Recommendation system for the design of experiments

Abstract

A method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more tuneable parameter(s) characterizing the one or more target chemical reaction(s), the method comprising: - providing a request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s), wherein the request comprises an indication of the one or more target chemical reaction(s) and one or more target parameter value(s) associated with a target parameter characterizing one or more target chemical reaction(s), and wherein adapting the one or more parameter value(s) associated with one or more tuneable parameter(s) influences parameter values associated with the target parameter, - providing functional specification data related to one or more functions of one or more operating engine(s), wherein at least one of the operating engine(s) is configured to provide parameter values, - providing one or more input data structure related to operating input data suitable for being provided to the one or more operating engine(s) - providing one or more model instruction(s) for instructing a data-driven model to generate operating input data for triggering at least one operating engine selected from a plurality of operating engines to provide the one or more parameter value(s) associated with one or more tuneable parameter(s), - providing parameter task instructions comprising the indication of the one or more target chemical reaction(s), the one or more input data structure(s), the functional specification data, the one or more model instruction(s) and the one or more target parameter value(s) to a data-driven model for generating the operating input data, wherein the data-driven model is configured to follow task instructions, - providing the operating input data to the at least one selected operating engine for generating the one or more parameter value(s) associated with one or more tuneable parameter(s), - providing the one or more parameter value(s) for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s).

Description

[0001] RECOMMENDATION SYSTEM FOR THE DESIGN OF EXPERIMENTS

[0002] TECHNICAL FIELD

[0003] The invention relates to sustainable design of experiments and a method for controlling one or more target chemical reaction(s), use of parameter task instructions, use of a data-driven model, an apparatus for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more tuneable parameter(s) characterizing the one or more target chemical reaction(s).

[0004] TECHNICAL BACKGROUND

[0005] Chemicals are the basis for materials used and produced in industry. Therefore, chemical products need to be tailored towards their intended use by developing efficient production procedures. This requires a lot of time and resources. Hence, it is desired to shorten the development time for chemical production procedures and increase the resource-efficiency with respect to developing chemical production procedures.

[0006] SUMMARY

[0007] In an aspect, this disclosure relates to a, in particular computer-implemented, method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more tuneable parameter(s) characterizing the one or more target chemical reaction(s), the method comprising: providing a request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s), wherein the request comprises an indication of the one or more target chemical reaction(s) and one or more target parameter value(s) associated with a target parameter characterizing one or more target chemical reaction(s), and wherein adapting the one or more parameter value(s) associated with one or more tuneable parameter(s) influences parameter values associated with the target parameter, providing functional specification data related to one or more functions of one or more operating engine(s), wherein at least one of the operating engine(s) is configured to provide parameter values, providing one or more input data structure related to operating input data suitable for being provided to the one or more operating engine(s) providing one or more model instruction(s) for instructing a data-driven model to generate operating input data for triggering at least one operating engine selected from a plurality of operating engines to provide the one or more parameter value(s) associated with one or more tuneable parameter(s), providing parameter task instructions comprising the indication of the one or more target chemical reaction(s), the one or more input data structure(s), the functional specification data, the one or more model instruction(s) and the one or more target parameter value(s) to a data-driven model for generating the operating input data, wherein the data-driven model is configured to follow task instructions, providing the operating input data to the at least one selected operating engine for generating the one or more parameter value(s) associated with one or more tuneable parameter(s), providing the one or more parameter value(s) for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s).

[0008] In another aspect, it relates to use of a data-driven model for generating operating input data for triggering at least one operating engine to determine one or more parameter value(s) for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s), in particular a data-driven model and / or operating input data as described herein.

[0009] In another aspect, it relates to use of parameter task instructions as described herein for determining one or more parameter value(s) for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s).

[0010] In another aspect, it relates to an apparatus for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more tuneable parameter(s) characterizing the one or more target chemical reaction(s), the apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to perform the steps of any one of the methods as described herein.

[0011] In another aspect, it relates to a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform any one of the methods as presented herein.

[0012] EMBODIMENTS

[0013] Any disclosure, embodiments and examples described herein relate to the methods, the systems, apparatuses, chemical products and computer elements lined out above and below. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples.

[0014] In the following, terminology as used herein and / or the technical field of the present disclosure will be outlined by ways of definitions and / or examples. Where examples are given, it is to be understood that the present disclosure is not limited to said examples. These and other objects, which become apparent upon reading the following description, are solved by the subject matters of the independent claims. The dependent claims refer to embodiments of the disclosure.

[0015] Chemical products are starting materials for a plurality of different end products. As a consequence, chemical products have to provide a variety of changing properties tailored to the intended end product. The production of chemical products starts with raw materials that are processed via one or more processing steps including for example chemical reactions conducted in reactors and purification steps. Typically, chemical products are obtained from two or more chemical reactions changing the chemical structure of the reactants and thus, changing the properties of the chemical product. For example, a liquid such as monoethylenglycol and a solid such as terephthalic acid can be converted to yield polyester. Polyester is a functional polymer with distinct properties depending on the educts and reaction conditions, i.e. tuneable parameters. The properties of the chemical product such as melting point or scratch resistance, i.e. target parameters, cannot be tuned directly but are a consequence of adapting the tuneable parameters.

[0016] Processing a request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s) allows to guide operators controlling chemical production equipment in one or more target chemical reaction(s) to achieve target values of target parameters such as conversion rates or reaction velocities through controlling tuneable parameters. Said request can be provided in natural language, i.e. in an unstructured manner. Providing parameter task instructions to a data-driven model allows to convert the unstructured request to structured operating input data for recommending parameter values of tuneable parameters to arrive at the target parameter values of the target parameter. Thereby, human-machine interaction is improved as the user is not required to fulfill restrictive formatting and / or language requirements. Further, small errors, e.g. misspelling, which would usually lead to a system error, can still be understood from the context of the request. Hence, errors by human users can be corrected when generating the operating input data to control a recommendation system for obtaining tuneable parameter values. As a consequence, more request can be processed successfully while saving resources for providing and evaluating system errors. This increase in processing efficiency for performing target chemical reactions that yield target parameter values of target parameters, enables improved human-machine interaction and recommending of tuneable parameter values. Ultimately, this improves tailoring chemical products towards desired application properties.

[0017] In an embodiment, functional specification data may be related to one or more functions of the one or more operating engine(s). The functional specification data may comprise a functional specification associated with the one or more operating engine(s). The functional specification data may be indicative of a processing of operating input data by the one or more operating engine(s), in particular to operating output data. The functional specification associated with the one or more operating engine(s) may be indicative of one or more functions associated with the one or more operating engine(s), in particular the one or more functions carried out by the one or more operating engine(s).

