the tropical environment (Figure 3j-l) as well as their biodegradation (Figure 3g-i). In both lab and field tests the temperature was constant and yet BP exhibited different biodegradation and disintegration profiles. However, it has been shown that a second order process, accounting, for instance, for the microbial concentration, is general enough to describe biodegradation in all scenarios. First-order models are also suitable for some application cases, for instance, for polymeric materials with a large number of degraders present at the start of the experiment, where no lag-phase exists.

[0205] The described biodegradation model proved that it provides a good description of the biodegradation kinetics of different materials and environmental conditions. Despite its simplicity, all key features of the biodegradation process - lagphase, the biodegradation phase, and the plateau phase - are captured. The simplified variables of the exemplary model allow determining a biodegradation curve for the exemplary application cases very accurately and further allow for a quantitative comparison of different polymer / environment couples that go beyond the final extent of biodegradation (e.g. larger initial concentration of microbes with respect to carbon, reflect shorter lag phase). This allows to generate biodegradation curves, for instance, of the same polymer in different habitats, speeding up the testing procedure. 231051W001

[0206] 54

[0207] Figs. 10A and 10B depict examples of biodegradation curves in wastewaster of measured data for polymers of different molecular weights indicated as data points, and biodegradation curves for polymer of different molecular weights determined by the biodegradation model of the present disclosure, indicated as continuous or dashed lines.

[0208] The biodegradation model is configured to determine biodegradation of polymer of different molecular weights. For instance, Fig. 10 depicts the biodegradation determined by the biodegradation model in wastewater for a polymer of molecular weight 9000 g / mol. For this purpose, the biodegradation model takes into account specific parameters capable of describing molecular weight of the polymer and therefore effecting the determined biodegradation.

[0209] For instance, Fig. 10A depicts a comparison of the experimental data of biodegradation of a polyethylene glycol of molecular weight 9000 g / mol in wastewater with the curve determined by the biodegradation model of the present disclosure, wherein the biodegradation model accurately determined time of initiation, time to plateau and endpoint of the biodegradation of the polymer.

[0210] Similarly, Fig. 10B depicts biodegradation curves determined for a polyethylene glycol of different molecular weights such as 1000 g / mol, 35000 g / mol and 70000 g / mol. As an example, measured data for molecular weight of 35000 g / mol is also depicted as squared data points for comparison of the determined biodegradation by the biodegradation curve with a measured biodegradation curve.

[0211] In the determined biodegradation curves of polymers with different molecular weights (1000, 35000 and 70000 g / mol) as depicted in Fig. 10B, it is clearly seen that biodegradation model of the present disclosure can accurately determine the biodegradation curve of the polymer - compare for instance the experimental data and data determined by the biodegradation model for 35000 g / mol. In particular, the biodegradation model is capable of accurately determining the biodegradation curve of a given polymer including main parameters which describing the curve such as time of initiation, time to plateau and endpoint, which as depicted in Fig. 10B are all the same between the values determined by the biodegradation model of the present disclosure and the experimental values.

[0212] The biodegradation model of the present disclosure is configured to determine biodegradation behaviour of polymers of different molecular weights, which is particularly advantageous, as it allows, for instance, determining and / or understanding how a polymer of different molecular weights will biodegrade, without having to perform experimental testing for each molecular weight of interest, which is particular beneficial as it renders determination of biodegradation more efficient in terms of resources such as reduced time for determining biodegradation curves of polymers of different molecular weights as well as reducing the infrastructure and materials required to conduct such as experimental biodegradation testing. As shown in Figs. 10A and 10B, the biodegradation of the of the present invention is configured to determine biodegradation of a polymer with a certain molecular weight within a certain time, which is particularly beneficial as it allows to yield the given biodegradation data without having to do time- and cost-intensive lab tests on that polymer, which results a faster, more efficient and accurate determination of the biodegradation of polymer of different molecular weights. 231051W001

[0213] 55

[0214] Moreover, the biodegradation model of the present disclosure allows to design a product to biodegrade within a certain time, for example, a product which is stable for a certain time, and then biodegrades once it is in wastewater, as the model accurately shows the time before biodegradation is initiated, which allows tailoring of biodegradation properties of the polymer, for instance, before production of the given polymer. Additionally or alternative, the biodegradation model of the present disclosure allows to design a product that biodegrades more slowly, as such a target biodegradation time may be provided to the biodegradation model, so that the model determines a suitable molecular weight for the polymer to produce, for instance, a given higher molecular weight, as depicted for example in Fig. 10B.

