Methods for determining habitat descriptor values ​​that provide target biodegradability of polymers

Abstract

The present invention relates to a method for determining habitat descriptor values ​​that provide a target biodegradability for a polymer. A target biodegradability is provided that indicates the biodegradation properties of the polymer. A digital representation of the polymer is provided that indicates the physicochemical properties of the polymer. A biodegradation habitat is provided that indicates habitat descriptors that influence the biodegradation of the polymer. Based on the provided biodegradation habitat, a biodegradation model is provided that is adapted to determine habitat descriptor values ​​that enable the biodegradation of the polymer that meets the target biodegradability in the biodegradation habitat. The habitat descriptor values ​​are determined for the polymer based on a selected biodegradation model, the target biodegradability and the polymer descriptors.

Description

[Technical field]

[0001] FIELD OF THEINVENTION The present invention relates to a method, an apparatus and a computer program product for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer.Furthermore, the present invention relates to a training method, a training apparatus and a training computer program for training a data-driven biodegradation model that can be utilized by the method, apparatus and computer program product for determining habitat descriptor values.Furthermore, the present invention relates to a method and an apparatus that provides an interface for determining habitat descriptor values. [Background technology]

[0002] 2. Background of the Invention In general, polymers are widely used in industrial and / or daily use products due to their wide range of application properties. The use of polymers encompasses, among others, coatings, personal care products, cleaning detergents, lubricants, packaging and foams. However, this widespread application, on the other hand, results in a large amount of waste containing the used polymers. Indeed, in most cases, it is the durability of polymers that makes them widespread in many applications, but this very durability leads to multiple problems in waste management, especially since the polymers are durable in waste. In particular, if non-biodegradable polymers are not properly collected in the intended waste stream, this can lead to an increase in microplastic pollution and bioaccumulation in the environment. Therefore, not only is it necessary for polymers to degrade, but it is also necessary to take into account knowledge of the biodegradability of polymers at an early stage of the product design process. Furthermore, for the subsequent waste management of products containing biodegradable polymers, it may also be important to know under what conditions the polymer optimally biodegrades and under what conditions the polymer does not biodegrade. This makes it possible to take into account the subsequent waste management of polymers that are already in the design process. It is therefore advantageous to provide the possibility to predict the conditions under which a polymer will biodegrade accurately and in a computationally inexpensive manner. Summary of the Invention [Problem to be solved by the invention]

[0003] Summary of the Invention It is an object of the present invention to provide a method, an apparatus and a computer program product that allows for an accurate determination of the conditions under which a polymer biodegrades, is computationally inexpensive and can be reliably applied to new polymers. Furthermore, it is an object of the present invention to provide a training method, a training apparatus and a computer program product that can be used in the method, the apparatus and the computer program product and that allows for providing a biodegradation model that can be trained to provide good determination accuracy using fewer computational resources. [Means for solving the problem]

[0004] In a first aspect of the present invention, a computer-implemented method for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer is presented, the method comprising: a) providing a target biodegradability, the biodegradability indicating a biodegradation characteristic of the polymer; b) providing a digital representation of the polymer indicating associated physicochemical properties of the polymer; c) providing biodegradation habitats, the biodegradation habitats indicating habitat descriptors that influence the biodegradation of the polymer in the respective habitat, the habitat descriptors indicating environmental characteristics of the habitat; d) providing a biodegradation model based on the provided biodegradation habitats, the biodegradation model being adapted to determine habitat descriptor values ​​that enable biodegradation of the polymer meeting the target biodegradability in the biodegradation habitat, the biodegradation model being a data-driven model parameterized for the biodegradation habitat to determine the habitat descriptor values ​​based on the target biodegradability and the physicochemical properties; and e) determining the habitat descriptor values ​​of the polymer based on the selected biodegradation model, the target biodegradability and the polymer descriptors.

[0005] Since the biodegradation model is specifically adapted to determine habitat descriptor values ​​that enable the biodegradation of polymers that meet the target biodegradability in each habitat, the habitat descriptor values ​​for each habitat can be determined very accurately. Furthermore, since the biodegradation model is specifically trained for one or more specific biodegradation habitats, less training data is required for training and the biodegradation model becomes more flexible with respect to determining the biodegradation of new polymers that are not part of the training data set. Thus, the method allows for accurate determination of habitat descriptor values ​​that are computationally inexpensive and can be flexibly applied to new polymers.

[0006] Furthermore, the training of the respective biodegradation model can be improved since the physicochemical properties, including the physicochemical information of the polymer, for example the quantum chemical information of the polymer, such as the solubility in water and octanol or the molar mass of the polymer, are utilized. In particular, utilizing the physicochemical properties allows the training of such models with less training data, since part of the correlation information that needs to be learned is already presented to the model by using the physicochemical properties. This further allows saving the tests and experiments required to provide the training data set.

[0007] The method refers to a computer-implemented method and may therefore be implemented by a general-purpose or dedicated computer adapted to carry out the method, for example by executing a respective computer program. The method is adapted to determine, in particular predict, a habitat value of a given polymer that allows the given polymer to biodegrade at a given target biodegradability. A biodegradable polymer refers to a polymer that can be degraded by biological processes, in particular a biodegradable polymer can refer to a polymer that can be assimilated by bacteria and / or fungi to give environmentally friendly products, i.e. decomposed into non-polluting residues, for example by producing mineralized carbon and / or biomass. In general, the determined habitat descriptor value can refer to one or more quantifications of the environmental characteristics of the habitat. For example, the habitat descriptor value can refer to only one value, for example the salt concentration in the respective habitat, or can refer to more than one value, for example a range of values, for example a range of salt concentrations.

[0008] In general, biodegradability refers to the biodegradation properties of a polymer. In particular, biodegradability refers to an indicator of degradation, i.e., the degradation of a polymer caused by a biological process, i.e., a process involving biological materials, especially microorganisms, involved in the degradation process. Thus, biodegradability does not refer to a purely chemical degradation process that does not involve microbial activity. Target biodegradability can refer to any quantification of the biodegradability of a polymer. Biodegradability is an intrinsic characteristic of a polymer. In this context, the intrinsic characteristic of a polymer refers to a property of a polymer that is caused by and thus reflects the nature of the polymer, i.e., its structure, composition, etc., with respect to a particular context. In particular, biodegradability reflects the properties of the polymer when present in a particular biologically active environment. For example, target biodegradability can refer to only one value, e.g., the half-life of the polymer in the respective habitat, or can refer to more than one value, e.g., the degradation function of the polymer over time in a particular habitat. Preferably, the target biodegradability of a polymer refers to any one of the mineralization properties, biotransformation properties and / or degradation half-life of the polymer. Preferably, biodegradability refers to the percentage value of biodegradation after a given time frame. Furthermore, biodegradation is a technical characteristic of a polymer, i.e. knowledge of the biodegradation of a polymer strongly influences the technical applicability and utilization of the polymer.

[0009] In general, the polymer may be any polymer. Preferably, the target polymer is a synthetic polymer. In one embodiment, the synthetic polymer may be a chemical compound produced by chemical production from one or more starting materials, e.g., monomers, and comprising at least two monomer units. A monomer unit may be considered as a subunit of a polymer. A polymer may be prepared from a monomer by commonly known polymerization techniques. A polymer may be produced from a single type of monomer or from different monomers. A polymer may be produced by a single polymerization technique or by a combination of different techniques. The monomer units may be randomly distributed or present as blocks within the polymer. A polymer may be a linear polymer. A polymer may be a branched polymer. A polymer may be a crosslinked polymer. A polymer may be chemically modified after polymerization.

[0010] In a first step, the method includes providing a target biodegradability indicating a biodegradation characteristic of the polymer. In particular, providing can refer to receiving the target biodegradability from a user's input, for example, using a respective input unit. Providing can also refer to accessing a storage unit in which the target biodegradability is already stored. Furthermore, providing can also include receiving the target biodegradability, for example, via a network connection, from another source, and providing the received biodegradability. In general, the target biodegradability can refer to one target value, for example, a target half-life of the polymer in a particular habitat, or can refer to a range of values ​​that the polymer should meet in a particular habitat. Furthermore, the target biodegradability can also refer to any kind of target function, for example, a timely sequence of biodegradation. For example, the target biodegradability can indicate that the polymer has a first biodegradability value range during a first time range, and then a second target biodegradability value range during a next time range. Such more complex targeted biodegradation may be advantageous where habitat descriptor values ​​may change within the habitat due to timely changes in the environment, such as day / night changes, or due to control of the habitat descriptor, such as in a chemical reactor.

[0011] Furthermore, the method includes providing a digital representation of the polymer, which is indicative of the physicochemical properties of the polymer. In particular, providing can refer to receiving the digital representation from a user's input, for example using a respective input unit. Furthermore, providing can refer to accessing a storage unit in which the digital representation is already stored. Furthermore, providing can also include receiving the physicochemical properties from another source, for example via a network connection, and providing the received physicochemical properties as a digital representation. In particular, the physicochemical properties of the polymer can be quantified by polymer physicochemical parameters. Furthermore, indicative of or associated with the physicochemical properties of the polymer is defined as allowing access to information of the physicochemical properties. For example, the digital representation can directly include the physicochemical properties, for example in the form of values ​​for the respective quantities. However, the digital representation can also be a link to the respective physicochemical properties, which allows access to the physicochemical properties, or the digital representation can refer to an identifier associated with the physicochemical properties and which allows utilizing a respective look-up storage to access the physicochemical properties. Furthermore, the digital representation can also refer to information allowing deriving the physicochemical properties using one or more known relationships. For example, the synthetic specifications or structural formulas of polymers can be utilized as digital representations that allow for the derivation of their respective physicochemical properties using known chemical and physical laws and relationships.

[0012] Generally, throughout the following description, referring to a parameter or characteristic includes referring to both the respective quantity and the specific value of that quantity, unless explicitly defined otherwise. For example, a parameter that is temperature always refers to the quantity that is temperature and the specific value of temperature that is set for that quantity. In most cases, the explicit value of a parameter is not generally mentioned, since it may be different for different embodiments and application cases. However, providing a parameter or characteristic generally means providing information about a quantity, e.g., that the value is temperature, and also the value of the quantity or characteristic itself.

[0013] Preferably, the digital representation comprises polymer physicochemical parameters, preferably referring to polymer descriptors, which indicate the physicochemical properties of the polymer. In particular, the polymer physicochemical parameters refer to parameters that quantify the physicochemical properties of the polymer. In this context, the term "physicochemical properties" refers to the physical and / or chemical properties of the polymer. However, the digital representation may also be provided to allow for deriving the polymer physicochemical parameters, for example by providing a representation of a polymer in which the respective polymer physicochemical parameters are already stored or can be determined, for example, by a respective polymer descriptor calculation. Preferably, the digital representation refers to at least one of the following: the recipe, the structural formula, the brand name, the IUPAC name, the chemical identifier, and the CAS number of the polymer.

[0014] In a preferred embodiment, the polymer physicochemical parameters refer to parameters that quantify the physicochemical characteristics of subgroups of the polymer. In this embodiment, a digital representation can also be provided to allow deriving the polymer physicochemical parameters by determining subgroups of the polymer and determining the polymer physicochemical parameters based on the physicochemical properties of the determined subgroups. Generally, a subgroup refers to a part of a polymer, where all subgroups of the polymer together form the polymer. For example, a subgroup can refer to a part of a polymer, where the subgroups are linked together in a chain or network in a continuous manner to form the polymer. Preferably, a subgroup of a polymer refers to a repeating unit that describes a part of the polymer that, when repeated, produces a complete polymer chain. However, in some cases, a subgroup can also refer to a single part of a polymer that is not repeated. Furthermore, it is preferred that the subgroup comprises a repeating part, for example, a subgroup of a polymer can comprise a repeating core that is also present in other subgroups and further additional parts that are not present in other subgroups. Preferably, the subgroup refers to at least one of the polymerized monomers or oligomer fragments. More preferably, the subgroup refers to the polymerized monomers. In this context, polymerized monomers refer to the monomers after their polymerization and are sometimes referred to as "mer units" or "mers". In particular, polymerized monomers do not refer to the monomers present in the reaction mixture before polymerization, i.e. raw materials, but rather to repeating units derived from monomers that have been altered during or after polymerization. Thus, the subgroup descriptors determined for polymerized monomers are different from the subgroup descriptors determined for unreacted monomers before polymerization. In particular, it has been found by the inventors that polymerized monomers allow the polymer descriptors to be determined from the subgroup descriptors of polymerized monomers, which allows the determination model to accurately determine the habitat descriptor values ​​of the polymer. In a preferred embodiment, the digital representation of the polymer includes subgroups provided as molecular models showing the chemical structure of the subgroups after polymerization.Even more preferably, the molecular model of the subgroup is determined in a manner suitable for quantum chemical calculations regarding the number of atoms characteristic of the subgroup and their connectivity within the polymer. Furthermore, in addition to the molecular model of the subgroup treating the subgroup as a monomeric structure, alternatively a molecular model can also be used that refers to an oligomeric model taking into account the effect of the neighboring molecular structures of the subgroup in the polymer.

