Patent Application
20250157597
The invention relates to a method, an apparatus and a computer program product for determining habitat descriptor values providing a target biodegradability for a predetermined polymer. Further, the invention refers to a training method, a training apparatus and a training computer program for training a data driven biodegradation model utilizable by the method, apparatus and computer program product for determining the habitat descriptor values. Moreover, the invention refers to a method and apparatus for providing an interface for the determination of the habitat descriptor values.
Generally, polymers are widely used in industrial and/or daily use products due to their broad range of application properties. The use of polymers encompasses amongst others coatings, personal care products, washing detergents, lubricants, packages and foams. However, this widely spread application leads on the other hand to a huge amount of waste containing the used polymers. While it is in fact in most cases the durability of the polymers that makes them popular for many uses, exactly this durability leads to a plurality of problems in waste management, in particular, since the polymers are also durable in waste. In particular, if non-biodegradable polymers are not suitably collected in intended waste stream, this can result in increased micro-plastic contamination and bioaccumulation in the environment. Thus, there is not only a need for polymers that decompose, but also a need to take into account knowledge about the biodegradability of a polymers in early stages of a product design process. Moreover, for a later waste management of products comprising a biodegradable polymer it can also be important to know under which conditions the polymer will optimally biodegrade and under which conditions it might not biodegrade. This allows to take into account considerations of a later waste management of polymers already in the design process. Thus, it would be advantageous to provide a possibility to predict the conditions under which a polymer will biodegrade accurately and in a computationally inexpensive manner.
It is an object of the present invention to provide a method, an apparatus and a computer program product that allow for an accurate determination of conditions under which a polymer will biodegrade that is computationally inexpensive and can robustly be applied to new polymers. Moreover, it is further an object of the invention to provide a training method, a training apparatus and a computer program product that allow to provide a biodegradation model that is usable in the method, the apparatus and computer program product and that can be trained to provide a good determination accuracy by utilizing less computational resources.
In a first aspect of the present invention, a computer implemented method for determining habitat descriptor values providing a target biodegradability for a predetermined polymer is presented, wherein the method comprises a) providing a target biodegradability, wherein a biodegradability is indicative of a biodegradation characteristic of a polymer, b) providing a digital representation of a polymer indicative of associated with physicochemical characteristics of the polymer, c) providing a biodegradation habitat, wherein the biodegradation habitat is indicative of habitat descriptors influencing a biodegradation of a polymer in the respective habitat, wherein the habitat descriptors are indicative of environmental characteristics of the habitat, d) providing a biodegradation model based on the provided biodegradation habitat, wherein the biodegradation model is adapted to determine habitat descriptor values that allow for a biodegradation of the polymer meeting the target biodegradability in the biodegradation habitat, wherein the biodegradation model is a data driven model parameterized with respect to the biodegradation habitat such that it determines based on the target biodegradability and the physicochemical characteristics the habitat descriptor values, and e) determining the habitat descriptor values for the polymer based on the selected biodegradation model, the target biodegradability and the polymer descriptors.
Since the biodegradation model is specifically adapted to determine habitat descriptor values that allow for a biodegradation of a polymer meeting a target biodegradability in a respective habitat, the habitat descriptor values for the respective habitat can be determined very accurately. Moreover, since the biodegradation model is specifically trained for one or more specific biodegradation habitats, less training data becomes necessary for the training and the biodegradation model becomes more flexible with respect to determine the biodegradation of new polymers not being part of the training data set. Thus, the method allows for an accurate determination of habitat descriptor values that is computationally inexpensive and can be applied flexible also to new polymers.
Moreover, since the physicochemical characteristics are utilized that contain physicochemical information of the polymer, e.g. quantum chemical information of the polymer like solubility in water and octanol or molar mass of the polymer, the training of a respective biodegradation model can be improved. In particular, utilizing the physicochemical characteristics allows training of such models with less training data, because some of the correlation information that needs to be learned is already presented to the model by using the physicochemical characteristics. This, further allows to save tests and experiment necessary for providing the training data set.
The method refers to a computer implemented method and thus can be performed by a general or dedicated computer adapted to perform the method, for instance, by executing a respective computer program. The method is adapted to determine, in particular, predict, habitat values of a predetermined polymer that allow the predetermined polymer to bio-degradate with a predetermined target biodegradability. A biodegradable polymer refers to a polymer that can be degraded by biological processes, in particular, a biodegradable polymer can refer to a polymer that can be assimilated by bacteria and/or fungi to give environmentally friendly products, i.e. to decompose into non-polluting residuals, for example by produce mineralized carbon and/or biomass. Generally, the determined habitat descriptor values can refer to one or more quantifications of the environmental characteristics of a habitat. For example, the habitat descriptor values can refer to only one value, for instance, a salt concentration in a respective habitat, or can refer to more than one value, for instance, can refer to a values range, for instance, a salt concentration range.
Generally, a biodegradability is indicative of a biodegradation characteristic of a polymer. In particular, the biodegradability refers to a measure of a degradation, i.e. decomposition of a polymer caused by biological processes, i.e. processes that include biological material, in particular, microorganisms, taking part in the degradation process. Thus, the biodegradability does not refer to purely chemical degradation processes that do not include microbial activity. The target biodegradability can refer to any quantification of the biodegradability of a polymer. The biodegradability is an intrinsic characteristic of a polymer. In this context, an intrinsic characteristic of a polymer refers to a property of the polymer that is caused by and thus reflects the nature of a polymer, i.e. its structure, composition, etc., with respect to a specific context. In particular, the biodegradability reflects the nature of the polymer when present in a specific biological active environment. For example, the target biodegradability can refer to only one value, for instance, a half-life of the polymer in a respective habitat, or can refer to more than one value, for instance, can refer to a degradation function with time of the polymer in a specific habitat. It is preferred that the target biodegradability of the polymer refers to any one of a mineralization characteristic, a biotransformation characteristic and/or a decomposition half-life of the polymer. Preferably, the biodegradability refers to a value of the percentage of biodegradation after a predetermined timeframe. Moreover, the biodegradation is a technical characteristic of a polymer, i.e. knowledge of the biodegradation of a polymer strongly influences the technical applicability and utilization of a polymer.
Generally, the polymer can be any polymer. Preferably, the target polymer is a synthetic polymer. In an embodiment, a synthetic polymer may be a chemical compound which is produced by a chemical production from one or more starting material(s), such as monomers, and which comprises at least two monomer units. The monomer units may be regarded as subunits of the polymer. The polymer may be prepared from the monomers by commonly known polymerization techniques. The polymer may be produced from a single type of monomers or from different monomers. The polymer may be produced by a single polymerization technique or by a combination of different ones. The monomer units may be distributed randomly or may be present as blocks within the polymer. The polymer may be a linear polymer. The polymer may be a branched polymer. The polymer may be a crosslinked polymer. The polymer may be chemically modified after polymerization.
In a first step the method comprises providing a target biodegradability that is indicative of a biodegradation characteristic of a polymer. In particular, the providing can refer to receiving the target biodegradability from an input of a user using, for instance, a respective input unit. Moreover, the providing can also refer to accessing a storage unit on which a target biodegradability is already stored. Further, the providing can also comprise receiving a target biodegradability, for instance, via a network connection from other sources and providing the received biodegradability. Generally, the target biodegradability can refer to one target value, for instance, a target half-life of a polymer in a specific habitat, or can refer to a value range that should be met by the polymer in a specific habitat. Moreover, the target biodegradability can also refer to any kind of target function, for instance, a timely sequence of biodegradations. For example, the target biodegradability can indicate that the polymer shall have a first biodegradability value range during a first time range and then a second target biodegradability value range during a following time range. Such more complex target biodegradabilities can be advantageous in cases in which habitat descriptor values can change in a habitat, for instance, due to timely changes in the environment, like day/night changes, or due to a control of the habitat descriptors, like in a chemical reactor.
Further, the method comprises providing a digital representation of the polymer indicative of physicochemical characteristics of the polymer. In particular, the providing can refer to receiving the digital representation from an input of a user using, for instance, a respective input unit. Moreover, the providing can also refer to accessing a storage unit on which the digital representation is already stored. Further, the providing can also comprise receiving physicochemical characteristics, for instance, via a network connection from other sources and providing the received physicochemical characteristics as digital representation. In particular, the physicochemical characteristics of a polymer can be quantified by polymer physicochemical parameters. Moreover, being indicative of or associated with physicochemical characteristics of a polymer is defined as allowing to access the information of the physicochemical characteristics. For example, the digital representation can directly comprise the physicochemical characteristics, for example, in form of values for respective quantities. However, the digital representation can also be a link to the respective physicochemical characteristics via which the physicochemical characteristics can be accessed, or the digital representation can refer to an identifier that is associated with the physicochemical characteristics and allows to utilize a respective look up storage in order to access the physicochemical characteristics. Moreover, the digital representation can also refer to information that allows to derive the physicochemical characteristics using one or more known relations. For example, a synthesis specification or a structural formula of a polymer can be utilized as digital representation allowing to derive, using known chemical and physical laws and relations, respective physicochemical characteristics.
Generally, throughout the following description referring a parameter or a characteristic comprises referring both to the respective quantity and also to a specific value of the quantity if not explicitly defined otherwise. For example, a parameter being a temperature always refers to the quantity being a temperature and also to a specific value of the temperature being set for the quantity. Since in most cases the explicit value of the parameter can be different for different embodiments and application cases the value is generally not mentioned. However, providing a parameter or characteristic generally means providing the quantity, e.g. the information that a value is a temperature, and also the value of the quantity or characteristic itself.