[0215] Figs. 11 A and 11 B depict examples of biodegradation curves in wastewater at different temperatures of measured data for a polymer - indicated as circular data points - and biodegradation curves for the polymer determined by the biodegradation model of the present disclosure - indicated as continuous or dashed lines.

[0216] The biodegradation curve is configured to determine biodegradation of polymers in a given habitat, for instance as depicted in Figs 11A and 11 B, in wastewater.

[0217] Fig. 11 A depicts the biodegradation of a polyethylene glycol at 20°C. Specific parameters which describe the temperature are included in the biodegradation model and therefore have an effect on the determined biodegradation data. The biodegradation model of the present disclosure accurately determines the biodegradation curve the polyethylene glycol at 20 °C in wastewater as shown in Fig. 11 A, in particular, all of time of initiation, time to plateau and endpoint.As depicted in Fig. 11 B, the biodegradation model of the present invention is also configured to accurately determine biodegradation curves, i.e. , biodegradation kinetics, of another polyethylene glycol at different environmentally relevant temperatures, as compare to measure biodegradation kinetics of the polymer, for instance, for 15 °C and 27 °C.

[0218] Fig. 11 B depicts biodegradation curves determined by the biodegradation model of the present disclosure for the polymer at four different temperatures, in particular, at 10°C, 15°C, 27°C and 35°C. Additionally, Fig. 11 B shows experimental data of the biodegradation of polyethylene glycol at 15 and 27°C in wastewater as an exemplary comparison. As clearly seen in Fig. 11 B, the determined biodegradation at different environmentally relevant temperatures varies largely, for instance, at 35°C the polymer biodegradation is complete within 5 days, however for the same structure at 10°C, biodegradation is still not complete even after 28 days.

[0219] Moreover, in Fig. 11 B is clearly seen that the biodegradation model of the present disclosure accurately determines the biodegradation of the polymer at 15 °C and 27 °C when compared with measured biodegradation curves of the polymer at these temperatures. Most importantly, the biodegradation model of the present disclosure includes main parameters which describe the curve such time of initiation, time to plateau and endpoint, which the biodegradation model accurately determined as observed when comparing the value determined by the biodegradation model and experimental values. 231051W001

[0220] 56

[0221] In particular, the biodegradation model is configured to accurately determine the biodegradation behaviour of polymers in different environmentally temperatures, which is particularly beneficial, as it allows to determine and / or understand how a polymer biodegrades in given different temperatures, for example, the biodegradation model allows to determine biodegradation of polymer(s) in an environment at 27 °C, which for instance represents a given habitat condition of a given geographical location such as a given European country in the summer, wherein the biodegradation model determines that the polymer will start biodegrading within 2 days. Additionally or alternatively, the biodegradation model allows to determine the biodegradation of the polymer at a different temperature, for example, at 10 C, which may represent a temperature of the given geographical condition at a different time such as in the winter in the given European country, wherein the biodegradation model determines that the polymer will take longer to degrade, around 12 days. This is particularly advantageous, as it allows to determine biodegradation of polymers at different temperature, for instance, for the same habitat and / or for similar habitats, for example, otherwise with different locations and consequently at different temperatures.

[0222] Hence, the biodegradation model of the present disclosure allows to determine biodegradation of materials, such as polymers, for different relevant conditions such inherent characteristics of the material, for example, molecular weights of polymers, and / or habitat characteristics such as temperature. It should be understood that these are just exemplary conditions relevant to biodegradation of materials, thus, the biodegradation model of the present disclosure is also be configurated to determine biodegradation of materials based on other parameters, inter alia, but no limited to, habitat composition, a pH value, a moisture content, a nutrient concentration, a microbial community, a concentration of microbes, a nitrogen content, a water holding capacity and an enzyme environment.

[0223] Additionally or alternatively, the biodegradation model is configured to accurately determine biodegradation of materials which may be classified as "readily biodegradable”, for instance, under OECD guidelines, which requires that materials achieve a biodegradation level of 60-70%, depending on the specific test protocol, within a 10-day window during a standard 28-day test period. Therefore, the biodegradation model of the present disclosure is particular advantageous, as it allows to determine whether, for example, a given structure of a polymer, is likely to meet or fail these regulatory criteria. This is particularly beneficial, as it allows designing biodegradable products in compliance with these standards without requiring to perform experimental biodegradation testing for each candidate structure of polymers to be produced.

[0224] Although the above examples, refer to specific substances and environments to show the applicability of the biodegradation model. The principles described above can also be applied to completely different substances and environments. Moreover, the biodegradation model can also be trained for more complex application cases, for a plurality of different environments, and / or for a plurality of chemical substances.