[0015] In general, when the digital representation of a polymer does not directly include the polymer physicochemical parameters, the polymer physicochemical parameters are preferably determined by determining subgroups of the polymer. For example, the respective subgroups of the polymer can be determined using known methods. However, the determination of the subgroups of the polymer is preferably performed according to the following embodiment of the invention. In particular, the subgroups are preferably determined such that the bonds between the atoms of the different subgroups in the polymer are as unpolarized as possible, preferably with the smallest possible bond order (e.g., single C-C bonds). Furthermore, the subgroup representing the polymer preferably contains the same number of active non-hydrogen atoms as the polymer. In addition to the active atoms, the subgroups may also contain further atoms that can be ignored while calculating the physicochemical parameters of the subgroups. Furthermore, the subgroups are preferably determined such that polymers containing moieties built using different polymerization techniques are sufficiently covered and satisfy the aforementioned conditions. An example is polyethers used as components of polyurethanes. In general, a database or archive can be created with multiple reactions between polymer moieties, and the subgroups can be obtained from the respective structures of the reactions. For example, the subgroups of the polymers can be easily derived using specific chemical languages ​​such as SMILES and SMARTS. For example, a database of reaction SMARTS can be generated and then corresponding reaction SMARTS can be selected based on the polymerization of each polymer. From the selected reaction SMARTS, the SMILES of the monomers of the polymer can be directly derived and, for example, RDkit can be used to determine the SMILES of the subgroups, i.e., the number and connectivity of atoms, from the SMILES of the monomers.

[0016] The determined subgroups of polymers are associated with subgroup physicochemical parameters indicative of parameters quantifying the physicochemical properties of the subgroups in the polymer. In particular, if the polymer physicochemical parameters are not directly provided by the digital representation, the polymer physicochemical parameters are preferably determined by determining the respective subgroup physicochemical parameters for each of the subgroups and determining the polymer physicochemical parameters based on the subgroup physicochemical parameters of the subgroups, for example by averaging. Thus, the method preferably comprises first providing or determining the subgroups of polymers from the digital representation of the polymer, then determining or providing the physicochemical parameters of the subgroups, i.e. the values ​​of the parameters quantifying the physicochemical properties, and then determining the physicochemical parameters of the polymers based on the physicochemical parameters of the subgroups of each polymer.

[0017] Preferably, the polymer physicochemical parameters refer to at least one of the following: compositional descriptors, counting descriptors, lists of structural fragments, fingerprints, graph invariants, 3D descriptors and / or higher dimensional descriptors, which indicate parameters that quantify the physicochemical properties of the polymer. In a preferred embodiment, the polymer physicochemical parameters refer to 3D descriptors, in particular quantum chemical descriptors. Furthermore, the inventors have found that in particular the molar mass describes the biodegradation of a polymer very accurately. It is therefore particularly preferred that the physicochemical parameters include the molar mass of the polymer. In general, the physicochemical parameters of the polymer can be derived from the physicochemical parameters of the subgroups, which can therefore also refer to the same physicochemical parameters as above. However, the physicochemical parameters can also be derived without the use of subgroups, for example by quantum chemical simulation of the entire polymer. Below, the possible physicochemical parameters are defined in more detail. Also, in these cases, the defined physicochemical parameters can refer directly to the physicochemical parameters of the polymer, or, optionally, to the physicochemical parameters of the subgroups.

[0018] The compositional descriptors may refer to any of the following: electric potential, average molecular weight, polydispersity, charge, spin, boiling point, melting point, enthalpy of fusion, dissociation constant, Hansen parameters, protic, polar and dispersive contributions, Abraham parameters, retention index, TPSA, receptor binding constant, Michaelis-Menten constant, inhibitor constant, mutagenicity, LD50, bioconcentration, toxicity, biodegradation profile and viscosity.

[0019] The count descriptor can refer to any of the following: sum of atomic electronegativities, sum of atomic polarizabilities, amount of components, ratio of amount of components, number of atoms and non-H atoms, number of H, B, C, N, O, P, S, Hal and heavy atoms, number of H donor and H acceptor atoms, number of bonds, number of non-H or multiple bonds, number of double, triple and aromatic bonds, number of functional groups, ratio of functional groups, sum of bond orders, aromatic ratio, number of rings or cycles, number of unpaired electrons, number of rotatable bonds, rotatable bond fraction, and number of conformers.

[0020] The polymer descriptor, which refers to 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. The fingerprint descriptor preferably includes at least one of a MACCS key, preferably in bit form or total form, Morgan and other circular fingerprints, preferably in bit form or total form, phase twist, atom pairs, infrared and related spectra, fingerprint counts, PubChem fingerprints, substructure fingerprints, and Klekota-Roth fingerprints. The graph invariant / topological indicators descriptor preferably includes at least one of a topostructural indicator and a topochemical indicator.

[0021] In a preferred embodiment, the polymer physicochemical parameters are 3D descriptors including at least one of the following: volume as a sum over atoms, average volume per atom, area as a sum over atoms, area as an average per atom, area over all atoms, area as an average per atom, solvent accessible surface, dispersion energy, dielectric energy, H donor, H acceptor, polar and non-polar surface area, atomically resolved H donor, H acceptor, polar and non-polar surface area, shape, sphericity, dipole and higher electric moments, polarizability, dielectric energy, proticity, polar and non-polar surface area, orbital energy and orbital gap, ionization energy, electron affinity, hardness, electronegativity, electrophilicity, excitation energy and intensity, infrared and ultraviolet absorption bands, reactivity measurements, redox potential, bond reference point, partial charge, charge surface area, atomic orbital contribution, bond order, atomic radius. In particular, the polymer physicochemical parameters preferably refer to 3D descriptors including at least one of the following: sum of volume over all atoms, average volume per atom, sum of area over all atoms, average area per atom, solvent accessible surface, dispersion energy, dielectric energy, H donor, H acceptor, polar and / or non-polar surface area, atomically resolved H donor, H acceptor, polar and / or non-polar surface area, shape, sphericity, cone angle, polarizability, dielectric energy, proticity, polar and / or non-polar surface area, excitation energy and intensity, infrared and / or UV absorption bands, reactivity measurements, particle charge and / or charge surface area. The preferred high-dimensional descriptors may include at least one of the following: conformational partition function, solubility, vapor pressure, activity coefficient, diffusion coefficient, partition coefficient, surface activity, rotational constant, moment of inertia, radius of gyration, composition drift of the polymer, density, viscosity, conformer-weighted volume and area, conformer-weighted H donor, H acceptor, protonity, polar and / or non-polar surface area, charge distribution, conformational dipole moment and molecular refraction. Preferably, higher-dimensional descriptors are used that include at least one of solubility, vapor pressure and activity coefficient, surface activity, conformer-weighted H donor, H acceptor, protonity, polar and non-polar surface area and charge distribution.

[0022] The method further comprises providing a biodegradation habitat, the biodegradation habitat indicating a habitat descriptor that affects the biodegradation of the polymer in the respective habitat. In particular, providing can refer to receiving the biodegradation habitat from a user's input, for example using a respective input unit. Furthermore, providing can also refer to accessing a storage unit in which the biodegradation habitat is already stored. Furthermore, providing can also refer to pre-setting of the biodegradation habitat. For example, if the method is utilized in a very specific situation where only one specific biodegradation habitat is advisable, the respective biodegradation habitat can be pre-set and thus does not need to be provided as a specific input. Furthermore, providing can also include receiving a habitat descriptor directly from another source, for example via a network connection, and providing the received habitat descriptor as the biodegradation habitat. The provided biodegradation habitat can refer to a general habitat, for example a marine habitat, in which case the respective habitat descriptor of this habitat is already stored in the respective storage that can be accessed. The provided habitat descriptor determines for which habitat descriptor a habitat descriptor value should be determined. For example, a provided biodegradation habitat may indicate that salt concentration is an environmental characteristic that may affect biodegradability in that habitat. In this example, salt concentration refers to a habitat descriptor, and the value of salt concentration is determined as the habitat descriptor value.

[0023] In general, the habitat descriptors indicate the environmental characteristics of the habitat. In particular, the environmental characteristics of the biodegradation habitat can affect the biological activity in the respective habitat, for example, the presence, growth or absence of a particular microbiome. Thus, the environmental characteristics defined by the habitat descriptors also indirectly affect the biodegradation of the polymer in the respective habitat. For example, if a polymer is biodegradable by a particular bacterium that requires a particular salt concentration, the polymer will biodegrade faster in a habitat that provides such a salt concentration, such as a marine habitat, but much slower in a habitat that does not have the right salt concentration, such as wastewater.

[0024] Preferably, the biodegradation habitat is any one of a marine habitat, a wastewater habitat, a lake habitat, a compost habitat, or a soil habitat. In a preferred embodiment, the biodegradation habitat refers to a marine habitat, and the habitat descriptor refers to at least one of the following: salt concentration, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, such as nitrogen, phosphate, potassium, and / or dissolved organic carbon concentration, pH value, environment type, oxygen content, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to a lake habitat, and the habitat descriptor refers to at least one of the following: salt concentration, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to a wastewater, and the habitat descriptor refers to at least one of the following: water temperature, microbial community, sludge concentration, nutrient concentration, pH value, test duration, solids content, and enzyme environment. Furthermore, in this habitat, the sludge may be a separate habitat. Thus, in one embodiment, the habitat may be a sludge habitat, such as the aerobic part of a wastewater treatment plant, and the habitat descriptors refer to at least one of the following: solids content, pH, nutrient content, heavy metal content, microbial population. In a further preferred embodiment, the biodegradation habitat refers to soil, and the habitat descriptors refer to at least one of the following: temperature, composition, such as sand and / or clay content, pH value, moisture content, nutrient concentration, microbial community, nitrogen content, water holding capacity, and enzyme environment. In a further preferred embodiment, the biodegradation habitat refers to compost, and the habitat descriptors refer to at least one of the following: temperature, compost activity, pH value, moisture content, humidity, compost maturity, compost composition, compost origin, nutrient concentration, microbial community, solids content, water holding capacity, and enzyme environment.

[0025] The method further comprises providing a biodegradation model based on the provided biodegradation habitat. In particular, the step of providing a biodegradation model is preferably a step of selecting a biodegradation model based on the provided biodegradation habitat. For example, a plurality of biodegradation models can be stored in the biodegradation storage, each biodegradation model being trained for one or more different biodegradation habitats, in particular for different values ​​or ranges of values ​​of the habitat descriptor values ​​of the biodegradation habitat. A respective suitable biodegradation model can then be selected from the plurality of biodegradation models based on the provided biodegradation habitat indicating the habitat descriptor. For example, a biodegradation model is suitable if the indicated habitat descriptor determinable by the biodegradation model refers to the habitat descriptor indicated by the provided habitat. For example, a respective look-up table can be provided that allows an easy comparison between the indicated habitat descriptor and the habitat descriptor on which the biodegradation model stored in the storage was trained, so that a suitable biodegradation model can be directly selected. However, in another embodiment, providing a biodegradation model based on the provided biodegradation habitat can also refer to a user selection of a biodegradation model. For example, the user may be provided with a pre-selection of biodegradation models that refer to the provided biodegradation habitats and then be allowed to select the respective biodegradation models to be utilized. In general, the possible stored biodegradation models refer to biodegradation models that have already been parameterized based on respective training data sets for one or more habitats. Since the training data sets utilized to parameterize the biodegradation models are historical data, as will be explained in more detail below, the biodegradation models can be trained and thus generated at any time prior to the determination of a particular habitat for a particular polymer and target biodegradability, and stored on a separate database after training. However, the training and thus the generation of the biodegradation models can of course also be performed when it is determined that a particular biodegradation model for a particular habitat is required, for example.

