Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/08—Learning methods
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/70—Machine learning, data mining or chemometrics

Definitions

• the present invention relates to the field of the simulation of chemical kinetics, with the aim of predicting a mixture of chemical species at the output of a reactive system.
• the prediction of the behavior of chemical reactive systems is important for the design of many industrial systems, such as processes or energy conversion systems in transport with chemical propulsion, as well as in the transport issues of these chemical compounds.
• a fluid containing a mixture of chemical species undergoes various transformations, called chemical reactions.
• it may involve the prediction of the composition of chemical species within combustion, in particular in a combustion engine, chemical reactions within an electric battery, within a fuel cell , within a reactive flow, within a catalysis process, etc.
• This modeling consists of predicting the evolution of chemical reagents (chemical species) in a reactive system between an input and an output.
• the notion of input and output can correspond to one of the following definitions: the input and the output can correspond to a physical input and output of a system (before and after a reaction reaction), as well as the evolution of the system between two time steps during a numerical simulation (before or after a time step of a chemical reaction).
• Patent application WO2020176914 describes a prediction of gas concentrations from neural networks.
• the method is applied to the post-treatment of engine exhaust gases.
• the direct calculation of chemical kinetics is expensive, hence the use of neural networks. Nevertheless, for this method the conservation of mass is not taken into account by the neural networks, which does not allow an accurate prediction of the concentrations of the exhaust gases.
• the patent application CN109918702 envisages the prediction of chemical states by neural networks, taking into account the conservation of the mass of the atoms by means of a constraint. However, this constraint is not included in the neural network.
• the aim of the invention is to construct a simulation model of at least one chemical reaction making it possible to predict in an accurate and reliable manner the chemical compositions during the at least one chemical reaction.
• the present invention relates to a method building a simulation model of at least one chemical reaction, which builds an intermediate model by means of an artificial neural network and a learning base, then which builds the simulation model by adding an additional mass conservation layer to the artificial neural network.
• the invention relates to a method for simulating a chemical reaction, implementing the built simulation model.
• the invention relates to a method for constructing a simulation model of at least one chemical reaction between several chemical species within a reactive system, by means of a learning base, said learning base comprising learning input data and learning output data of said chemical reaction, said learning input and learning output data being representative of the mass fractions of said chemical species respectively at the input and at the output of said chemical reaction.
• An intermediate model of said simulation model is constructed by means of an artificial neural network, which comprises an input layer, at least one hidden layer and an output layer, the construction of said intermediate model being implemented by means of said learning base; and B.
• Said simulation model is constructed by means of said intermediate model and an additional layer implemented after said output layer of said neural network, said additional layer being formed by a linear operator which applies a linear correction to the output data of said output layer of said network of artificial neurons to ensure the conservation of the mass of the chemical elements forming said chemical species used in said chemical reaction.
• said additional layer is constructed by means of a linear correction calculation step and a step of applying said calculated linear correction.
• the linear correction is calculated for a number of chemical species of said chemical reaction corresponding to the number of chemical elements of said chemical reaction.
• the synaptic weights of the artificial neural network are optimized after the addition of the additional layer by means of the input and output data of the learning base.
• said input and output data are determined respectively before and after said chemical reaction, or respectively before and after a time step of said chemical reaction.
• said input data are mass fractions of said chemical species.
• said output data are mass fractions of said chemical species or variations of the mass fractions of said chemical species.
• the invention relates to a method for simulating at least one chemical reaction between several chemical species within a reactive system.
• a simulation model is constructed by means of the method for constructing a simulation model according to one of the preceding characteristics; and B. Said simulation model is applied to simulation input data, said simulation input data being representative of said mass fractions of said chemical species as input to said chemical reaction.
• said simulation model is constructed for a time step, and the step of applying said simulation model is reiterated for a plurality of successive time steps, and at each reiteration, said data of simulation input are the output data of the previous time step.
• said chemical reaction is a chemical reaction within an energy conversion system, for example a combustion within an engine or a chemical reaction within an electric battery, or a chemical reaction within within a fuel cell, or a chemical reaction within a system for transporting said chemical species.
• FIG. 1 illustrates the steps of the method for constructing a simulation model of a chemical reaction according to a first embodiment of the invention.