Preferably, the digital representation comprises polymer physicochemical parameters, preferably, referring to polymer descriptors, wherein the polymer physicochemical parameters are indicative of physicochemical characteristics of the polymer. In particular, the polymer physicochemical parameters are indicative of parameters quantifying the physicochemical characteristics of the polymer. In this context, the term “physicochemical characteristics” refers to physical and/or chemical characteristics of the polymer. However, the digital representation can also be provided such that it allows to derive the polymer physicochemical parameters, for instance, by providing a representation of the polymer for which respective polymer physicochemical parameters are already stored or can be determined, for instance, by respective polymer descriptor calculations. Preferably, the digital representation refers to at least one of a recipe, a structural formula, a brand name, an IUPAC name, a chemical identifier and a CAS number of the polymer.
In a preferred embodiment, the polymer physicochemical parameters are indicative of parameters quantifying the physicochemical characteristics of subgroups of the polymer. In this embodiment, the digital representation can also be provided such that it allows to derive the polymer physicochemical parameters by determining subgroups of the polymer and to determine the polymer physicochemical parameters based on physicochemical characteristics of the determined subgroups. Generally, a subgroup refers to a part of the polymer, wherein all subgroups of a polymer together form the polymer. For example, a subgroup can refer to a part of the polymer, wherein the subgroups are linked together successively along a chain or network to form the polymer. Preferably, the subgroups of the polymer refer to repeating units that describe a part of the polymer which when repeated produces the complete polymer chain. However, in some cases, a subgroup can also refer to a single part of the polymer that is not repeated. Moreover, it is preferred that the subgroups comprise parts that are repeated, for example, a subgroup of a polymer can comprise a repeating core also present in other subgroups and further additional parts that are not present in other subgroups. Preferably, the subgroups refer to at least one of polymerized monomers or oligomer fragments. More preferably, the subgroups refer to polymerized monomers. In this context, polymerized monomers refer to monomers after their polymerization sometimes also called “mer unit” or “mer”. In particular, polymerized monomers do not refer to monomers, i.e. raw materials, as present in a reaction mixture before polymerization, but refer to repeating units derived from monomers that have been changed during or after the polymerization. Thus, subgroup descriptors determined for polymerized monomers are different from subgroup descriptors determined for unreacted monomers before polymerization. It has been found by the inventors that in particular the polymerized monomers allow to determine polymer descriptors from the subgroup descriptors of the polymerized monomers that allow the determination model to accurately determine the habitat descriptor values for the polymer. In a preferred embodiment, the digital representation of the polymer comprises subgroups provided as molecular model which is indicative of a chemical structure of the subgroup after its polymerization. Even more preferably, the molecular model of a subgroup is determined in a way that is suited for quantum chemical computations regarding a number of atoms and their connectivity that is representative of the properties of the subgroup within the polymer. Moreover, additionally an alternatively to a molecular model of a subgroup treating the subgroup as a monomer structure, also a molecular model referring to an oligomer model can be utilized that takes into account effects of neighbouring molecular structures of the subgroup in the polymer.
Generally, if the digital representation of the polymer does not directly comprise the polymer physicochemical parameters, it is preferred that the polymer physicochemical parameters are determined by determining the subgroups of the polymer. For example, respective subgroups of the polymer can be determined utilizing known methods. However, it is preferred that the determination of the subgroups of the polymer is performed in accordance with later described embodiments of the invention. In particular, it is preferred that the subgroups are determined such that between atoms of different subgroups in the polymer the bond is as least polarized as possible and, preferably, with a bond order as small as possible (e.g. a CC single bond). Additionally, it is preferred that the subgroups representing a polymer comprise the same number of active non-hydrogen-atoms then the polymer. Besides the active atoms, a subgroup can also contain further atoms, which can be ignored during computing the physicochemical parameters of the subgroup. Further, it is preferred that the subgroups are determined in a way that polymers comprising parts, which were built up with different polymerization techniques, are well covered and fulfill the foresaid conditions. An example is a polyether used as ingredient for a polyurethane. Generally, a database or archive with a plurality of reactions between polymer parts can be generated and the subgroups can be derived from the respective structure of the reactions. For example, specific chemical languishes like SMILES and SMARTS can be utilized to easily derive the subgroup of a polymer. For example, a database of reaction SMARTS can be generated and then based on the polymerization of the respective polymer a corresponding reaction SMARTS can be selected. From the selected reaction SMARTS then the SMILES of monomers of the polymer are directly derivable and, for example, RDkit can be used to determine from the SMILES of the monomers the SMILES, i.e. the number and connectivity of the atoms, of the subgroups.
The determined subgroups of the polymer are associated with subgroup physicochemical parameters indicative of parameters quantifying physicochemical characteristics of the subgroups in the polymer. In particular, it is preferred that if the polymer physicochemical parameters are not directly provided by the digital representation, the polymer physicochemical parameters are determined by determining a respective subgroup physicochemical parameter for each of the subgroups and to determine the polymer physicochemical parameters based on the subgroup physicochemical parameters of the subgroups, for instance, by averaging. Thus, the method preferably comprises first providing or determining for the polymer the subgroups from the digital representation of the polymer, then to determine or provide the subgroup physicochemical parameters, i.e. values of the parameters quantifying the physicochemical characteristics, of the subgroups, and then to determine the polymer physicochemical parameters based on the subgroup physicochemical parameters of each polymer.
Preferably, the polymer physicochemical parameters refer to at least one of constitutional descriptors, count descriptors, list of structural fragments, fingerprints, graph invariants, 3D-descriptors and/or higher dimensional descriptors that are indicative of parameters quantifying physicochemical characteristics of the polymer. In a preferred embodiment the polymer physicochemical parameters refer to 3D descriptors, in particular quantum chemical descriptors. Moreover, the inventors have found that in particular a molar mass describes the biodegradation of a polymer very accurately. Thus, it is in particular preferred that the physicochemical parameters comprise a molar mass of the polymer. Generally, the polymer physicochemical parameters can be derived from the subgroup physicochemical parameters, thus, also the subgroup physicochemical parameters can refer to the same physicochemical parameters as stated above. However, the physicochemical parameters can also be derived without utilizing subgroups, for instance, by quantum chemical simulations of the whole polymer. In the following the possible physicochemical parameters are defined in more detail. Also in these cases the defined physicochemical parameters can refer directly to the polymer physicochemical parameters or, optionally, to the subgroup physicochemical parameters.
A constitutional descriptor can refer to any of a potential, average molecular weight, polydispersity, charge, spin, boiling point, melting point, enthalpy of fusion, dissociation constant, Hansen parameter, protic, polar and dispersive contributions, Abraham parameter, retention index, TPSA, receptor binding constant, Michaelis-Menten constant, Inhibitor constant, Mutagenicity, LD50, bioconcentration, toxicity, biodegradation profile and viscosity.
A count descriptor can refer to any of a sum of atomic electro negativities, a sum of atomic polarizabilities, an amount of ingredients, a ratio of amounts of ingredients, a number of atoms and non H-atoms, a number of H, B, C, N, O, P, S, Hal and heavy atoms, a number of H-donor and H-acceptor atoms, a number of bonds, non-H or multiple bonds, a number of double, triple and aromatic bonds, a number of functional groups, a ratio of functional groups, a sum of bond orders, an aromatic ratio, a number of rings or circuits, a number of unpaired electrons, a number of rotatable bonds, rotatable bond fractions, and a number of conformers.
Polymer descriptors referring to a list of structural fragment descriptors can refer to at least one of a list of molecular fractions, a list of functional groups, a list of bonds, and a list of atoms. Fingerprint descriptors comprise preferably, at least one of MACCS keys, preferably, in bit format or total amount format, Morgan and other circular fingerprints, preferably, in bit format or total amount format, topological torsion, atom pairs, infrared and related spectra, fingerprint count, PubChem fingerprint, substructure fingerprint, and Klekota-Roth fingerprint. Graph invariants/topological indices descriptors comprise preferably at least one of topostructural indices and topochemical indices.
In a preferred embodiment the polymer physicochemical parameters are 3D descriptors comprising at least one of a volume as sum overall atoms, a mean volume per atom, an area as sum overall atoms, an area as mean per atom, an area over all atoms, an area as mean per atom, a solvent accessible surface, a dispersion energy, a dielectric energy, a H-donor, H-acceptor, polar and non-polar surface area, an atom resolved H-donor, H-acceptor, polar and non-polar surface area, a shape, a sphericity, dipole and higher electric moments, polarizability, dielectric energy, protic, polar and non-polar surface area, orbital energies and orbital gaps, ionization energy, electron affinity, hardness, electronegativity, electrophilicity, excitation energies and intensities, infrared and ultraviolet absorption bands, reactivity measurements, redox potential, bond criterial points, partial charges, charge surface areas, atomic orbital contributions, bond orders, atom radius. In particular, it is preferred that the polymer physicochemical parameters refer to 3D descriptors comprising at least one of a sum of a volume over all atoms, a mean of a volume per atom, a sum of the area over all atoms, a mean of an area per atom, a solvent accessible surface, a dispersion energy, a dielectric energy, a H-donor, H-acceptor, polar and/or non-polar surface area, atom resolved H-donor, H-acceptor, polar and/or non-polar surface area, shape, sphericity, cone angles, polarizability, dielectric energy, protic, polar and/or nonpolar surface area, excitation energies and intensities, infrared and/or UV absorption bands, reactivity measurements, particle charges and/or charge surface areas. A preferably utilized higher dimensional descriptor can comprise at least one of a conformational partition function, solubility, vapor pressure, activity coefficient, diffusion coefficient, partition coefficient, interfacial activity, rotational constant, moment of inertia, radius of gyration, compositional drift of polymer, density, viscosity, conformer weighted volume and area, conformer weighted H-donor, H-acceptor, protic, polar and/or non-polar surface area, charge distribution, conformational dipole moment and molecular refraction. Preferably higher dimensional descriptors are utilized that comprise at least one of solubilities, vapor pressure and activity coefficients, interfacial activity, conformer weighted H-donor, H-acceptor, protic, polar and non-polar surface area, and charge distribution.