[0026] The provided biodegradation model is then adapted to determine a habitat descriptor value based on the target biodegradability and the polymer physicochemical parameters. In particular, the biodegradation model is a data-driven model parameterized with respect to the biodegradation habitat so that the habitat descriptor value of the polymer can be determined based on the polymer physicochemical parameters and the target biodegradability. The term "to" should be interpreted here as the parameterization allows the biodegradation model to be adapted when the polymer physicochemical parameters are provided as input, thus allowing the biodegradation model to provide a biodegradability with respect to the habitat. For example, the biodegradation model relates the polymer physicochemical parameters of the historical digital representation of the synthetic specification and the historical digital representation of the habitat to biodegradability. This allows the digital representation of the synthetic specification to be determined based on the target biodegradability. The term "data-driven" is used herein to emphasize that the model is primarily based on the respective data input, and not, for example, on intuition, personal experience, or knowledge. Preferably, the biodegradation model refers to a machine learning-based model based on known classification algorithms, such as neural networks, regression models, machine learning algorithms, etc. For most applications in this context, regression models based on linear regression, random forest, Lasso, boosted trees, ridge regression and MARS algorithms in particular have proven suitable, while for classification models, random forest, logistic regression and SVM algorithms in particular have proven suitable. Preferably, the biodegradation model is based on a neural network algorithm. In general, the biodegradation model is parameterized during a training process in which polymer physicochemical parameters derived from parameters quantifying the physicochemical properties of the polymer are utilized together with the corresponding biodegradability for specific biodegradation habitats and the respective habitat descriptor values ​​of these specific biodegradation habitats.Based on such a training data set specific to a biodegradation habitat and a particular habitat descriptor value, each parameter of the data-driven model can be determined utilizing known training methods such that the biodegradation model can also determine habitat descriptor values ​​for polymers that are not part of the training data set.

[0027] The method then comprises determining a habitat descriptor value for each polymer based on the selected biodegradation model, the target biodegradability and the polymer physicochemical parameters. In particular, if the digital representation of the polymer comprises the polymer physicochemical parameters, the polymer physicochemical parameters are provided together with the target biodegradability as input to the biodegradation model, which then provides the determined habitat descriptor value as output. If the digital representation does not directly comprise the physicochemical parameters of the polymer, determining the habitat descriptor value may also comprise first determining the physicochemical parameters of the polymer, for example as described above. The thus determined polymer physicochemical parameters can then be provided as input to the biodegradation model. The thus determined habitat descriptor value can then be provided, for example, to an output unit or a calculation unit for further processing. Preferably, providing the habitat descriptor value leads to further processing utilizing the habitat descriptor value. In such a case, providing as a separate step can be omitted and replaced by processing of the habitat descriptor value.

[0028] In a preferred embodiment, the method further comprises providing a control signal for controlling a reactor system adapted to biodegrade the polymer, the control signal being provided based on the determined habitat descriptor value. In particular, the reactor system may generate or provide a control signal such that the reactor system provides the determined habitat descriptor value to a biodegradation habitat for a material in the reactor system, preferably including a polymer. For example, if the determined habitat descriptor value indicates that the polymer meets a target biodegradability in a compost environment having a particular temperature, pH value and moisture content, the control signal may comprise a component that causes the reactor system to simulate the respective environment, in particular to set the respective temperature, moisture content and pH value such that the polymer biodegrades at the respective target biodegradability.

[0029] Furthermore, processing of habitat descriptor values ​​may also refer to a step of selecting one or more polymers based on their respective habitat descriptor values. For example, if for a plurality of potential polymers respective habitat descriptor values ​​for a particular habitat have been determined, selecting may comprise comparing the determined habitat descriptor values ​​of the different polymers with predefined selection criteria and selecting polymers whose determined habitat descriptor values ​​meet these criteria. In particular, in an embodiment, the method comprises, for example, receiving a target habitat descriptor value or a target habitat descriptor value range that can be met by the respective reactor system. In this case, the method may comprise comparing the received target habitat descriptor value with the determined habitat descriptor values ​​of the different polymers and selecting the polymer based on this comparison. In particular, based on such a comparison, a control signal may be provided. The control signal may refer to any signal that allows further control of the technical system. For example, the control signal may be adapted to control an interface to provide the result of the comparison on the interface. In a preferred embodiment, the comparison refers to a verification of the target habitat descriptor value, and the verification is positive if the determined habitat descriptor value falls within a predefined range around the target habitat descriptor value. In this case, the control signal may be adapted to simply control the user interface to provide an indication of a positive or negative validation result for the respective polymer. However, preferably, the control signal refers to a recipe, i.e. a synthesis specification, of one or more polymers that meet the specified target habitat descriptor values, i.e. are positively validated. A recipe, i.e. a synthesis specification, is generally defined as an instruction on how a polymer can be synthesized. In particular, the recipe comprises starting materials and respective parameters for polymerization from the starting materials. Preferably, the control signal comprises a recipe in a form that directly enables the automatic control of a respective industrial system or work facility for producing the polymer. In particular, if the result of the comparison refers to the determined habitat descriptor values ​​being within a predefined range around the target habitat descriptor values, the control signal preferably indicates a machine executable synthesis specification for the polymer.

[0030] In a preferred embodiment, the method further comprises providing a synthesis specification as a digital representation of the polymer and determining the polymer physicochemical parameters from the synthesis specification. In particular, the synthesis specification, i.e. recipe, comprises information on the polymer synthesis of the polymer, for example on the starting materials and the process by which the respective starting materials are covalently linked to form a polymer chain or a polymer network. The method then comprises determining the physicochemical parameters of the polymer from the synthesis specification. Optionally, subgroups can be determined from the synthesis specification and then the polymer physicochemical parameters can be determined based on the subgroup physicochemical parameters of the subgroups, for example from a database or by utilizing known descriptor determination algorithms. In a preferred embodiment, further from the synthesis specification, the catalyst and / or non-reactive process components to be utilized are determined. In this case, this information is preferably also utilized by the biodegradation model together with the polymer physicochemical parameters to determine the habitat descriptor values. Preferably, physicochemical parameters are also determined for the catalyst and / or non-reactive process components and the respective physicochemical parameters are also used to determine the physicochemical parameters of the polymer. Preferably, the physicochemical parameters for the catalyst and / or non-reactive process components refer to the amounts of the respective components, e.g. molar mass, molar percentage, etc., and are considered to determine the polymer physicochemical parameters for the polymer.

[0031] In a preferred embodiment, determining the polymer physicochemical parameters from the synthesis specification includes identifying the type and amount of subgroups based on the synthesis specification, for example as the physicochemical parameters of the subgroups, and determining the polymer physicochemical parameters based on the identified type and amount of the subgroups. In general, the type of subgroups may refer to a certain type or class that is related to a specific physicochemical characteristic (for example, a descriptor) of the subgroups and thus associated with a specific physicochemical characteristic of the polymer that includes these subgroups. However, the general physicochemical properties of a polymer, and therefore the physicochemical parameters of the polymer, may also depend on the amount of the subgroups present in the polymer, so that the amount may also be taken into account. In a preferred embodiment, the determination of the type and amount of the subgroups takes into account information provided by the synthesis specification that indicates the type of polymerization. Information regarding the type of polymerization that can be utilized may refer, for example, to whether the polymerization refers to polycondensation, polyaddition, radical polymerization, cationic polymerization, anionic polymerization, or coordination chain polymerization. Preferably, for each type of polymerization, a rule is predefined that can be applied to determine the subgroups of the polymer. For example, rules can be predefined that determine which functional groups of monomers in a synthesis specification react with which functional groups of the synthesized polymer in what order of priority. The rules can be based, for example, on kinetic considerations. Based on the number and type of polymerized functional groups, subgroups can be determined and the number and type of subgroups can be calculated.

[0032] In one embodiment, determining the amount of the subgroups includes determining the amount of at least one of amide, ester, thioester, carbonate, ether, amine, urea, urethane, thiourethane, isocyanurate, biuret, allophanate, acetal, mica adduct, radical polymerized double bond, siloxane, silane, silazane, phosphazene groups, and residual amine, aldehyde, ketone, epoxide, aziridine, isocyanate, alcohol, thiol, carboxylic acid, acyl halide, α,β-unsaturated carbonyl group, α,β-unsaturated carboxyl, and double bond group in the polymer based on a synthesis specification.

[0033] In one embodiment, the method further comprises providing a biodegradation test method, the provided biodegradation test method representing a standardized biodegradation test method for experimentally determining the biodegradation of a polymer, and a biodegradation model is further provided based on the provided biodegradation test method. Generally, there are a number of standardized biodegradation test methods for testing the biodegradation of a polymer. For example, such test methods can be found in DIN or ISO standards. Further providing a biodegradation test method and providing a trained biodegradation model based on the provided biodegradation test method allows, for example, to determine a biodegradation that is easily comparable to the biodegradation measured respectively using the respective test methods. In particular, with respect to this embodiment, it is preferred that the biodegradation model is trained based on a data set in which the test method for which the biodegradation was determined is clearly specified, so that the biodegradation model can be specifically trained for one or more test methods.

[0034] In one embodiment, a target application of the polymer is further provided with reference to the intended application of the polymer, and a biodegradation habitat is provided based on the target application. The target application of the polymer can refer to the context of the intended application of the polymer, for example, if it is intended to utilize the polymer as a coating, in a personal care product, in a cleaning detergent, or in the packaging of a product. Such a target application indicates a particular biodegradation habitat. For example, it may be interesting for the packaging of a product if the polymer biodegrades in compost. In another example, if the target application refers to the use of the polymer in a personal care product, it is very likely that the polymer will be found in an aqueous environment sooner or later. Thus, each target application indicates a respective biodegradation habitat. In this regard, a predefined list can be provided on the storage in which each target application and the corresponding biodegradation habitat are stored. A target application for the polymer can then be provided, for example, by providing a list of target applications to the user and allowing the user to select an individual target application, the individual target application being connected to one or more biodegradation habitats. Biodegradability can then be determined for each of the biodegradation habitats to which the target application is connected, or again, the user can select individual biodegradation habitats to which the target application is connected. Additionally or alternatively, information can be provided that indicates the intended end-of-life treatment of the polymer. For example, the end-of-life treatment may indicate whether the polymer is intended to biodegrade in a particular environment or should be subjected to a particular treatment, for example, in a bioreactor. Thus, the intended end-of-life treatment information can also be utilized to determine the biodegradation habitat for the polymer, as described above.

[0035] In one embodiment, the biodegradation model can be further trained to provide information in addition to the habitat descriptor values ​​that indicate the accessible surface area of ​​the product comprising the polymer that allows the target biodegradability of the product to be met. For example, the information can indicate whether the product should be provided in solid, crushed, foamed, pelletized, or any other form in order to meet the target biodegradability in the respective habitat. Preferably, the information indicates the accessible surface area of ​​the product per mass or the geometric shape of the smallest independent part of the product. Generally, the biodegradability of a polymer is an inherent characteristic of the polymer, but the exact timing of the biodegradation of a product comprising the polymer may also depend, for example, on the surface area that can be accessed by the microbial components of the habitat involved in the biodegradation. Thus, further determining the accessible surface area of ​​the product comprising the polymer that allows the target biodegradability to be met allows for further precision in the biodegradation of the respective product. For example, based on the information on the accessible surface area, control data, i.e. control signals, can be generated to control a grinder or mill to shred or grind the product to be biodegraded.