• FIG. 2 illustrates the steps of the method for constructing a simulation model of a chemical reaction according to a second embodiment of the invention.
• FIG. 3 illustrates the steps of the method for simulating a chemical reaction according to a first embodiment of the invention.
• FIG. 4 illustrates the steps of the method for simulating a chemical reaction according to a second embodiment of the invention.
• FIG. 5 illustrates the steps of the method for simulating a chemical reaction according to a third embodiment of the invention.
• FIG. 6 illustrates a simulation model of a chemical reaction according to one embodiment of the invention.
• FIG. 7 illustrates a simulation model of a chemical reaction according to one embodiment of the invention.
• FIG. 8 illustrates the curves of the mass fractions of four chemical elements as a function of time for a simulation by means of a method according to the prior art.
• FIG. 9 illustrates the curves of the mass fractions of four chemical elements as a function of time for a simulation by means of a method according to an embodiment of the invention.
• the present invention relates to a method for constructing a simulation model of at least one chemical reaction between several chemical species within a reactive system.
• the present invention relates to the prediction of chemical kinetics; that is to say the prediction of the chemical species during a chemical reaction, or at a given moment of the chemical reaction.
• chemical reaction the exchanges of matter between various chemical species in a reactive system.
• the chemical reaction can be written in the form of a balance equation.
• methane combustion without taking into account the presence of nitrogen in the air
• the chemical reaction can be written: CH 4 + 2O 2 ® CO 2 + 2 H 2 O.
• the reactive system is the system in which the chemical reaction takes place.
• the reactive system is the combustion chamber.
• the molecules used in the chemical reaction are called chemical species.
• the chemical species are methane CH 4 , dioxygen O 2 , carbon dioxide CO 2 and water H 2 O. works in the chemical reaction.
• the chemical elements are carbon C, hydrogen H, oxygen O.
• the present invention implements a learning base.
• the learning base comprises input data representative of the mass fractions of the chemical species entering the reactive system.
• the learning base further comprises output data representative of the mass fractions of the chemical species at the output of the reactive system.
• the mass fraction of a chemical species is defined by the ratio of the mass of the chemical species to the total mass of the mixture in the reactive system.
• input and output can correspond to one of the following definitions: this notion of input and output can correspond to a physical input and output of a system (before and after a chemical reaction), or to the evolution of the system between two time steps during a numerical simulation (before or after a time step of a chemical reaction).
• the input data of the learning base can correspond to the mass fractions of the chemical species.
• the output data from the learning base can correspond to the mass fractions of the chemical species.
• the output data from the learning base can correspond to the variations of mass fractions of the chemical species, these variations being those produced during the chemical reaction.
• the input and output data of the learning base can come from experimentally measured data and/or from simulation data.
• the method may include a prior step of constructing the learning base, from data measured experimentally and/or data from simulation.
• the method for constructing a simulation model comprises the following steps:
• Figure 1 illustrates, schematically and in a non-limiting manner, the steps of the method according to a first embodiment of the invention.
• an intermediate model is trained by means of an RNA artificial neural network and an OPT (learning) optimization of the RNA artificial neural network. This optimization step makes it possible to determine the synaptic weights of the RNA artificial neural network according to the data from the BAP learning base.
• the MSI simulation model is built, using the optimized (learned) RNA artificial neuron model and an additional layer, called the additional RNC conservation layer.
• the synaptic weights of the artificial neural network can be optimized after the addition of the additional conservation layer by means of the data from the learning base.
• learning is applied to the entire model formed of the artificial neural network and the additional conservation layer.
• the simulation model can be optimized by taking into account data from the learning base.
• FIG. 2 illustrates, schematically and in a non-limiting manner, the steps of the method of this embodiment.
• the artificial neural network RNA is optimized OPT (learning) simultaneously with the additional layer of RNC conservation in order to build the MSI simulation model.
• OPT learning
• an update of the synaptic weights of the artificial neural network with the additional RNC conservation layer is implemented from the learning database BAP.
• the invention relates to a method for numerical simulation of at least one chemical reaction between several chemical species within a reactive system.
• a numerical simulation method corresponds to a method for predicting chemical species during a chemical reaction within a reactive system.
• the simulation method implements the model construction method according to any one of the variants or combinations of variants described in the present application, and a step of applying the simulation model to simulation input data.
• the numerical simulation then generates simulation output data.
• the simulation input data and the simulation output data are of the same nature as the input and output data of the learning base.
• the simulation input and output data are representative of the mass fractions of the chemical species respectively at the input and at the output of the simulated reactive system.