[0018] In an embodiment, the one or more input data structure(s) may be indicative of one or more input data formats suitable for being provided to the one or more operating engine(s). The one or more input data structure(s) may refer to an arrangement of input data, in particular input data to the one or more operating engine(s). Operating input data may be input data to the one or more operating engine(s). The input data structure may be indicative of an arrangement of the one or more target properties, optionally a target type of chemical product and / or a targer field of application associated with the chemical product. Operating input data associated with the one or more input data structure(s) may be provided to the one or more operating engine(s), in particular the at least one selected operating engine.

[0019] In an embodiment, operating input data may be provided to the one or more operating engine(s), in particular the selected operating engine. The operating input data may comprise structured data. The operating input data may be associated with, in particular include, the one or more target properties, the operating input data may be derived from and / or may depend on the one or more target properties. The operating input data may be associated with a data structure related to selected operating engine. The one or more operating engine(s) may be configured to receive the operating input data and generating at least a part of the chemical product data, in particular the operating output data, from the operating input data. The operating output data may be associated with a digital representation of the chemical structure of the chemical product with the one or more target properties, in particular may include a digital representation of the chemical structure of the chemical product with the one or more target properties. The operating output data may be derived from and / or may depend on the one or more target properties.

[0020] In an embodiment, the target parameter may be a target property of a chemical product, in particular a target chemical product. In an embodiment, the one or more target properties may be one or more properties required for processing the chemical product, in particular to an end product. The one or more target properties may include one or more chemical properties, one or more physical properties, one or more environmental attributes and / or one or more biological properties. The target property may include a userspecific target property. The user-specific target property may be provided and / or received via a user interface. The target property may be provided by a chemical product processing facility. The chemical product processing facility may trigger the receiving to the request associated with the one or more target properties.

[0021] In an embodiment, environmental attribute may comprise at least one of emission data of the chemical product, recyclate content of the chemical product, bio-based content of the chemical product, renewable content of the chemical product, chemical product declaration data, chemical product safety data or a combination thereof. Emission data may comprise any data related to environmental footprint. The environmental footprint may refer to an entity and its associated environmental footprint. The environmental footprint may be entity specific. For instance, the environmental footprint may relate to a chemical product, a company, a process such as a manufacturing process, a raw material or basic substance, a chemical product or material, a component, a component assembly, an end product, combinations thereof or additional entityspecific relations. Emission data may include data relating to carbon footprint of a chemical product. Emission data may include data relating to greenhouse gas emissions e.g. released in production of the chemical product. Emission data may include data related to greenhouse gas emissions. Greenhouse gas emissions may include emissions such as carbon dioxide (CO2) emission, methane (CH4) emission, nitrous oxide (N2O) emission, hydrofluorocarbons (HFCs) emission, perfluorocarbons (PFCs) emission, sulphurhexafluoride (SFe) emission, nitrogen trifluoride (NF3) emission, combinations thereof and additional emissions. Emission data may include data related to greenhouse gas emissions of an entities or companies own operations (production, power plants and waste incineration). Scope 2 comprise emissions from energy production which is sourced externally. Scope 3 comprise all other emissions along the value chain. Specifically, this includes the greenhouse gas emissions of raw materials obtained from suppliers. Product Carbon Footprint (PCF) sum up greenhouse gas emissions and removals from the consecutive and interlinked process steps related to a particular product. Cradle-to-gate PCF sum up greenhouse gas emissions based on selected process steps: from the extraction of resources up to the factory gate where the product leaves the company. Such PCFs are called partial PCFs. In order to achieve such summation, each company providing any products must be able to provide the scope 1 and scope 2 contributions to the PCF for each of its products as accurately as possible and obtain reliable and consistent data for the PCFs of purchased energy (scope 2) and their raw materials (scope 3).

[0022] In an embodiment, chemical property may be a property established by changing the chemical structure, in particular of a material. The chemical product may be obtained by changing the chemical structure of one or more educts. Chemical property of the chemical product may include properties associated with a chemical reaction of the chemical product. Examples may include reactivity, electronegativity or the like. Physical property may be one of the following: mechanical properties, electrical properties, optical properties, thermal properties or the like. For example, physical property may comprise one or more of the following density, scratch resistance, electrical conductivity, color, absorption, heat capacity or the like. Biological property may include a property related to an activity of a living organism.

[0023] In an embodiment, task instruction may include at least a part of the request, at least a part of the functional specification data and / or at least a part of the one or more input data structure(s). The task instruction may be generated by combining at least the part of the request, at least the part of the functional specification data and / or at least the part of the one or more input data structure(s). The task instruction may be provided to the one or more data-driven models. The one or more data-driven models may be configured to select at least one operating engine from the one or more operating engine(s). The selected operating engine may be associated with one or more function(s) for providing the chemical product with the one or more target properties, in particular for providing a digital representation of the chemical structure of the chemical product with the one or more target properties. In an embodiment, the task instruction may comprise an allocation task instruction and a structure task instruction. The allocation task instruction may be generated by combining at least a part of the request and the functional specification data. The allocation task instruction may be provided to an allocation model for selecting at least one operating engine. The allocation model may provide indication of the at least one selected operating engine in response to being provided with the allocation task instruction. The indication of the at least one selected operating engine may be associated and / or indicative of the selected operating engine and at least a part of the request, in particular, the one or more target properties. The structure task instruction may be generated by merging at least a part of the indication of the at least one selected operating engine and the one or more input data structure(s). The structure task instruction may be provided to a structure model for generating operating input data, in particular structured operating input data. The structure model may provide operating input data in response to being provided with the structure task instruction.

[0024] In an embodiment, providing the task instruction may include mapping the task instruction to vectorized task instruction. The one or more data-driven models may be configured to map the vectorized task instruction to vectorized operating input data, and to map the vectorized operating input data to operating input data. The vectorized task instruction may be related to and / or may represent at least a part of the request, at least a part of the functional specification data and / or at least a part of the one or more input data structure(s). The vectorized task instruction may be obtained by passing the task instruction through one or more embedding layers. The one or more data-driven models may include one or more embedding layers. The one or more embedding layers may be configured to map unstructured data to a structured numerical representation, in particular vectorized data. The vectorized task instruction may be indicative and / or may depend on the sequence of one or more elements and / or string data related to the task instruction. The sequence may be encoded by positional encoding of the vectorized task instruction. Positional encoding may be performed prior to providing the task instruction to the one or more data-driven models or by processing of the one or more data-driven models, in particular the one or more embedding layer(s). Alternatively, the self-attention mechanism of the encoder may include relative positional encoding as described 1803.02155.pdf (arxiv.org). The vectorized task instruction may be associated with, in particular comprise, structured numerical data, in particular a tensor, related to the request, the functional specification data and the one or more input data structure(s). In particular, the vectorized task instruction may represent the task instruction, preferably at least a part of the request, the one or more input data structure(s) and / or the functional specification data. The vectorized task instruction may be associated with a smaller amount of data than the task instruction. Further, the vectorized task instruction may be processed by one or more matrix operation(s) associated with the one or more data-driven models. Hence, the vectorized task instruction may be faster processable for matrix operations of the one or more data-driven models. The vectorized task instruction may be a structured digital representation of the task instruction including unstructured data, in particular associated with a machine processable numerical, preferably float, format. The structured vectorized task instruction can be efficiently processed by one or more data-driven models and allows to save significant computational resources for processing unstructured requests.