[0225] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the disclosure, from a study of the drawings, the disclosure, and the appended claims. 231051W001

[0226] 57

[0227] For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in differing order. Furthermore, the outlined operations are only provided as examples, and some of the operations may be optional, combined into fewer steps and operations, supplemented with further operations, or expanded into additional operations without detracting from the essence of the disclosed embodiments.

[0228] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

[0229] A single unit or device may fulfil the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0230] Procedures like the receiving of property data, the providing of a biodegradation model, the applying of the biodegradation model, the providing of the received biodegradation curve, etc. performed by one or several units or devices can be performed by any other number of units or devices. These procedures can be implemented as program code means of a computer program and / or as dedicated hardware.

[0231] A computer program product may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0232] Any units described herein may be processing units that are part of a classical computing system. Processing units may include a general-purpose processor and may also include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term "memory” may include any computer- readable storage media such as a non-volatile mass storage. If the computing system is distributed, the processing and / or memory capability may be distributed as well. The computing system may include multiple structures as "executable components”. The term "executable component” is a structure well understood in the field of computing as being a structure that can be software, hardware, or a combination thereof. For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component may include software objects, routines, methods, and so forth, that may be executed on the computing system. This may include both an executable component in the heap of a computing system, or on computer-readable storage media. The structure of the executable component may exist on a computer-readable medium such that, when interpreted by one or more processors of a computing system, e.g., by a processor thread, the computing system is caused to perform a function. Such structure may be computer-readable directly by the processors, for instance, as is the case if the executable component were binary, or it may be structured to be interpretable and / or compiled, for instance, whether in a single stage or in multiple stages, so as to generate such binary that is directly interpretable by the processors. In other 231051W001

[0233] 58 instances, structures may be hard coded or hard-wired logic gates, that are implemented exclusively or near-exclu- sively in hardware, such as within a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Accordingly, the term "executable component” is a term for a structure that is well understood by those of ordinary skill in the art of computing, whether implemented in software, hardware, or a combination. Any embodiments herein are described with reference to acts that are performed by one or more processing units of the computing system. If such acts are implemented in software, one or more processors direct the operation of the computing system in response to having executed computer-executable instructions that constitute an executable component. A computing system may also contain communication channels that allow the computing system to communicate with other computing systems over, for example, network. A "network” is defined as one or more data links that enable the transport of electronic data between computing systems and / or modules and / or other electronic devices. When information is transferred or provided over a network or another communications connection, for example, either hardwired, wireless, or a combination of hardwired and wireless, to a computing system, the computing system properly views the connection as a transmission medium. Transmission media can include a network and / or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or special-purpose computing system or combinations. While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface system for use in interfacing with a user. User interfaces act as input or output mechanism to users for instance via displays.

[0234] Those skilled in the art will appreciate that at least parts of the disclosure may be practiced in network computing environments with many types of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, main-frame computers, mobile telephones, PDAs, pagers, routers, switches, datacentres, wearables, such as glasses, and the like. The disclosure may also be practiced in distributed system environments where local and remote computing system, which are linked, for example, either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links, through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0235] Those skilled in the art will also appreciate that at least parts of the disclosure may be practiced in a cloud computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be distributed internationally within an organization and / or have components possessed across multiple organizations. In this description and the following claims, "cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources, e.g., networks, servers, storage, applications, and services. The definition of "cloud computing” is not limited to any of the other numerous advantages that can be obtained from such a model when deployed. The computing systems of the figures include various components or functional blocks that may implement the various embodiments disclosed herein as explained. The various components or functional blocks may be implemented on a local computing system or may be implemented 231051W001

[0236] 59 on a distributed computing system that includes elements resident in the cloud or that implements aspects of cloud computing. The various components or functional blocks may be implemented as software, hardware, or a combination of software and hardware. The computing systems shown in the figures may include more or less than the components illustrated in the figures and some of the components may be combined as circumstances warrant.

[0237] Any reference signs in the claims should not be construed as limiting the scope.