[0036] In one embodiment, the habitats and / or habitat descriptors are stored in association with respective geographical locations, and providing a biodegradable habitat refers to providing a geographical location of the habitat and retrieving the habitat and / or habitat descriptor for that geographical location from storage. The geolocation can refer to, for example, coordinates or other locality identification. For example, the geolocation can refer to the name of a city, a country, a country region, an ocean area, a geographical feature, etc. Based on such a geolocation, a respective habitat descriptor, for example a typical environmental characteristic, can be determined. Thus, by providing a geolocation, a respective habitat descriptor for this geolocation can be provided. This has the advantage that the exact habitat or the exact habitat descriptor for the area does not need to be known to the user. Thus, the user can simply provide a location where it is expected that the polymer may biodegrade in this area.

[0037] In one embodiment, the polymer physicochemical parameters represented by the digital representation of the polymer refer to at least one of the following: recipe parameters from the polymer synthesis, compositional descriptors, counting descriptors, lists of structural fragments, fingerprints, graph invariants, 3D descriptors and / or higher dimensional descriptors indicative of the chemical properties of the polymer. The respective connections of the digital representation with, for example, previously calculated physicochemical parameters of the polymer or further information about the polymer may already be stored and connected to the respective digital representation. For example, if the digital representation refers to a brand name, the respective structural formula, subgroups and / or subgroup physicochemical parameters or polymer physicochemical parameters corresponding to the brand name may already be stored, for example, in the storage of the brand name owner.

[0038] In a preferred embodiment, the polymer belongs to at least one of the following polymer types: polyalkoxylates, polycondensates, addition polymers, vinyl polymers, natural polymers, polymer dispersions, polymer foils, biopolymers, polysilicones, resins, rubbers, and polyketones, and the biodegradation model is specifically trained for each polymer type to which the polymer belongs. In particular, the training data for parameterizing the biodegradation model includes polymers of each polymer type. However, the biodegradation model can also be parameterized with training data of polymers from more than one polymer type.

[0039] In a preferred embodiment, the polymer belongs to polyalcoholates and the habitat is a wastewater habitat, in particular a sludge habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of molar mass, composition, chemical moiety, water solubility, and partition coefficient, more preferably, the physicochemical properties include molar mass, even more preferably, molar mass, composition, and even more preferably, molar mass, composition, and partition coefficient.

[0040] In a preferred embodiment, the polymer belongs to the polycondensates, preferably polyesters, polyamides and phenoplasts, and the habitat is a wastewater habitat, in particular a sludge habitat or a soil habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of molar mass, composition, chemical moiety, water solubility, partition coefficient, index of hydrolytic stability and crystallinity, more preferably, the physicochemical properties include molar mass and composition, even more preferably, the molar mass, composition and chemical moiety, even more preferably, the molar mass, composition, chemical moiety and crystallinity.

[0041] In a preferred embodiment, the polymer belongs to addition polymers, preferably polyurethanes and polyureas, and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of composition, chemical moieties, ratio of chemical moieties, ratio of components, and crystallinity, more preferably, the physicochemical properties include composition, even more preferably, ratio of components and chemical moieties, even more preferably, ratio of components, ratio of chemical moieties, ratio of components.

[0042] In a preferred embodiment, the polymer belongs to vinyl polymers, preferably polyvinyl, polyacrylate, polystyrene, polyvinyl ether and polyvinyl alcohol, and the habitat is a wastewater habitat, in particular a sludge habitat, or a soil habitat.Furthermore, in this embodiment, the physicochemical property preferably comprises at least one of molar mass, composition, chemical moiety, water solubility, partition coefficient, more preferably the physicochemical property comprises molar mass, even more preferably the molar mass and composition, even more preferably the molar mass, composition and chemical moiety.

[0043] In a preferred embodiment, the polymer belongs to natural polymers, preferably polysaccharides, polynucleotides, lignin, suberin, cutin, melanin, natural rubber and polypeptides, and the habitat is a wastewater habitat, in particular a sludge habitat or a soil habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of molar mass, chemical moiety, and water solubility and partition coefficient, more preferably the physicochemical properties include chemical moiety, even more preferably the chemical moiety and molar mass.

[0044] In a preferred embodiment, the polymer is a polymer dispersion and the habitat is a marine habitat or a soil habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of composition, chemical moiety, water solubility, and particle size, more preferably, the physicochemical properties include composition and particle size, and even more preferably, the composition, chemical moiety, and particle size.

[0045] In a preferred embodiment, the polymer belongs to a polymer foil and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of the following: composition, molar mass, chemical moiety, water solubility, crystallinity, and surface / volume ratio, more preferably, the physicochemical properties include composition and surface / volume ratio, even more preferably, the composition, chemical moiety, and surface / volume ratio, even more preferably, the composition, chemical moiety, crystallinity, and surface / volume ratio.

[0046] In a preferred embodiment, the polymer belongs to polysilicones, and the habitat is a soil habitat, a wastewater habitat, particularly a sludge habitat, or a marine habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of composition, molar mass, chemical moiety, water solubility, partition coefficient, and surface / volume ratio, more preferably, the physicochemical properties include molar mass, even more preferably, molar mass and composition, and even more preferably, molar mass, composition, and partition coefficient.

[0047] In a preferred embodiment, the polymer belongs to a resin and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, the physicochemical property preferably includes at least one of composition, molar mass, chemical moiety, water solubility, crystallinity, and surface / volume ratio, more preferably, the physicochemical property includes molar mass, even more preferably, the molar mass and composition, and even more preferably, the molar mass, composition, and surface / volume ratio.

[0048] In a preferred embodiment, the polymer belongs to rubber and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of composition, chemical moiety, water solubility, crystallinity, and surface / volume ratio, more preferably, the physicochemical properties include composition, even more preferably, the composition and surface / volume ratio, even more preferably, the composition, chemical moiety, crystallinity, and surface / volume ratio.

[0049] In a preferred embodiment, the physicochemical properties include at least one of molar mass, chemical moiety, solubility in water and / or octanol, crystallinity, and surface / volume ratio. More preferably, the physicochemical characteristics include chemical moiety, and even more preferably, molar mass, chemical moiety, and solubility in water. For polymers belonging to polyalkoxylates, polycondensates, vinyl polymers, or polyscones, it is preferred that the physicochemical properties further include at least one of partition coefficient and composition. For polymers belonging to polycondensates, addition polymers, polymer foils, resins, rubbers, or polyketones, it is preferred that the physicochemical properties further include at least one of crystallinity and hydrolytic stability index. For polymers belonging to resins, rubbers, addition polymers, or polysilicones, it is preferred that the physicochemical properties further include surface / volume ratio.

[0050] In a further aspect, an interface method for providing an interface is provided, the interface method comprising: a) receiving a target biodegradability, a digital representation and a habitat as input via a user interface and providing the received target biodegradability, digital representation and habitat to a processor performing the above method; and b) providing a determined habitat descriptor value of the polymer as a result, the result being received from the processor performing the above method.

[0051] In a further aspect, a computer-implemented training method for training a data-driven based biodegradation model for parameterizing the biodegradation model is provided, the training method comprising: a) providing training data associated with a given biodegradation habitat, the training data including: i) digital representations of a plurality of training polymers indicating physicochemical properties of each of the training polymers, ii) habitat descriptor values ​​for each habitat descriptor of the biodegradation habitat, and iii) biodegradability of each biodegradation habitat descriptor value associated with each training polymer; b) providing a data-driven based trainable biodegradation model; c) training the provided data-driven based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine habitat descriptor values ​​based on biodegradability and the physicochemical properties; and d) providing a trained biodegradation model.

[0052] In a further aspect, an apparatus is provided for determining habitat descriptor values ​​providing a target biodegradability for a given polymer, the apparatus comprising: a) a target biodegradability providing unit for providing a target biodegradability indicative of a biodegradation characteristic of the polymer; b) a digital representation providing unit for providing a digital representation of the polymer indicative of or associated with a physicochemical property of the polymer; c) a habitat providing unit for providing a biodegradation habitat, the biodegradation habitat indicative of habitat descriptors that affect the biodegradation of the polymer in the respective habitat, the habitat descriptors indicative of environmental properties of the habitat; d) a model providing unit for providing a biodegradation model based on the provided biodegradation habitat, the biodegradation model being adapted to determine habitat descriptor values ​​that enable biodegradation of the polymer meeting the target biodegradability in the biodegradation habitat, the model providing unit being a data-driven model parameterized for the biodegradation habitat such that the biodegradation model determines the habitat descriptor values ​​based on the target biodegradability and the physicochemical property; and e) a determining unit for determining a habitat descriptor value for the polymer based on the selected biodegradation model, the target biodegradability and the polymer description.

[0053] In a further aspect, an interface device for providing an interface is presented, the interface device comprising: a) an input interface unit for receiving a target biodegradability, a digital representation and a habitat as input via a user interface and providing the received target biodegradability, digital representation and habitat to said device, and b) a result interface for providing a habitat descriptor value of the polymer as a result, the result being received from said device.

[0054] In a further aspect, a training device for training a data-driven based biodegradation model for parameterizing the biodegradation model is presented, the training device including: a) a training data providing unit for providing training data associated with a predetermined biodegradation habitat, the training data including i) a digital representation of a plurality of training polymers indicating physicochemical properties for each of the training polymers, ii) habitat descriptor values ​​for each habitat descriptor of the biodegradation habitat, and iii) biodegradability for each biodegradation habitat descriptor value associated with each training polymer; b) a trainable model providing unit for providing a data-driven based trainable biodegradation model; c) a training unit for training the provided data-driven based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine the habitat descriptor values ​​based on the biodegradability and the physicochemical properties; and d) a training model providing unit for providing a trained biodegradation model.

[0055] In a further aspect of the invention, the use of the above method is presented, which is used to determine habitat descriptor values ​​of any of the following: i) polymers referring to polyesters, especially used in mulch film and packaging applications, such as aromatic-aliphatic copolyesters; ii) polymers referring to polyalkoxylates, especially used in home care and personal care applications; iii) polymers referring to polyurethane dispersions; iv) polymers used in fragrance applications; v) polymers used in paper coatings for packaging applications based on multi-layer blends; and vi) polymers referring to polyurethanes used in adhesives.

[0056] In a further aspect of the invention, a system is provided comprising: i) a control signal comprising a polymer synthesis specification indicating one or more components for producing a polymer, the control signal being generated according to the method described above; and ii) one or more components indicated by the synthesis specification in the control signal.

[0057] In a further aspect of the present invention, the use of a control signal generated according to the above-described method for controlling a production process, in particular a production process involving the production of polymers, or for controlling a reactor, in particular a waste decomposition reactor, is provided.

[0058] In a further aspect of the invention, a control signal is provided, the control signal being generated according to the method described above. Preferably, the control signal i) comprises a machine executable synthesis specification for producing a polymer or ii) refers to a signal for controlling a reactor.

[0059] In a further aspect, a computer program product for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer is presented, the computer program product comprising program code means for causing the above-mentioned apparatus to perform the above-mentioned method.

[0060] In a further aspect, a computer program product for training a biodegradation model is presented, the computer program product comprising program code means for causing the above-mentioned apparatus to perform the above-mentioned method.

[0061] It is to be understood that the above-mentioned method, the above-mentioned device and the above-mentioned computer program product have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.Furthermore, the above-mentioned training method, the above-mentioned training device and the above-mentioned training computer program product also have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0062] It is to be understood that a preferred embodiment of the invention can also be any combination of the dependent claims or the above embodiments with the respective independent claim.