The method further comprises providing a biodegradation habitat, wherein a biodegradation habitat is indicative of habitat descriptors influencing a biodegradation of a polymer in the respective habitat. In particular, the providing can refer to receiving the biodegradation habitat from an input of a user using, for instance, a respective input unit. Moreover, the providing can also refer to accessing a storage unit on which a biodegradation habitat is already stored. Furthermore, the providing can also refer to a presetting of a biodegradability habitat. For example, if the method is utilized in a very specific context that is only sensible with one specific biodegradation habitat, the respective biodegradation habitat can be preset and thus has not to be provided as specific input. Further, the providing can also comprise receiving directly habitat descriptors, for instance, via a network connection, from other sources and providing the received habitat descriptors as biodegradation habitat. The provided biodegradation habitat can refer to a general habitat, for instance, can refer to a marine habitat, wherein respective habitat descriptors for this habitat are then already stored on a respective storage which can be accessed. The provided habitat descriptors determine for which habitat descriptors a habitat descriptor value should be determined. For example, the provided biodegradation habitat can indicate that a salt concentration is an environmental characteristic that can influence a biodegradability in this habitat. In this example, the salt concentration refers to a habitat descriptor and a value for the salt concentration is to be determined as habitat descriptor value.
Generally, the habitat descriptors are indicative of environmental characteristics of the habitat. In particular, the environmental characteristics of a biodegradation habitat can influence a biological activity in the respective habitat, for example, can influence a presence, grows or absence of specific microbiome. Thus, the environmental characteristics defined by the habitat descriptors indirectly also influence the biodegradation of a polymer in the respective habitat. For example, if a polymer is biodegradable by a specific bacterium that needs a specific salt concentration, the polymer will biodegrade fast in a habitat providing such a salt concentration, like a marine habitat, but will biodegrade much slower in a habitat with not the right salt concentration, like waste water.
Preferably, the biodegradation habitat refers to any one of a marine habitat, a waste water habitat, a limnic habitat, a compost habitat or a soil habitat. In a preferred embodiment, the biodegradation habitat refers to a marine habitat, wherein the habitat descriptors refer to at least one of a salt concentration, a sedimentation type, oxygen level, location, sample depth, a water temperature, a nutrient concentration, for example, a nitrogen, phosphate, potassium, and/or dissolved organic carbon concentration, a pH value, an environmental type, oxygen content, and a microbial community. In a further preferred embodiment, the biodegradation habitat refers to a limnic habitat and wherein the habitat descriptors refer to at least one of a salt concentration, a sedimentation type, oxygen level, location, sample depth a water temperature, a nutrient concentration, a pH value, an environmental type and a microbial community. In a further preferred embodiment, the biodegradation habitat refers to waste water, wherein the habitat descriptors refer to at least one of a water temperature, a microbial community, a sludge concentration, a nutrient concentration, a pH value, a test duration, a solid content, and an enzyme environment. Moreover, in this habitat the sludge can also be a separate habitat. Thus, in an embodiment the habitat can also be a sludge habitat, for example, as the aerobic part of a waste water treatment plant and the habitat descriptors refer to at least one of a solid content, pH, nutrient content, heavy metal content, microbial community. In a further preferred embodiment, the biodegradation habitat refers to soil and the habitat descriptors refer to at least one of a temperature, composition, for example, a sand and/or clay content, a pH value, a moisture content, a nutrient concentration, a microbial community, a nitrogen content, a water holding capacity and an enzyme environment. In a further preferred embodiment, the biodegradation habitat refers to compost and the habitat descriptors refer to at least one of a temperature, compost activity, a pH value, a moisture content, humidity, compost maturity, compost composition, compost origin, a nutrient concentration, a microbial community, a solid content, a water holding capacity, and an enzyme environment.
The method further comprises providing a biodegradation model based on the provided biodegradation habitat. In particular, it is preferred that the providing of the biodegradation model refers to a selecting of a biodegradation model based on the provided biodegradation habitat. For example, a plurality of biodegradation models can be stored on a biodegradation storage, wherein each biodegradation model has been trained for one or more different biodegradation habitats, in particular, for different values or value ranges of habitat descriptor values of a biodegradation habitat. Based on the provided biodegradation habitat indicative of the habitat descriptors, a respective suitable biodegradation model can then be selected from the plurality of biodegradation models. For example, a biodegradation model is suitable if the indicated habitat descriptors that are determinable by the biodegradation model refer to the habitat descriptors in indicated by the provided habitat. For example, a respective lookup table can be provided that allows for an easy comparison between the indicated habitat descriptors and the habitat descriptors for which the biodegradation models stored on the storage have been trained such that directly a suitable biodegradation model can be selected. However, in another embodiment the providing of a biodegradation model based on the provided biodegradation habitat can also refer to a user selection of the biodegradation model. For instance, the user can be provided with a preselection of biodegradation models that refer to the provided biodegradation habitat and then be allowed to select the respective biodegradation model that should be utilized. Generally, the possible stored biodegradation models refer to biodegradation models that have already been parameterized based on a respective training data set for one or more habitats. Since the training data sets utilized for parameterizing a biodegradation model are historical data, as described in more detail below, the biodegradation models can be trained and thus generated at any time before the determination of a specific habitat for a specific polymer and target biodegradability, and after the training be stored on a respective database. However, the training and thus the generation of a biodegradation model can of course also be performed at the time that it is determined that a specific biodegradation model, for instance, for a specific habitat, is needed.
The provided biodegradation model is then adapted to determine habitat descriptor values based on the target biodegradability and the polymer physicochemical parameters. In particular, the biodegradation model is a data driven model that is parameterized with respect to the biodegradation habitat such that it can determine habitat descriptor values for a polymer based on the polymer physicochemical parameters and a target biodegradability. The term “such that” is to be interpreted here that the parameterization adapts and thus enables the biodegradation model to provide the biodegradability with respect to a habitat when provided with polymer physicochemical parameters as input. For example, the biodegradation model relates polymer physicochemical parameters of historic digital representations of synthesis specification and historic digital representations of habitats to a biodegradability. This allows that, based on a target biodegradability, a digital representation of the synthesis specification may be determined. The term “data driven” is used here to emphasize that the model is mainly based on respective data input and not, for instance, on intuition, personal experience or knowledge. Preferably, the biodegradation model refers to a machine learning based model that is based on known machine learning algorithms, like neural networks, regression models, classification algorithms, etc. It has been found that for most applications in this context, in particular, regression models based on Linear Regression, Random Forests, Lasso, Boosted Tress, Ridge Regression and MARS algorithms are suitable, whereas for classification models, in particular, Random Forests, Logistic Regression, and SVM algorithms are suitable. Preferably, the biodegradation model is based on a neural network algorithms. Generally, the biodegradation model is parameterized during a training process in which polymer physicochemical parameters derived from parameters quantifying the physicochemical characteristics of the polymer are utilized together with corresponding biodegradabilities for specific biodegradation habitats and respective habitat descriptor values of these specific biodegradation habitats. Based on such a training data set that is specific for a biodegradation habitat and for specific habitat descriptor values, the respective parameters of the data driven model can be determined utilizing known training methods such that the biodegradation model is also able to determine habitat descriptor values for polymers that are not part of the training data set.
Then the method comprises determining the habitat descriptor values for the respective polymer based on the selected biodegradation model, the target biodegradability and the polymer physicochemical parameters. In particular, if the digital representation of the polymer comprises the polymer physicochemical parameters, the polymer physicochemical parameters are provided together with the target biodegradability as input to the biodegradation model, wherein the biodegradation model then provides the determined habitat descriptor values as output. If the digital representation does not directly comprise the polymer physicochemical parameters, the determining of the habitat descriptor values can comprise also determining firstly the polymer physicochemical parameters, for instance, as described above. The such determined polymer physicochemical parameters can then be provided to the biodegradation model as input. The such determined habitat descriptor values can then be provided, for instance, to an output unit or to a computing unit for further processing. Preferably, the providing of the habitat descriptor values leads to a further processing utilizing the habitat descriptor values. In such a case, the providing as individual step can be omitted and replaced by the processing of the habitat descriptor values.
In a preferred embodiment, the method further comprises providing control signals for controlling a reactor system adapted to biodegrade a polymer, wherein the control signals are provided based on the determined habitat descriptor values. In particular, the control signals can be generated or provided such that the reactor system provides the biodegradation habitat with the determined habitat descriptor values to the substances within the reactor system that preferably comprise the polymer. For example, if the determined habitat descriptor values indicate that the polymer will meet the target biodegradability in a compost environment with a specific temperature, pH value and moisture content, the control signals can comprise components that cause a reactor system to simulate the respective environment, in particular, to set a respective temperature, moisture content and pH value such that the polymer will biodegrade with the respective target biodegradability.
Moreover, a processing of the habitat descriptor values can also refer to a step of selecting one or more polymers based on respective habitat descriptor values. For example, if for a plurality of potential polymers respective habitat descriptor values for a specific habitat have been determined, the selecting can comprise comparing the determined habitat descriptor values of the different polymers to predetermined selection criteria and selecting the polymers for which the determined habitat descriptor values fulfil these criteria. In particular, in an embodiment the method comprises receiving target habitat descriptor values or target habitat descriptor value ranges that can, for instance, be met by a respective reactor system. In this case the method can comprise comparing the received target habitat descriptor values with the determined habitat descriptor values of the different polymers and selecting a polymer based on this comparison. In particular, based on such a comparison a control signal can be provided. The control signal can refer to any signal that allows for a further control of a technical system. For example, the control signal can be adapted to control an interface for providing the result of the comparison on the interface. In a preferred embodiment, the comparison refers to a validation of the target habitat descriptor values, wherein the validation is positive if the determined habitat descriptor values fall within a predetermined range around the target habitat descriptor values. In this case, the control signal can be adapted to simply control a user interface to provide an indication of a positive or negative validation result for a respective polymer. However, preferably, the control signal refers to a recipe, i.e. synthesis specification, of the one or more polymers which fulfil the specified target habitat descriptor values, i.e. which are validated positively. A recipe, i.e. synthesis specification, is generally defined as an instruction on how a polymer can be synthesized.
