Device for determining technical application characteristics of superabsorbent material

Abstract

The present disclosure relates to devices and methods for predicting the absorption capacity, swelling kinetics or permeability of superabsorbent materials used in, for example, diapers. The superabsorbent material includes polymeric particles having an interconnected core and a surface crosslinked shell. The absorption capacity, swelling kinetics or permeability of the material is predicted based on the size distribution of the particles using a trained model. The output of the apparatus and method is control data for adjusting the manufacturing process in order to produce particles having a size distribution that provides the desired target absorption capacity, swelling kinetics, or permeability.

Description

Technical Field

[0001] The present invention relates to a device, a method and a computer program product for determining technical application properties of superabsorbent materials. Furthermore, the present invention relates to an interface device, an interface method and an interface computer program product for providing an interface for determining technical application properties of superabsorbent materials. Furthermore, the present invention relates to a training device, a training method and a training computer program product for parameterizing a property determination model that can be used to determine technical application properties of superabsorbent materials. Background Art

[0002] In many modern sanitary products, superabsorbent materials in the form of superabsorbent particles are usually used for fluid absorption. However, the production process of the necessary superabsorbent particles is complicated, and although the process includes multiple grinding and classification steps, which are usually screening steps, the size of the resulting particles is still different. The corresponding equipment in such powder processes is subject to strong wear and tear, which leads to the drift of grinding and screening performance. In addition, due to the complex production process, the process must be continuously adjusted to provide products with continuous product quality, especially products that always provide the same technical application characteristics, especially the same absorption characteristics. At present, the readjustment of the production process to provide superabsorbent materials with the same technical application characteristics is mainly based on the experience of the personnel controlling the production process or by random sampling and random sample-based measurement to determine the adjustment. In addition, in the current design process for determining the characteristics of superabsorbent materials used in, for example, sanitary products, laboratory experiments are usually used and the characteristics of the particles designed are measured. However, these measurements usually only provide information about the technical application characteristics of the best superabsorbent particles, while during normal production, the superabsorbent particles produced will usually change and will therefore be distributed around the best superabsorbent particles. This can lead to deviations of the technical application properties of the produced superabsorbent particles from the predicted technical application properties, wherein these deviations are difficult to predict due to the design process and a readjustment of the production is generally not possible or is only possible by a redesign of the corresponding superabsorbent particles. Such a redesign generally includes process and formulation adjustments.

[0003] It would therefore be advantageous if the technical application properties of superabsorbent particles could be predicted more easily and accurately enough to allow controlling the production process of superabsorbent particles based on the technical application property predictions. Summary of the invention

[0004] It is an object of the present invention to provide a device, a method and a computer program product which allow an improved determination of the technical application properties of a superabsorbent material, which improved determination allows an improved control of the production process of the superabsorbent material, in particular the production of superabsorbent material which continuously meets the predetermined technical application properties. Furthermore, it is another object of the present invention to provide a training method, a training device and a computer program product which allow providing a property determination model suitable for use in the method, the device and the computer program product.

[0005] In a first aspect of the present invention, a device for determining technical application characteristics of a superabsorbent material is proposed, wherein the superabsorbent material is provided in the form of superabsorbent particles, the superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the device comprises a) a receiving interface for receiving the particle size of superabsorbent particles of the superabsorbent material, b) one or more processors configured to determine the technical application characteristics of the superabsorbent material based on the particle size using a characteristic determination model, wherein the characteristic determination model is a data-driven model that has been parameterized so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles, and c) an output interface for generating control data based on the determined technical application characteristics.

[0006] The inventors have found that a characteristic determination model can be trained that allows the technical application characteristics of superabsorbent particles to be determined in an accurate manner based on the size of the corresponding superabsorbent particles. Thus, the technical application characteristics of a superabsorbent material comprising superabsorbent particles of different sizes (i.e. comprising a size distribution) can be easily predicted by determining the technical application characteristics of each size present in the superabsorbent material. Thus, by determining the technical application characteristics of superabsorbent particles based on the size of the particles using such a characteristic determination model, control data can be generated that allow, for example, to improve the control of the production of superabsorbent materials by adjusting the size distribution of the particles produced to form the superabsorbent material. In particular, based on easily measurable variables, i.e. based on the size of the particles, the production of superabsorbent materials can be directly controlled, allowing a fast and direct adjustment possibility if, for example, the determined technical application characteristics deviate from the predetermined target technical application characteristics of the superabsorbent material, without having to rely purely on the experience of the controller of the production process or on a randomly performed quality control. In particular, such an improved production process allows avoiding waste production and reducing the resources required to produce superabsorbent materials for corresponding hygiene products. Furthermore, the results of the property determination model may be combined with other process and feedstock data from one or more previous or subsequent steps in the production process to determine the optimal process adjustments necessary to obtain or maintain a given target property of the superabsorbent material.

[0007] In general, the apparatus may refer to any general or special computing device suitable for performing the functions of the apparatus, for example by executing a corresponding computer program. In particular, the apparatus may be implemented in any form of software and / or hardware that enables a general or special computing device to perform the functions as defined above. Furthermore, the apparatus may be implemented in the form of a stand-alone device, for example in the form of dedicated hardware, or by being provided on a corresponding computer system of a user, but may also be implemented in the form of a network of computers or processors, for example in a shared computing architecture like cloud computing, network computing, etc., where more than one computer or processor may provide the functionality of the apparatus.

[0008] Typically, the device is suitable for determining the technical application characteristics of superabsorbent materials. Superabsorbent materials are materials that can absorb and retain large amounts of aqueous liquids relative to their own mass. For example, for deionized water and distilled water, superabsorbent materials can absorb up to 1000 times their own weight. For practical applications in hygiene, superabsorbent materials absorb at least 15g / g, typically at least 20g / g, preferably at least 25g / g and most preferably at least 30g / g, but not more than 120g / g, preferably not more than 100g / g, more preferably not more than 80g / g, most preferably not more than 60g / g of 0.9 wt% saline solution (NaCl) in the absence of external pressure, such as in the tea bag method CRC. Compared with conventional absorbents such as cellulose fluff, absorption by superabsorbent polymers is particularly useful because even under the use pressure in hygiene applications, the absorbed liquid will not be released, and the wearer's skin will remain dry. In most cases, superabsorbent materials include superabsorbent polymers (SAPs) that are usually provided in the form of multiple particles forming superabsorbent materials. Superabsorbent polymers are usually composed of hydrophilic, high molecular weight polymer chains with ionic groups that are linked together to make the superabsorbent polymer insoluble in water. The ionic groups are usually -COOH, but can also be based on sulfur (-SO 3H) or phosphorus as acid providers. These groups are partially neutralized to achieve a skin-friendly pH on the wearer's skin, typically pH = 4.0-7.5, preferably pH = 5.0-6.5. As a means of neutralization, any alkali metal-based neutralizer (Li, Na, K, Rb, Cs) can be used, but Na is preferably used in hygienic applications. Commonly used neutralizers are alkali metal salts of hydroxides, carbonates and bicarbonates and mixtures thereof. Examples of such superabsorbent polymers are cross-linked poly(meth)acrylic acid sodium salts, cross-linked polyitaconate sodium salts, polyacrylamide copolymers, ethylene maleic anhydride copolymers, cross-linked carboxymethyl cellulose, cross-linked starch derivatives and carboxymethyl starch, polyvinyl alcohol grafted or starch grafted cross-linked partially neutralized polyacrylic acid salts, polyvinyl alcohol copolymers, cross-linked polyethylene oxides, etc. In addition, copolymers of acrylic acid, maleic acid, and itaconic acid can be used, and can be combined with copolymerized nonionic hydrophilic or hydrophobic monomers. Partially neutralized cross-linked polyacrylic acid and polyitaconate and their copolymers are preferred. More preferred are partially neutralized cross-linked polyacrylic acid and polyitaconate and copolymers thereof, the raw materials of which are derived from biological sources, such as plants, microorganisms, algae, fungi.

[0009] Most preferred are partially neutralized cross-linked polyacrylic acid and polyitaconate and copolymers thereof, and superabsorbent production processes having as low a carbon footprint as possible for producing the superabsorbent polymers of the present invention.

[0010] Technical application characteristics may refer to any technical application characteristics related to the superabsorbent properties of superabsorbent materials. Preferably, the technical application characteristics refer to at least one of absorption capacity, or swelling kinetics and permeability. Absorption capacity can be determined as any one of centrifuge retention capacity (CRC), free swelling capacity (FSC) and pressure absorption capacity (AAP). Typically, CRC and FSC are determined without external pressure, i.e., 0.0psi. In both cases, the higher the respective CRC or FSC value, the more fluid the particles can absorb. Swelling kinetics may refer to any one of VAUL, T20 and Vortex. For irregularly shaped rough surface particles, these three quantities are related. For particles with regular, smooth surfaces or round shapes, Vortex may not be related to other quantities. Typically, the faster the particle swells, the smaller the corresponding value of any of these quantities. Permeability (SFC) is nonlinearly related to CRC capacity. Typically, the higher the permeability value, the better the distribution of fluid in the particle spiral surface. These technical application characteristics are examples of different categories of technical application characteristics. CRC can be interpreted as the pure absorption capacity of the superabsorbent material, SFC can be interpreted as the pure permeability of the open pores in the swollen gel bed of the swollen superabsorbent material, FSC or AAP can be interpreted as the absorption capacity of the superabsorbent material under defined external pressure conditions, and each includes the contribution from the swollen superabsorbent material and the contribution from the partially or completely liquid-filled pores (interstitial liquid) in the resulting gel bed, and T20, Vortex and VAUL characteristic swelling times can be interpreted as kinetic swelling rate parameters that describe the swelling rate, and can include additional effects other than the absorption rate - for example, particle morphology and surface viscosity can affect these measurements. The rate at which liquid is absorbed into the swollen superabsorbent material particles is defined as the absorption rate. These characteristics and the corresponding methods for determining these characteristics are further described below. There are various other forms of characteristics or methods for determining corresponding characteristics, and can also be used as technical application characteristics according to the present invention. Other methods may differ, for example, in the size of the equipment, such as smaller or larger AAP cells or osmotic cells, in the processing technology, in the test liquid, such as water, artificial urine instead of saline, in the swelling time, such as 1, 3, 5 or 10 minutes instead of 30 minutes. In general, the choice of technical application characteristics may depend on the respective intended application, for example for hygiene applications on the respective consumer needs. A particularly useful method for measuring technical application characteristics is described in WO 2021 / 001221, which method allows the determination of the time-dependent swelling curve of superabsorbent polymers under varying external pressure. In this way, a characteristic fingerprint curve is obtained, which can be used to define the target technical application characteristics. Particularly useful is an online analysis method as disclosed in WO 2020 / 109601, which enables the determination of technical application characteristics by Raman spectroscopy analysis inside the production process.The combination of such online techniques with online particle size determination enables efficient use of the present invention.