[0025] The one or more data-driven models may include one or more encoder block(s) and / or one or more decoder block(s) for mapping the vectorized task instruction to the context task instruction. Context task instruction may be related to vectorized task instruction. Context task instruction may be vectorized task instruction processed by one or more matrix operation(s) associated with the one or more data-driven models. The one or more encoder block(s) and / or one or more decoder block(s) may be configured to map the vectorized task instruction to the context task instruction, in particular by taking a sequence of one or more elements and / or string data related to the task instruction into account. The context task instruction may be associated with, in particular comprise, structured numerical data, in particular a tensor. Context task instruction may represent a sequence of elements associated with the task instruction and a relation between the elements of the sequence. The relation between the elements may be obtained by applying the one or more matrix operation(s) to the vectorized task instruction. The elements may comprise at least a part of a word, a number, a symbol or the like. Thereby, the relation between the elements, e.g. words in a text, can be understood by the one or more data-driven models. This improves the mapping between the task instruction and the operating input data. Consequently, the operating engine(s) can be operated more efficiently to process the request.

[0026] Further, the one or more data-driven models may comprise one or more encoder output(s), in particular where the one or more data-driven models may include one or more encoder block(s). Further, the one or more data- driven models may comprise one or more decoder output(s), in particular where the one or more data-driven models may include one or more decoder block(s). Additionally or alternatively, the one or more encoder output(s) and / or the one or more decoder output(s) may be configured to map the context task instruction to a plurality of confidence scores associated with a plurality of elements, in particular a distribution of confidence scores associated with the plurality of elements. At least a part of the plurality of the element(s) may be associated with, in particular included by, the operating input data. The one or more encoder block(s) and / or decoder block(s) may generate a distribution of confidence scores associated with the plurality of elements comprising one or more element(s) associated with the operating input data. The operating input data may be determined by selecting the one or more element(s) associated with the operating input data according to the one or more confidence score(s) associated with the one or more element(s). In an embodiment, the one or more encoder output(s) and / or one or more decoder output(s) may be configured to select the one or more element(s) associated with the operating input data according to the one or more confidence score(s) associated with the one or more element(s). Selecting the one or more element(s) associated with the operating input data according to the one or more confidence score(s) associated with the one or more element(s) may comprise receiving a range of confidence scores and selecting the one or more element(s) associated with one or more confidence score(s) within the range. The one or more encoder block(s) and / or one or more encoder output(s) and / or one or more decoder output(s) may map the vectorized task instruction to operating input data, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the operating input data. The one or more decoder block(s) and one or more decoder output(s) may map the vectorized task instruction to operating input data, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the operating input data.

[0027] In an embodiment, the unstructured data, in particular the unstructured request, the unstructured task instruction, the unstructured functional specification data, the unstructured indication of the at least one selected operating engine and / or the unstructured chemical product data, may include string data and / or a sequence of one or more elements. An element may comprise a number, a letter, a symbol or the like. Humans need to understand and intervene into decisions of machines, in particular in fields with high safety requirements and humans as domain experts. This disclosure allows for translating machine interpretable data into human interpretable data. Further, an intervention and / or triggering with human interpretable data input is enabled. Hence, this disclosure enables trustworthy Al.

[0028] In an embodiment, the one or more data-driven models may be task specific, partially task agnostic or task agnostic. The one or more task specific data-driven models may be configured to perform one of select at least one operating engine from the one or more operating engine(s) or structure the task instruction to generate operating input data or classify if a data structure related to the operating input data corresponds to the input data structure related to the selected operating engine or generate unstructured data in response to being provided with at least partially structured processing task instruction per data-driven model. The one or more partially agnostic data-driven models may be configured to perform one or more of select at least one operating engine from the one or more operating engine(s) or structure the task instruction to generate operating input data or classify if the data structure related to the operating input data corresponds to the input data structure related to the selected operating engine or generate unstructured data in response to being provided with at least partially structured processing task instruction per data-driven model. In particular, at least one of the one or more partially agnostic may be configured to perform at least two of select at least one operating engine from the one or more operating engine(s) or structure the task instruction to generate operating input data or classify if the data structure related to the operating input data corresponds to the input data structure related to the selected operating engine or generate unstructured data in response to being provided with at least partially structured processing task instruction per data-driven model. In an embodiment, the one or more agnostic data-driven models may include at least one data-driven model configured to select at least one operating engine from the one or more operating engine(s) and structure the task instruction to generate operating input data and classify if the data structure related to the operating input data corresponds to the input data structure related to with the selected operating engine and generate unstructured data in response to being provided with at least partially structured processing task instruction. In an embodiment, the one or more data-driven models may include a structure data-driven model, an allocation model, a validation model and / or a processing model. At least partially agnostic models provide the advantage of requiring less models for generating the chemical product data. Thus, computational resources for building and maintaining a plurality of models can be saved or used to obtain more accurate at least partially agnostic models. Using task-specific models in turn allows for more accurate and robust generation of operating input data. This in turn improves the robustness of generating the chemical product data and hence, the robustness of providing the chemical product with the one or more target properties.

[0029] In an embodiment, providing the allocation task instruction to the allocation model may include mapping the allocation task instruction to vectorized allocation task instruction. The allocation model may be configured to map the vectorized allocation task instruction to vectorized indication of the at least one selected operating engine, and to map the vectorized indication of the at least one selected operating engine to indication of the at least one selected operating engine.

[0030] The allocation model may include one or more encoder block(s) and / or one or more decoder block(s) for mapping the vectorized allocation task instruction to the vectorized indication of the at least one selected operating engine. The one or more encoder block(s) and / or one or more decoder block(s) may be configured to map the vectorized selection task instruction to the vectorized indication of the at least one selected operating engine, in particular by taking a sequence of one or more elements and / or string data related to the selection task instruction into account. Further, the allocation model may comprise one or more encoder output(s), in particular where the allocation model may include one or more encoder block(s). Further, the allocation model may comprise one or more decoder output(s), in particular where the allocation model may include one or more decoder block(s).