[0063] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter. [Brief description of the drawings]

[0064] [Figure 1] 1 illustrates, in a schematic and exemplary manner, an embodiment of a system including an apparatus for determining habitat descriptor values. [Diagram 2] 1 shows, generally and exemplarily, a flow chart of a method for determining habitat descriptor values. [Diagram 3] 1 shows, generally and exemplarily, a flow chart of a method for training a biodegradation model for determining habitat descriptor values. [Figure 4] 1 shows, generally and exemplarily, a flow chart of an embodiment of a method for determining habitat descriptor values. [Diagram 5] 10 shows, diagrammatically and exemplarily, an optional extension of the method for determining habitat descriptor values; [Figure 6] 1 shows, generally and exemplarily, a block diagram of a system architecture of a system and apparatus for determining habitat descriptor values. [Figure 7] 1 shows, generally and exemplarily, a block diagram of a system architecture of a system and apparatus for determining habitat descriptor values. [Figure 8] 1 shows, generally and exemplarily, a block diagram of a system architecture of a system and apparatus for determining habitat descriptor values.

[0065] [Figure 9] 1 shows schematic and exemplary output and input screens of an exemplary user interface; [Figure 10] 2 shows, diagrammatically and exemplarily, a further flow chart of a preferred and more detailed embodiment of a habitat determination method; [Figure 11] 2 shows, diagrammatically and exemplarily, a further flow chart of a preferred and more detailed embodiment of a habitat determination method; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0066] Detailed Description of the Embodiments 1 shows, in a schematic and exemplary manner, an embodiment of a system 100 including an apparatus 110 for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer. Furthermore, the system 100 includes a training apparatus 130 for training a biodegradation model used in the apparatus 110, a database 140 capable of storing the determination results, i.e. the determined habitat descriptor values, of the polymer, and a waste management system 120 for managing waste containing the polymer, for example by utilizing a reactor system that can be controlled utilizing the determined habitat descriptor values.

[0067] The apparatus 110 comprises a target biodegradable providing unit 111, a digital representation providing unit 112, a habitat providing unit 113, a model providing unit 114, and a determining unit 115. Optionally, the apparatus 110 may further comprise an output and / or control unit 116, which may be adapted to output the determined habitat descriptor values ​​and / or to provide control signals for controlling a waste management process of the waste management system 120 based on the determined habitat descriptor values.

[0068] The target biodegradability providing unit 111 is adapted to provide a target biodegradability that exhibits a desired biodegradability characteristic of the polymer. The target biodegradability providing unit 111 can refer to, for example, an input unit through which a user can input the respective target biodegradability. Furthermore, the target biodegradability providing unit 111 can refer to or be part of a user interface that allows a user to interact with the device 110 for providing a target biodegradability. However, the target biodegradability providing unit 111 can also refer to or be communicatively coupled to, for example, a storage unit in which a target biodegradability for a particular application is already stored.

[0069] The digital representation providing unit 112 is adapted to provide a digital representation indicative of the polymer physicochemical parameters of the polymers for which the habitat descriptor values ​​are to be determined. The digital representation providing unit 112 may for example refer to an input unit through which a user can input the respective digital representation. Furthermore, the digital representation providing unit 112 may refer to or be part of a user interface that allows a user to interact with the device 110 and / or the database 140. However, the digital representation providing unit 112 may also refer to or be communicatively coupled to a storage unit in which the digital representations of the polymers are already stored. In general, the digital representations may directly include the polymer physicochemical parameters indicative of parameters quantifying the physicochemical properties of the respective polymers. However, instead of directly providing the physicochemical parameters of the polymers, it is also possible to provide a synthesis specification of the polymers. In this case, the digital representation providing unit 112 is preferably further adapted to determine the polymer physicochemical parameters from the synthesis specification. In particular, the digital representation providing unit 112 is preferably adapted to identify types and amounts of subgroups of polymers from the synthesis specification and to determine the polymer physicochemical parameters based on the identified types and amounts of the subgroups. In particular, the digital representation providing unit 112 may be adapted to determine for each identified subgroup a respective subgroup physicochemical parameter, for example by accessing a database in which the respective physicochemical parameters for a plurality of most relevant subgroups are stored. The physicochemical parameters of the polymer may then be determined based on the subgroup physicochemical parameters of the subgroups, preferably also based on the determined amount and type of the subgroups, for example by a weighted average of the subgroup physicochemical parameters of the subgroups. The digital representation providing unit 112 is then adapted to provide a digital representation comprising the polymer physicochemical parameters, for example to the determining unit 115.

[0070] The habitat providing unit 113 is adapted to provide a biodegradation habitat. The habitat providing unit 113 may, for example, refer to an input unit through which a user can input the respective biodegradation habitat. For example, a user interface may be provided that allows the user to select from several predefined biodegradation habitats. In a preferred embodiment, the habitat providing unit may be communicatively coupled to or refer to a user interface that allows the user to indicate a geolocation, for example by marking a location on a map, by indicating coordinates, or by providing a name of an area, for example a political or geological area, and the habitat providing unit may then be adapted to provide a biodegradation habitat based on the geolocation. For example, if the geolocation indicates a particular sea area, such as the North Sea or the Atlantic Ocean, the habitat providing unit may be adapted to determine a marine habitat as a biodegradation habitat.

[0071] In general, the biodegradation habitats indicate habitat descriptors that affect the biodegradation of the polymer in the respective habitat. In particular, the habitat descriptors indicate the environmental characteristics of the habitat, for example, for a marine habitat, the salt concentration may strongly affect the biodegradation of the polymer in the marine habitat. The indicated habitat descriptors then refer to the habitat descriptors for which the habitat descriptor values ​​are to be determined. In particular, the determined habitat descriptor values ​​are determined such that the provided polymers biodegrade in the respective habitats with the respective determined habitat descriptor values ​​such that the target biodegradability is met within the limits. For example, if the marine habitats provide the respective salt concentration values ​​and temperature values ​​or the respective ranges of values, it can be determined that a particular polymer in a marine habitat meets a particular half-life for biodegradation.

[0072] The model providing unit 114 is adapted to provide a biodegradation model based on the provided biodegradation habitat. In particular, the model providing unit 114 is preferably adapted to select a biodegradation model from a plurality of biodegradation models already stored in the database. For example, the biodegradation model may be trained on training data corresponding to one or more specific biodegradation habitats. These specific biodegradation habitats may be defined in terms of habitat descriptors that define which habitat descriptor values ​​can be accurately determined by the respective biodegradation model. For example, a look-up table may be provided that allows the model providing unit to select which biodegradation model is appropriate based on the biodegradation habitat, for example based on the habitat descriptor value to be determined. However, the model providing unit 114 may also comprise or point to an input unit, from which a biodegradation model may be received, for example by user selection or user input indicating which biodegradation model should be used.

[0073] The biodegradation model is a data-driven model parameterized to determine the biodegradability of the polymer in a habitat based on the digital representation, in particular based on the polymer descriptors that indicate the physicochemical properties associated with the polymer, and based on one or more habitat descriptor values ​​of the target biodegradability. In a preferred embodiment, the data-driven model refers to a classification model that utilizes, for example, a regression model-based algorithm or a machine learning model-based algorithm. The regression model-based algorithm can be based on any of the following algorithms: neural network algorithm, linear regression algorithm, LASSO algorithm, ridge regression algorithm, MARS algorithm, random forest algorithm, and boosted tree algorithm. The classifier-based model algorithm can be based on any of the following algorithms: random forest algorithm, logistic regression algorithm, and SVM algorithm. The inventors have found that for most applications, in particular, linear regression, random forest, neural network, and MARS-based algorithms are suitable.

[0074] The biodegradation model can be trained, for example, with the aid of a training device 130. In particular, the training device 130 comprises a training data providing unit 131 for providing training data for training the data-driven biodegradation model. The training data includes a) polymer physicochemical parameters of a plurality of training polymers, b) a biodegradability associated with each training polymer for one or more different habitats, and c) a habitat descriptor value associated with each habitat and each biodegradability. Preferably, in the training data, the biodegradability provided for each training polymer refers to a biodegradability measured according to the same measurement method. However, the biodegradability may also be provided for different measurement methods, in which case it is preferably clearly indicated which biodegradability is associated with which measurement method, so that the biodegradation model can be trained to distinguish between the different measurement methods. In general, the training data can be designed to cover a predefined habitat space of the biodegradation model to be trained, the habitat space being defined by the range of values ​​of the respective habitat descriptors on which the biodegradation model is trained. For example, the training data can be designed to cover predefined polymer types for predefined habitats. Known methods for designing and optimizing training data for a given habitat space can be used to ensure that the habitat space is adequately covered in the training data and that random outliers are avoided.

[0075] Further, the training device 130 includes a model providing unit 132 adapted to provide a data-driven based trainable biodegradation model, e.g., a biodegradation model including parameters that can be set during a training process to train the biodegradation model. For example, the trainable biodegradation model may already be stored in a storage unit that the model providing unit 132 can access to provide it. Further, the training device 130 includes a training unit 133 for training the provided data-driven biodegradation model based on the provided training data. In particular, training may refer to varying parameters of the biodegradation model based on the respective training data until the biodegradation model is adapted to determine one or more habitat descriptor values ​​of the polymer based on the digital representation, in particular the polymer physicochemical parameters and the target biodegradability of the polymer. In general, any known training algorithm for training a data-driven, in particular machine learning based, model may be utilized. Preferably, during training of the biodegradation model, the physicochemical parameters of the polymer that most affect the biodegradability and / or habitat descriptor values ​​in the respective habitats are also determined, and then the model is trained based on these most influencing descriptors. To determine these most influential physicochemical parameters and / or habitat descriptors, for example, cluster analysis or PCA analysis tools can be used. In particular, the physicochemical parameters of the polymer can be used to determine the application space of the training data, and then the application space can be defined by the physicochemical parameters of the polymer and the habitat descriptors covered by the data. The determination of the most influential physicochemical parameters and / or habitat descriptors can then be performed as a dimensionality reduction of the application space. An algorithm can then be applied to optimize the training data in the application space, for example, to cover the application space with as little training data as possible.

[0076] The training device 130 then comprises a trained model providing unit 134 adapted to provide the trained biodegradation model, for example to a storage unit in which trained biodegradation models for different habitats and / or different types of polymers, respectively, are stored. However, the trained model providing unit 134 may also be adapted to provide the trained biodegradation model directly, for example to the biodegradation model providing unit 114 of the device 110.

[0077] In all cases, the biodegradation model providing unit 114 is then adapted to provide the appropriate trained biodegradation model to the determining unit 115. The determining unit 115 can then utilize the biodegradation model, the provided digital representation and the provided target biodegradability to determine the habitat descriptor value. In particular, the determining unit 115 can be adapted to utilize the polymer physicochemical parameters indicated by the digital representation together with the target biodegradability as input to the biodegradation model, which is then trained as already mentioned above to provide as output a decision on the habitat descriptor value. An output unit, for example referring to a display, can then be adapted to output the determined habitat descriptor value. Furthermore, the determined habitat descriptor value can also be provided, for example, to a database 140 for storing the polymer in association with the determined habitat descriptor value for a particular habitat for future use. For example, from the database 140, a polymer that meets a predetermined target biodegradability in a particular habitat can be selected, and then the respective determined habitat descriptor value can be utilized, for example, by the waste management system 120. However, polymers may also be selected based on determined habitat descriptor values, for example, such that only polymers within a reactor of the waste management system 120 are capable of providing suitable biodegradation conditions for these polymers.

[0078] Optionally, the apparatus 110 may comprise a control unit 116 adapted to provide control signals based on the determined habitat descriptor values, for example to control a reactor system, which is part of the waste management system 120, to biodegrade the polymer. In particular, it is preferred that the provided control signals control the reactor system, such that the reactor system provides at least one of the determined habitat descriptor values ​​to the polymer-containing material in the reactor system. For example, the control signals may control the temperature or humidity in the reactor system to meet the respective determined habitat descriptor values. This allows to control the waste management facility 120, so that the polymers to be biodegraded in the facility, for example in different reactor systems, are degraded, for example within a predefined time and with a desired target biodegradability.