[0011] In general, the absorption capacity, swelling kinetics and permeability all need to be optimized for the needs of sanitary products. Sanitary products include several technical elements designed to manage fluid acquisition and distribution and superabsorbent polymers for irreversible liquid storage. These elements need to cooperate with each other and with the superabsorbent polymer to achieve rapid liquid acquisition, extensive distribution to utilize the full storage capacity of the sanitary product, and the superabsorbent polymer must quickly absorb liquid to re-dry the sanitary product under the conditions of use. Superabsorbent polymer capacity and swelling kinetics depend on various factors, such as formulation, process conditions (for example, in the gel drying step), in particular particle size and shape. Therefore, the optimization of one aspect (such as capacity) often affects another aspect, such as swelling kinetics, in the opposite direction. Therefore, it is necessary not only to find the optimum between the capacity and swelling kinetics of a given sanitary product design, but also to find the optimal settings in the production process of such superabsorbent polymers.

[0012] Superabsorbent materials are provided in the form of superabsorbent particles comprising superabsorbent polymers. For most applications, the size of the superabsorbent particles is preferably between 100 μm and 850 μm. However, for some applications, larger or smaller superabsorbent particles may also be suitable. Recently, for some hygiene applications, a narrower particle size distribution is required, which makes production more difficult and more expensive, and is challenging to optimize their technical properties, such as having a lower particle size of 100, 150, 200 or 250 μm and a higher particle size of 700 μm, 600 μm or 500 μm.

[0013] In particular, each superabsorbent particle comprises a) an interconnected core and b) a surface crosslinked shell having a higher connectivity than the core. In this context, the terms "interconnected", "connectivity" mean that the polymer chains of the core or shell portion of the superabsorbent polymer particle are physically entangled, ionically crosslinked or covalently crosslinked, such that the corresponding portions of the superabsorbent particle are water-swellable but not water-soluble. Combinations of such methods of interconnecting the polymer chains are possible and are commonly found in superabsorbent particles. Ionic crosslinking can be achieved by multivalent cations, a practical example being Mg. 2+ , Ca 2+ , Sr 2+ 、Al 3+ 、Ti 4+ 、Zr 4+. Covalent crosslinking can be achieved by adding a polymerizable difunctional or multifunctional ethylenically unsaturated crosslinking agent to the monomer mixture. Alternatively, this functional group can also be provided by a group that can be esterified or transesterified. Mixed functional groups in one molecule are also possible. For surface crosslinking, the same compounds as described above can be used. Typically, ionic crosslinkers and covalent crosslinkers are used in combination. Examples of core crosslinking and surface crosslinking are described in WO 2019 / 197194, which is incorporated herein by reference. In the present invention, it is understood that the shell and core of the superabsorbent particles are connected to each other by physical entanglement or ionic crosslinking or preferably by covalent crosslinking. Such core-shell structures can destroy the surface-shell of the superabsorbent particles when swollen, but due to the connectivity of the shell to the core, even if the shell is completely broken, it will continue to apply physical force to the swollen core. In particular, superabsorbent particles refer to post-crosslinked superabsorbent polymer particles. Such post-crosslinked superabsorbent polymer particles include a crosslinked and thus interconnected core, and then provide a shell with higher connectivity than the core during the post-crosslinking process, while the shell is covalently bonded to the core below it. Optionally, such provided superabsorbent particles may also have an additional non-superabsorbent coating. Non-limiting examples of such coatings are polymers or polymer films that improve flowability or destabilize, powder coatings (silicon dioxide, alumina, clay or other inorganic powders in dry or hydrated form) that prevent agglomeration, additives that prevent aging or discoloration, additives that combat malodor, or functional coatings that react with the surface, such as Ca 2+ -Mg 2+ 、Al 3+ - and Zr 4+ - salts or their soluble hydroxides, as disclosed, for example, in WO 2019 / 197194.

[0014] The receiving interface is configured to receive the particle size of the superabsorbent particles of the superabsorbent material. In particular, the receiving interface can be configured to be connected to a storage unit on which the superabsorbent particle size has been stored. However, the receiving interface can be additionally or alternatively configured to dock with a user input unit so that the user can provide the superabsorbent particle size. In addition, the receiving interface can also be additionally or alternatively configured to dock with a measuring unit such as a sensor, which is configured to measure the particle size or particle size distribution of the superabsorbent material, for example, during the production process. Generally, the particle size of the superabsorbent particles can be received in the form of any amount indicating the volume of the superabsorbent particles, for example, it can be received as the volume, radius, diameter, etc. of the superabsorbent particles. In addition, the particle size refers to the dry state of the superabsorbent particles, and therefore refers to the state in which the superabsorbent particles are not in contact with the absorbable fluid. In the dry state, the superabsorbent particles contain less than 25% by weight, usually less than 20% by weight, preferably less than 15% by weight, more preferably less than 10% by weight, even more preferably less than 5% by weight and most preferably less than 3% by weight of moisture content.

[0015] The one or more processors are then further configured to determine the technical application characteristics of the superabsorbent material based on the particle size using the characteristic determination model. In particular, the characteristic determination model is implemented as a data-driven model, which has been parameterized, for example, based on historical measurement data, so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles. In particular, the term "data-driven" defines that the model is mainly based on corresponding data input, rather than, for example, based on intuition, personal experience or knowledge. In particular, the characteristic determination model can be implemented as any machine learning-based model, which is based on known machine learning algorithms, such as neural networks, regression models, classification algorithms, etc. White box models evaluated with nonlinear regression optimizers are particularly useful in the present invention. Such machine learning models can be used as part of a comprehensive process control model - considering other formulations and process parameters - using other machine learning and artificial intelligence algorithms. Typically, the characteristic determination model includes one or more model parameters that can be determined based on corresponding training data. The parameterization of the characteristic determination model is therefore based on the training data in the model training process to determine the values ​​of the corresponding one or more model parameters. In particular, the determination model is parameterized so that it can determine the technical application characteristics of the superabsorbent particles based on the size of the particles. In general, in order to parameterize the property determination model accordingly, respectively known training methods for parameterizing a given model can be utilized. In particular, optimization methods can be utilized to find the best fit of the model parameters to the corresponding training data. For the training of the property determination model, preferably historical data can be used as training data, e.g. historical data comprising measured data from corresponding measurements of the technical application properties of the superabsorbent particles or data derived from known physical relationships of the technical application properties of the superabsorbent particles and the corresponding measurable properties. In particular, the historical data comprise superabsorbent particles of a plurality of different particle sizes corresponding to one or more technical application properties.

[0016] Since the production process of the superabsorbent particles can have a large influence on the specific composition of the superabsorbent particles, for example on the specific interconnectivity of the core and the shell of the superabsorbent particles, it is preferred that the property determination model is parameterized with a corresponding historical data set for a specific production process of the respectively produced superabsorbent material. This allows the property determination model to provide a very accurate determination of the technical application properties of the superabsorbent material produced in a specific production process. However, in other embodiments, the property determination model can also be trained with a more general historical data set, which historical data set includes data of superabsorbent materials using different production processes, wherein in this case the property determination model can then learn to distinguish between superabsorbent materials produced in different production processes, and the corresponding production process can be provided as a further input to the property determination model. Furthermore, the property determination model can be trained so that it can determine one technical application property of the superabsorbent material, but can also be trained to predict more than one technical application property of the superabsorbent material.

[0017] The output interface is then configured to generate control data based on the determined technical application characteristics. In particular, the output interface can be configured to dock with a user interface, wherein control data for controlling the user interface to provide the determined technical application characteristics can then be generated. However, the control data may relate to other applications and may be provided on the user interface, so that, for example, the user may then decide whether to implement the control data or so that the user may arrange for the correction of the control data. However, the output interface may be additionally or alternatively configured to dock with other computing systems or production systems. For example, the output interface may be configured to dock with a production management system that controls the production of superabsorbent materials to provide control data to the production management system, and / or may be configured to dock directly with a production system that is configured to produce superabsorbent materials to provide control data directly to the production system. In a preferred embodiment, the generated control data includes manufacturing specifications for controlling the manufacture of superabsorbent materials. In particular, it is preferred that the manufacturing specifications include information indicating the size of the superabsorbent particles to be produced. The manufacturing specifications may also include additional information about the superabsorbent particles related to production, such as one or more process parameters indicating the production process of the superabsorbent particles, the formula of the substance or material used during the production process, etc. Preferably, the manufacturing specifications are provided in the form of a control sequence for the production system, so that the superabsorbent material can be directly produced based on the control data.Generally, the control data can be generated, for example, based on predetermined rules, which determine which control data is generated based on which determined technical application characteristics.