[0238] The disclosure refers to a method for generating a biodegradation curve associated with a chemical substance, wherein a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time. Property data associated with one or more chemical and / or physical properties of the chemical substance is received. A biodegradation model is provided based on the property data. The biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process. The biodegradation model is applied to the property data of the chemical substance as input and the biodegradation curve is received as biodegradation model output. The biodegradation curve is provided for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

Claims

231051W00160CLAIMS1. A method, in particular, a computer implemented method, for generating a biodegradation curve associated with a chemical substance, wherein a biodegradation curve is indicative of the biodegradation of a chemical substance in a habitat with time, wherein the method comprises: receiving, at a computer interface, property data associated with one or more chemical and / or physical properties of the chemical substance, providing a biodegradation model based on the property data, wherein the biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process and wherein one or more trainable parameters of the biodegradation model are parameterized based on a historical training dataset, wherein the historical training dataset comprises a) measured degradation curves of at least one chemical substance, and b) respective property data of the at least one chemical substance, wherein the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based on property data of the chemical substance as input, generating the biodegradation curve based on the biodegradation model and the property data, and providing the generated biodegradation curve for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

2. The method according to claim 1 , wherein the processing of the biodegradation curve comprises generating control data for controlling and / or monitoring a biodegradation experiment for determining the biodegradation of the chemical substance based on the generated biodegradation curve.

3. The method according to claim 2, wherein the generating of the control data comprises determining an end measurement time for the biodegradation experiment based on the biodegradation curve and generating the control data such that the final biodegradation measurement is performed in the biodegradation experiment at the determined end measurement time.

4. The method according to any of the preceding claims, wherein the processing of the biodegradation curve comprises validating, based on the biodegradation curve, whether the chemical substance fulfils a predetermined biodegradation goal, and generating control data for monitoring and / or controlling chemical processes associated with the chemical substance based on the validation.

5. The method according to claim 4, wherein the predetermined biodegradation goal is related to one or more of a biodegradation rate constant, a maximum biodegradation percentage and a biodegradation lag phase.231051W001616. The method according to any of claims 4 and 5, wherein the predetermined biodegradation goal is defined by a biodegradation norm.

7. The method according to any of claims 4 to 6, wherein, if it is determined that the chemical substance does not fulfil the predetermined biodegradation goal, processing of the biodegradation curve comprises generating, based on the biodegradation curve, measures for adjusting the chemical substance such that the adjusted chemical substance does fulfil the predetermined biodegradation goal or measures for specifying an adjusted biodegradation habitat such that the chemical substance does fulfil the predetermined biodegradation goal in the adjusted biodegradation habitat and providing the measures for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

8. The method according to any of the preceding claims, wherein the method comprises generating control data for controlling and / or monitoring an end of a biodegradation measurement based on the generated biodegradation curve.

9. The method according to any of the preceding claims, wherein the biodegradation model is associated with a specific biodegradation habitat, and wherein the method comprises receiving habitat data associated with one or more habitat conditions of a specific habitat, and wherein the biodegradation model is provided based on the habitat data.

10. The method according to any of the preceding claims, wherein the one or more trainable parameters of the biodegradation model refer to at least one of an initial microbe concentration, a likelihood of a microorganism to consume the chemical substance, and a degradation coefficient.11 . The method according to any of the preceding claims, wherein the kinetic model is a second order kinetic model.

12. The method according to any of the preceding claims, wherein the method further comprises receiving an initial biodegradation curve indicative of a measured biodegradation in a predetermined biodegradation phase, wherein the method further comprises adjusting the trained one or more trainable parameters of the biodegradation model further based on the initial biodegradation curve and utilizing the adjusted one or more trainable parameters of the biodegradation model to generate the biodegradation curve for other biodegradation phases based on the property data.

13. The method according to any of the preceding claims, wherein the kinetic model is based on a carbon mass balance associated with a mass transfer of the carbon from the chemical substance to the microorganisms and one or more residual substances.231051W0016214. An apparatus for generating a biodegradation curve associated with a chemical substance, wherein the apparatus comprises: an input interface configured to i) receive property data associated with one or more chemical and / or physical properties of the chemical substance, ii) provide a biodegradation model based on the property data, wherein the biodegradation model is based on a machine learning based kinetic model, wherein the kinetic model is associated with a mass transfer from the chemical substance to biomass and one or more residual substances during the biodegradation process and wherein one or more trainable parameters of the biodegradation model are parameterized based on a historical training dataset, wherein the historical training dataset comprises a) measured degradation curves of at least one chemical substance, and b) respective property data of the at least one chemical substance, wherein the trained biodegradation model is configured to generate as output a biodegradation curve of a chemical substance based on property data of the chemical substance as input, a processor configured to generate the biodegradation curve based on the biodegradation model and the property data, and an output interface configured to provide the generated biodegradation curve for further processing for monitoring and / or controlling chemical processes associated with the chemical substance.

15. A computer program product for generating a biodegradation curve associated with a chemical substance, wherein the computer program product comprises program code means for causing a computing system to execute the method according to any of claims 1 to 12.

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