[0079] Fig. 2 shows a schematic and exemplary flow chart of a method for determining habitat descriptor values ​​providing a target biodegradability for a given polymer. The method 200 comprises a first step 210 of providing a target biodegradability. Furthermore, the method 200 comprises a step 220 of providing a digital representation of the polymer, which represents the polymer physicochemical parameters of the polymer. In particular, the provision of the target biodegradability and the provision of the digital representation in steps 210 and 220 can be performed according to the principles described above with respect to the target biodegradability providing unit 111 and the digital representation providing unit 112, respectively. Furthermore, in step 230, biodegradation habitats are provided, which represent the habitat descriptors influencing the biodegradation of the polymer in the respective habitat. For this step 230 too, the principles described above with respect to the habitat providing unit 113 can be applied, for example. Furthermore, in step 240, a biodegradation model is provided, which is adapted to determine based on the target biodegradability and the polymer physicochemical parameters habitat descriptor values. As already described above in more detail, providing a biodegradation model can also refer to the selection of a biodegradation model based on the provided biodegradation habitat. Furthermore, the biodegradation model is a data-driven model parameterized with respect to the biodegradation habitat, as described in more detail above. In general, steps 210, 220, 230, and 240 can be performed in any order, or even simultaneously. In a next step 250, the habitat descriptor values ​​are determined utilizing the provided target biodegradability, the provided polymer physicochemical parameters, and the selected biodegradation model. Then, in an optional step 260, the habitat descriptors can be provided to a user interface, for example, displayed on a display. Furthermore, in step 260, the method can additionally or alternatively include generating a control signal that allows controlling a reactor system in a waste management facility, for example, to provide at least one of the determined habitat descriptor values ​​to a substrate in the reactor system, including the polymer, such that the polymer biodegrades in the reactor system with the target biodegradability.

[0080] FIG. 3 shows a schematic and exemplary flow chart of a method for training a data-driven based biodegradation model, for example used in the method 200 described with reference to FIG. 2. In general, the method 300 can be performed by each unit of the training device 130 described with reference to FIG. 1. The method 300 comprises a step 310 of providing training data for training the data-driven based biodegradation model. The training data comprises a) polymer physicochemical parameters of a plurality of training polymers, b) biodegradability associated with each training polymer in a respective biodegradation habitat, and c) habitat descriptor values ​​for the habitat associated with each biodegradability. In particular, the training data can be provided according to the principles described above with reference to the training data providing unit 131 described with reference to FIG. 1. The method further comprises a step 320 of providing a data-driven based trainable biodegradation model, for example a machine learning based biodegradation model such as a neural network. In general, steps 310 and 320 can be performed in any order or even simultaneously. Then, the method 300 further includes a step 330 of training the provided data-driven based biodegradation model based on the provided training data, for example by varying parameters in the data-driven based trainable biodegradation model, such that the trained biodegradation model is adapted to determine one or more habitat descriptors of the polymer based on the digital representation of the polymer and the target biodegradability. In step 340, the trained biodegradation model can then be provided, for example, by storing the trained biodegradation model in a storage or by directly providing the trained biodegradation model to the device 130, as described with respect to FIG.

[0081] In the following, a more detailed preferred embodiment of the above-mentioned method and the corresponding device is described. In an exemplary embodiment, the method may consist of the steps described below. A schematic and exemplary flow chart of an exemplary embodiment of the method is provided by FIG. 4. In this exemplary embodiment, the method starts with requesting a digital representation of a new polymer, for example via a user interface. Furthermore, a target application of the new polymer may also be requested, for example via a user interface. The target application may refer, for example, to how the new polymer, for which the digital representation is provided, should be utilized in a product or which waste treatment is expected for the polymer. Each target application indicates, for example, a respective biodegradation habitat and a respective target biodegradability. For example, a list may be presented to the user via a user interface, from which the user may select an individual target application, and based on the individual selection, furthermore a selection of a biodegradation habitat and a target biodegradation value associated with the target application may also be provided for the user to select. Based on the target application, in particular based on the biodegradation habitat and optionally based on the target biodegradability indicated by the target application, a respective decision model, i.e. a biodegradation model, may be selected. Optionally, further preselected conditions indicated by the selected biodegradation model may be required. For example, the biodegradation model may be adapted to utilize further descriptors, e.g. optional habitat descriptors, application constraints, etc., which may be required as necessary and may enable the biodegradation model to determine the habitat descriptor values ​​with further accuracy or in particular with respect to constraints. Furthermore, from the provided digital representation, polymer physicochemical parameter values ​​may be derived, and the polymer physicochemical parameters may depend on the provided target application. Further details on the possibility of deriving polymer physicochemical parameter values ​​are described below with respect to FIG. 5.Then, by utilizing the selected biodegradation model, the derived physicochemical parameter values ​​and the target biodegradability, it is possible to provide one or more respective determined habitat physicochemical parameter values, i.e. respective target performances. Figure 10 shows the same method, in a schematic and exemplary manner, specific to a target technology application characteristic, which is a habitat descriptor value.

[0082] Fig. 5 shows, in a schematic and exemplary manner, a preferred method 500 for deriving physicochemical parameter values ​​from a digital representation of a novel polymer. In a first step 510, a digital representation of the polymer is provided. The digital representation may directly contain the polymer physicochemical parameters, in which case the steps shown in Fig. 5 up to step 550 may be omitted. However, in many cases the physicochemical parameters of the polymer must first be determined on the basis of the provided digital representation, in such cases for example with reference to a recipe for the synthesis of the polymer or a chemical representation of the polymer showing the chemical components and bonds in the polymer. In this step 510, the digital representation may include any one or more of the following information: amounts of monomeric components, amounts of non-monomeric components such as initiators, fillers, additives, etc., reaction conditions such as temperature, vessel, pressure, stirring speed, condition profiles, e.g. temperature profile, pH value, solvent, feed profile, type of polymerization, e.g. radical, cationic, anionic, polycondensation, polyaddition, polyether formation, post-treatment such as amounts of components, conditions, and temperature and feed profile, type of post-treatment, e.g. radical, cationic, anionic, polycondensation, polyaddition, polyether formation, chemical information on the components such as mixtures, connectivity of non-polymeric pure compounds, composition of polymeric pure compounds based on subgroups, connectivity of monomers related to subgroups in polymeric pure components, for block copolymers, information on which block each monomer and reactive prepolymer is incorporated into, and for structured / layered materials and composites, information on which phase / layer each component is included in. If such information is not directly provided by the digital representation, then in optional step 520, the non-active ingredients and subgroups may also be derived from the digital representation, for example from a recipe.

[0083] If the information provided indicates the presence of a mixture, in a next step the mixture is decomposed into its pure components and each polymer component is treated as an input polymer. Furthermore, the polymer composition can also be converted to mole %, weight %, volume %, or absolute moles, as appropriate.

[0084] In the next step 530, the polymerizable components can be converted into subgroups, e.g. repeating units, where the subgroups are determined as different types. For example, the polymerizable subgroups can be determined based on the connectivity information of the non-polymeric pure compounds by using SMARTS, e.g. via a KNIME workflow. Also, the connectivity information of all possible subgroups can be derived from the connectivity information of the non-polymeric pure compounds by using reaction SMARTS, e.g. via a KNIME workflow.

[0085] After the subgroups and their types are determined, in step 540, the type of physicochemical parameters to be utilized may be provided. However, the physicochemical parameters may also be determined without first selecting the type of subgroup. In order to reduce the computational resources for the method, in step 541, it is preferably determined whether the subgroup physicochemical parameters associated with each type of subgroup are already stored in the database, for example, whether an entry for the subgroup with the same connectivity information already exists in the database. If so, then, for example, in step 544, the respective associated subgroup physicochemical parameters may be directly downloaded. If the determined type of subgroup is not stored in the database, then the subgroup physicochemical parameters associated with each type of subgroup may be determined, for example, in step 542. For example, a 3D structure of each type of subgroup may be derived based on the connectivity information, and either an automatic calculation of the subgroup physicochemical parameters may be initiated, for example, using a computer cluster, or an existing machine learning determination may be utilized as the subgroup physicochemical parameters. In general, if a calculation for a new subgroup is required, then, in step 543, the results are preferably stored in the database after the calculation is completed. Optionally, further subgroup physicochemical parameters can be provided from topological analysis of the subgroups, quantum chemical calculations, molecular dynamics calculations, coarse-grained methods, finite element calculations and kinetic simulations. In particular, polymer reaction engineering methods can be used to derive subgroup descriptors that allow the microstructure of the polymer to be taken into account.

[0086] In step 531, the amount of subgroups, i.e. the amount of each type of subgroup, is determined, for example based on recipe information provided for the polymer, and provided in step 532. For example, this amount can be determined by counting the amount of polymerizable groups per polymerizable component, optionally including a prepolymer. In this case, information on the polymerizable groups can be obtained from the non-polymeric components, and the amount so determined can be determined based on the composition of the polymeric component, in addition to counting the number of polymerizable groups, optionally not polymerized, of the subgroups of the polymeric component. It is also preferable to exclude from the amount obtained the amount of polymerizable groups resulting from agents used for post-treatment after polymerization.

[0087] However, although it is preferred that the polymer physicochemical parameters are derived from the polymer subgroups, in other embodiments of the invention the polymer physicochemical parameters can also be derived in other ways, for example by directly determining the polymer physicochemical parameters from the complete polymer. Furthermore, the polymer physicochemical parameters for each polymer may also already be stored on a storage unit, such that the derivation of the polymer physicochemical parameters from the digital representation of the polymer can refer to determining information about the polymer from the digital representation, which allows accessing a database and retrieving the corresponding polymer physicochemical parameters.

[0088] Optionally, the derived amounts of subgroups can be used for further interpretation of the polymer composition, for example, to determine the total number of polymerized functional groups, such as double bonds, amine groups, alcohol groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups, and functional groups formed, such as amide groups, ester groups, thioester groups, urea groups, urethane groups, thiourethane groups, ether groups. Also, molar weighted total number of polymerized functional groups, mass weighted total number of polymerized functional groups, total number of residual functional groups, e.g. double bonds, amine groups, alcohol groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups, molar weighted total number of residual functional groups, mass weighted total number of residual functional groups, sum of all residual functional groups, ratio between functional groups after polymerization, number of crosslinks in the polymer, mole fraction of crosslinks in the polymer, optionally also mass weighted, per subgroup, optionally also average number of atoms per weight, per subgroup, optionally also average number of non-H atoms per weight, per subgroup, optionally also average number of bonds between non-H atoms per weight, per subgroup, optionally also average number of rotors per weight, per subgroup, optionally also average number of rotors between non-H atoms per weight, per subgroup, optionally also average number of rings per weight, per subgroup , optionally as well as the average polar surface area per weight, per subgroup, optionally as well as the average refractive index per weight, the total number of blocks, the molar size of the first block, the molar size of the last block, the HLB value of the polymer, optionally the area weighted HLB value, the HLB value of the block having the lowest HLB value, optionally the area weighted HLB value, the HLB value of the block having the highest HLB value, optionally the area weighted HLB value, the HLB value of the first block, optionally the area weighted HLB value, the HLB value of the last block, optionally the area weighted HLB value, the mass of the first block, the mass of the last block, the area of ​​the block having the lowest HLB value, the area of ​​the block having the highest HLB value, the difference in the HLB values ​​of the blocks, optionally the area weighted HLB value, the hydrophilic area of ​​the polymer, the lipophilic area of ​​the polymer, the number of arms for ring opening polymerisation, or the length of the arms for ring opening polymerisation can be determined.