[0031] The allocation model may include one or more encoder block(s) and / or one or more decoder block(s) for mapping the vectorized allocation task instruction to the context allocation task instruction. Context allocation task instruction may be related to vectorized allocation task instruction. Context allocation task instruction may be vectorized allocation task instruction processed by one or more matrix operation(s) associated with the allocation model. The one or more encoder block(s) and / or one or more decoder block(s) may be configured to map the vectorized selection task instruction to the context selection task instruction, in particular by taking a sequence of one or more elements and / or string data related to the selection task instruction into account. The context selection task instruction may be associated with, in particular comprise, structured numerical data, in particular a tensor. Context selection task instruction may represent a sequence of elements associated with the selection task instruction and a relation between the elements of the sequence. The relation between the elements may be obtained by applying the one or more matrix operation(s) to the vectorized selection task instruction. The elements may comprise at least a part of a word, a number, a symbol or the like. Thereby, the relation between the elements, e.g. words in a text, can be understood by the allocation model. This improves the mapping between the selection task instruction and the indication of the at least one selected operating engine. Consequently, the operating engine(s) can be operated more efficiently to process the request.

[0032] Further, the allocation model may comprise one or more encoder output(s), in particular where the allocation model may include one or more encoder block(s). Further, the allocation model may comprise one or more decoder output(s), in particular where the allocation model may include one or more decoder block(s). Additionally or alternatively, the one or more encoder output(s) and / or the one or more decoder output(s) may be configured to map the context allocation task instruction to a plurality of confidence scores associated with a plurality of elements, in particular a distribution of confidence scores associated with the plurality of elements. At least a part of the plurality of the element(s) may be associated with, in particular included by, the indication of the at least one selected operating engine. The one or more encoder block(s) and / or decoder block(s) may generate a distribution of confidence scores associated with the plurality of elements comprising one or more element(s) associated with the indication of the at least one selected operating engine. The indication of the at least one selected operating engine may be determined by selecting the one or more element(s) associated with the indication of the at least one selected operating engine according to the one or more confidence score(s) associated with the one or more element(s). In an embodiment, the one or more encoder output(s) and / or one or more decoder output(s) may be configured to select the one or more element(s) associated with the indication of the at least one selected operating engine according to the one or more confidence score(s) associated with the one or more element(s). Selecting the one or more element(s) associated with the indication of the at least one selected operating engine according to the one or more confidence score(s) associated with the one or more element(s) may comprise receiving a range of confidence scores and selecting the one or more element(s) associated with one or more confidence score(s) within the range. The one or more encoder block(s) and / or one or more encoder output(s) and / or one or more decoder output(s) may map the vectorized allocation task instruction to indication of the at least one selected operating engine, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the indication of the at least one selected operating engine. The one or more decoder block(s) and one or more decoder output(s) may map the vectorized allocation task instruction to indication of the at least one selected operating engine, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the operating input data. The vectorized indication of the at least one selected operating engine may represent the indication of the at least one selected operating engine, in particular the sequence of the plurality of elements and / or the string data related to the indication of the at least one selected operating engine. The vectorized indication of the at least one selected operating engine may comprise a numerical representation of the indication of the at least one selected operating engine. The vectorized allocation task instruction may be indicative and / or may depend on the sequence of one or more elements and / or string data related to the allocation task instruction. The vectorized allocation task instruction may be related to and / or may represent at least a part of the request and / or at least a part of the functional specification data. The vectorized allocation task instruction may be obtained by passing the allocation task instruction through one or more embedding layers. Hence, the one or more allocation model may include one or more embedding layers. The one or more embedding layers may be configured to map unstructured data to a structured numerical representation, in particular vectorized data. The vectorized allocation task instruction may be indicative and / or may depend on the sequence of one or more elements and / or string data related to the allocation task instruction. The sequence may be encoded by positional encoding of the vectorized allocation task instruction. Positional encoding may be performed prior to providing the allocation task instruction to the structure model or by processing of the structure model, in particular the one or more embedding layer(s). Alternatively, the self-attention mechanism of the encoder may include relative positional encoding as described 1803.02155. pdf (arxiv.org). The vectorized allocation task instruction may comprise structured numerical data, in particular a tensor, related to the request and the functional specification data. In particular, the vectorized allocation task instruction may represent the allocation task instruction, preferably at least a part of the request and the functional specification data. The vectorized allocation task instruction may be associated with a smaller amount of data than the allocation task instruction. Further, the vectorized allocation task instruction may be processed faster by the allocation model, in particular by one or more matrix operation(s). The vectorized allocation task instruction may require less computational storage than the allocation task instruction. The vectorized allocation task instruction may be a structured digital representation of the allocation task instruction including unstructured data. The vectorized allocation task instruction can be efficiently processed by the allocation model and allows to save significant computational resources for processing unstructured requests. Thereby, the chemical product data can be generated reliably by the selected operating engine.

[0033] In an embodiment, providing parameter task instructions may comprise providing allocation task instructions comprising the indication of the one or more target chemical reaction(s), the functional specification data, the one or more target parameter value(s), and one or more model instruction(s) for triggering the allocation model to select the at least one operating engine from the plurality of operating engines to an allocation model, and wherein the allocation model is configured to follow the task instructions, and generating operating input data by providing structuring task instructions comprising the indication of the at least one selected operating engine, one or more input data structure(s) characterizing an input data structure associated with the at least one selected operating engine, the indication of the one or more target chemical reaction (s), the one or more target parameter value(s) and the one or more model task instruction(s) for instructing the structure model to generate the operating input data.

[0034] The allocation task instructions may trigger the allocation model to select at least one operating engine from a plurality of operating engine(s). The allocation model may be configured to follow task instructions, in particular the allocation task instructions. Generating the one or more parameter value(s) by the at least one selected operating engine based on the request may comprise generating operating input data by providing structuring task instructions to a structure model. The structuring task instructions may comprise an indication of the at least one selected operating engine, one or more input data structure(s) characterizing an input data structure associated with the plurality of operating engines, in particular the at least one selected operating engine, the indication of the one or more target chemical reaction(s) and the one or more target parameter value(s) and one or more model task instruction(s) for triggering the structure model to generate the operating input data from the indication of the at least one selected operating engine, one or more input data structure(s), the indication of the one or more target chemical reaction(s) and the one or more target parameter value(s). The allocation model may provide the indication of the at least one selected operating engine, in particular in response to receiving the allocation task instructions. The operating engine may be configured to provide, in particular generate, the one or more parameter value(s) in response to receiving the operating input data.

[0035] In an embodiment, providing the structure task instruction to the structure model may include mapping the structure task instruction to vectorized structure task instruction. The structure model may be configured to map the vectorized structure task instruction to vectorized operating input data, and to map the vectorized operating input data to operating input data. The vectorized operating input data may be associated with a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the operating input data.