• simulation input and output can correspond to one of the following definitions: on the one hand to a physical input and output of a system (before and after a chemical reaction), on the other hand to the evolution of the system between two time steps during a numerical simulation (before or after a time step of a chemical reaction).
• the simulation input data can correspond to the mass fractions of the chemical species.
• the simulation output data may correspond to the mass fractions of the chemical species.
• the simulation output data may correspond to variations in mass fractions of the chemical species.
• Simulation input data may be obtained experimentally, or may be obtained by simulation.
• the simulation process includes the following steps:
• steps 1 and 2 can be implemented beforehand only once, and step 3 can be repeated with the simulation model. These steps are detailed in the remainder of the description.
• FIG. 3 illustrates, schematically and in a non-limiting manner, the steps of the digital simulation method according to a first embodiment of the invention.
• an intermediate model is trained by means of an RNA artificial neural network and an OPT optimization of the RNA artificial neural network. This optimization step makes it possible to determine the synaptic weights of the RNA artificial neural network according to the data from the BAP learning base.
• the MSI simulation model is built, using the optimized RNA artificial neuron model and an additional RNC conservation layer.
• the simulation model MSI is applied SIM to simulation input data DES to determine simulation output data DSS.
• FIG. 4 illustrates, schematically and in a non-limiting manner, the steps of the digital simulation method according to a second embodiment of the invention.
• This second embodiment of the digital simulation method implements the second embodiment of the method for constructing the simulation model of FIG. 2.
• An artificial neural network RNA, and the additional conservation layer RNC are constructed.
• the artificial neural network RNA is optimized OPT simultaneously with the additional conservation layer RNC so as to build the simulation model MSI.
• an update of the synaptic weights of the artificial neural network with the additional RNC conservation layer is implemented from the learning database BAP.
• the simulation model MSI is applied SIM to simulation input data DES to determine simulation output data DSS.
• a simulation for which a simulation is carried out for a plurality of successive time steps (that is to say for several intermediate stages of the chemical reactions)
• a chemical reaction can be simulated step by step, in a precise and robust manner.
• FIG. 5 illustrates, schematically and in a non-limiting manner, the steps of the digital simulation method according to a third embodiment of the invention.
• This third embodiment of the digital simulation method implements the second embodiment of the method for constructing the simulation model of FIG. 2.
• An artificial neural network RNA, and the additional conservation layer RNC are constructed.
• the artificial neural network RNA is optimized OPT simultaneously with the additional conservation layer RNC so as to build the simulation model MSI.
• an update of the synaptic weights of the artificial neural network with the additional RNC conservation layer is implemented from the learning database BAP.
• the simulation model MSI is applied SIM to simulation input data DES to determine simulation output data DSS.
• the simulation step is repeated for new simulation input data, which correspond to the simulation output data of the previous time step.
• an intermediate model is constructed using an artificial neural network.
• the model is said to be intermediate, because it is not the simulation model obtained by the process: this intermediate model is modified in step 2.
• the artificial neural network makes it possible to obtain a model allowing rapid evaluation of the data of output data, and with a limited computer memory requirement, in particular faster than a complex numerical model representative of the chemical reaction.
• the artificial neural network is suitable for complex chemical reactions, in particular involving a large number of chemical species. Indeed, the simulations of these complex systems can require significant computer resources, in particular in terms of memory, and high simulation times.
• An artificial neural network is an algorithm whose parameters are optimized using a so-called training database derived from the learning base, which contains the learning input/output pairs.
• the artificial neural network includes an input layer, at least one hidden layer and an output layer.
• the input layer and the output layer have a number of neurons which corresponds to the number of chemical species in the chemical reaction.
• the construction of the artificial neural network is implemented by means of the learning base.
• the synaptic weights of the neural network are learned in a supervised way to match the input and output data of the learning base.
• the intermediate model makes it possible to determine output data from input data, while being representative of the data of the learning base.
• the inputs of the intermediate model are of the same nature as the input data of the learning base, and the outputs of the intermediate model are of the same nature as the output data of the learning base.
• the artificial neural network can be of any form, for example a deep neural network, a recurrent neural network, a multi-layer Perceptron neural network, etc.
• the construction of the intermediate model may include a validation step of the artificial neural network, in particular to avoid problems of overlearning.
• the validation can be implemented by means of part of the data from the learning base.
• the simulation model is constructed by means of the intermediate model constructed in step 1 and an additional layer, called additional conservation layer.