[0018] In a preferred embodiment, the utilized characteristic determination model is further parameterized based on the core size and shell size of the superabsorbent particles. Surprisingly, the inventors have found that further parameterizing the characteristic determination model based on the core size and shell size of the superabsorbent particles allows particularly accurate determination of the technical application characteristics. In addition, further parameterizing the characteristic determination model based on the core size and shell size of the superabsorbent particles allows separation of the respective effects of each of these parameters on the technical application characteristics. Typically, the core size and shell size of the superabsorbent particles depend on the particle size, i.e., the penetration depth of the material used for the post-crosslinking process is substantially the same for all particle sizes, so that the size of the shell and the core (i.e., the volume) depends primarily on the size of the particles. However, the general penetration depth of the post-crosslinking material and therefore the thickness of the shell relative to the core depends on the crosslinking program utilized, such as the material utilized, the pressure conditions, the additive utilized, the temperature, etc. Therefore, further separating the effects of the core and shell sizes on the technical application characteristics not only allows the use of the size of the superabsorbent particles to control the technical application characteristics, but also allows the determination of the effects of the shell size and the core size on the technical application characteristics, and therefore also allows the optimization of the post-crosslinking program of the superabsorbent particles with respect to the technical application characteristics.

[0019] Preferably, the parameterization of the utilized property determination model comprises determining performance parameters that quantify the contribution of the core size and the shell size of the superabsorbent particles, respectively, to the technical application properties. Utilizing a corresponding training data set comprising corresponding core sizes, shell sizes and technical application properties for a plurality of superabsorbent particle sizes allows parameterizing the property determination model such that the influence of the core size and the shell size on the technical application properties can be accurately quantified by determining the performance parameters. Preferably, the utilized property determination model is based on the following relationship between the technical application properties and the size of the superabsorbent particles:

[0020]

[0021] Where V shell is the volume of the shell of the superabsorbent particle, and V core is the volume of the core of the superabsorbent particle, wherein the volume of the shell of the superabsorbent particle and the volume of the core depend on the size of the superabsorbent particle, and wherein var shell and var core are performance parameters that quantify the contribution of core size and shell size, respectively, and are determined during the parameterization of the property determination model, and where var Particle Indicates the technical application characteristics being measured.

[0022] In one embodiment, the received particle size is a particle size distribution of superabsorbent particles of a superabsorbent material, wherein the one or more processors are further configured to a) determine one or more particle size grades in which particles with corresponding sizes are present in the superabsorbent material from predetermined particle size grades based on the particle size distribution, b) determine the technical application characteristics for the determined particle size grades, and c) determine the overall technical application characteristics of the superabsorbent material based on the determined technical application characteristics for the corresponding determined particle size grades and based on the particle size distribution. Generally, the particle size distribution of superabsorbent particles can be received in the form of any data information indicating the particle sizes present in a statistically relevant sample of the superabsorbent material. For example, the particle size distribution can be provided in the form of a list of all sampled superabsorbent particles and superabsorbent particles of corresponding sizes. However, the particle size distribution can also be provided directly in the form of a grade distribution, which indicates the amount of particles present in the corresponding grade in a statistically relevant sample for multiple particle size grades. Generally, a particle size grade refers to a particle size range and is defined by a minimum particle size and a maximum particle size that define the particle size range. If the particle size distribution is provided in the form of a grade distribution, the respective predetermined particle size grade may refer to the particle size grade that has been used, wherein in this case, determining whether particles are present in the predetermined particle size grade is equivalent to determining whether an amount of particles greater than zero is indicated by the particle size grade distribution in the respective particle size grade. However, the predetermined particle size grade may also be independent of any particle size grade previously used to provide the particle size grade distribution. In this case, corresponding statistical methods may be used to determine for which of the predetermined particle size grades particles are present in the superabsorbent material. Furthermore, if a list of particle sizes is provided as a particle size distribution, the predetermined particle size grades may be used to classify the particle sizes accordingly and to determine for which predetermined particle size grade at least one particle is present. Preferably, the particle size distribution of the water-absorbing polymer particles may be determined, for example, by the test method number WSP 220.3 (11) "Particle Size Distribution" recommended by EDANA. Furthermore, optical methods may advantageously be used, such as laser diffraction, photographic analysis, etc., preferably calibrated according to EDANA or corresponding ISO test methods based on screening analysis. Such calibration may also depend on other particle properties besides particle size and therefore be performed specifically for each product grade. In the present invention, such calibration methods are particularly useful because they can be used online at one or more locations in the production process and provide the necessary particle size information in real time.

[0023] Then, the technical application characteristics for the determined particle size class are determined by using the characteristic determination model for the corresponding particle size falling into the predetermined particle size class. Generally, the technical application characteristics can be determined by providing at least one particle size falling into the predetermined particle size class to the characteristic determination model and using the determined technical application characteristics as the technical application characteristics of all sizes falling into the determined particle size class. However, the minimum and maximum sizes of the particles falling into the corresponding determined particle size class can also be used as inputs to the characteristic determination model, and the corresponding determined technical application characteristics can be statistically combined, for example, by averaging, to determine the technical application characteristics representing the corresponding determined particle size class. However, other statistical methods can also be used accordingly. The overall technical application characteristics can then be determined based on the technical application characteristics determined for the corresponding determined particle size class and based on the particle distribution. In particular, the amount of particles falling into the corresponding determined particle size class is considered when determining the overall technical application characteristics. For example, weighted averaging can be used to determine the overall technical application characteristics based on the determined technical application characteristics, wherein the weight of the weighted averaging is determined based on the amount of particles of the particle distribution falling into the corresponding particle size class. For example, if more particles fall within one particle size class, the weight of the corresponding technical application characteristic may be higher than the technical application characteristic corresponding to a particle size class with fewer particles. Furthermore, other known or learned relationships may also be taken into account, for example, in the weights for determining the overall technical application characteristic. For example, it may be determined that larger particles generally have a higher influence on the overall technical application characteristic than smaller particles. In this case, a higher weight may be provided for the particle size class with larger particles relative to the weight of the particle size class with smaller sized particles. The control data are then preferably generated based on the determined overall technical application characteristic.

[0024] Typically, for absorbent capacity with and without external pressure, a weighted average can be used to predict overall product properties from a single particle size grade. This is not the case for performance-critical properties such as swelling kinetics and liquid permeability, and rather complex mixed properties are found for blends of superabsorbent particles with different properties, for example as described in WO 2019 / 137833. This is due to the fact that for such blends, not only are the size-grade-specific properties relevant, but also complex mixing phenomena based on the amount of particles present in the different size grades show a significant impact on the overall performance. While finer particles can be beneficial for achieving rapid liquid absorption, they can play an opposing role in liquid distribution because they quickly block fluid-conducting pores. In order to predict such properties, a weighted average of size grades can be used, which does not treat all particle sizes equally and can depend on the number of particles in each grade. In the present invention, the weights are preferably determined experimentally by first measuring the overall performance properties of the superabsorbent polymer and then determining the properties of each grade after classifying it into the corresponding discrete size grades. Mixing the corresponding grades with each other in different amounts will allow conclusions to be drawn on the desired weights. For such mixing, an experimental design can be used.

[0025] In one embodiment, the received particle size is provided as a starting particle size, and wherein the receiving interface is further adapted to receive a target application characteristic, wherein the processor is further adapted to iteratively determine a target particle size so that the superabsorbent material meets the target application characteristic within predetermined limits, wherein the iteration comprises: a) determining a technical application characteristic in each iteration step, b) comparing the determined technical application characteristic with the target technical application characteristic, and c) based on the comparison, providing a corrected particle size, or determining the particle size as a target size. In the case where the particle size is provided in the form of a particle size distribution, the target technical application characteristic refers to a target overall technical application characteristic, and the overall technical application characteristic can be determined as described above. Control data is then generated based on the target size or target size distribution. In particular, control data can be generated in such a case so that a production system for producing superabsorbent particles produces superabsorbent particles having a target size or a target size distribution.

[0026] Additionally or alternatively, the one or more processors may also be adapted to determine a deviation of the determined technical application characteristic from a target technical application characteristic, and control data may then be generated based on the deviation. Thus, in this case, iteration may be omitted, and for this case control data may be generated directly based on the deviation. For example, a predetermined rule may be utilized to generate control data based on the deviation. For example, if a deviation between the determined technical application characteristic and the target technical application characteristic is determined, control data may be generated to modify the production process, for example such that the particle size is reduced or increased by approximately a predetermined amount. After producing superabsorbent particles with a reduced or increased size, the technical application characteristics of the superabsorbent particles may then be determined again and compared again with the target technical application characteristics. The process may then be repeated until the determined technical application characteristics meet the target technical application characteristics.

[0027] In another aspect of the present invention, a device for determining the overall technical application characteristics of a superabsorbent material is proposed, wherein the superabsorbent material is provided in the form of superabsorbent particles, the superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the device comprises a) a receiving interface for receiving a particle size distribution of superabsorbent particles of the superabsorbent material, b) one or more processors, the one or more processors being configured to i) determine one or more particle size grades in which particles with corresponding sizes are present in the superabsorbent material from predetermined particle size grades based on the particle size distribution, ii) determine the technical application characteristics of the superabsorbent material for the determined particle size grades using a characteristic determination model, wherein the characteristic determination model is a data-driven model that has been parameterized so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles, and iii) determine the overall technical application characteristics of the superabsorbent material based on the determined technical application characteristics for the corresponding determined particle size grades and based on the particle size distribution, and c) an output interface for generating control data based on the determined overall technical application characteristics.

[0028] In another aspect of the present invention, an interface device for providing an interface for determining the technical application characteristics of a superabsorbent material is proposed, wherein the interface device comprises: a) an interface input unit for docking with a device as described above to provide particle size to the device as described above; and b) an interface output unit for processing control data generated by the device as described above based on the particle size.

[0029] In another aspect of the present invention, a training device for a parameterized characteristic determination model is proposed, wherein the training device comprises: a) a receiving interface for receiving historical training data, the historical training data comprising a plurality of particle sizes of superabsorbent particles of a superabsorbent material and one or more measured application characteristics of the corresponding superabsorbent material; b) one or more processors configured to parameterize the characteristic determination model using the received historical training data, so that the parameterized characteristic determination model is suitable for determining the technical application characteristics of the superabsorbent material based on the particle size; and c) an output interface for outputting the parameterized characteristic determination model.