[0036] The vectorized structure task instruction may be obtained by passing the structure task instruction through one or more embedding layers. The structure model may include one or more embedding layers. The one or more embedding layers may be configured to map unstructured data to a structured numerical representation, in particular vectorized data. The vectorized structure task instruction may be indicative and / or may depend on the sequence of one or more elements and / or string data related to the structure task instruction. The sequence may be encoded by positional encoding of the vectorized structure task instruction. Positional encoding may be performed prior to providing the structure task instruction to structure model or by processing of the structure model, in particular the one or more embedding layer(s). Alternatively, the selfattention mechanism of the encoder may include relative positional encoding as described 1803.02155.pdf (arxiv.org). The vectorized structure task instruction may be associated with. In particular, the vectorized task instruction may represent the structure task instruction. The vectorized structure task instruction may be associated with a smaller amount of data than the structure task instruction. Further, the vectorized structure task instruction may be processed by one or more matrix operation(s) associated with the structure model. Hence, the vectorized structure task instruction may be faster processable for matrix operations of the structure model. The vectorized structure task instruction may be a structured digital representation of the structure task instruction including unstructured data, in particular associated with a machine processable numerical, preferably float, format. The structured vectorized task instruction can be efficiently processed by the structure model and allows to save significant computational resources for processing unstructured requests.

[0037] In an embodiment, the structure model may include one or more encoder block(s) and / or one or more decoder block(s) for mapping the vectorized structure task instruction to the context structure task instruction. Context structure task instruction may be related to vectorized structure task instruction. Context structure task instruction may be vectorized structure task instruction processed by one or more matrix operation(s) associated with the structure model. The one or more encoder block(s) and / or one or more decoder block(s) may be configured to map the vectorized structure task instruction to the context structure task instruction, in particular by taking a sequence of one or more elements and / or string data related to the task instruction into account. The context structure task instruction may be associated with, in particular comprise, structured numerical data, in particular a tensor. Context structure task instruction may represent a sequence of elements associated with the structure task instruction and a relation between the elements of the sequence. The relation between the elements may be obtained by applying the one or more matrix operation(s) to the vectorized structure task instruction. The elements may comprise at least a part of a word, a number, a symbol or the like. Thereby, the relation between the elements, e.g. words in a text, can be understood by the structure model. This improves the mapping between the structure task instruction and the operating input data. Consequently, the operating engine(s) can be operated more efficiently to process the request.

[0038] Further, the structure model may comprise one or more encoder output(s), in particular where the structure model may include one or more encoder block(s). Further, the structure model may comprise one or more decoder output(s), in particular where the structure model may include one or more decoder block(s). Additionally or alternatively, the one or more encoder output(s) and / or the one or more decoder output(s) may be configured to map the context structure task instruction to a plurality of confidence scores associated with a plurality of elements, in particular a distribution of confidence scores associated with the plurality of elements. At least a part of the plurality of the element(s) may be associated with, in particular included by, the operating input data. The one or more encoder block(s) and / or decoder block(s) may generate a distribution of confidence scores associated with the plurality of elements comprising one or more element(s) associated with the operating input data. The operating input data may be determined by selecting the one or more element(s) associated with the operating input data according to the one or more confidence score(s) associated with the one or more element(s). In an embodiment, the one or more encoder output(s) and / or one or more decoder output(s) may be configured to select the one or more element(s) associated with the operating input data according to the one or more confidence score(s) associated with the one or more element(s). Selecting the one or more element(s) associated with the operating input data according to the one or more confidence score(s) associated with the one or more element(s) may comprise receiving a range of confidence scores and selecting the one or more element(s) associated with one or more confidence score(s) within the range. The one or more encoder block(s) and / or one or more encoder output(s) and / or one or more decoder output(s) may map the vectorized structure task instruction to operating input data, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the operating input data. The one or more decoder block(s) and one or more decoder output(s) may map the vectorized structure task instruction to operating input data, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the operating input data.

[0039] In an embodiment, any one of the methods may further comprise providing a validation task instruction related to the operating input data, the at least one selected operating engine and the one or more input data structure(s), in particular the at least one input data structure(s) associated with the at least one selected operating engine, to a validation model for validating a data structure related to the operating input data. The validation model may be configured to classify if the data structure related to the operating input data may correspond to the input data structure related to the at least one selected operating engine. The validation model may be configured to provide an indication on whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine. The indication may be a class label indicating whether the data structure related to the operating input data may be validated. Hence, the validation model may be a classification model. The classification model may be trained to provide the indication in response to receiving the validation task instruction. The validation model may comprise one or more classification layers. The one or more classification layers may be configured to determine the indication on whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine from the validation task instruction, in particular the vectorized validation task instruction. Hence, the indication on whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine may be derived from and / or may depend on the validation task instruction. Validating the data structure related to the operating input data ensures robust triggering of the at least one selected operating engine by the operating input data. Thereby, the chemical product data can be reliably generated. Using a task specific model for validating the data structure related to the operating input data allows for a high accuracy of the validating.

[0040] In an embodiment, providing the validation task instruction may comprise generating validation task instruction by merging the one or more input data structure(s), at least a part of the indication of the at least one selected operating engine and the operating input data and providing the validation task instruction data to the validation model and / or the one or more data-driven models. The validation task instruction may comprise the one or more input data structure(s), an indication on the selected operating engine and the operating input data. Further, the validation task instruction may include instructions for triggering the validation model and / or the one or more data-driven models to validate the data structure associated with the operating input data. By combining provided and / or generated data, the available context can be provided to the validation model and / or the one or more data-driven models. This allows to accurately validate the operating input data. This ensures robust triggering of the at least one selected operating engine by the operating input data. Thereby, the chemical product data can be reliably generated.

[0041] In an embodiment, providing the validation task instruction may include mapping the validation task instruction to vectorized validation task instruction. The validation model may be configured to map the vectorized validation task instruction to a vectorized indication on whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine. The validation model may be further configured to map the vectorized indication to the indication on whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine.

[0042] The vectorized validation task instruction may be obtained by passing the validation task instruction through one or more embedding layers. The validation model may include one or more embedding layers. The one or more embedding layers may be configured to map unstructured data to a structured numerical representation, in particular vectorized data. The vectorized validation task instruction may be indicative and / or may depend on the sequence of one or more elements and / or string data related to the validation task instruction. The sequence may be encoded by positional encoding of the vectorized validation task instruction. Positional encoding may be performed prior to providing the validation task instruction to the validation model or by processing of the validation model, in particular the one or more embedding layer(s). Alternatively, the selfattention mechanism of the encoder may include relative positional encoding as described 1803.02155.pdf (arxiv.org). The vectorized validation task instruction may be associated with, in particular comprise, structured numerical data. In particular, the vectorized validation task instruction may represent the validation task instruction. The vectorized validation task instruction may be associated with a smaller amount of data than the validation task instruction. Further, the vectorized validation task instruction may be processed by one or more matrix operation(s) associated with the validation model. Hence, the vectorized validation task instruction may be faster processable for matrix operations of the validation model. The vectorized validation task instruction may be a structured digital representation of the validation task instruction including unstructured data, in particular associated with a machine processable numerical, preferably float, format. The structured vectorized validation task instruction can be efficiently processed by the validation model and allows to save significant computational resources for processing unstructured requests.