In particular, the recipe comprises the starting substances and the respective parameters for polymerization from the starting substances. Preferably, the control signals comprise a recipe in a form that directly allows an automatic controlling of respective industrial systems or labor equipment for producing the polymer. In particular, it is preferred that the control signal is indicative of a machine executable synthesis specification of the polymer, when the result of the comparison refers to the determined habitat descriptor values being within a predetermined range around the target habitat descriptor values.
In a preferred embodiment, the method further comprises providing as digital representation of the polymer a synthesis specification and determining the polymer physicochemical parameters from the synthesis specification. In particular, the synthesis specification, i.e. recipe, comprises information on the polymer synthesis of the polymer, for instance, on the starting substance and process by which respective starting substances are covalently bonded to form the polymer chain or network of the polymer. The method then comprises determining the polymer physicochemical parameters from the synthesis specification. Optionally, from a synthesis specification the subgroups can be determined and the polymer physicochemical parameters can then be determined based on subgroup physicochemical parameters of the subgroups, for instance, from a database or utilizing known descriptor determination algorithms. In a preferred embodiment, further from the synthesis specification utilized catalysts and/or none reactive process ingredients are determined. In this case it is preferred that this information is also utilized together with the polymer physicochemical parameters by the biodegradation model for determining the habitat descriptor values. Preferably, physicochemical parameters are also determined for the catalysts and/or none reactive process ingredients and the respective physicochemical parameters are also used for determining the physicochemical parameters of the polymer. Preferably, the physicochemical parameter for the catalysts and/or none reactive process ingredients refers to an amount of the respective ingredient, for example, a molar mass, a molar percentage, etc. and is taken into account for determining a polymer physicochemical parameter for the polymer.
In a preferred embodiment, the determining of the polymer physicochemical parameters from the synthesis specification comprises identifying types and amounts of subgroups based on the synthesis specification, for instance, as physicochemical parameters of the subgroups, and determining the polymer physicochemical parameters based on the identified types and amounts of subgroups. Generally, the types of subgroups can refer to predetermined types or classes that are associated with specific physicochemical characteristics, e.g. descriptors, of the subgroups, and thus with specific physicochemical characteristics of a polymer comprising these subgroups. However, since the general physicochemical characteristics of a polymer and hence the polymer physicochemical parameters can also depend on the amount of a subgroup present in the polymer that amount can also be taken into account. In a preferred embodiment the determination of the type and amount of subgroups takes into account information provided by the synthesis specification indicative of the type of polymerization. The information on the type of polymerization that can be utilized can refer, for instance, to whether the polymerization refers to a polycondensation, polyaddition, radical polymerization, cationic polymerization, anionic polymerization, or coordinative chain-polymerization. Preferably, for each type of polymerization rules are predetermined that can be applied to determine the subgroups of the polymer. For example, rules can be predetermined that determined which functional groups of monomers in the synthesis specification react with which prioritization to which functional groups of the synthesized polymer. The rules can be based, for instance, on kinetic considerations. Based on the number and type of polymerized functional groups the subgroups can be determined and a number and type of the subgroups can be calculated.
In an embodiment the determination of the amount of subgroups comprises determining the amount of at least one of, amide, ester, thioester, carbonate, ether, amine, urea, urethane, thiourethane, isocyanurate, biuret, allophanate, acetal, Michal-adduct, radically polymerized double bond, siloxane, silane, silazane, phosphazene groups as well as residual amine, aldehyde, ketone, epoxide, aziridine, isocyanate, alcohol, thiol, carboxylic acid, acyl halogenide, α,β-unsaturated carbonyl groups, α,β-unsaturated carboxyl and double bond groups in the polymer based on the synthesis specification.
In an embodiment, the method further comprises providing a biodegradation test method, wherein the provided biodegradation test method is indicative of a standardised biodegradation test method for determining experimentally a biodegradation of a polymer, wherein the biodegradation model is further provided based on the provided biodegradation test method. Generally, a plurality of standardized biodegradation test methods exist for testing a biodegradation of a polymer. For example, such test methods can be found in DIN or ISO norms. Further providing a biodegradation test method and providing a biodegradation model that has been trained based on the provided biodegradation test method allows to determine a biodegradation that is easily comparable, for instance, with respectively measured biodegradations utilizing the respective test method. In particular, for this embodiment it is preferred that the biodegradation models are trained based on data sets in which the test method based on which the biodegradation has been determined is clearly specified such that a biodegradation model can be trained specifically for one or more test methods.
In an embodiment, further a target application of the polymer is provided referring to an intended application of the polymer, wherein the biodegradation habitat is provided based of the target application. A target application of a polymer can refer, for instance, to an intended application context of the polymer, for example, if it is intended to utilize the polymer as a coating, in personal care products, in a washing detergent or in a packaging of a product. Such target applications indicate specific biodegradation habitats. For example, for a packaging of a product it could be interesting if a polymer biodegrades in a compost. In another example, if the target application refers to utilizing the polymer in personal care products, it is very likely that the polymer will sooner or later be found in a water environment. Thus, a respective target application is indicative for a respective biodegradation habitat. In this context, a predetermined list can be provided on a storage on which respective target applications and corresponding biodegradation habitats are stored. A target application for a polymer can then be provided, for instance, by providing the list of target applications to a user and allowing the user to select a respective target application, wherein a respective target application is connected to one or more biodegradation habitats. A biodegradability can then be determined for each of the biodegradation habitats to which the target application is connected or again a user can select a respective biodegradation habitat connected with the target application. Additionally or alternatively, information indicative of an intended end-of-life treatment of the polymer can be provided. For example, an end-of-life treatment can be indicative of, whether the polymer is intended to biodegrade in a specific environment, or should be subjected to a specific treatment, for example, in a bioreactor. Thus, also the information of the intended end-of life treatment can be utilized to determine a biodegradation habitat for the polymer, as described above.
In an embodiment, the biodegradation model can further be trained to provide in addition to the habitat descriptor values information indicative of an accessible surface area of a product comprising the polymer that allows to meet the target biodegradability for the product. For example, the information can refer to whether the product should be provided in a solid, pulverized, foamy, pelletized, or any other form to meet the target biodegradability in the respective habitat. Preferably, the information is indicative of an accessible surface area of the product per mass or a geometry of a smallest independent part of the product. Generally, although the biodegradability of a polymer is an intrinsic characteristic of the polymer, the exact timing of the biodegradability of a product comprising the polymer can also depend on the surface area that can be accessed, for instance, by microbial components of the habitat responsible for the biodegradation. Thus, further determining the accessible surface area of a product comprising the polymer allowing to meet the target biodegradability allows for further accuracy in biodegrading a respective product. For example, based on the information on the accessible surface area control data, i.e. a control signal, can be generated for controlling a grinder or mill in order to shred or pulverize a product to be biodegraded.
In an embodiment, habitats and/or habitat descriptors are stored associated with respective geolocations, wherein the providing of a biodegradation habitat refers to providing a geolocation of the habitat and retrieving the habitat and/or habitat descriptors for the geolocation from storage. Geolocations can refer, for instance, to coordinates, or other regional identifications. For example, a geolocation can refer to the name of a city, country, country region, sea region, geographical feature, etc. Based on such geolocations, respective habitat descriptors, for instance, typical environmental characteristics can be determined.
Thus, by providing the geolocation, the respective habitat descriptors for this geolocation can be provided. This has the advantage that an exact habitat or exact habitat descriptors for a region do not have to be known to a user. Thus, the user can simply provide a location for which it is expected that the polymer might biodegrade in this region.
In an embodiment, the polymer physicochemical parameters indicated by the digital representation of the polymer refer at least to one of recipe parameters from polymer synthesis, constitutional descriptors, count descriptors, list of structural fragments, fingerprints, graph invariance, 3D-descriptors and/or higher dimensional descriptors that are indicative of a chemical nature of the polymer. Respective connections of the digital representation with polymer physicochemical parameters, for instance, calculated previously, or further information on the polymer, can be stored already and connected with the respective digital representation. For example, if the digital representation refers to a brand name, a respective structural formula, subgroups and/or subgroup physicochemical parameters or polymer physicochemical parameters corresponding to the brand name can be stored already, for example, on a storage of the brand name owner.
In a preferred embodiment, the polymer belongs to a polymer type being a least one of a polyalkoxylate, polycondensate, addition polymer, vinylic polymer, natural polymer, polymer dispersion, polymer foil, biopolymer, polysilicone, resin, rubber and polyketone, wherein the biodegradation model is specifically trained for the respective polymer type to which the polymer belongs. In particular, the training data for parameterizing the biodegradation model comprises polymers of the respective polymer type. However, the biodegradation model can also be parameterized with training data of polymers from more than one polymer type.
In a preferred embodiment, the polymer belongs to polyalkolates and the habitat is a waste water habitat, in particular, a sludge habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of a molar mass, an ingredient, a chemical moiety, solubility in water and a partition coefficient, more preferably, the physicochemical characteristics comprise a molar mass, even more preferably, comprise a molar mass, an ingredient, even more preferably, comprise a molar mass, an ingredient, and a partition coefficient.