• the additional conservation layer is added after the output layer of the artificial neural network.
• the additional conservation layer is formed by a linear operator for the conservation of the mass of the chemical elements present in the chemical reaction.
• the simulation model comprises the input layer, the at least one hidden layer and the output layer of the artificial neural network, the additional conservation layer.
• the additional conservation layer makes it possible to verify the conservation of the mass of the chemical elements within the simulation model itself, which makes the simulation model more precise.
• the linearity of the additional conservation layer makes it possible to retain the advantages of the artificial neural network: simulation of complex systems with limited computer memory and limited calculation time.
• the additional conservation layer comprises a number of inputs and a number of outputs which correspond to the number of chemical species of the chemical reaction.
• data representative of the mass fractions of the chemical species of the chemical reaction are determined. Once determined, the additional layer of conservation is static and constant. In other words, the additional preservation layer is not modified.
• the additional conservation layer can be constructed by means of a step for calculating the linear correction and a step for applying the calculated linear correction.
• the correction step can be applied for a number of chemical species which corresponds to the number of chemical elements.
• the chemical species chosen can contain at least once each atom present in the mixture. Thus, an underdetermination of the problem can be avoided.
• these two steps can be preceded by a normalization inversion step, and can be followed by a standardization step.
• a mixture of chemical species numbered 1, ..., N s and described by the mass fraction Y k of each of these species is considered.
• an input chemical state is denoted in and the corresponding output out.
• the corresponding mass fractions are denoted and respectively.
• the passage between entry and exit involves a number of chemical reactions between these species.
• the fraction Y j corresponds to the mass of atom j relative to the total mass of the mixture.
• This mass fraction can be written from the mass fractions of species as follows: Where M k corresponds to the molar mass of the chemical species k, is the number of atoms j present in the molecule of chemical species k and M j corresponds to the molar mass of atom j.
• M k corresponds to the molar mass of the chemical species
• M j corresponds to the molar mass of atom j.
• This relation can be written in a matrix way by setting the vector containing the mass fractions of atoms and the vector of the mass fractions chemical species. We can then write:
• g the output vector of the artificial neural network. This embodiment consists in correcting the vector g in order to satisfy the previous relationship.
• the correction can be written by adding an a priori term to be determined, denoted ⁇ :
• This equation is a linear system with unknowns .
• this system contains N s unknowns and N a equations.
• the number of atoms is much less than the number of species (N a « N s ), and the system is therefore underdetermined.
• N a the number of species
• N a the number of species
• a new correction vector can be defined as follows:
• M' corresponds to the sub-matrix of M containing only the columns corresponding to the species .
• Mr corresponds to the sub-matrix of M containing only the columns corresponding to the species .
• a modification of the neural network in this case a modification of the synaptic weights, can be carried out after the application of the correction.
• this correction being applied inside the neural network, the parameters of the at least one upstream hidden layer can be adjusted so that the output is well predicted, and this despite the fact that an arbitrarily chosen set of species is selected to correct the mass of the atoms.
• FIG. 6 illustrates, schematically and in a non-limiting manner, the construction of the simulation model according to one embodiment of the invention.
• the RNA artificial neural network includes an input layer CE, for which the mass fractions of the species chemicals are the input data.
• the RNA artificial neural network includes at least one CC hidden layer.
• the artificial neural network RNA further comprises an output layer CS, for which mass fractions of the chemical species g 1 , ..., g Ns constitute the output data.
• the layers of the artificial neural network RNA comprise a plurality of artificial neurons NA, represented by circles. Each artificial neuron NA is associated with a synaptic weight.
• the RNC simulation model further includes an additional layer of conservation L c which determines the linear correction of the output data g 1 ,...,g Ns of the artificial neural network RNA to determine the mass fractions of the chemical species at the outlet, while ensuring the conservation of the mass of the chemical elements.
• the additional conservation layer L c takes the input data into account.
• Figure 7 is a figure similar to figure 6, in the case where the output data are variations of the mass fraction of the chemical species w 1 ..., w Ns with .
• the elements identical to FIG. 6 are not detailed again.
• the additional conservation layer L c does not need to take into account the input data
• This step relates only to the method of digital simulation of at least one chemical reaction.
• the simulation model constructed in step 2 is applied to simulation input data.
• the chemical kinetics of the chemical reaction are simulated from simulation input data and from the simulation model. It can be a simulation for the entire chemical reaction or for a time step of the chemical reaction.
• a chemical reaction can be simulated step by step, in a precise and robust manner.
• the chemical reaction may be a chemical reaction from the field of reactive fluid mechanics.
• the chemical species can be liquids or gases.