[0043] The validation model may include one or more encoder block(s) and / or one or more decoder block(s) for mapping the vectorized validation task instruction to the context validation task instruction. Context validation task instruction may be related to vectorized validation task instruction. Context validation task instruction may be vectorized validation task instruction processed by one or more matrix operation(s) associated with the validation model. The one or more encoder block(s) and / or one or more decoder block(s) may be configured to map the vectorized validation task instruction to the context validation task instruction, in particular by taking a sequence of one or more elements and / or string data related to the validation task instruction into account. The context validation task instruction may be associated with, in particular comprise, structured numerical data, in particular a tensor. Context validation task instruction may represent a sequence of elements associated with the validation task instruction and a relation between the elements of the sequence. The relation between the elements may be obtained by applying the one or more matrix operation(s) to the vectorized validation task instruction. The elements may comprise at least a part of a word, a number, a symbol or the like. Thereby, the relation between the elements, eg words in a text, can be understood by the one or more data-driven models. This improves the mapping between the validation task instruction and the indication on whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine. Consequently, the operating engine(s) can be operated more efficiently to process the request.

[0044] Further, the validation model may comprise one or more encoder output(s), in particular where the validation model may include one or more encoder block(s). Further, the validation model may comprise one or more decoder output(s), in particular where the one or more data-driven models may include one or more decoder block(s). Additionally or alternatively, the one or more encoder output(s) and / or the one or more decoder output(s) may be configured to map the context validation task instruction to a plurality of confidence scores associated with a plurality of elements, in particular a distribution of confidence scores associated with the plurality of elements. At least a part of the plurality of the element(s) may be associated with, in particular included by, the indication. The one or more encoder block(s) and / or decoder block(s) may generate a distribution of confidence scores associated with the plurality of elements comprising one or more element(s) associated with the indication. The indication may be determined by selecting the one or more element(s) associated with the indication according to the one or more confidence score(s) associated with the one or more element(s). In an embodiment, the one or more encoder output(s) and / or one or more decoder output(s) may be configured to select the one or more element(s) associated with the indication according to the one or more confidence score(s) associated with the one or more element(s). Selecting the one or more element(s) associated with the indication according to the one or more confidence score(s) associated with the one or more element(s) may comprise receiving a range of confidence scores and selecting the one or more element(s) associated with one or more confidence score(s) within the range.

[0045] The one or more encoder block(s) and / or one or more encoder output(s) and / or one or more decoder output(s) may map the vectorized validation task instruction to the indication, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the indication. The one or more decoder block(s) and one or more decoder output(s) may map the vectorized validation task instruction to the indication, preferably to a distribution of confidence scores associated with the plurality of elements comprising one or more elements associated with the indication. The vectorized validation task instruction may be indicative and / or may depend on the sequence of one or more elements and / or string data related to the validation task instruction. The indication may comprise string data indicative of whether the data structure related to the operating input data corresponds to the input data structure related to the at least one selected operating engine. The vectorized indication may comprise numerical data, in particular structured numerical data. The vectorized indication may represent the indication, in particular the sequence related to the indication, in particular the indication including unstructured data. The one or more encoder output(s) and / or the one or more decoder output(s) may be further configured to map the vectorized indication to the indication related to a sequence of a plurality of elements or including string data.

[0046] In an embodiment, the at least one selected operating engine may include one or more subengine(s) configured to select at least one subengine from the one or more subengine(s) based on the operating input data and subengine specification data related to one or more functions of the one or more subengine(s) for providing the chemical product with the one or more target properties, and structure subengine task instruction related to the operating input data and one or more input data structure(s) related to the one or more subengine(s) to generate subengine input data to the at least one selected subengine, and optionally classify if the data structure related to the subengine input data corresponds to the input data structure related to the at least one selected subengine.

[0047] The subengine input data may be dervied from the operating input data and / or may depend on the operating input data. The subengine input data may include at least a part of the operating input data. The at least one subengine may provide at least a part of the chemical product data, preferably the operating output data, in response to providing the subengine input data. In an embodiment, the one or more subengine(s) may perform at least one of select at least one subengine from the one or more subengine(s) based on the operating input data and subengine specification data related to one or more functions of the one or more subengine(s), structure subengine task instruction related to the operating input data and one or more input data structure(s) related to the one or more subengine(s) to generate subengine input data to the at least one selected subengine, optionally classify if the data structure related to the subengine input data corresponds to the input data structure related to the at least one selected subengine, provide at least a part of the chemical product data, preferably the operating output data, in response to prviding the subengine input data or a combination thereof per subengine. This provides the advantage to separate tasks into a plurality of tasks. By doing so, intermediate steps become obvious. Hence, this feature allows for reasoning of the decision taken according to this disclosure. This in turn reduced the errors of processing the received request. Hence, this contributes to a robust generation of the chemical product data and enables trustworthy Al.

[0048] In an embodiment, providing input data to the data-driven model may comprise mapping the input data to a numerical representation of the input data. The numerical representation of the input data may comprise a tensor associated with the input data and / or obtained from the input data. In particular, the numerical representation of the input data may be indicative of two or more elements of the input data and a relation between the two or more elements of the input data. Preferably, providing input data to the data-driven model may comprise at least one of identifying two or more elements of the input data, mapping the two or more elements of the input data to a numerical representation of the two or more elements, mapping the numerical representation of the two or more elements to a numerical representation of a predefined size related to the numerical representation of the two or more elements, mapping the numerical representation of the predefined size related to the numerical representation of the two or more elements to a numerical representation of the two or more elements and a relation between the two or more elements or a combination thereof. Providing the extraction task instruction includes mapping the extraction task instruction to a numerical representation of the extraction task instruction. The extraction data-driven model may be configured to map the numerical representation of the extraction task instruction to the at least one set of parameter values, in particular a numerical representation of the at least one set of parameter values. Any one of the methods may further comprise mapping the numerical representation of the at least one set of parameter values to the at least one set of parameter values. The numerical representation may be processed and / or may be processable by the extraction data-driven model. The numerical representation may be a structured representation.

[0049] In an embodiment, processing the input data and / or generating the output data from the input data may comprise processing the numerical representation of the input data, in particular the numerical representation of the two or more elements and the relation between the two or more elements. Processing the numerical representation of the two or more elements and the relation between the two or more elements may comprise mapping the numerical representation of the two or more elements, and optionally the relation between the two or more elements to a numerical representation of the output data. The numerical representation of the output data may be mapped to the output data, in particular based on a relation between the numerical representation of data and the data. In particular, the data may be of a data type according to the input data. Hence, the output data may be of the data type according to the input data, e.g. of the same data type as the input data and / or of the data type specified by the input data. Preferably, processing the numerical representation of the two or more elements and the relation between the two or more elements may comprise at least one of generating two or more numerical representations of the two or more elements and the relation between the two or more elements from the numerical representation of the two or more elements and the relation between the two or more elements, modifying the two or more numerical representation of the two or more elements and the relation between the two or more elements by applying a filter to the two or more numerical representations of the two or more elements and the relation between the two or more elements, concatenating the two or more numerical representations of the two or more elements and the relation between the two or more elements, mapping the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data or a combination thereof.