In a preferred embodiment, the polymer belongs to polycondensates, preferably, polyesters, polyamides and phenoplasts, and the habitat is a waste water habitat, in particular, a sludge habitat, or soil habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of a molar mass, an ingredient, chemical moiety, solubility in water, a partition coefficient, a measure for stability against hydrolysis, and degree of crystallinity, more preferably, the physicochemical characteristics comprise a molar mass and an ingredient, even more preferably, comprise a molar mass, an ingredient and a chemical moiety, even more preferably, comprise a molar mass, an ingredient, chemical moiety, and a degree of crystallinity.
In a preferred embodiment, the polymer belongs to addition polymers, preferably, polyurethanes and polyureas, and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a chemical moiety, a ratio of chemical moieties, a ratio of ingredients, and a degree of crystallinity, more preferably, the physicochemical characteristics comprise an ingredient, even more preferably, comprise an ingredient and a ratio of chemical moieties, even more preferably, comprise an ingredient, a ratio of chemical moieties, a ratio of ingredients.
In a preferred embodiment, the polymer belongs to the vinylic polymers, preferably, polyvinyls, polyacrylates, polystryrenes polyvinylethers, and polyvinylalcohols and the habitat is a waste water habitat, in particular, a sludge habitat, or a soil habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of a molar mass, an ingredient, a chemical moiety, solubility in water, a partition coefficient, more preferably, the physicochemical characteristics comprise a molar mass, even more preferably, comprise a molar mass, and an ingredient, even more preferably, comprise a molar mass, an ingredient and a chemical moiety.
In a preferred embodiment, the polymer belongs to natural polymers, preferably, polysaccharides, polynucleotides, lignin, suberin, cutin, cutan, melanin, natural rubber and polypeptides, and the habitat is a waste water habitat, in particular, a sludge habitat, or a soil habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of a molar mass, a chemical moiety, and solubility in water and a partition coefficient, more preferably, the physicochemical characteristics comprise a chemical moiety, even more preferably, comprise a chemical moiety, and a molar mass.
In a preferred embodiment, the polymer belongs to polymer dispersions and the habitat is a marine habitat or soil habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a chemical moiety, a solubility in water and a particle size, more preferably, the physicochemical characteristics comprise an ingredient and a particle size, even more preferably, comprise an ingredient, a chemical moiety, and a particle size.
In a preferred embodiment, the polymer belongs to polymer foils and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a molar mass, a chemical moiety, solubility in water, degree of crystallinity, and surface/volume ratio, more preferably, the physicochemical characteristics comprise an ingredient and surface/volume ratio, even more preferably, comprise an ingredient, a chemical moiety and a surface/volume ratio, even more preferably, comprise an ingredient, a chemical moiety, a degree of crystallinity, and surface/volume ratio.
In a preferred embodiment, the polymer belongs to polysilicones and the habitat is a soil habitat, waste water habitat, in particular, sludge habitat, or marine habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a molar mass, a chemical moiety, solubility in water, a partition coefficient, and surface/volume ratio, more preferably, the physicochemical characteristics comprise a molar mass, even more preferably, comprise a molar mass and an ingredient, even more preferably, comprise a molar mass, an ingredient, and a partition coefficient.
In a preferred embodiment, the polymer belongs to the resins and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a molar mass, a chemical moiety, solubility in water, degree of crystallinity, and surface/volume ratio, more preferably, the physicochemical characteristics comprise a molar mass, even more preferably, comprise a molar mass and an ingredient, even more preferably, comprise a molar mass, an ingredient, and surface/volume ratio.
In a preferred embodiment, the polymer belongs to the rubbers and the habitat is a soil habitat or marine habitat. Moreover, it is preferred in this embodiment that the physicochemical characteristics comprise at least one of an ingredient, a chemical moiety, solubility in water, degree of crystallinity, and surface/volume ratio, more preferably, the physicochemical characteristics comprise an ingredient, even more preferably, comprise an ingredient and a surface/volume ratio, even more preferably, comprise an ingredient, a chemical moiety, degree of crystallinity, and surface/volume ratio.
In a preferred embodiment, the physicochemical characteristics comprise at least one of a molar mass, a chemical moiety, solubility in water and/or in octanol, a degree of crystallinity, and a surface/volume ratio. More preferably, the physicochemical characteristics comprise a chemical moiety, even more preferably, comprise a molar mass, a chemical moiety, and a solubility in water. For the polymer belonging to polyalkoxylates, polycondensates, vinylic polymers, or polyslicones it is preferred that the physicochemical characteristics comprise further at least one of a partition coefficient and an ingredient. For the polymer belonging to polycondensates, addition polymers, polymer foils, resins, rubbers, or polyketones it is preferred that the physicochemical characteristics comprise further at least one of a degree of crystallinity and a measure for stability against hydrolysis. For the polymer belonging to resins, rubbers, addition polymer or polysilicones, it is preferred that the physicochemical characteristics comprise further a surface/volume ratio.
In a further aspect, an interface method for providing an interface is presented, wherein the interface method comprises a) receiving as input a target biodegradability, digital representation and a habitat via a user interface and providing the received target biodegradability, digital representation and the habitat to a processor performing the method as described above, and b) providing the determined habitat descriptor values of the polymer as result, wherein the result is received from the processor performing the method as described above.
In a further aspect, a computer implemented training method for training a data driven based biodegradation model for parameterizing the biodegradation model is presented, wherein the training method comprises a) providing training data associated with a predetermined biodegradation habitat, wherein the training data comprises i) digital representations of a plurality of training polymers indicative of physicochemical characteristics each of the training polymers, ii) habitat descriptor values for the respective habitat descriptors of the biodegradation habitat, and iii) a biodegradability for the respective biodegradation habitat descriptor values associated with each training polymer, b) providing a data driven based trainable biodegradation model, c) training the provided data driven based biodegradation model based on the provided training data such that the trained biodegradation model is adapted to determine habitat descriptor values based on a biodegradability and physicochemical characteristics, and d) providing the trained biodegradation model.
In a further aspect, an apparatus for determining habitat descriptor values providing a target biodegradability for a predetermined polymer is presented, wherein the apparatus comprises a) a target biodegradability providing unit for providing a target biodegradability, wherein the biodegradability is indicative of a biodegradation characteristic of a polymer, b) a digital representation providing unit for providing a digital representation of a polymer indicative of or associated with physicochemical characteristics of the polymer, c) a habitat providing unit for providing a biodegradation habitat, wherein the biodegradation habitat is indicative of habitat descriptors influencing a biodegradation of a polymer in the respective habitat, wherein the habitat descriptors are indicative of environmental characteristics of the habitat, d) a model providing unit for providing a biodegradation model based on the provided biodegradation habitat, wherein the biodegradation model is adapted to determine habitat descriptor values that allow for a biodegradation of the polymer meeting the target biodegradability in the biodegradation habitat, wherein the biodegradation model is a data driven model parameterized with respect to the biodegradation habitat such that it determines based on the target biodegradability and the physicochemical characteristics the habitat descriptor values, and e) a determining unit for determining the habitat descriptor values for the polymer based on the selected biodegradation model, the target biodegradability and the polymer description.
In a further aspect, an interface apparatus for providing an interface is presented, wherein the interface apparatus comprises a) an input interface unit for receiving as input a target biodegradability, a digital representation and a habitat via a user interface and for providing the received target biodegradability, digital representation and the habitat to an apparatus as described above, and b) an result interface for providing the habitat descriptor values of the polymer as result, wherein the result is received from the apparatus as described above.
In a further aspect, a training apparatus for training a data driven based biodegradation model for parameterizing the biodegradation model is presented, wherein the training apparatus comprises a) a training data providing unit for providing training data associated with a predetermined biodegradation habitat, wherein the training data comprises i) digital representations of a plurality of training polymers indicative of physicochemical characteristics for each of the training polymers, ii) habitat descriptor values for the respective habitat descriptors of the biodegradation habitat, and iii) a biodegradability for the respective biodegradation habitat descriptor values associated with each training polymer, b) a trainable model providing unit for providing a data driven based trainable biodegradation model, c) a training unit for training the provided data driven based biodegradation model based on the provided training data such that the trained biodegradation model is adapted to determine habitat descriptor values based on a biodegradability and physicochemical characteristics, and d) a trained model providing unit for providing the trained biodegradation model.
In a further aspect of the invention a use of the method as described above is presented, wherein the method is used for determining habitat descriptor values for any of the following i) polymers referring to polyesters, in particular, used for mulch film and packaging applications, e.g. aromatic aliphatic copolyesters, ii) polymers referring polyalkoxylates, in particular, used for home and personal care applications, iii) polymers referring to polyurethane dispersions, iv) polymers used for aroma applications, v) polymers used for paper coatings for packaging applications based on multilayer blends, and vi) polymers referring to polyurethane used for adhesives.
In a further aspect of the present invention, a system is presented, wherein the system comprises i) a control signal comprising a synthesis specification of a polymer indicating one or more ingredients for producing the polymer, wherein the control signals are generated according to the above described method, and ii) the one or more ingredients indicated by the synthesis specification in the control signal.
In a further aspect of the invention, a use of a control signal generated according to the above described method for controlling a production process, in particular, a production process comprising the production of a polymer, or for controlling a reactor, in particular, a waste degradation reactor, is presented.
In a further aspect of the invention, a control signal is presented, wherein the control signal is generated according to the above described method. Preferably, the control signal i) comprises a machine executable synthesis specification for producing a polymer or ii) refers to a signal controlling a reactor.
In a further aspect, a computer program product for determining habitat descriptor values providing a target biodegradability for a predetermined polymer is presented, wherein the computer program product comprises program code means for causing the apparatus as described above to execute the method as described above.
In a further aspect, a computer program product for training a biodegradation model is presented, wherein the computer program product comprises program code means for causing the apparatus as described above to execute the method as described above.