[0030] In another aspect of the present invention, an optimization device for determining a target superabsorbent material having a target technical application characteristic is provided, wherein the superabsorbent material is provided in the form of superabsorbent particles, the superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the device comprises a) a receiving interface configured to receive the target technical application characteristic of the target superabsorbent material and the particle size of the potential superabsorbent particles of the superabsorbent material, b) one or more processors, the one or more processors being configured to: i) determine the technical application characteristic of the potential superabsorbent material based on the particle size using a characteristic determination model , wherein the characteristic determination model is a data-driven model that has been parameterized so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles, and ii) comparing the determined technical application characteristics with the target technical application characteristics, and I) if the predicted technical application characteristics are within a predetermined range around the target application characteristics, then determining the particle size as the target particle size, or II) if the predicted technical application characteristics are outside the predetermined range around the target application characteristics, then determining a corrected particle size and repeating the determination of the technical application characteristics using the corrected particle size, and c) an output interface configured to generate a control signal based on the target particle size. Preferably, the received potential particle size is a particle size distribution of superabsorbent particles of the target superabsorbent material. As described above with respect to the device, the one or more processors may then also be configured to a) determine one or more particle size classes in which particles of corresponding sizes are present in the superabsorbent material from the predetermined particle size classes based on the particle size distribution, b) determine the technical application characteristics for the determined particle size classes, and c) determine the overall technical application characteristics of the superabsorbent material based on the determined technical application characteristics for the corresponding determined particle size classes and based on the particle size distribution. The determined overall technical application characteristics can then be compared to the target technical application characteristics, and the potential particle size distribution can be determined to be the target particle size distribution, or a modified particle size distribution can be determined as a new potential particle size distribution. For example, the amount of particles in one or more particle size classes can be modified. However, in one embodiment, the target particle size distribution can also be determined analytically by a) providing one or more potential target particle size classes, b) determining the technical application characteristics of each of the potential target particle size classes, and c) determining a target amount of particles in each potential target particle size class by optimizing the amount of particles in each particle size class so that the overall technical application characteristics meet the target technical application characteristics.

[0031] In another aspect of the present invention, an interface method for providing an interface for determining technical application characteristics of superabsorbent materials is proposed, wherein the interface method comprises a) providing particle size to a device as described above via an input interface, and b) processing control data generated by the device as described above based on the particle size via an output interface.

[0032] In another aspect of the present invention, a training method for a parameterized property determination model is proposed, wherein the training method comprises a) receiving historical training data, the historical training data comprising a plurality of particle sizes of superabsorbent particles of a superabsorbent material and one or more corresponding measured application characteristics of the superabsorbent material, b) utilizing the received historical training data to parameterize the property determination model, such that the parameterized property determination model is suitable for determining the technical application characteristics of the superabsorbent material based on the particle size, and c) outputting the parameterized property determination model.

[0033] In another aspect of the present invention, an optimization method for determining a target superabsorbent material having a target technical application characteristic is proposed, wherein the superabsorbent material is provided in the form of superabsorbent particles, the superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the method comprises: a) receiving the target technical application characteristic of the target superabsorbent material and the particle size of potential superabsorbent particles of the superabsorbent material, b) determining the technical application characteristic of the potential superabsorbent material based on the particle size using a characteristic determination model, wherein the characteristic determination model is a data-driven model that has been parameterized so that it is suitable for determining the technical application characteristic of the superabsorbent particle based on the size of the particle, c) comparing the determined technical application characteristic with the target technical application characteristic, and I) if the predicted technical application characteristic is within a predetermined range around the target application characteristic, determining the particle size as the target particle size, or II) if the predicted technical application characteristic is outside the predetermined range around the target application characteristic, determining a revised particle size and repeating the determination of the technical application characteristic using the revised particle size, and d) generating a control signal based on the target particle size.

[0034] In another aspect of the invention, a computer program product for determining technical application properties of a superabsorbent material is proposed, wherein the computer program product comprises program code means for causing an apparatus as described above to perform a method as described above.

[0035] In another aspect of the present invention, a computer program product for training a characteristic determination model is proposed, wherein the computer program product comprises program code means for causing the training device as described above to execute the training method as described above.

[0036] In another aspect of the invention, control data generated according to the device, method and / or computer program product as described above is proposed.

[0037] In another aspect of the invention, the use of a device, a method and / or a computer program as described above for determining application properties of a superabsorbent polymer is proposed.

[0038] In another aspect of the present invention, the use of a device, a method and / or a computer program as described above for generating a library of technical application properties of different superabsorbent polymers is proposed.

[0039] In another aspect of the invention, the use of a device, a method and / or a computer program product as described above for controlling a production process of a superabsorbent material, in particular for controlling the particle size distribution, is proposed.

[0040] It should be understood that the method as described above, the device as described above and the computer program product as described above have similar and / or identical preferred embodiments, in particular the preferred embodiments as defined in the dependent claims. In addition, the training method as described above, the training device as described above and the training computer program product as described above also have similar and / or identical preferred embodiments, in particular the preferred embodiments as defined in the dependent claims.

[0041] It shall be understood that a preferred embodiment of the invention can also be the dependent claims or any combination of the above-mentioned embodiments with corresponding dependent claims.

[0042] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In the following figures:

[0044] Figure 1 A system for producing a superabsorbent material comprising a device for determining technical application properties of a superabsorbent material is schematically and exemplarily shown,

[0045] Figure 2 A flow chart showing schematically and exemplarily a method for determining technical application properties of a superabsorbent material and optionally controlling the production of a superabsorbent material,

[0046] Figure 3 Schematically and exemplarily a training device for training a property determination model for determining technical application properties of superabsorbent materials is shown,

[0047] Figure 4 A flow chart schematically and exemplarily shows a method for training a property determination model for determining technical application properties of a superabsorbent material,

[0048] Figure 5 The swelling model of superabsorbent particles is schematically and exemplarily shown, and

[0049] Figure 6 The use of certain technical application properties of superabsorbent particles is shown schematically and exemplarily. DETAILED DESCRIPTION

[0050] Figure 1 Schematically and exemplarily, a system 100 for producing a superabsorbent material 140 is shown. The system 100 includes an apparatus 110 for determining technical application characteristics of the superabsorbent material 140. Optionally, the system may also include an interface device 120 for interfacing with the apparatus 110. In addition, the system may include a production system 132 and a production control system 131 provided in a production plant 130 for producing the superabsorbent material 140.

[0051] The device 110 comprises a receiving interface 111, one or more processors 112 and an output interface 113. Typically, the device is configured to determine the technical application characteristics of the superabsorbent material. The superabsorbent material is provided in the form of superabsorbent particles, which include superabsorbent polymers provided in the form of an interconnected core and a surface cross-linked shell, and the surface cross-linked shell has a higher connectivity than the interconnected core. Generally speaking, the device can be provided as a stand-alone device, for example, as a dedicated computing device, but can also be provided as part of a more general computing device providing additional functionality. In particular, the device can be provided as part of a quality control system, or, for example, as part of a production control system 131.

[0052] The receiving interface 111 is configured to receive the particle size of the superabsorbent particles of the superabsorbent material. Generally, the receiving interface can be implemented as any interface that allows the corresponding data of the indicative particle size to be received. In particular, the receiving interface can be configured to provide an interface to a storage unit on which the particle size has been stored, a control system (such as the control system 131 of the production system 132) that provides sensor measurements indicating the particle size, or a user interface 120 that allows a user to input the corresponding particle size. The particle size may refer to any amount that allows the particle volume of the superabsorbent particles of the superabsorbent material to be quantified. Preferably, the particle size refers to the volume of the particle, or if the particle can be approximated as a spherical particle, it refers to the radius or diameter of the particle. The particle size of the superabsorbent particles is usually provided in the dry state of the superabsorbent particles, that is, before the fluid is absorbed into the superabsorbent particles and causes the size of the superabsorbent particles to increase. The particle size of the received superabsorbent particles is then provided to one or more processors 112.

[0053] The one or more processors 112 are then configured to determine the technical application characteristics of the superabsorbent material based on the particle size using the characteristic determination model. For example, the one or more processors may be configured to access the storage unit 114 on which the characteristic determination model is stored. Typically, more than one characteristic determination model may be stored on the storage unit 114, for example, characteristic determination models of different superabsorbent materials produced according to manufacturing specifications, such as using different superabsorbent polymers, different crosslinking methods and / or production parameters, may be stored. In this case, the one or more processors may then be configured to select the corresponding characteristic determination model for the superabsorbent material using corresponding information about the superabsorbent material, such as the ID of the superabsorbent material or the manufacturing specifications provided for the superabsorbent material. In addition, different characteristic determination models may be stored on the storage unit 114 for different technical application characteristics. In this case, the one or more processors may be configured to select all characteristic determination models available for the corresponding superabsorbent material and then apply each of these characteristic determination models to determine all technical application characteristics available for the corresponding superabsorbent material, or based on further information about the desired technical application characteristics provided, for example, via the user interface 120, the one or more processors may be configured to select the corresponding characteristic determination model to be utilized.

[0054] Typically, the characteristic determination model is parameterized so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles. In particular, the data-driven determination model can be any machine learning-based model that allows learning based on historical data to determine the technical application characteristics of the superabsorbent particles based on the size of the particles. For example, the characteristic determination model may refer to an algorithm based on a regression model, such as a neural network algorithm, a lasso algorithm, a ridge regression algorithm, a MASS algorithm, or a random forest algorithm. However, the characteristic determination model may also refer to a model algorithm based on a classifier, such as a random forest algorithm or a SVM algorithm. About Figure 5 Particularly preferred embodiments of the characterization model are described. In particular, it is preferred that the utilized characterization model is further parameterized based on the core size and the shell size of the respective superabsorbent particles in order to allow quantification and determination of the respective contribution of the core size and the shell size to the respective technical application properties. This allows the characterization model to be stored by storing the respective performance parameters of the quantified contribution. The selection of the characterization model can then be achieved by selecting the performance parameters corresponding to the respective core size and the respective shell size and using these performance parameters in the characterization model.