[0089] The determined amounts and types of subgroups and the associated subgroup physicochemical parameters can be utilized to calculate the polymer physicochemical parameters in step 550. For example, the physicochemical parameters of the polymer can be determined by one or more of the molar weighted average (e.g., arithmetic mean, harmonic mean or logarithmic mean), mass weighted average (e.g., arithmetic mean, harmonic mean or logarithmic mean), volume weighted average (e.g., arithmetic mean, harmonic mean or logarithmic mean), surface area weighted average (e.g., arithmetic mean, harmonic mean or logarithmic mean) of the associated physicochemical parameters of the subgroups. Further, the physicochemical parameters of the polymer may be determined by determining one or more of the molar weighted standard deviation, mass weighted standard deviation, volume weighted standard deviation, surface area weighted standard deviation, molar weighted maximum, mass weighted maximum, volume weighted maximum, surface area weighted maximum, molar weighted minimum, mass weighted minimum, volume weighted minimum, surface area weighted minimum, molar weighted sum, mass weighted sum, volume weighted sum, surface area weighted sum, and maximum difference from the physicochemical parameters of the relevant subgroups.

[0090] In step 560, the derived or provided physicochemical parameters, for example referring to the polymer descriptors, can then be provided to a trained biodegradation model to determine habitat descriptor values, for example as described with respect to FIG. 4. In general, different trained biodegradation models can also be utilized, as already described above to determine habitat descriptor values ​​for different habitats. However, the biodegradation model can also be adapted to determine habitat descriptor values ​​for more than one biodegradation habitat. The biodegradation model can be trained based on an automated statistical pre-processing of the training data, in particular the training polymer physicochemical parameters, for example using feature engineering. For example, feature engineering can include first determining a plurality of different polymer physicochemical parameters for the polymer, for example based on subgroup physicochemical parameters of the subgroups, and pre-selecting from this plurality of physicochemical parameters those that have a predetermined probability associated with the biodegradability and / or habitat descriptor values ​​of the polymer in a particular habitat. Based on the related physicochemical parameters, a cluster analysis is preferably performed to identify groups of strongly correlated physicochemical parameters. Such a group allows to select only one of the members of the group, i.e. only one physicochemical parameter of the group, to represent the entire group of physicochemical parameters. Therefore, based on the cluster analysis, the number of relevant physicochemical parameters can be further reduced. Optionally, the same process can be performed on the habitat descriptors to determine the habitat descriptors that are most relevant to the biodegradability of the polymer in a particular habitat. Based on the remaining polymer descriptors, and optionally also on the habitat descriptors, the application space can be determined and optimized. The application of the trained biodegradation model to, for example, a particular habitat or polymer physicochemical parameters can then be determined by the space spanned by the training data that forms the application space.This space can be optimized, for example, by modifying the training data to cover the application space periodically, by removing strong outliers, by adding training data to parts of the space that are not yet covered, etc. This also allows maximizing the applicability space. A biodegradation model is then trained on the basis of the optimized training data. The biodegradation model can generally refer to sparse, e.g., spline, LASSO regression, PLS, and non-sparse, e.g., ridge regression, tree methods, kernel-based methods, statistical learning models for relating polymer physicochemical parameters to biodegradability in a specific habitat. Furthermore, the biodegradation model can further provide a reliability estimate of the determination depending on the respective biodegradation model used. Then, in step 570, the determined habitat descriptor values, i.e. technical application characteristics, can be provided to the user, for example via a user interface. Figure 11 shows the same method, specifically for the target technical application characteristics, which are habitat descriptor values, in a schematic and exemplary manner.

[0091] FIG. 6 shows a block diagram of an exemplary system architecture of an automated laboratory system 1000 for synthesizing polymers, having a laboratory equipment control device 1102, a network 1150, and a synthesis specification or recipe module 1100 / 1110, and a client device 1108. The automated laboratory system includes a laboratory equipment control device layer 1152 as part of the laboratory equipment control device 1102, a synthesis specification module layer 1154 associated with a synthesis specification module, and a remote control or client layer 1156 associated with the client device 1108. The laboratory equipment control device layer can be divided into several layers, namely hardware, middleware, and interface layers. The hardware layer relates to hardware resources, such as sensors and actuators, especially for controlling the synthesis of polymers. The middleware relates to any of the known middleware for laboratory or plant synthesis operations. One example is LABS / QM, which provides different abstractions, such as low-level device control and message passing, for hardware, network, and operating system. The communication layer relates to communication protocols, one of which may be REST, which may be implemented over different transport protocols (i.e. UDP, TCP, Telemetry) that allow the exchange of messages between the lab equipment control device and the lab equipment devices. Such a software architecture makes it possible to control and monitor the lab equipment without the need to interact with hardware.

[0092] The synthetic specification module layer 1154 may include a mass storage layer, a computing layer, and an interface layer. The storage layer is configured to provide mass storage for a data-driven biodegradation model for providing a recipe, i.e., a synthetic specification, for a polymer based on habitat descriptor values ​​that result in a specific target biodegradability in a habitat, as described in detail above. In particular, the functions performed by the device as described above may be provided as program code means stored in the mass storage. Furthermore, synthetic specifications for multiple polymers may be stored in the mass storage. Such data may be stored in a structured database, such as an SQL database, or a distributed file system, such as HDFS, or a NoSQL database, such as HBase, MongoDB, etc. The computing layer may include an application layer that allows customizing the functions provided by standard cloud services to perform computing processes based on target properties. Such functions may include determining a digital representation of a target polymer based on the target biodegradability and / or the target habitat descriptor values ​​and the biodegradation model, generating a synthetic specification from the digital representation of the target polymer, and providing the synthetic specification as control data to a laboratory equipment control device. The interface layer may implement a web service, a network interface as UDP or TCP, or a web socket interface. For communication with the lab equipment control device, a REST API is implemented.

[0093] The client layer 1156 provides an interface for end users. For end users, the client layer 1156 can launch a client-side web application that provides an interface to the synthetic specification module layer 1154 or the laboratory equipment control device layer 1152. The user may be provided with a UI to select a target biodegradability, a biodegradation habitat for the target biodegradability, and optionally a target habitat descriptor value. The target biodegradability may also include a range of biodegradability values. In other examples, the user may be provided with a UI to select two or more target biodegradabilities and / or two or more target habitats and target habitat values. The application may be configured for the user to remotely monitor and control the laboratory equipment control device and operation. In other examples, the client device layer and the synthetic specification module layer may be integrated into one device. The alternatives described herein are for illustrative purposes only and should not be considered limiting.

[0094] 7 shows a block diagram of an exemplary system architecture of a system and device for generating a biodegradation model for determining habitat descriptor values ​​that result in the biodegradation of a polymer in a specific habitat that meets the target biodegradability, a network 2150, and a model generation module 2100 / 2110 that can be considered or includes a training model device, a synthetic specification module 1100 / 1110, and a client device 2108. The system for generating a biodegradation model includes a model generation module layer 2154 as part of the model generation module and a client layer 2156 associated with the client device 2108.

[0095] The model generation module layer 2154 may include: a mass storage layer, a computing layer, an interface layer, and a storage layer configured to provide mass storage for the data-driven biodegradation model as described above. Further, the mass storage is configured to store the composition specifications of the polymer and the measured biodegradability of one or more habitats. Such data may be stored in a structured database, such as a SQL database, or a distributed file system, such as HDFS, HBase, NoSQL database, such as MongoDB, etc. The computing layer may include an application layer that allows customizing the functionality provided by standard cloud services to execute the computing process for generating the biodegradation model for determining the habitat descriptor values. Such functions may include: receiving, for at least two previously measured polymers, their respective digital representations associated with a synthetic specification, at least one measurement data of biodegradability in at least one habitat for a particular habitat descriptor value for each of the at least two previously measured polymers; receiving, in a model generation module, a digital representation of at least one unmeasured polymer; training a model according to the above-mentioned training principle based on the digital representations of the at least two previously measured polymers, the measurement data of biodegradability in at least one habitat, and the corresponding habitat descriptor value for each of the at least two previously measured polymers, and preferably on a similarity index between the digital representations associated with the synthetic specification for each of the at least two previously measured polymers and the respective digital representations associated with the synthetic specification for the at least one unmeasured polymer; and providing, via an output interface, a biodegradation model for the habitat descriptor value. The model generation module layer may be configured to deploy the generated model and the synthetic specification database to the synthetic specification module layer. This may include storing the generated model and the synthetic specification database in a mass storage device associated with the synthetic specification module.

[0096] The model generation module layer may further be configured to determine from the synthesis specification a digital representation of the polymer associated with the synthesis specification. The digital representation may include a set of polymer physicochemical parameters and polymer physicochemical parameter values ​​associated with the synthesis specification for each measured polymer. One way to derive these polymer physicochemical parameters may be to apply the SMILES algorithm or any other principle already mentioned above. If the model is generated based on the digital representation derived from the recipe, the relationship between the synthesis specification and the physicochemical parameters may be stored in a mass storage device associated with the model generation module. In such a case, developing the model includes providing the relationship.

[0097] The interface layer may implement a web service, a network interface as UDP or TCP, or a web socket interface. For communication with client devices, a REST API is implemented in this embodiment. The client layer 2156 provides access to a mass storage device containing the polymer synthesis specifications and at least one biodegradability and corresponding habitat descriptor values ​​of at least two polymers. The client layer further provides an interface for end users. For end users, the client layer 2156 may launch a client-side web application that provides an interface to the model generation module layer 2154 or a mass storage device associated with the client layer. The user may be provided with a UI for selecting the test method and / or habitat for which the habitat descriptor values ​​are determined. The user may further be provided with a UI for selecting the synthesis specification data. The user interface may also provide an option for uploading the selected data to the model generation module layer and, optionally, an option for starting the model generation.

[0098] FIG. 8 shows an exemplary system 700 for biodegrading chemical products based on control data for controlling a reactor generated according to the invention. In this embodiment, the system comprises a user interface 710 and a processor 720 associated with a control unit 740. The user interface 710 and the processor 720 may be associated with or implemented according to the principles described above, in particular adapted to execute a computer-implemented method for determining target habitat descriptor values, as described above. The control unit 740 is configured, for example, to receive control data generated according to the invention, as described above, in particular to receive control data generated based on habitat descriptor values ​​that allow a polymer product to degrade in a target biodegradable manner. In this embodiment, the control data is provided from a database 730, but in other embodiments, the control data can also be provided from a server or any other computing unit for distributing data. The vessels 750, 752 can contain components for influencing conditions in the reactor, for example components for influencing pH value, microorganisms, etc. Valves 760, 762 are associated with the vessels 750, 752. Valves 750 and 752 can be controlled to dose appropriate amounts of each component to reactor 770 according to the target habitat descriptor values. Motors 800 of mixers and / or grinders 780 can also be controlled by the control unit according to the control data. Optional heaters 790 can also be controlled according to the determined habitat descriptors. Finally, an outlet valve 810 in fluid communication with the reactor can be controlled by the control unit to provide the final biodegraded products to a waste container or drain system 820.

[0099] FIG. 9 exemplarily and diagrammatically shows a possible user interface for interfacing with a processor implementing, for example, the above-mentioned method for determining a habitat descriptor value of a polymer. In this example, an input screen is shown on the left. The input screen allows the definition of a target biodegradability for one or more habitats for which a habitat descriptor value is to be determined. In this example, a target biodegradability range for two different habitats is provided, where biodegradability is defined as the percentage of the polymer that has degraded after a specified time frame of the ISO-Norm. Optionally, additional information can also be determined, for example, how to measure biodegradability or how to weight the different targets. Furthermore, the input screen allows the definition of a polymer for which a habitat descriptor value is to be determined. In this case, as a polymer class, polyalkoxylates are defined, for example, by using drop-down menus. Furthermore, further fields can be provided for the specification and input of further options. In this example, the polymer is then further defined below by defining the components of the polymer in the form of component type and associated components, amounts and blocks. An exemplary further definition of polyalkoxylates is provided in FIG. 9. In general, the inputs can also refer to further information, e.g., defining for which habitat descriptors values ​​should be determined, the intended application, the measurement method, etc. In this example, it can be shown that good predictive accuracy can be achieved when utilizing, as polymer descriptor types, a descriptor referring to the molar weight of the polymer, a descriptor referring to the amount of subgroups, and a descriptor referring to the hydrophilicity of the polymer. Values ​​of such polymer descriptors can then be determined according to the above principles for the defined polymers. An exemplary output screen is shown on the right side of FIG. 9. In this case, the output screen provides the habitat descriptor values ​​for the polymer and the results of the determination of the biodegradability of the polymer for the respective habitats.