• the invention may relate to a chemical reaction of combustion, in particular within a combustion engine, a turbine, or any similar system.
• the methods according to the invention can make it possible to model complex systems with numerous chemical species.
• the invention may relate to a chemical reaction within an energy storage and supply system, in particular within an electric battery or a fuel cell.
• the methods according to the invention can make it possible to model complex systems with numerous chemical species.
• the invention may relate to a chemical catalysis reaction, in particular a catalysis within an exhaust gas post-treatment system, or a catalysis implemented in the field of the production of hydrocarbons.
• a chemical catalysis reaction in particular a catalysis within an exhaust gas post-treatment system, or a catalysis implemented in the field of the production of hydrocarbons.
• the invention may relate to a chemical reaction specific to fluid flows in a porous medium, in particular a precipitation or dissolution reaction of one or more minerals.
• a chemical reaction specific to fluid flows in a porous medium in particular a precipitation or dissolution reaction of one or more minerals.
• the invention relates to a digital chemical reaction simulator implementing the simulation model constructed by the method according to one of the variants or combinations of variants of the method according to the invention.
• the digital simulator uses simulation input data to determine simulation output data.
• the simulation input data and the simulation output data are representative of the mass fractions of the chemical species of the chemical reaction.
• the invention relates to a method for designing a reactive system, in which at least one chemical reaction takes place, in which the following steps are implemented: -
• the chemical reaction is numerically simulated in the reactive system by means of the simulation method according to any one of the variants or combinations of variants previously described, and -
• the reactive system is designed, and the chemical reaction is implemented in the reactive system.
• a plurality of numerical simulations can be carried out, for a plurality of chemical reactions (for example by modifying the chemical species initially present) and/or for a plurality of reactive systems (for example for several dimensions or shapes of reactive systems).
• the comparison of the simulations makes it possible to determine the chemical reaction and/or the reactive system to be designed.
• the present invention may also relate to a method for controlling a reactive system, in which at least one chemical reaction takes place, and in which the following steps are implemented: - the chemical reaction is numerically simulated in the reactive system by means of of the simulation method according to any one of the variants or combinations of variants previously described, and - the reactive system is controlled, preferably in real time, as a function of the simulated chemical reaction, in particular to avoid dangerous situations, or not suitable for the intended use.
• control may consist in controlling the quantity of air or fuel in the combustion chamber.
• the purpose of this control may be to limit polluting emissions, or to avoid the phenomenon of knocking, etc.
• control may correspond to the control of the voltage or of the current, in particular to limit overheating or premature aging of the electric battery.
• control may in particular correspond to the control of the air intake, to optimize the chemical kinetics within the fuel cell, so as to increase the performance thereof.
• the case considered here is a case of methane combustion at constant pressure, where the atoms generally present are carbon (C), hydrogen (H), oxygen (O) and nitrogen (N).
• the learning base is the same, and the number of layers as well as the number of neurons of the artificial neural network are the same.
• the neural network selected is a network of multi-layer Perceptron type (from the English “Multi-Layer Perceptron”) which comprises two hidden layers with 64 units each.
• the database consists of simulations of the combustion of H 2 /air mixtures for different initial temperatures T 0 and richness ⁇ .
• One thousand simulations are selected from the learning base, using a Latin Hypercube Sampling algorithm.
• This sampling is carried out for values of T 0 between 1600 K (approximately 1326°C) and 1800K (approximately 1526°C), and richness values between 0.7 and 1.5.
• the optimization of the network weights is performed using a gradient descent method.
• FIG. 8 illustrates the curves of the process according to the prior art, as a function of time t in ms, of the normalized mass fraction of carbon Y c (top left), of the normalized mass fraction of hydrogen Y H ( top right), the normalized mass fraction of oxygen Y o (bottom left), and the normalized mass fraction of nitrogen Y N (bottom right).
• the curves of the mass fraction of carbon and hydrogen are not constant over time.
• the artificial neural network according to the prior art does not make it possible to guarantee the conservation of the mass of the carbon atoms and of the hydrogen atoms, whereas the conservation of the masses of atoms during the simulation is a point critical. Indeed, a deviation of the mass at a given moment can have negative repercussions on the rest of the simulation, and make the simulation imprecise.
• FIG. 9 illustrates the curves of the process according to the invention (with the additional preservation layer), as a function of time t in ms, of the mass fraction of the normalized carbon Yc (top left), of the mass fraction of the normalized hydrogen Y H (top right), normalized mass fraction of oxygen Y O (bottom left), and normalized mass fraction of nitrogen Y N (bottom right). Note that the four curves are constant over time. Consequently, the use of the layer L c according to the invention allows the conservation of the mass of the atoms, and consequently better accuracy and better robustness of the simulation model.