[0055] Generally speaking, one can use, for example, Figure 3 The training device 300 shown in the figure is used to train the characteristic determination model, and the training device can be configured to perform the following steps: Figure 4 The training method shown. Figure 3The training device 300 shown includes a receiving interface 310, one or more processors 320, and an output interface 340. Generally speaking, the training device 300 may be integrated into, for example, the device 110. In this case, the device 110, in particular the one or more processors 112 of the device 110, may be configured to, if no characteristic determination model is available, for example, on the memory 114, access a storage unit storing corresponding historical training data instead, and then train the corresponding characteristic determination model using the device 300. However, the training device 300 may also be provided independently of the device 110, and may then be configured to provide the trained characteristic determination model to a storage unit, such as the storage unit 114 accessible to the device 110.

[0056] The receiving interface 310 is configured to receive historical training data for training the characteristic determination model. Typically, the receiving interface may provide an interface to a storage unit on which the historical training data is stored, or to a measurement or sensor interface that allows receiving corresponding measurements that can be used as historical training data. The historical training data includes at least two, preferably multiple particle sizes for the superabsorbent material and the corresponding one or more measured application characteristics of the superabsorbent material. Such training data may be generated, for example, by measuring the corresponding superabsorbent material using a known measurement method for determining the technical application characteristics and also for measuring the corresponding particle size of the superabsorbent material. Generally speaking, such historical training data is usually generated during the quality control of the superabsorbent material or during the design process of the superabsorbent material where the corresponding measurements are performed.

[0057] The one or more processors 320 are then configured to parameterize the characteristic determination model using the received historical training data, so that the parameterized characteristic determination model is suitable for determining the technical application characteristics of the superabsorbent material based on the particle size. For example, known machine learning (i.e., parameterization) methods can be used. The output interface 340 is then configured to output the parameterized characteristic determination model to, for example, a corresponding memory, such as the memory 114, or directly to a device for determining the technical application characteristics, such as the device 110. In general, in order to train the characteristic determination model, a method such as Figure 4 For example, historical training data may be received, for example, as described with respect to the receiving interface of the training device 300. The corresponding historical training data may then be used to parameterize the corresponding characteristic determination model using known training methods, as described in more detail with respect to the training device 300. Furthermore, the trained characteristic determination model may then be provided to, for example, the device 110.

[0058] Based on such trained characteristic determination model, one or more processors 112 of the device 110 are then configured to determine the technical application characteristics of the superabsorbent material based on the provided particle size. The determined technical application characteristics and optionally also the utilized particle size are then provided to the output interface 113. The output interface 113 is then configured to generate control data based on the determined technical application characteristics. For example, the output interface may generate control data that allows the determined technical application characteristics to be provided to the user interface 120 in order to inform the user of the determined technical application characteristics. However, in a preferred embodiment, the generated control data includes manufacturing specifications for controlling the manufacture of the superabsorbent material, in particular for controlling the production system 132 for producing the superabsorbent material 140. The manufacturing specifications can then be provided, for example, to the control system 131 to control the production system 132 to produce the corresponding superabsorbent particles 140.

[0059] However, in a preferred embodiment, the apparatus 110 is also configured to not only determine the technical application properties, but also to perform iterations to determine a particle size that allows the superabsorbent material to provide the target technical application properties. In particular, the apparatus may be configured to perform, for example, Figure 2 The method is exemplarily and schematically shown in the flowchart of FIG. Figure 2 The method shown can be carried out in the context of product process design, where a superabsorbent product is designed with respective predetermined target technical application properties and a corresponding superabsorbent material has to be found to provide the respective target technical application properties. In this case, the iteration is based only on the respective target technical application properties and no further measurements are provided as part of the feedback loop. However, the method can also be applied to e.g. Figure 1 In the illustrated scenario of the system 100, a control feedback is provided to the production system 132 in order to produce a superabsorbent material 140 having corresponding desired technical application properties. In this case, for example, the measurement of the particle size of the current batch of superabsorbent material can be used as an input for iteration, and the production process can be modified simultaneously until the measured particle size is reached to provide the corresponding desired technical application properties.

[0060] Usually, in Figure 2In the method described in , a target technical application characteristic is received, which refers to the technical application characteristic that the corresponding superabsorbent material should meet. In addition, the method includes receiving a corresponding particle size, for example as an arbitrary starting particle size or as a measured particle size provided by the superabsorbent material produced by the current batch. In addition, as will also be described below, the particle size provided can also be a particle size distribution of a statistically relevant sample of the superabsorbent material. In many cases, it is impossible to produce particles of only one size in the production of superabsorbent materials. Therefore, in many cases, the production produces superabsorbent materials including different particle size distributions. Such particle size distribution can be determined, for example, by measuring the corresponding statistically relevant amount of superabsorbent material to determine the size of the corresponding superabsorbent particles. The particle size distribution so determined can be provided, for example, in the form of a list of all measured particle sizes. However, the particle size distribution can also be provided, for example, in the form of a bar graph, which determines different particle size grades with reference to the corresponding particle size range, and determines how many particles are found in the corresponding amount of superabsorbent material in each of the corresponding particle size grades. Measuring the particle size distribution in the form of a bar graph can be performed, for example, by using sieves of different sizes and measuring the amount of particles captured by each sieve. The size of the sieve then provides the boundaries for the corresponding particle size classes.

[0061] In the next step, the technical application characteristics of the corresponding superabsorbent material are determined using the characteristic determination model. If only one particle size is provided, the technical application characteristics of the superabsorbent material are determined, as described above, for example, with respect to the device 110. In the case where the particle size is provided in the form of a particle size distribution to determine the overall technical application characteristics of the particle size distribution, different particle sizes must be taken into account. For example, if a list of particle sizes is provided as a particle size distribution, then for each particle size, the characteristic determination model can be used to determine the corresponding technical application characteristics, wherein the overall technical application characteristics of the superabsorbent material can then be determined, for example, by averaging a plurality of determined technical application characteristics. Optionally, for example, if it is known that some particle sizes have a higher impact on the overall technical application characteristics than other particle sizes, a weighted average can also be used. However, for most cases, it is not necessary to accurately determine the technical application characteristics of each particle size in order to provide suitable accuracy. In fact, in most cases, the measurement of the particle size will not produce an accurate particle size, but will produce a particle size distribution, for example, in the form of a bar graph, which can be easily measured using a sieve, wherein for each sieving step, the amount of particles obtained is continuously measured. In this case, it is preferred to use particle size classes and determine the technical application characteristics for each particle size class. Typically, the characteristic determination model used may have been trained using the same particle size class accordingly. However, the characteristic determination model may also be trained without using the particle size class. If the characteristic determination model has been trained based on the particle size class, it is only necessary to determine for which classes at least one particle exists in the particle size distribution based on the provided particle size distribution. For the particle size classes thus determined, the characteristic determination model may then be used to determine the technical application characteristics. Moreover, in the case where the characteristic determination model has been trained based on a specific particle size, i.e. without using the particle size class, the corresponding particle size class may be used. For example, in this case, the particle size class provided in the particle size distribution may be used, or other particle size classes may be predetermined. Then, it is also determined based on the particle size distribution in which particle size class at least one particle may appear in the superabsorbent material. Then, one or more particle sizes representing the particle size class may be determined. For example, a particle size located in the middle of the particle size class may be used to represent the particle size class. However, the minimum and maximum particle sizes of the particle size class may also be used to represent the particle size class. Based on the represented particle size values, the technical application characteristics can then be determined using a characteristic determination model. If more than one value is used to represent a particle size class, it is preferred that the technical application characteristics corresponding to this particle size class are determined using statistical methods based on the technical application characteristics determined for more than one size value representing the particle size class. For example, an average value of the determined technical application characteristics can be used. Thus, also in this case, for each particle size class, the technical application characteristics corresponding to this particle size class are determined. Based on all the determined technical application characteristics of all particle size classes in which at least one particle is present in the superabsorbent material, then an overall technical application characteristic can be determined.For example, corresponding statistical methods can be used, such as averaging or in particular weighted averaging. In addition, known relationships of different particle size classes and their contribution to the corresponding technical application characteristics can also be taken into account. For example, if larger particles have a higher influence on the technical application characteristics than smaller particles in the averaging process, the technical application characteristics corresponding to the particle size class of larger particles can have a higher weight than the technical application characteristics corresponding to the particle size class of smaller particles. In addition, when determining the overall technical application characteristics, the particle size distribution is also taken into account. In particular, the relative amount of particles in a certain particle size class compared to particles in other particle size classes is taken into account when determining the overall technical application characteristics. For example, in the averaging process, a particle size class comprising more particles can have a higher weight than a particle size class comprising fewer particles. In particular, the weight can be selected based on the corresponding percentage of particles in the corresponding particle size class relative to the total number of particles in the corresponding sample. Therefore, also in the case of particle size distribution, the above method allows to determine a very accurate overall technical application characteristics of the superabsorbent material comprising this particle size distribution.

[0062] In the next step, the technical application characteristics thus determined (in the case of particle size distribution, the total technical application characteristics) are compared with the target technical application characteristics. In particular, it is determined whether the determined technical application characteristics deviate from the target technical application characteristics. If the deviation between the determined technical application characteristics and the target technical application characteristics exceeds the predetermined limits, that is, if the determined technical application characteristics do not meet the target technical application characteristics within these predetermined limits, the next iteration step can be initiated. In particular, in the next iteration step, the particle size can be corrected, for example, increased or reduced. In the case of particle size distribution, the corresponding particle size distribution is corrected, for example, by changing the corresponding amount of particles in different particle size grades. This step may refer to a purely computer-implemented step. However, in the case of controlling a production process, this step may also refer to providing control data for controlling the production process of the superabsorbent material so that the produced particle size is corrected respectively. In this case, also as a feedback loop, the corrected particle size can be measured again, because for the corresponding production process, it is usually impossible to accurately ensure that the particle size is corrected as planned. In this case, the measured corrected particle size is then utilized in the next iteration step. Based on the respectively corrected particle size or particle size distribution, the technical application properties are then determined again using the property determination model as described above.