[0050] In an embodiment, any one of the methods may further comprise generating allocation task instructions from the request, in particular the indication of the one or more target chemical reaction(s) and the one or more target parameter value(s) and one or more model instruction(s) for triggering the allocation data-driven model. The model instruction(s) for triggering the allocation data-driven model may be provided e.g. from an instruction providing engine. The model instruction(s) for triggering the allocation data-driven model may comprise natural language for instructing the allocation data-driven model. The allocation data-driven model may be configured to follow task instructions comprising natural language.

[0051] In an embodiment, any one of the methods may further comprise generating parameter task instructions from the request, in particular the indication of the one or more target chemical reaction(s) and the one or more target parameter value(s) and one or more model instruction(s) for triggering the data-driven model and one or more input data structures. Generating may refer to merging.

[0052] In an embodiment, the one or more suggestion parameter value(s) may be provided to an operator of a chemical production entity. The chemical production entity may comprise a reactor. The chemical production entity may comprise laboratory and / or chemical plant equipment. The chemical production entity may be suitable for and / or may be configured to product the target chemical product. The chemical production entity may be suitable for and / or configured to control and / or perform at least a part of the one or more target chemical reaction(s). Providing the one or more suggestion parameter value(s) may include displaying the one or more suggestion parameter value(s), in particular to the operator of the chemical production entity. This may allow the operator to control the one or more target chemical reaction(s) according to the one or more parameter value(s). The provided parameter value(s) may include the one or more suggestion parameter value(s).

[0053] In an embodiment, task instruction may include experimental data, proposed experimental data and / or one or more instructions for triggering the extraction data-driven model to provide the operation data-driven model and / or to extract the at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter from the experimental data. The at least one set of parameter values may comprise at least one parameter value associated with the at least one target parameter and at least one parameter value associated with the at least one tuneable parameter. The task instruction may be input data to the extraction data-driven model. Processing of the task instruction by the extraction data-driven model may trigger the extraction data-driven model to follow the task instructed by the task instruction, e.g. extract the at least one set of parameter values and / or provide the operation data-driven model.

[0054] In an embodiment, the experimental data may be recorded in relation to the plurality of chemical reactions. Hence, the experimental data may comprise sensor data obtained in relation to the plurality of chemical reactions and / or obtained by processing the sensor data via one or more mathematical operation(s). For example, the sensor data may be converted into another numerical schema or a measure in relation to the sensor data may be obtained based on a mathematical formular applied to the sensor data.

[0055] In an embodiment, one or more target chemical product(s) may be obtained by the one or more target chemical reaction(s). The indication of the one or more target chemical product(s) may comprise and / or may be an indication of the one or more target chemical reaction(s) and vice versa. The indication of the one or more target chemical product(s) may be suitable for retrieving a digital representation of the one or more target chemical reaction(s) and / or one or more target chemical product(s) obtained by the one or more target chemical reaction(s).

[0056] In an embodiment, data-driven model may refer to a model suitable for describing one or more relations between input data and output data, in particular non-linear relations between input data and output data. Input data may refer to data to be provided to the data-driven model and / or to data being received by the data- driven model. Output data may be data to be received from the data-driven model and / or to be provided by the data-driven model. Hence, the data-driven model may determine the output data based on transforming the input data via one or more non-linear relations. The data-driven model may obtain the relation between the input data and the output data during the training of the data-driven model.

[0057] In an embodiment, adapting the parameter values associated with the tuneable parameter may change the parameter values associated with the target parameter, in particular increase or decrease the parameter values associated with the target parameter.

[0058] In an embodiment, the extraction task instruction data may include string data and / or a sequence of one or more elements. The one or more element(s) may comprise at least a part of a word, a number, a symbol or the like. Thereby, the extraction task instruction may be user-interpretable. In chemical production, chemical plants need to be operated by longly experienced operators. Therefore, it is desired to support operators of chemical plants in adjusting chemical reaction procedures. This allows for an easy instructing of the extraction data-driven model by human users as the human is supported in interacting with the models. Further, output from the model becomes interpretable. Thus, users are enabled to identify errors and intervene. Ultimately, this improves controlling of chemical reactions. In an embodiment, the operating engine may comprise an extraction data-driven model and / or the operating input data may comprise model determining task instructions. Providing model determining task instructions to the extraction data-driven model may comprise: providing extraction task instructions including the experimental data to an extraction data-driven model for extracting at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter from the experimental data. The extraction data-driven model may be configured to follow task instructions, and training an operation data-driven model to relate parameter values associated with the at least one tuneable parameter to the at least one target parameter according to the at least one extracted set of parameter values. Training the operation data-driven model may include initializing a predefined number of parameters of the operation data-driven model and updating the predefined number of parameters to reduce a deviation between parameter values associated with the at least one tuneable parameter or the at least one target parameter determined by the operation data-driven model and the parameter values associated with the at least one tuneable parameter or the at least one target parameter according to the experimental data. The deviation may be reduced until the deviation may be within a predefined deviation range. The operation data- driven model may comprise one or more mathematical relation(s), in particular function(s) relating the parameter values associated with the at least one tuneable parameter and the parameter values associated with the at least one target parameter. The extraction task instructions may trigger the extraction data-driven model to extract at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter from the experimental data. The extraction data-driven model may select the at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter. One set of parameter values associated with the at least one target parameter and the at least one tuneable parameter may comprise at least two parameter values.

[0059] In an embodiment, the operating input data may trigger the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and select at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter from the experimental data according to the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s) and optionally according to the applicable parameter range, wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s), and determine an operation data-driven model based on the at least one selected set of parameter values, wherein the operation data-driven model is configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the operation data-driven model. The at least one parameter values may be associated with the at least one target parameter and at least one parameter values may be associated with the at least one tuneable parameter. By extracting the at least one set from the experimental data, already available information may be used to build an operation data-driven model based on the at least one extracted set. Thereby, less or no further experiments may be required to determine control parameters for target chemical reactions. Ultimately, significant resources for developing target chemical reactions that allow to obtain target chemical products with target properties can be saved.