Landscapes

• Engineering & Computer Science (AREA)
• Chemical & Material Sciences (AREA)
• Theoretical Computer Science (AREA)
• Computing Systems (AREA)
• Life Sciences & Earth Sciences (AREA)
• Bioinformatics & Computational Biology (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Crystallography & Structural Chemistry (AREA)
• General Health & Medical Sciences (AREA)
• Health & Medical Sciences (AREA)
• Evolutionary Computation (AREA)
• Data Mining & Analysis (AREA)
• Artificial Intelligence (AREA)
• Software Systems (AREA)
• Analytical Chemistry (AREA)
• Medical Informatics (AREA)
• Databases & Information Systems (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Chemical Kinetics & Catalysis (AREA)
• Physics & Mathematics (AREA)
• Biophysics (AREA)
• Molecular Biology (AREA)
• General Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Mathematical Physics (AREA)
• Computational Linguistics (AREA)
• Biomedical Technology (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)
• Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=77411771

Family Applications (1)

Country Status (6)

Families Citing this family (1)

Family Cites Families (2)

• 2021
  • 2021-05-05 FR FR2104735A patent/FR3122767B1/fr active Active
• 2022
  • 2022-04-22 US US18/555,771 patent/US20240212794A1/en active Pending
  • 2022-04-22 EP EP22724743.4A patent/EP4334949A1/de active Pending
  • 2022-04-22 CA CA3213684A patent/CA3213684A1/fr active Pending
  • 2022-04-22 JP JP2023566838A patent/JP2024517767A/ja active Pending
  • 2022-04-22 WO PCT/EP2022/060739 patent/WO2022233602A1/fr active Application Filing

Also Published As

Similar Documents

Legal Events