[0063] These iterative steps can be performed and repeated until a predetermined termination criterion is reached, for example, a predetermined number of iterative steps have been performed, or the comparison results in a deviation below a predetermined limit, i.e., until it is determined that the technical application characteristics meet the target technical application characteristics within predetermined limits. In this case, corresponding control data are generated based on the technical application characteristics and / or the target particle size thus determined. In particular, it is preferred that control data are generated that include manufacturing specifications that specifically indicate a target particle size or particle size distribution that should be achieved during the production of superabsorbent particles. The corresponding production of superabsorbent particles can then optionally be controlled using the corresponding manufacturing specifications. However, if the method is used to directly control a production process, the control data in this case can also simply indicate the fact that with the current production settings, for example the process parameters of the production process of the superabsorbent particles, the corresponding target technical application characteristics are achieved, so that these production parameters should not be further modified.

[0064] In an optional embodiment, control data, in particular manufacturing specifications, can also be determined directly based on the determined technical application characteristics without further iteration steps for determining the target technical application characteristics. This is particularly true if the target technical application characteristics are directly met or the control data can involve providing the determined technical application characteristics to a user interface.

[0065] In a preferred embodiment of the characteristic determination model, Figure 4 The characteristic determination model described is further parameterized based on the core size and shell size of superabsorbent particles. Generally speaking, the core size and shell size of superabsorbent particles depend on particle size. However, the core size and shell size further depend on the process parameters of the production process of superabsorbent particles, particularly on the process parameters that have an impact on the crosslinking process and the post-crosslinking process of providing its shell for the corresponding particles. In such embodiments, these process parameters or directly the core size and shell size may also depend on the radius used as the corresponding input parameter and may be additionally or alternatively corrected to particle size. This allows the contribution of core size and shell size to technical application characteristics to be considered, and more possibilities of reaching desired technical application characteristics are provided. For example, in some cases, the corresponding target technical application characteristics may not be met based on the correction of particle size alone, and the corresponding shell size or core size may further need to be corrected. Therefore, iteration may also include, in addition to the corrected particle size or as a substitute for the modified particle size, providing a corrected core and / or shell size. Further details about the preferred characteristic determination model will be provided below.

[0066] Superabsorbent materials are usually produced by grinding and sieving the pre-dried gel produced by the polymerization step. For example, after belt drying, the dried gel is initially provided in the form of at least one infinitely long, usually several centimeters thick flat plate, which can then be ground and sieved to provide a superabsorbent polymer powder as a superabsorbent material. For the grinding step, for example, a combination of a finger crusher, a roller mill or a pin mill can be used to grind the polymer plate. However, in most common production processes, the grinding step results in a wide and not necessarily normally distributed particle size distribution of superabsorbent particles forming the resulting superabsorbent material. For corresponding applications, most grinding processes produce the following two: too fine superabsorbent particles, such as less than about 100 μm, and too coarse superabsorbent particles, such as greater than about 850 μm. Too fine particles can usually be separated and recycled into gel or monomer during the production of basic superabsorbent polymers. Too coarse particles can be further crushed, such as by repeated grinding or additional grinding steps. However, these two process steps are economically disadvantageous and may also undesirably deteriorate the quality of the final product. It is therefore advantageous to limit the further processing steps for particles that are too fine and too coarse to the minimum necessary for the respective application.

[0067] In recent years, for very thin hygiene articles which may not contain too much fluff (if any), narrower particle sizes are required as described above, while for industrial hygiene and performance reasons they may not contain too many very fine particles, for example, the particle size fraction of less than 50 μm, preferably less than 100 μm, most preferably less than 150 μm must be minimized and ideally should not exist. However, such very fine particles cannot be avoided in any grinding process and must be removed from the final product. Ideally, these very fine particles are recycled in the production process, but this ability is very limited because they are cross-linked and recycling may be detrimental to product performance. Therefore, it is preferred to control the production process of superabsorbents in a way that minimizes these very fine particle fractions.

[0068] The separation of fine, fine and coarse particle fractions is usually carried out as follows: by using a screening machine to classify and separate by utilizing the size differences of the particles, at least partially by utilizing the different air resistances of the particles and the classification of air of different densities, or by separating via the flow behavior of the particles, for example in a trickle separator or by means of a vibrating feeder or other separation processes utilizing the different flow behaviors of particles of different sizes such as friction, adhesion or agglomeration. In a typical production process, a combination of these separation methods is utilized. Therefore, changes in the chemical or physical surface properties of the particles (for example due to changes in manufacturing specifications) usually require considerable adjustments of the grinding or classification process so that the desired particle size distribution can be produced in a stable manner. For example, due to the high space-time yield during the production process, the sieve surface can be loaded with significantly more material compared to laboratory scale, which can then lead to a significant reduction in the separation efficiency due to the strongly emphasized flow characteristics of the particles at that time.

[0069] In all these grinding and separation processes, the task is to provide an optimal particle size distribution, which makes the superabsorbent material meet the technical requirements of the customer and also allows to improve the efficiency of the production process, for example by optimizing the steps for reprocessing too small and too coarse particles. For this reason, it is advantageous to provide suitable process parameter settings for the grinding and separation processes used and to adjust these process parameter settings regularly during continuous production. At present, these process parameter settings and adjustments are usually provided by experienced factory operators and can be further based on the analysis of the obtained products and / or intermediate samples. For this reason, sampling is performed at a suitable point in the production process, for example, samples of the basic superabsorbent polymer to be fed to the post-crosslinking program and samples of the final product obtained from the post-crosslinking program are usually analyzed. In addition, as long as it is technically feasible, attempts are also made to record the material flow during the production process. Then, the adjustment itself is usually carried out empirically, which results in optimization problems. The disadvantage is that in practice this is a time-consuming and cost-intensive "trial and error" method. In the case of new equipment in the grinding and separation process, or in the case of changes in the base superabsorbent material (e.g. morphology, porosity, surface properties, etc.) due to its manufacture (e.g. formulation, polymerization, extrusion, drying, etc.), due to lack of operating experience, especially for new products, it is often necessary to repeat the necessary settings to ensure continuous product quality, especially to ensure that the final product has the same technical application characteristics, but this is often difficult. Especially for continuous manufacturing processes with high space-time yields, operating trials are often ineffective and difficult to perform. In addition, the selectivity differences between the laboratory and production provide additional complexity for scale-up: while the optimal particle size distribution can be developed and adjusted effortlessly in the laboratory, the actual production process usually requires long and careful operating trials and adjustments. Usually, for the actual production process, the same particle size distribution as in the laboratory cannot be set, but only the best possible approximation can be set.

[0070] In production plants, for example in crushing or screening plants, mechanical wear of the equipment also occurs over time, which has to be compensated by regular readjustment of process parameters in order to maintain a set particle size distribution, for example gap width in a roller mill, selection of screens etc.

[0071] The superabsorbent polymer powder produced as described above is usually further sent to the post-crosslinking process step after producing the best particle size distribution. In this step, the post-crosslinking agent dissolved in the solvent is sprayed on the superabsorbent powder, and the post-crosslinking on the surface of the superabsorbent particles is caused by annealing, and at least partially or completely dried. Depending on the formulation composition, the post-crosslinking solution only penetrates the particles to a certain depth. The complete penetration of the particles is usually unfavorable because it is usually important to combine the characteristics of the strongly crosslinked surface shell with the basic polymer core that is only slightly crosslinked. Too thin post-crosslinked shells are also unfavorable because they can be destroyed by mechanical wear, resulting in at least partial loss of the desired particle characteristics. Generally, the experimental determination of shell thickness is known, but labor-intensive, and can only obtain approximate results, as described in "Modern Superabsorbent Polymer Technology", FL Buchholz, AT Gra-ham, Wiley-VCH, Weinheim, 1998, pp. 192-193.

[0072] Generally, the above core-shell structure with a shell polymer covalently bonded to the core polymer and a surface that is only torn but not separated during swelling causes almost every technical application characteristic of the final product to be strongly dependent on the particle size distribution of the superabsorbent polymer powder. During the product development and manufacturing process, usually only the technical application characteristics of the superabsorbent polymer with a wide particle size distribution that is finally produced are determined. However, in this form, the contribution of superabsorbent polymer powder, surface post-crosslinking and particle size distribution are inseparable and only show as overall performance. Therefore, the optimization of these three components is usually carried out by fine tests in the laboratory and in operation, or it is impossible in terms of particle size distribution. This produces another task: providing the possibility of optimizing the crosslinking process and particle size distribution of superabsorbent polymer particles, for example, wherein the further optimization of the post-crosslinking process provides more possibilities than the optimization of the particle size distribution that is usually technically limited. In addition, if the technical application characteristics of superabsorbent materials can be determined by computer in sanitary products, this may be advantageous. Here, it is particularly advantageous to be able to determine the technical application characteristics of different superabsorbent particles in the corresponding mixture that is usually present, for example as a starting point for possible product development.

[0073] So, for example, Figures 1 to 4The invention as described above provides the possibility of determining the technical application properties of superabsorbent particles based on the radius of the superabsorbent particles by using a property determination model. Figure 5 In the preferred embodiment described in more detail, it is further preferred that the property determination model also takes into account the size of the core and shell of the particle.

[0074] Surprisingly, the inventors have found that the contribution of the core and shell of the superabsorbent particles to the properties for technical applications, which are difficult or impossible to obtain experimentally, can be easily separated in a property determination model which takes into account the reasonable assumption that the shell and core of the dry superabsorbent particles are, for example, represented by the volume V shell 、V core The quantitative dimensions change in different ways during use by swelling, for example different crosslinking densities lead to different degrees of swelling, but dictate the technical application properties obtainable by the swollen particles in use due to their structurally given properties before swelling.