[0100] In a preferred embodiment, a target biodegradability is provided as a target requirement. A habitat is then selected. Based on the habitat selection, a biodegradation model is selected. This allows having separate biodegradation models for different habitats. A digital representation of the polymer that needs to be waste treated is then provided. The digital representation of biodegradation and the target value are provided as inputs to the biodegradation model. The model produces as output the habitat descriptor values ​​of the habitat required to meet the target biodegradability. These can then be used as control data to control a plant for the biodegradation of the polymer. Assuming a reactor for the biodegradation of a particular polymer, the temperature, the composition of the soil / water / ocean, and the concentration composition of enzymes, bacteria can be proposed.

[0101] The potential expression of biodegradability may be one or more of: mineralization, which refers to information whether the polymer is completely mineralized or not, or the time until mineralization is achieved; biotransformation, which refers to a change in the chemical structure that results in the loss of a particular property of the polymer, e.g., toxicity, or the time until this is achieved; and half-life, which refers to the time until 50% of the polymer is degraded. Prominent habitats are ocean, wastewater, and soil. In the ocean, the following parameters may affect biodegradation: In some embodiments, the marine habitat descriptors may be stored in a database with geolocation information. The geolocation may then be entered and the values ​​of the parameters associated with this geolocation may be retrieved from the database. For wastewater, the following parameters may affect biodegradation: temperature, bacterial population, type of bacteria, enzyme concentration, enzymes. For soil, the following parameters may affect biodegradation: temperature, bacterial population, type of bacteria, enzyme concentration, enzymes.

[0102] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0103] With respect to the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in different orders. Furthermore, the outlined operations are provided only as examples, and some of the operations may be optional, combined into fewer steps and operations, supplemented with additional operations, or expanded into additional operations, without detracting from the essence of the disclosed embodiments.

[0104] 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.

[0105] A single unit or device may fulfill 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.

[0106] The steps such as providing the polymer physicochemical parameters and biodegradation model, determining the habitat descriptor values, providing the habitat descriptor values, etc., performed by one or more units or devices can be performed by any other number of units or devices. These steps can be implemented as program code means of a computer program and / or as dedicated hardware.

[0107] The 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 communication systems.

[0108] Any unit described herein may be a processing unit that is part of a conventional computing system. The processing unit may include a general-purpose processor, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other dedicated circuit. Any memory may be a physical system memory that may be volatile, non-volatile, or some combination of the two. The term "memory" may include any computer-readable storage medium, such as non-volatile mass storage. If the computing system is distributed, the processing and / or memory capabilities may be distributed as well. The computing system may include multiple structures as "executable components." The term "executable components" is a structure well understood in the computing arts as being a structure that may be software, hardware, or a combination thereof. For example, if implemented in software, one skilled in the art will understand that the structure of the executable components may include software objects, routines, methods, etc. that may be executed on the computing system. This may include both executable components in the heap of the computing system or executable components on a computer-readable storage medium. The structure of the executable components may reside on a computer-readable medium such that, when interpreted by one or more processors of a computing system, e.g., by processor threads, it causes the computing system to execute a function. Such structures may be directly computer readable by a processor, e.g., where the executable components are binary, or may be structured to be interpretable and / or compiled, e.g., in either a single step or multiple steps, to generate such a binary that is directly interpretable by a processor. In other cases, the structures may be hard-coded or hard-wired logic gates that are implemented exclusively or nearly exclusively in hardware, such as in a field programmable gate array (FPGA), application specific integrated circuit (ASIC), or any other dedicated circuit.Thus, the term "executable components" is a term for structures well understood by those skilled in the art of computing, whether implemented in software, hardware, or a combination. Any embodiment herein is described with reference to operations performed by one or more processing units of a computing system. When such operations are implemented in software, one or more processors direct the operation of the computing system in response to executing the computer-executable instructions that make up the executable components. A computing system may also contain communication channels that enable the computing system to communicate with other computing systems, for example, via a 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 to a computing system via a network or another communication connection, for example, either wired, wireless, or a combination of wired or wireless, the computing system properly regards the connection as a transmission medium. A transmission medium may include a network and / or data links that may be used to carry desired program code means in the form of computer-executable instructions or data structures and may be accessed by a general-purpose or special-purpose computing system or combination. Although not all computing systems require a user interface, in some embodiments a computing system includes a user interface system for use in interfacing with a user. The user interface serves as an input or output mechanism to a user, for example via a display.

[0109] Those skilled in the art will appreciate that at least a portion of the present invention may be implemented in a networked computing environment having many types of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cell phones, PDAs, pagers, routers, switches, data centers, wearables such as glasses, etc. The present invention may also be implemented in a distributed system environment where tasks are performed by both local and remote computing systems that are linked through a network, for example, by either hardwired data links, wireless data links, or a combination of hardwired and wireless data links. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0110] Those skilled in the art will also appreciate that at least a portion of the present invention may be implemented in a cloud computing environment. A cloud computing environment may be distributed, but this is not required. When distributed, a cloud computing environment may be distributed internationally within an organization and / or have components owned across multiple organizations. In this specification and in the claims that follow, "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 numerous other advantages that may be obtained from such a model when deployed. The computing system of the figure includes various components or functional blocks that may implement various embodiments disclosed herein, as described. The various components or functional blocks may be implemented on a local computing system or on a distributed computing system that includes elements that reside in the cloud or that implement 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 system depicted in the figures may include more or fewer components than those depicted in the figures, and some of the components may be combined where circumstances warrant.

[0111] Any reference signs in the claims should not be construed as limiting the scope.

[0112] The present invention relates to a method for determining habitat descriptor values ​​that provide a target biodegradability for a polymer. A target biodegradability is provided that indicates the biodegradation properties of the polymer. A digital representation of the polymer is provided that indicates the physicochemical properties of the polymer. A biodegradation habitat is provided that indicates habitat descriptors that influence the biodegradation of the polymer. Based on the provided biodegradation habitat, a biodegradation model is provided that is adapted to determine habitat descriptor values ​​that enable the biodegradation of the polymer that meets the target biodegradability in the biodegradation habitat. The habitat descriptor values ​​are determined for the polymer based on a selected biodegradation model, the target biodegradability and the polymer descriptors.

Claims

1. 1. A computer-implemented method for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer, the method (200) comprising: Providing a targeted biodegradability (210), where biodegradability refers to the biodegradation properties of the polymer; providing a digital representation of a polymer that indicates or is associated with physicochemical properties of said polymer (220); providing (230) biodegradation habitats, each of the biodegradation habitats exhibiting habitat descriptors that affect biodegradation of polymers within the biodegradation habitat, the habitat descriptors indicating environmental characteristics of the biodegradation habitat; providing (240) a biodegradation model based on the provided biodegradation habitat, the biodegradation model being adapted to determine habitat descriptor values ​​that enable biodegradation of the polymer in the biodegradation habitat to meet the target biodegradability, the biodegradation model being a data-driven model parameterized for the biodegradation habitat to determine the habitat descriptor values ​​based on the target biodegradability and physicochemical properties of the polymer; and determining (250) the habitat descriptor value for the polymer based on the selected biodegradation model, the target biodegradability, and physicochemical properties of the polymer.

2. 10. The method of claim 1, wherein the method further comprises providing a control signal for controlling a reactor system adapted to biodegrade a polymer, the control signal being provided based on the determined habitat descriptor value.

3. 10. The method of claim 1, wherein the biodegradation habitat refers to any one of a marine habitat, a wastewater habitat, a lake habitat, a compost habitat, an anaerobic habitat, or a soil habitat.

4. 4. The method of claim 3, wherein the biodegradation habitat refers to a marine habitat and the habitat descriptors refer to at least one of salinity, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community.

5. 4. The method of claim 3, wherein the biodegradation habitat refers to a lake habitat and the habitat descriptors refer to at least one of salinity, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community.

6. 4. The method of claim 3, wherein the biodegradation habitat refers to wastewater and the habitat descriptors are at least one of water temperature, microbial community, sludge concentration, nutrient concentration, pH value, test duration, and enzyme environment.

7. 4. The method of claim 3, wherein the biodegradation habitat refers to soil and the habitat descriptors are at least one of temperature, sand content, pH value, moisture content, nutrient concentration, microbial community, and enzyme environment.

8. 4. The method of claim 3, wherein the biodegradation habitat refers to compost and the habitat descriptors refer to at least one of temperature, compost activity, pH value, moisture content, humidity, compost maturity, compost composition, compost origin, nutrient concentration, microbial community, and enzyme environment.

9. 1. An interface method for providing an interface, the interface method comprising: receiving as input via a user interface a target biodegradability, a digital representation, and a biodegradation habitat, and providing said received target biodegradability, digital representation, and said biodegradation habitat to a processor that executes the method of any one of claims 1 to 8; and providing the determined habitat descriptor value of the polymer as a result, the result being received from the processor implementing a method according to any one of claims 1 to 8.

10. 1. A computer-implemented training method for training a data-driven based biodegradation model to parameterize said biodegradation model, said training method (300) comprising: providing (310) training data associated with a predetermined biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers indicating physicochemical properties of each of the training polymers; b) habitat descriptor values ​​for each habitat descriptor of the biodegradation habitat; and c) a biodegradability of each habitat descriptor value associated with each training polymer; Providing a data-driven based trainable biodegradation model (320); training (330) the provided data-driven based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine habitat descriptor values ​​based on biodegradability and physicochemical properties of polymers; providing (340) the trained biodegradation model.

11. 1. An apparatus for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer, said apparatus (110) comprising: a targeted biodegradable delivery unit (111) for providing targeted biodegradability, said targeted biodegradability indicating biodegradation properties of a polymer; a digital representation providing unit (112) for providing a digital representation of a polymer indicative of or associated with physicochemical properties of said polymer; a habitat providing unit (113) for providing biodegradation habitats, each of said biodegradation habitats exhibiting habitat descriptors that affect the biodegradation of polymers within said biodegradation habitat, said habitat descriptors exhibiting environmental characteristics of said biodegradation habitat; a model providing unit (114) for providing a biodegradation model based on the provided biodegradation habitat, the biodegradation model being adapted to determine habitat descriptor values ​​that enable biodegradation of the polymer in the biodegradation habitat to satisfy the target biodegradability, the biodegradation model being a data-driven model parameterized for the biodegradation habitat to determine the habitat descriptor values ​​based on the target biodegradability and physicochemical properties of the polymer; a determining unit (115) for determining the habitat descriptor value for the polymer based on the selected biodegradation model, the target biodegradability and a description of the polymer.

12. 1. An interface device for providing an interface, said interface device comprising: an input interface unit for receiving a target biodegradation property, a digital representation, and a habitat as input via a user interface and for providing the received target biodegradation property, the digital representation, and the biodegradation habitat to the device of claim 11; a result interface for providing the habitat descriptor values ​​of the polymer as results, the results being received from the device of claim 11 .

13. A training device for training a data-driven based biodegradation model to parameterize said biodegradation model, said training device (120) comprising: a training data providing unit (121) for providing training data associated with a predetermined biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers indicating physicochemical properties of each of the training polymers; b) habitat descriptor values ​​for each habitat descriptor of the biodegradation habitat; and c) a biodegradability of each habitat descriptor value associated with each training polymer; a trainable model providing unit (122) for providing a trainable biodegradation model based on a data-driven basis; a training unit (123) for training the provided data-driven based biodegradation model based on the provided training data, the trained biodegradation model being adapted to determine habitat descriptor values ​​based on biodegradability and physicochemical properties of a polymer; a trained model providing unit (124) for providing the trained biodegradation model.

14. 12. A computer program for determining habitat descriptor values ​​that provide a target biodegradability for a given polymer, said computer program comprising program code means for causing an apparatus according to claim 11 to carry out the method according to any one of claims 1 to 8.

15. A computer program for training a biodegradation model, said computer program comprising program code means for causing an apparatus according to claim 13 to carry out the method according to claim 10.

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