It shall be understood that the methods as described above, the apparatuses as described above and the computer program products as described above have similar and/or identical preferred embodiments, in particular, as defined in the dependent claims. Moreover, also the training method as described above, the training apparatus as described above, and the training computer program product as described above have similar and/or identical preferred embodiments, in particular, as defined in the dependent claims.
It shall be understood that a preferred embodiment of the present invention can also be any combination of the dependent claims or above embodiments with the respective independent claim.
These and other aspects of the present invention will be apparent from and elucidated with reference to the embodiments described hereinafter.
In the following drawings:
The apparatus 110 comprises a target biodegradability providing unit 111, a digital representation providing unit 112, a habitat providing unit 113, a model providing unit 114 and a determination unit 115. Optionally, the apparatus 110 can further comprise an output and/or control unit 116 that can be adapted to output the determined habitat descriptor values and/or to provide control signals for controlling a waste management process of the waste management system 120 based on the determined habitat descriptor values.
The target biodegradability providing unit 111 is adapted to provide a target biodegradability indicative of desired biodegradation characteristics of a polymer. The target biodegradability providing unit 111 can refer, for instance, to an input unit into which a user can input a respective target biodegradability. Moreover, the target biodegradability providing unit 111 can refer to or can be part of a user interface that allows the user to interact with the apparatus 110 for providing the target biodegradability. However, the target biodegradability providing unit 111 can also refer to or be communicatively coupled with a storage unit on which a target biodegradability, for instance, for a specific application, is already stored.
The digital representation providing unit 112 is adapted to provide a digital representation indicative of polymer physicochemical parameters of a polymer for which habitat descriptor values should be determined. The digital representation providing unit 112 can refer, for instance, to an input unit into which a user can input the respective digital representation. Moreover, the digital representation providing unit 112 can refer to or be part of a user interface that allows the user to interact with the apparatus 110 and/or the database 140. However, the digital representation providing unit 112 can also refer to or be communicatively coupled with a storage unit on which the digital representation of the polymer is already stored. Generally, the digital representation can directly comprise the polymer physicochemical parameters that are indicative of parameters quantifying physicochemical characteristics of the respective polymer. However, instead of directly providing the polymer physicochemical parameters also a synthesis specification of the polymer can be provided. In this case, it is preferred that the digital representation providing unit 112 is further adapted to determine the polymer physicochemical parameters from the synthesis specification. In particular, it is preferred that the digital representation providing unit 112 is adapted to identify from the synthesis specification types and amounts of subgroups of the polymer and to determine the polymer physicochemical parameters based on the identified types and amounts of subgroups. In particular, the digital representation providing unit 112 can be adapted to determine for each identified subgroup respective subgroup physicochemical parameters, for instance, by accessing a database on which for a plurality of the most relevant subgroups respective physicochemical parameters are stored. The physicochemical parameters of the polymer can then be determined based on the subgroup physicochemical parameters of the subgroups and preferably, also on the determined amount and type of the subgroups, for example, by weighted averaging of the subgroup physicochemical parameters of the subgroups. The digital representation providing unit 112 is then adapted to provide the digital representation comprising the polymer physicochemical parameters, for instance, to the determination unit 115.
The habitat providing unit 113 is adapted to provide the biodegradation habitat. The habitat providing unit 113 can refer, for instance, to an input unit into which a user can input a respective biodegradation habitat. For example, a user interface can be provided that allows a user to select from a number of predetermined biodegradation habitats. In a preferred embodiment, the habitat providing unit can be communicatively coupled to or refer to a user interface that allows to indicate a geolocation, for instance, by marking a location on a map, by indicating coordinates, or providing a name of a region, for instance, a political or geological region, wherein the habitat providing unit can then be adapted to provide a biodegradation habitat based on the geolocation. For example, if the geolocation indicates a specific sea region like the Northern Sea or the Atlantic, the habitat providing unit can be adapted to determine as biodegradation habitat a marine habitat.
Generally, a biodegradation habitat is indicative of habitat descriptors influencing a biodegradation of a polymer in the respective habitat. In particular, habitat descriptors are indicative of environmental characteristics of the habitat, for example, for a marine habitat a salt concentration can strongly influence the biodegradation of a polymer in the marine habitat. The indicated habitat descriptors then refer to the habitat descriptors for which habitat descriptor values should be determined. In particular, the to be determined habitat descriptor values are determined such that the provided polymer will biodegrade in the respective habitat with the respective determined habitat descriptor values such that the target biodegradability is met within limits. For example, it can be determined that a specific polymer in a marine habitat meets a specific half-life for biodegradation if the marine habitat provides a respective salt concentration value and temperature value or respective value ranges.
The model providing unit 114 is adapted to provide a biodegradation model based on the provided biodegradation habitat. In particular, it is preferred that the model providing unit 114 is adapted to select the biodegradation model from a plurality of biodegradation models stored already on a database. For example, a biodegradation model can be trained with respect to training data corresponding to one or more specific biodegradation habitats. These specific biodegradation habitats can be defined with respect to habitat descriptors that define which habitat descriptor values can be determined accurately by the respective biodegradation model. For example, a lookup table can be provided that allows the model providing unit to select based on the biodegradation habitat, for instance, based on the habitat descriptor values that should be determined, which of the biodegradation models is suitable. However, the model providing unit 114 can also comprise or refer to an input unit to which the biodegradation model can be received, for instance, by a user selection or user input that indicates which biodegradation model should be used.
The biodegradation model is a data-driven model parameterized such that it can determine the biodegradability of the polymer in a habitat based on the digital representation, in particular, based on the polymer descriptors indicative of the physicochemical characteristics associated with the polymer, and based on the target biodegradability one or more habitat descriptor values. In a preferred embodiment, the data-driven model refers to a machine learning model, for instance, utilizing regression model based algorithms or a classifier model based algorithms. A regression model based algorithm can be based on any of a neural network algorithm, a Linear Regression algorithm, a LASSO algorithm, a Ridge Regression algorithm, a MARS algorithm, a Random Forest algorithm, and a Boosted Trees algorithm. A classifier based model algorithm can be based on any of a Random Forest algorithm, a Logistic Regression algorithm, and a SVM algorithm. The inventors have found that for most applications, in particular, Linear regression, Random Forest, neural networks and MARS based algorithms are suitable.
The biodegradation model can be trained, for instance, utilizing training apparatus 130. In particular, the training apparatus 130 comprises a training data providing unit 131 for providing training data for training the data-driven based biodegradation model. The training data comprises a) polymer physicochemical parameters of a plurality of training polymers, b) biodegradabilities associated with each training polymer for one or more different habitats and c) the habitat descriptor values associated with the respective habitats and the respective biodegradabilities. Preferably, in the training data the biodegradability provided for each training polymer refer to a biodegradability that is measured in accordance with the same measurement method. However, biodegradabilities can also be provided for different measurement methods, wherein in this case it is preferably clearly indicated which biodegradabilities are associated with which measurement methods, such that the biodegradability model can be trained to differentiate between different measurement methods. Generally, the training data can be designed to cover a predetermined habitat space of a to be trained biodegradation model, wherein the habitat space is defined by the value ranges of the respective habitat descriptors for which the biodegradation model shall be trained. For example, the training data can be designed to cover predetermined polymer types for a predetermined habitat. Known methods for designing and optimizing training data for a predetermined habitat space can be utilized such that the habitat space is well covered with training data and that random outliers are avoided.
Further, the training apparatus 130 comprises a model providing unit 132 adapted to provide a data-driven based trainable biodegradation model, for instance, a biodegradation model comprising parameters that can be set during the training process for training the biodegradation model. For example, a trainable biodegradation model can already be stored on a storage unit to which the model providing unit 132 can have access for providing the same. Moreover, the training apparatus 130 comprises a training unit 133 for training the provided data-driven based biodegradation model based on the provided training data. In particular, the training can refer to varying the parameters of the biodegradation model based on the respective training data until the biodegradation model is adapted to determine one or more habitat descriptor values for a polymer based on a digital representation, in particular, polymer physicochemical parameters of the polymer and a target biodegradability. Generally, any know training algorithms for training data-driven, in particular, machine learning based models can be utilized. Preferably, during the training of the biodegradation model also the physicochemical parameters of the polymer that have the most influence on the biodegradability and/or habitat descriptor values in the respective habitat are determined and the model is then trained based on these most influential descriptors. For determining these most influential physicochemical parameters and/or habitat descriptors, for example, cluster analysis or PCA analysis tools can be utilized. In particular, the polymer physicochemical parameters can be utilized to determine the application space of the training data, wherein the application space can then be defined by the polymer physicochemical parameters of the polymer and the habitat descriptors that are covered by the data. The determination of the most influential physicochemical parameters and/or habitat descriptors can then be performed as a dimension reduction of the application space. Then algorithms for optimizing the training data in the application space can be applied, for instance, to cover the application space with as few training data as possible.
The training apparatus 130 then comprises a trained model providing unit 134 that is adapted to provide the trained biodegradation model, for instance, to a storage unit on which respectively trained biodegradation models for different habitats and/or different types of polymers are stored. However, the trained model providing unit 134 can also be adapted to directly provide the trained biodegradation model, for instance, to the biodegradation model providing unit 114 of apparatus 110.