[0075] Figure 5 The corresponding model for the swelling of superabsorbent particles is shown schematically and exemplarily. The particle in the dry state is shown on the left, which comprises a superabsorbent particle with a radius r i The core is d, the shell thickness is d and the particle radius is r 0 , so that r 0 =r i +d. The superabsorbent particles are shown on the right after swelling. As shown in the figure, during swelling, the post-crosslinked shell breaks up, but does not separate from the core and essentially maintains its size, i.e. volume. Thus, the volume V of the shell and the core is shell 、V core Can be respectively and is determined and therefore depends on the radius r of the particle 0 and the shell thickness d or the core radius r l .

[0076] In order to separate the contribution of the shell and the core in the property determination model, an algorithm is preferably used which includes respective performance parameters indicative of the respective contributions which can be learned during parameterization of the property determination model. Preferably, the performance parameters var shell Quantify the volumetric contribution of the shell size and the performance parameter var corequantifies the volumetric contribution of the core size. Furthermore, since the shell thickness d is often difficult to determine for actually produced superabsorbent particles, an additional performance parameter d′, which quantifies the effective thickness of the shell polymer, can be used to represent d in the model, where d′ replaces d for all practical purposes, but the same formula is used. Typically, the performance parameters are constant parameters with respect to the particle size, but are functions of the formulation and other process parameters such as temperature and residence time during surface post-crosslinking, etc. Based on these performance parameters, quantities var can be determined that are or are indicative of technical application properties. Particle For example, the following relationship can be used:

[0077]

[0078] In general, the size, for example in the form of the radius or diameter of the particles, is known from the corresponding size distribution of the superabsorbent particles forming the superabsorbent material. For example, the corresponding size distribution can be determined by sieving the superabsorbent material or by optical measurements such as image analysis, laser diffraction, light barriers, etc., usually with the aid of calibration relative to the sieving method. For example, the corresponding size distribution can be determined using Probe online measurement system. Such online measurements are useful because the data can be easily used for processing, and although the sieve is an efficient and reliable classifier in the laboratory, this is different in the production plant: the throughput and the load on the sieve plate will change, and due to the wear of the equipment, its grinding and classification characteristics will change. Adjusting and optimizing the process to monitor in real time and take into account these effects greatly benefit from the present invention. In the case of utilizing a performance parameter related to the shell thickness of the superabsorbent particles, it should be noted that the performance parameter quantifies the effective thickness, which may or may not correspond to the physical thickness. For example, the determined value of the performance parameter may be variable, for example, depending on the technical application characteristics and the size of the particle surface. In addition, although the exemplary characteristic determination model provided above is discussed with respect to the spherical geometry of the superabsorbent particles, the inventors have found that the embodiments discussed above can also be used to very well determine the technical application characteristics of particles with quite irregular shapes, such as after gel extrusion.

[0079] For the above-mentioned characteristic determination model, the performance parameters can then be determined during the parameterization, in particular the training, of the characteristic determination model. For example, nonlinear optimization using an optimization function of the "least square method" or equivalent effect can be used to determine the performance parameters based on historical training data involving, for example, laboratory or production sample measurements. In particular, for example, particles of a corresponding sample comprising a particle size distribution can be separated in a sample comprising only particles having a particle size in a predetermined particle size class by sieving the sample with different sieve sizes. The technical application characteristics corresponding to each of these particle size classes can then be measured for separate samples, as will be described below. The information about the particle size classes and the corresponding technical application characteristics can then be used in the training data to determine the performance parameters, which themselves are independent of the size of the particles. For example, during training, the volumes of the shell and the core are calculated using the first initial estimate of the performance parameters. These calculated volumes can then be substituted into the characteristic determination model using, for example, the above-mentioned formula, and the technical application characteristics of the particles in the size class can be calculated. The accuracy of the performance parameters and therefore the accuracy of the characteristic determination model can then be determined by comparing the calculated technical application characteristics with the measured technical application characteristics of the training data for the corresponding particle size class. Based on this comparison, an optimization function may be utilized to iteratively adjust the performance parameters until the characteristic determination model best describes the given measurement data.Based on such training methods, the characteristic determination model may be parameterized by determining the values ​​of the performance parameters.

[0080] Such a corresponding training process for the feature determination model is Figure 6 The steps of determining the particle size distribution and separating the sample of superabsorbent material into particle size fractions are symbolically indicated by the first two symbols. In addition, Figure 6The scheme includes determining the technical application characteristics of each particle size grade of the sample. Based on the training data thus obtained, performance parameters such as the contribution of the core size and the shell size to the technical application characteristics can then be determined, as described above. The performance parameters thus obtained can then be catalogued and stored with respect to the formula and process parameters they have obtained. This storage of performance parameters is equivalent to the storage of a characteristic determination model, because the characteristic determination model is defined by the performance parameters. However, by separating the determined performance parameters for the contribution of the core and the surface post-crosslinked shell, these performance parameters can also be combined in a new way, thereby providing a new characteristic determination model for superabsorbent particles that have not been measured before. For example, the performance parameters determined for the contribution of the core containing superabsorbent polymers can be combined with the performance parameters of the contribution of the shell determined for different post-crosslinking procedures. In this way, a characteristic determination model for superabsorbent materials that have not been synthesized and measured before can be provided. This makes it possible to accelerate product development and optimize the surface post-crosslinking program, the core crosslinking program and / or the particle size distribution during the subsequent amplification of superabsorbent production to solve the above-mentioned tasks. In particular, the property determination model thus determined can then be used to determine the technical application properties of a potential superabsorbent material using, for example, the particle size distribution measured with the method described above.

[0081] In the following, some exemplary methods that can be used to measure particularly preferred technical application characteristics are described. Generally, EDANA (European Disposables and Nonwoven Fabrics Association, Avenue Herrmann Debroux 46, 1160 Brussel, Belgium, www.edana.org) and INDA (Institute of Nonwovens Industry, 1100 Crescent Green, Suite 115, Cary, North Carolina 27518, USA, www.inda.org) have published a joint standard method "Standard Procedure for Nonwoven Fabrics", 2015 edition, which is available from these two organizations, which can be used to determine the technical application characteristics of superabsorbent particles. The test methods for determining the absorption capacity or permeability of superabsorbent materials disclosed in the above publications are incorporated herein by introduction as useful methods for measuring the technical application characteristics in the present invention. Generally speaking, if not otherwise described in the method, most test methods are carried out at an ambient temperature of 23+ / -2°C and a relative humidity of 50%+ / -10%. Unless otherwise specified, the granular superabsorbent material is fully mixed before the test method is performed. This mixing is particularly relevant for obtaining representative samples for determining technical application properties, since technical application properties may vary with particle size. CRC (centrifuge retention capacity) can be determined according to EDANA test method NWSP 241.0.R2 (15) "Gravimetric determination of fluid retention capacity in saline solutions after centrifugation". FSC (free swelling capacity in saline determined by gravimetric analysis) can be determined according to EDANA test method NWSP240.0.R2 (15). AAP (absorbency against pressure) can be determined according to EDANA test method Nr.NWSP 242.0.R2 (15) "Absorbency against pressure, gravimetric determination" under one or more predetermined external pressures depending on the characteristics of the superabsorbent material. The external pressure can vary depending on the choice of the applied weight, and typical pressures are 0.0, 0.3, 0.7 psi, which correspond to 0.0, 21.0, 49.2 g / cm 2. T20 can be determined as the liquid absorption time (T20) of 20 g / g according to the test procedure described in EP 2 535 027 A1, pages 13-18 "K(t) test method (Dynamic effective permeability and absorption kinetics measurement test method)". VAUL (volume absorbency under load) can be determined by the method described in EP 2 922 882 B1, page 22 and a "characteristic swelling time" usually expressed as a τ-value can also be obtained. The external pressure used in the method can vary between 0.0-0.7 psi, preferably 0.03 psi or 0.30 psi. Vortex can be determined according to the vortex time method described in FL Buchholz, AT Graham, Modern Superabsorbent Polymer Technology, Wiley-VCH, Weinheim, 1998, pages 156-157. SFC (Saline Flow Conductivity) can be determined according to EP 2 535 698 A1, pages 19-22, "Urine Permeability Measurement (UPM) Test Method". PSD is "Standard Test Method for Superabsorbent Materials and Polyacrylate Superabsorbent Powders and Determination of Particle Size Distribution - Sieve Fractionation" NWSP 220.0.R2 (15). This method can be used to fractionate superabsorbent materials into predetermined size fractions to determine properties for technical applications.

[0082] 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.

[0083] For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in different orders. In addition, the operations outlined are provided only as examples, and some of these operations may be optional, combined into fewer steps and operations, supplemented with further operations, or expanded into additional operations without departing from the essence of the disclosed embodiments.

[0084] 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.

[0085] 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.

[0086] Procedures performed by one or several units or devices, such as receiving granularity, determining technical application characteristics, generating control data, etc., may be performed by any other number of units or devices. These processes may be implemented as program code means of a computer program and / or dedicated hardware.

[0087] 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 telecommunication systems.

[0088] Any unit described herein may be a processing unit as part of a classical computing system. The processing unit may include a general-purpose processor and may also include a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other dedicated circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term "memory" may include any computer-readable storage medium, such as a non-volatile mass storage device. If the computing system is distributed, the processing and / or memory capabilities may also be distributed. The computing system may include multiple structures as "executable components". The term "executable component" is a structure that is well understood in the computing field as a structure that can be software, hardware, or a combination thereof. For example, when implemented in software, a person of ordinary skill in the art will understand that the structure of the executable component may include software objects, routines, methods, etc. that can be executed on the computing system. This may include executable components in the stack of the computing system or on a computer-readable storage medium. The structure of the executable component may exist on a computer-readable medium so that when interpreted by one or more processors of the computing system (e.g., by a processor thread), the computing system performs a function. Such a structure may be directly computer readable by a processor, for example, as in the case where the executable component is binary, or it may be structured to be, for example, interpretable and / or compilable, whether in a single stage or in multiple stages, so as to generate such binary that can be directly interpreted by a processor. In other instances, the structure may be a hard-coded or hard-wired logic gate that is implemented exclusively or nearly exclusively in hardware, such as in a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other dedicated circuit. Therefore, the term "executable component" is a term for a structure that is well understood by a person of ordinary skill in the field of computing, whether implemented in software, hardware, or a combination. Any embodiment of the present invention is described with reference to actions performed by one or more processing units of a computing system. If such actions are implemented in software, one or more processors direct the operation of the computing system in response to having executed the computer executable instructions constituting the executable component. The computing system may also include a communication channel that allows the computing system to communicate with other computing systems, such as through a network. "Network" is defined as one or more data links that enable electronic data to be transmitted between computing systems and / or modules and / or other electronic devices. When information is transferred or provided to a computing system over a network or another communications connection (e.g., hardwired, wireless, or a combination of hardwired or wireless), the computing system properly views the connection as a transmission medium. Transmission media may include networks and / or data links that may be used to carry desired program code components in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computing system or combination thereof.Although not all computing systems require a user interface, in some embodiments, a computing system includes a user interface system for interfacing with a user. The user interface serves as an input or output mechanism to the user, such as via a display.

[0089] Those skilled in the art will appreciate that at least parts of the present invention can be practiced in a network computing environment with 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, mobile phones, PDAs, pagers, routers, switches, data centers, wearable devices (such as glasses), etc. The present invention can also be practiced in a distributed system environment, where local and remote computing systems linked, for example, by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links, all perform tasks over a network. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0090] Those skilled in the art will also appreciate that at least a portion of the present invention can be practiced in a cloud computing environment. A cloud computing environment can be distributed, although this is not required. When distributed, a cloud computing environment can be distributed internationally within an organization and / or have components owned across multiple organizations. In this specification and the following claims, "cloud computing" is defined as a model for implementing on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage devices, applications, and services). The definition of "cloud computing" is not limited to any of the other numerous advantages that can be obtained from such models when deployed. The computing system of the accompanying drawings includes various components or functional blocks that can implement various embodiments disclosed herein as explained. Various components or functional blocks can be implemented on a local computing system, or can be implemented on a distributed computing system that includes elements residing in the cloud or implementing various aspects of cloud computing. Various components or functional blocks can be implemented as software, hardware, or a combination of software and hardware. The computing system shown in the figure may include more or fewer components than those shown in the figure, and some of the components can be combined when the environment guarantees.

[0091] Any reference signs in the claims should not be construed as limiting the scope.

[0092] The present invention relates to an apparatus for determining technical application characteristics of superabsorbent materials. The superabsorbent material is provided in the form of superabsorbent particles, which include a polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell. The apparatus includes a receiving interface for receiving the particle size of the superabsorbent material particles. One or more processors are configured to determine the technical application characteristics of the superabsorbent material based on the particle size using a characteristic determination model, wherein the characteristic determination model is a data-driven model that has been parameterized so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles. An output interface generates control data based on the determined technical application characteristics.

Claims

1. An apparatus for determining the technical application properties of a superabsorbent material (140), wherein the superabsorbent material (140) is provided in the form of superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the apparatus (110) comprises: a receiving interface (111) for receiving the particle size of the superabsorbent particles of the superabsorbent material (140), one or more processors (112) configured to determine the technical application properties of the superabsorbent material (140) based on the particle size using a property determination model, wherein the property determination model is a data driven model that has been parameterized such that it is suitable for determining technical application properties of superabsorbent particles based on the size of the particles, and An output interface (113) for generating control data based on the determined technical application characteristics.

2. The apparatus of claim 1, wherein the property determination model utilized is further parameterized based on the core size and shell size of the superabsorbent particles.

3. Apparatus according to any one of claims 1 and 2, wherein the parameterisation of the property determination model utilised comprises determining performance parameters which quantify the contribution of the core size and the shell size of the superabsorbent particles, respectively, to the technical application properties.

4. The device according to any of the preceding claims, wherein the property determination model utilized is based on the following relationship between the technical application property and the size of the superabsorbent particles: wherein Vshell is the volume of the shell of the superabsorbent particle, and Vcore is the volume of the core of the superabsorbent particle, wherein the volume of the shell and the volume of the core of the superabsorbent particle depend on the size of the superabsorbent particle, and wherein var shell and var core are the performance parameters quantifying the contribution of the core size and the shell size, respectively, and are determined during the parameterization of the property determination model, and wherein var Particle Indicates the application characteristics of the technology.

5. An apparatus according to any one of the preceding claims, wherein the received particle size is a particle size distribution of the superabsorbent particles of the superabsorbent material (140), and wherein the one or more processors are further configured to: a) determine one or more particle size grades in which particles with corresponding sizes are present in the superabsorbent material (140) from predetermined particle size grades based on the particle size distribution, b) determine technical application characteristics for the determined particle size grades, and c) determine the overall technical application characteristics of the superabsorbent material (140) based on the determined technical application characteristics for the corresponding determined particle size grades and based on the particle size distribution.

6. An apparatus according to any of the preceding claims, wherein the received particle size is provided as a starting particle size, and wherein the receiving interface is further adapted to receive target application characteristics, wherein the processor is further adapted to iteratively determine the target particle size so that the superabsorbent material (140) meets the target application characteristics within predetermined limits, wherein the iteration comprises a) determining a technical application characteristic in each iterative step, b) comparing the determined technical application characteristic with the target technical application characteristic, and c) based on the comparison, providing a corrected particle size, or determining the current particle size as the target size.

7. Apparatus according to any one of the preceding claims, wherein the generated control data comprises manufacturing specifications for controlling the manufacture of the superabsorbent material (140), in particular comprising specifications for controlling the size of the superabsorbent particles.

8. An interface device for providing an interface for determining technical application properties of a superabsorbent material (140), wherein the interface device comprises: an interface input unit, the interface input unit being used to interface with a device according to any one of claims 1 to 7 to provide granularity to the device according to any one of claims 1 to 7, and An interface output unit, the interface output unit is used to process control data generated by the device according to any one of claims 1 to 7 based on the granularity.

9. A training device for a parameterized property determination model, wherein the training device (300) comprises: a receiving interface (310) for receiving historical training data, the historical training data comprising a plurality of particle sizes of superabsorbent particles of a superabsorbent material (140) and corresponding one or more measured application characteristics of the superabsorbent material (140), one or more processors (320) configured to parameterize a property determination model using the received historical training data, such that the parameterized property determination model is suitable for determining technical application properties of the superabsorbent material (140) based on particle size, and An output interface (340), the output interface is used to output the parameterized characteristic determination model.

10. An optimization device for determining a target superabsorbent material (140) having target technical application properties, wherein the superabsorbent material (140) is provided in the form of superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the device comprises: a receiving interface that receives the target technical application characteristics of the target superabsorbent material (140) and the particle size of the potential superabsorbent particles of the superabsorbent material (140), one or more processors that are configured to determine the technical application characteristics of the potential superabsorbent material (140) based on the particle size using a characteristic determination model, wherein the characteristic determination model is a data-driven model that has been parameterized so that it is suitable for determining the technical application characteristics of the superabsorbent particles based on the size of the particles, and comparing the determined technical application characteristic with the target technical application characteristic and i) determining the granularity to be the target granularity if the predicted technical application characteristic is within a predetermined range around the target application characteristic, and ii) determining a revised granularity and repeating the determination of the technical application characteristic using the revised granularity if the predicted technical application characteristic is outside the predetermined range around the target application characteristic, and An output interface is configured to generate a control signal based on the target granularity.

11. A computer-implemented method for determining technical application characteristics of a superabsorbent material (140), wherein the superabsorbent material (140) is provided in the form of superabsorbent particles comprising i) a core having a superabsorbent polymer and ii) a surface crosslinked shell, wherein the method comprises: the particle size of the superabsorbent particles receiving the superabsorbent material (140), determining the technical application characteristics of the superabsorbent material (140) using a characteristics determination model, wherein the characteristics determination model is a data driven model that has been parameterized such that it is suitable for determining the technical application characteristics of superabsorbent particles based on the size of the particles, and Control data are generated based on the determined technical application characteristics.

12. An interface method for providing an interface for determining technical application characteristics of a superabsorbent material (140), wherein the interface method comprises: providing the granularity to a device according to any one of claims 1 to 7 via an input interface, and Control data generated by the device according to any one of claims 1 to 7 based on the granularity is processed via an output interface.

13. A training method for a parameterized property determination model, wherein the training method comprises: receiving historical training data, the historical training data comprising a plurality of particle sizes of superabsorbent particles of a superabsorbent material (140) and corresponding measured one or more application characteristics of the superabsorbent material (140), parameterizing a property determination model using the received historical training data, such that the parameterized property determination model is suitable for determining technical application properties of the superabsorbent material (140) based on particle size, and The parameterized characteristic determination model is output.

14. An optimization method for determining a target superabsorbent material (140) having target technical application properties, wherein the superabsorbent material (140) is provided in the form of superabsorbent particles, the superabsorbent particles comprising a superabsorbent polymer provided in the form of i) an interconnected core and ii) a surface cross-linked shell having a higher connectivity than the core, wherein the method comprises: receiving said target technical application of said target superabsorbent material (140) and the particle size of the latent superabsorbent particles of the superabsorbent material (140), determining the technical application properties of a potential superabsorbent material (140) based on the particle size using a property determination model, wherein the property determination model is a data driven model that has been parameterized such that it is suitable for determining the technical application properties of superabsorbent particles based on the size of the particles, and comparing the determined technical application characteristic with the target technical application characteristic and i) determining the granularity to be the target granularity if the predicted technical application characteristic is within a predetermined range around the target application characteristic, and ii) determining a revised granularity and repeating the determination of the technical application characteristic using the revised granularity if the predicted technical application characteristic is outside the predetermined range around the target application characteristic, and A control signal is generated based on the target granularity.

15. A computer program product for determining technical application properties of a superabsorbent material (140), wherein the computer program product comprises program code means for causing a device according to claim 1 to execute a method according to claim 11.

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