In all cases, the biodegradation model providing unit 114 is then adapted to provide a suitable trained biodegradation model to the determination unit 115. The determination unit 115 can then utilize the biodegradation model, the provided digital representation and the provided target biodegradability for determining the habitat descriptor values. In particular, the determination unit 115 can be adapted to utilize the polymer physicochemical parameters indicated by the digital representation together with the target biodegradability as input to the biodegradation model that has, as already described above, been trained to then provide as output a determination for the habitat descriptor values. An output unit referring, for instance, to a display, can then be adapted to output the determined habitat descriptor values. Moreover, the determined habitat descriptor values can also be provided to a database 140 for storing, for instance, the polymer in association with the determined habitat descriptor values for a specific habitat for future usage. For example, from the database 140 polymers which fulfil predetermined target biodegradabilities in specific habitats can be selected and the respective determined habitat descriptor values can then be utilized, for instance, by the waste management system 120. However, polymers can also be selected based on the determined habitat descriptor values, for instance, in order to only allow polymers within reactors of the waste management system 120 that can provide the respective suitable biodegradation conditions for these polymers.
Optionally, the apparatus 110 can comprise a control unit 116 that is adapted to provide control signals based on the determined habitat descriptor values for controlling a reactor system, for instance, being part of the waste management system 120, to biodegrade a polymer. In particular, it is preferred that the provided control signals control the reactor system such that the reactor system is caused to provide at least one of the determined habitat descriptor values to a substance comprising the polymer within the reactor system. For example, the control signals can control a temperature or humidity within the reactor system such that it meets the respective determined habitat descriptor values. This allows to control a waste management facility 120 such that polymers that should biodegrade within the facility, for instance, in different reactor systems, degrade with the desired target biodegradability, for instance, within a predetermined time.
In the following, more detailed preferred examples of the above described method and the corresponding apparatus will be described. An exemplary embodiment the method can consist of steps described in the following. A schematic and exemplary flow chart of an exemplary embodiment of the method is provided by
If the provided information indicates the presence of a mixture, then in a following step the mixture is decomposed into its pure components and each polymer component is treated as input polymer. Moreover, the polymer composition can also be transformed into mol %, wt %, vol % or, absolute mol if necessary.
In the next step 530 the polymerizable components can be transformed into subgroups, e.g. repeating units, and the subgroups are determined as different types. For example, polymerizable subgroups can be determined based on connectivity information of non-polymeric pure compounds by using SMARTS, for instance, via KNIME workflow. Also connectivity information of all possible subgroups can be derived from connectivity information of non-polymeric pure compounds by using reaction SMARTS, for example, also via KNIME workflow
After the subgroups and their types have been determined, in step 540 the type of physicochemical parameters that should be utilized can be provided. However, the physicochemical parameters can also be determined without first selecting the type of the subgroups. In order to decrease the computational resources for the method it is preferred that in a step 541 it is determined whether subgroup physicochemical parameters associated with a respective type of subgroup are already stored in a database, for example, if entries for subgroups with identical connectivity information already exist in the database. If this is the case the respective associated subgroup physicochemical parameter can be directly downloaded, for example, in step 544. If the determined type of subgroup is not stored on the database the subgroup physicochemical parameters that are associated with a respective type of subgroup can be determined, for example, in step 542. For example, either a 3D structure of respective types of subgroups can be derived based on connectivity information and an automatically computation of subgroup physicochemical parameters can be started using, for instance, a computer cluster, or already existing machine-learning determinations can be utilized as subgroup physicochemical parameters. Generally, it is preferred that if computations on new subgroups are necessary, in step 543, the results are stored in the database after the computations are finished. Optionally further subgroup physicochemical parameters can be provided from a topological analysis of the subgroups, a quantum chemical computation, a molecular dynamics computation, coarse-grained methods, finite-element computations and kinetic simulations. In particular, polymer reaction engineering methods can be used to derive subgroup descriptors that allow to take into account a microstructure of the polymer.
In step 531 the amount of subgroups, i.e. of each type of subgroup, is determined, for example based on the provided recipe information for the polymer, and provided in step 532. For example, the amount can be determined by counting an amount of polymerizable groups per polymerizable component, optionally, including prepolymers. In this case, information on polymerizable groups can be derived from non-polymeric components and the such determined amount can be added to a count of the number of, optionally, non-polymerized, polymerizable groups of the subgroups for polymeric components based on the composition of the polymeric components to determine a resulting amount. Further, it is preferred that the amount of polymerizable groups originating from agents used for postprocessing after polymerization is removed from the resulting amount.
However, although it is preferred that the polymer physicochemical parameters are derived from polymer subgroups, in other embodiments of the invention the polymer physicochemical parameters can also be derived in other ways, for instance, by directly determining the polymer physicochemical parameters from the complete polymer. Moreover, the polymer physicochemical parameters for respective polymers can also be already stored on a storage unit such that the deriving of the polymer physicochemical parameters from a digital representation of the polymer can refer to determining from the digital representation information on the polymer that allows to access the database and retrieve the corresponding polymer physicochemical parameters.
Optionally the derived amounts of subgroups can be used for a further interpretation of the polymer composition. For example, a total number of polymerized functional groups, e.g. double bonds, amine groups, alcohols groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups, and formed functional groups, e.g. amid groups, ester groups, thioester groups, urea groups, urethane groups, thiourethane groups, ether groups, can be determined. Also the molar weighted total number of polymerized functional groups, the mass weighted total number of polymerized functional groups, the total number of residual functional groups, e.g. double bonds, amine groups, alcohol groups, thiol, groups, carboxylic acid groups, isocyanate groups, epoxide groups, the molar weighted total number of residual functional groups, the mass weighted total number of residual functional groups, the sum of all residual functional groups, the ratio between functional groups after polymerization, the number of crosslinks in polymer, the molar fraction of crosslinks in polymer, optionally, with mass-weighting as well, the average number of atoms per subgroup, optionally, per weight as well, the average number of non-H-atoms per subgroup, optionally, per weight as well, the average number of bonds per subgroup, optionally, per weight as well, the average number of bonds between non-H-atoms per subgroup, optionally, per weight as well, the average number of rotors per subgroup, optionally, per weight as well, the average number of rotors between non-H-atoms per subgroup, optionally, per weight as well, the average number of rings per subgroup, optionally, per weight as well, the average polar surface areas per subgroup, optionally, per weight as well, the average refractivity per subgroup, optionally, per weight as well, the total number of blocks, the molar size of first block, the molar size of last block, the HLB value of polymer, optionally, with area weighted HLB value, the HLB value of block with lowest HLB value, optionally, with area weighted HLB value, the HLB value of block with largest HLB value, optionally, with area weighted HLB value, the HLB value of first block, optionally, with area weighted HLB value, the HLB value of last block, optionally, with area weighted HLB value, the mass of first block, the mass of last block, the area of block with lowest HLB value, the area of block with largest HLB value, the difference of the HLB values of the blocks, optionally, with area weighted HLB value, the hydrophilic area of the polymer, the lipophilic area of the polymer, the number of arms for ring-opening-polymerization, or the length of arms for ring-opening-polymerization can be determined.
In step 550 the determined amount and type of the subgroups and the associated subgroup physicochemical parameters can be utilized to compute the polymer physicochemical parameters. For example, the polymer physicochemical parameters can be determined by one or more of molar weighted, e.g. arithmetic, harmonic or logarithmic, averaging, mass weighted, e.g. arithmetic, harmonic or logarithmic averaging, volume weighted, e.g. arithmetic, harmonic or logarithmic, averaging, surface area weighted, e.g. arithmetic, harmonic or logarithmic, averaging of the associated physicochemical parameters of the subgroups. Moreover, the polymer physicochemical parameters can be determined by determining from the associated subgroup physicochemical parameters one or more of a molar weighted standard deviation, a mass weighted standard deviation, a volume weighted standard deviation, a surface area weighted standard deviation, a molar weighted maximum value, a mass weighted maximum value, a volume weighted maximum value, a surface area weighted maximum value, a molar weighted minimum value, a mass weighted minimum value, a volume weighted minimum value, a surface area weighted minimum value, a molar weighted sum, a mass weighted sum, a volume weighted sum, a surface area weighted sum, and a maximal difference.
In step 560 the derived or provided physicochemical parameters, for example, referring to polymer descriptors, can then be provided to the trained biodegradation model for determining the habitat descriptor values, for example, as described with respect to
The same process can optionally be performed for the habitat descriptors in order to determine habitat descriptors that are most relevant for the biodegradability of polymers in a specific habitat. Based on the remaining polymer descriptors and, optionally also based on the habitat descriptors, an application space can be determined and optimized. The application of the trained biodegradation model, for instance, to a specific habitat or polymer physicochemical parameters, can then be determined by the space spanned by the training data that forms the application space. This space can be optimized, for example, by amending the training data to cover the application space regularly, by removing strong outliers, by adding training data in parts of the space that are not yet covered, etc. This also allows to maximize the applicability space. The biodegradation model is then trained based on the optimized training data. The biodegradation model can generally refer to sparse, e.g.
Splines, LASSO regression, PLS, and non-sparse, e.g. ridge regression, tree methods, kernel based methods, statistical learning models for relating the polymer physicochemical parameters to the biodegradability in a specific habitat. Moreover, the biodegradation model can further provide a reliability estimation of the determination depending on the respective used biodegradation model. In step 570 the determined habitat descriptor values, i.e. technical application property, can then be provided to a user, for example, via a user interface.
The synthesis specification module layer 1154 may include: a mass storage layer, the computing layer, the interface layer. The storage layer is configured to provide mass storage for the data-driven biodegradation model for providing a recipe, i.e. synthesis specification, of a polymer based on habitat descriptor values leading to a specific target biodegradability in a habitat, as described in detail above. In particular, the functions performed by the apparatus, as described above, can be provided as program code means stored on the mass storage. Furthermore, synthesis specifications for a plurality of polymers can be stored in the mass storage. Such data may be stored in structured databases such as SQL databases or in a distributed file system such as HDFS, NoSQL databases such as HBase, MongoDB. The computing layer may include an application layer that allows to customize the functionalities provided by standard cloud services to perform computing processes based on target properties. Such functionalities can include determining based on a target biodegradability and/or target habitat descriptor values and the biodegradation model a digital representation of a target polymer, generating a synthesis specification from the digital representation of the target polymer, and providing the synthesis specification as control data to the laboratory equipment control device. The interface layer may implement web services, network interfaces as UDP or TCP or Websocket interfaces. For communication with the laboratory equipment control device a REST API is implemented.
The client layer 1156 provides interfaces for end-users. For end-users, the client layer 1156 can run client side Web applications, which provide interfaces to the synthesis specification module layer 1154 or the laboratory equipment control device layer 1152. Users may be provided with a UI for selecting a target biodegradability, a biodegradation habitat for the target biodegradability, and optionally target habitat descriptor values. The target biodegradability may also comprise a range of biodegradability values. In other examples, the users may be provided with a UI for selecting more than one target biodegradability and/or more than one target habitat and target habitat values. The applications may be configured for users to monitor and control the laboratory equipment control device and the operation remotely. In other examples, the client device layer and the synthesis specification module layer may be integrated into one device. The alternatives described here are only for illustration purposes and should not be considered limiting.
The model generating module layer 2154 may include: a mass storage layer, a computing layer, an interface layer. The storage layer is configured to provide mass storage for the data-driven biodegradation model as described above. Furthermore, the mass storage is configured for storing synthesis specifications for polymers and measured biodegradabilities for one or more habitats. Such data may be stored in structured databases such as SQL databases or in a distributed file system such as HDFS, NoSQL databases such as HBase, MongoDB. The computing layer may include an application layer that allows to customize the functionalities provided by standard cloud services to perform computing processes for generating a biodegradation model for determining the habitat descriptor values. Such functionalities may include receiving for at least two previously measured polymers their respective digital representations associated with a synthesis specification, measurement data of at least one biodegradability in at least one habitat for specific habitat descriptor values for each of the at least two previously measured polymers, receiving at the model generating module the digital representation of at least one unmeasured polymer, training the model according to the above described training principles based on the digital representation of the at least two previously measured polymers, the measurement data of the biodegradability in the at least one habitat, and the corresponding habitat descriptor values for each of the at least two previously measured polymers, and, preferably, a similarity measure between the digital representation associated with the synthesis specification of each of the at least two previously measured polymers and the respective digital representation associated with a synthesis specification of the at least one unmeasured polymer, and providing via an output interface the biodegradation model for the habitat descriptor values. The model generating module layer may be configured for deploying the generated model and the synthesis specification database to the synthesis specification module layer. This may include storing the generated model and the synthesis specification database in the mass storage devices associated with the synthesis specification module.
The model generating module layer may further be configured for determining a digital representation of the polymer associated with the synthesis specification from the synthesis specification. The digital representation may include a set of polymer physicochemical parameters and polymer physicochemical parameter values associated with a synthesis specification of each measured polymer. One way of deriving these polymer physicochemical parameters can be to apply the SMILES algorithm or any other already above described principle. In case, where the model is generated based on the digital representation derived from the recipe, a relation between the synthesis specification and the physicochemical parameters may be stored in the mass storage devices associated with the model generating module. In such cases, deploying the model comprises providing that relation.
The interface layer may implement web services, network interfaces as UDP or TCP or Websocket interfaces. For communication with the client device a REST API is implemented in this example. The client layer 2156 provides access to mass storage devices, that contain synthesis specifications for polymers, and for at least two polymers at least one biodegradability and on corresponding habitat descriptor value. The client layer further provides an interface for end-users. For end-users, the client layer 2156 may run client side Web applications, which provide interfaces to the model generation module layer 2154 or the mass storage devices associated with the client layer. Users may be provided with a UI for selecting a test method and/or habitat for which the habitat descriptor values shall be determined. The user may further be provided with a UI for selection of the synthesis specification data. The user interface may also provide an option for uploading the selected data to the model generating module layer and optionally an option to initiate model generation.
In a preferred embodiment, a target biodegradability is provided as a target requirement. Then a habitat is selected. Based on the habitat selection a biodegradation model is selected. This allows to have separate biodegradation models for different habitats. Then a digital representation of a polymer that needs to be waste treated is provided. The digital representation and the target value for biodegradation are provided as inputs to the biodegradation model. The model generates as output habitat descriptor values for the habitat that are required to meet the target biodegradability. These can then be used as control data to control a plant for biodegradation of polymers. Assuming a reactor for biodegradation of a certain polymer, the temperature, the composition of soil/water/marine and concentrations composition of enzymes, bacteria may be proposed.
Potential representations of the biodegradability may be one or more of a mineralization referring to information whether the polymer fully mineralizes or not or a time until the mineralization is achieved, a biotransformation referring to an alteration in the chemical structure resulting in the loss of a specific property of the polymer, e.g. toxicology, or time until this is achieved, a half-life referring to the time until 50% of the polymer are decomposed. Prominent habitats are Marine, waste water, and soil. For marine the following parameters can have an influence on the biodegradation: salt concentration, sediments, water temperature, bacterial cultures, etc. In some examples, the marine habitat descriptors can be stored in a database together with the geolocation. In that case the geolocation can be entered and the values of the parameters related to this geolocation can be retrieved from a database. For waste water, the following parameters can have an influence on biodegradation: Temperature, bacteria population, bacteria type, enzyme concentration, enzymes. For soil the following parameters can have an influence on biodegradation, temperature, bacteria population, bacteria type, enzyme concentration, enzymes.
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.
For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in differing order. Furthermore, the outlined operations are only provided as examples, and some of the operations may be optional, combined into fewer steps and operations, supplemented with further operations, or expanded into additional operations without detracting from the essence of the disclosed embodiments.
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.
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.
Procedures like the providing of the polymer physicochemical parameters and the biodegradation model, the determining of the habitat descriptor values, the providing of the habitat descriptor values, etc. performed by one or several units or devices can be performed by any other number of units or devices. These procedures can be implemented as program code means of a computer program and/or as dedicated hardware.
A computer program product may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.
Any units described herein may be processing units that are part of a classical computing system. Processing units may include a general-purpose processor and may also include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may include any computer-readable storage media such as a non-volatile mass storage. If the computing system is distributed, the processing and/or memory capability may be distributed as well. The computing system may include multiple structures as “executable components”. The term “executable component” is a structure well understood in the field of computing as being a structure that can be software, hardware, or a combination thereof.
For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component may include software objects, routines, methods, and so forth, that may be executed on the computing system. This may include both an executable component in the heap of a computing system, or on computer-readable storage media. The structure of the executable component may exist on a computer-readable medium such that, when interpreted by one or more processors of a computing system, e.g., by a processor thread, the computing system is caused to perform a function. Such structure may be computer readable directly by the processors, for instance, as is the case if the executable component were binary, or it may be structured to be interpretable and/or compiled, for instance, whether in a single stage or in multiple stages, so as to generate such binary that is directly interpretable by the processors. In other instances, structures may be hard coded or hard wired logic gates, that are implemented exclusively or near-exclusively in hardware, such as within a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Accordingly, the term “executable component” is a term for a structure that is well understood by those of ordinary skill in the art of computing, whether implemented in software, hardware, or a combination. Any embodiments herein are described with reference to acts that are performed by one or more processing units of the computing system. If such acts are implemented in software, one or more processors direct the operation of the computing system in response to having executed computer-executable instructions that constitute an executable component. Computing system may also contain communication channels that allow the computing system to communicate with other computing systems over, for example, network. A “network” is defined as one or more data links that enable the transport of electronic data between computing systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection, for example, either hardwired, wireless, or a combination of hardwired or wireless, to a computing system, the computing system properly views the connection as a transmission medium. Transmission media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or special-purpose computing system or combinations. While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface system for use in interfacing with a user. User interfaces act as input or output mechanism to users for instance via displays.
Those skilled in the art will appreciate that at least parts of the invention may be practiced in network computing environments with many types of computing system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, main-frame computers, mobile telephones, PDAs, pagers, routers, switches, datacenters, wearables, such as glasses, and the like. The invention may also be practiced in distributed system environments where local and remote computing system, which are linked, for example, either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links, through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.
Those skilled in the art will also appreciate that at least parts of the invention may be practiced in a cloud computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be distributed internationally within an organization and/or have components possessed across multiple organizations. In this description and the following claims, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources, e.g., networks, servers, storage, applications, and services. The definition of “cloud computing” is not limited to any of the other numerous advantages that can be obtained from such a model when deployed. The computing systems of the figures include various components or functional blocks that may implement the various embodiments disclosed herein as explained. The various components or functional blocks may be implemented on a local computing system or may be implemented on a distributed computing system that includes elements resident in the cloud or that implement aspects of cloud computing. The various components or functional blocks may be implemented as software, hardware, or a combination of software and hardware. The computing systems shown in the figures may include more or less than the components illustrated in the figures and some of the components may be combined as circumstances warrant.
Any reference signs in the claims should not be construed as limiting the scope. The invention refers to a method for determining habitat descriptor values providing a target biodegradability for a polymer. A target biodegradability is provided indicative of a biodegradation characteristic of a polymer. A digital representation of a polymer indicative of physicochemical characteristics of the polymer is provided. A biodegradation habitat is provided indicative of habitat descriptors influencing a biodegradation of a polymer. A biodegradation model is provided based on the provided biodegradation habitat that is adapted to determine habitat descriptor values that allow for a biodegradation of the polymer meeting the target biodegradability in the biodegradation habitat. The habitat descriptor values are determined for the polymer based on the selected biodegradation model, the target biodegradability and the polymer descriptors.
| Number | Date | Country | Kind |
|---|---|---|---|
| 22157567.3 | Feb 2022 | EP | regional |
| 22157573.1 | Feb 2022 | EP | regional |
| 22157580.6 | Feb 2022 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2023/054052 | 2/17/2023 | WO |