[0060] In an embodiment, determining task instructions to the extraction data-driven model may comprise providing model determining task instructions including the experimental data for generating an operation data-driven model to an extraction data-driven model. The operation data-driven model may be provided by the extraction data-driven model. Optionally, any one of the methods may further comprise providing the operation data- driven model for determining one or more parameter value(s) associated with one or more parameter(s) characterizing the one or more target chemical reaction(s). At least a part of the parameter value(s) may be used for controlling the one or more target chemical reaction(s) according to one or more parameter value(s). The experimental data may be indicative of and / or may include one or more relation(s) between the parameter values associated with the at least one target parameter and the at least one tuneable parameter. The operation data-driven model may comprise the one or more relation(s) between the parameter values associated with the at least one target parameter and the at least one tuneable parameter. Hence, the extraction data-driven model may extract the one or more relation(s) between the parameter values associated with the at least one target parameter and the at least one tuneable parameter from the experimental data. Experimental data are typically indicative and / or experimental data are based on scientific relations. This allows to extract relations already present in the experimental data or already obtained in historical experiments by the extraction data-driven model. The extraction data-driven model may be trained based on one or more physical, chemical and / or biological equation(s). Hence, the extraction data-driven model may have obtained relations between parameters during the training.

[0061] In an embodiment, the operating input data may trigger the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and provide model determining task instructions including the experimental data for generating an operation data- driven model to an extraction data-driven model, wherein the operation data-driven model is configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and wherein the extraction data-driven model is configured to follow task instructions, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the operation data-driven model. The model determining task instructions may comprise one or more instructions for triggering the extraction data-driven model to generate the operation data-driven model from the experimental data, in particular the selected part of the experimental data. The operating input data may trigger the at least one selected operating engine to select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s).

[0062] By extracting the operation data-driven model from the experimental data, already available information may be used to build a operation data-driven model . Thereby, less or no further experiments may be required to determine control parameters of target chemical reactions. Ultimately, significant resources for developing target chemical reactions that allow to obtain target chemical products with target properties can be saved.

[0063] In an embodiment, the extraction data-driven model may be trained based on historical extraction task instructions and corresponding extracted sets of parameter values associated with two or more parameters. The extraction data-driven model may be trained and / or may be configured to extract parameter values from experimental data according to extraction task instructions. The extraction data-driven model may be a finetuned data-driven model.

[0064] In an embodiment, the data-driven model, such as the allocation model, the selection model, the validation model and / or the extraction data-driven model may be a pretrained data-driven model. The pretrained data- driven model may be parametrized and / or trained based on data with a plurality of contexts and / or unstructured data, in particular text data and optionally numerical data such as tabular data or image data. The pretrained data-driven models may be configured to perform a plurality of task and / to process data of a plurality of contexts. The pretrained data-driven models may be configured to perform the task according to the provided task instruction. Hence, the pretrained data-driven model may be configured to be provided with a plurality of different task instructions and / or provide a plurality of different types of output data upon receiving different task instructions. Thereby, already available models can be directly used. Hence, no resources for further training are necessary and on premise-models can be used. As a consequence, pretrained data-driven models are useful on small to medium-sized scales.

[0065] In an embodiment, the data-driven model, such as the allocation model, the selection model, the validation model and / or the extraction data-driven model may be a finetuned data-driven model. In an embodiment, the finetuned data-driven model may be obtained by training a pretrained data-driven model. The finetuned data- driven models may be trained additionally on a training data set comprising a plurality of extraction task instructions and corresponding extracted sets of parameter values. Finetuning a model for a specific application allows to improve the performance, i.e. accuracy and precision, of the model. Hence, finetuning the data-driven model improves the reliability of extracting parameter values, in particular according to predefined criteria. Followingly, less extraction instructions are needed to trigger extracting by the extraction data-driven model allowing for less data processing in order to extract parameter values. This is advantageous where large amounts of parameter values need to be extracted. Therefore, finetuning decreases the resources for extracting parameter values on large scales.

[0066] In an embodiment, any one of the methods may further comprise determining proposed experimental data according to a distribution of the parameter values associated with the at least one target parameter and the at least one tuneable parameter within the experimental data, and / or determining the proposed experimental data according to a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter. The proposed experimental data may comprise at least one proposed set of parameter values associated with at least one target parameter and at least one tuneable parameter. The extraction task instructions may further include the proposed experimental data. The experimental data may be indicative of and / or may comprise the distribution of the parameter values associated with the at least one target parameter and the at least one tuneable parameter. Determining the proposed experimental data may comprise determining at least two proposed parameter values associated with the at least one target parameter and the at least one tuneable paramter. In particular, at least one of the two proposed parameter values may be associated with the at least one target parameter and / or the at least one tuneable parameter. Determining the proposed experimental data may be based on a distribution function of parameter values associated with the at least one target parameter and / or the at least one tuneable parameter, in particular a cumulative distribution function relating parameter values associated with the at least one target parameter and / or the at least one tuneable parameter. The distribution function may be indicative of a distribution of parameter values, in particular a likelihood of an appearance of parameter values associated with the at least one target parameter and / or the at least one tuneable parameter. Determining the distribution function may comprise dividing the distribution function into a number of parts equal to a number of corresponding parameter values and / or a number of sets of parameter values and / or assigning at least one parameter value per part. The relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter may be a mathematical function relating the parameter values associated with the at least one target parameter to the parameter values associated with the at least one tuneable parameter or vice versa. The relation may be obtained from a database and / or may be extracted by one or more data-driven model(s) such as the extraction data-driven model. For this purpose, a data extraction task instruction for extracting the proposed experimental data from the experimental data. The one or more data-driven model(s) may be configured to follow a plurality of different task instructions.

[0067] In an embodiment, the operating input data may trigger the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and determine proposed experimental data according to a distribution of the parameter values associated with the at least one target parameter and the at least one tuneable parameter within the experimental data.

[0068] In an embodiment, the operating input data may trigger the at least one selected operating engine to provide a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter, and determine proposed experimental data according to the relation, wherein the proposed experimental data comprises the one or more parameter value(s).

[0069] By doing so, parameter values similar to the parameter values comprised by the experimental data may be obtained. The so-determined parameter values may be indirectly included in the experimental data via relations between the target parameter and the tuneable parameter. This allows to obtain more datapoints for building the operation data-driven model than directly obtained from the experimental data. Consequently, model performance of the operation data-driven model is improved leading to more reliable and diverse parameter values provided by the operation data-driven model . Ultimately, this reduces the number of experiments to be run to arrive at the one or more target chemical reaction(s).

[0070] In an embodiment, the experimental data may comprise proposed experimental data including at least one proposed set of parameter values associated with at least one target parameter and at least one tuneable parameter. The proposed experimental data may be determined according to a distribution of the parameter values associated with the at least one target parameter and the at least one tuneable parameter within historical experimental data, and / or determining the proposed experimental data according to a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter. In an embodiment, the relation and / or the distribution may be obtainable and / or may be obtained from the experimental data. The experimental data may be indicative of the relation and / or the distribution.

[0071] In an embodiment, operating input data may trigger the at least one selected operating engine to: provide extraction task instructions including experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions to an extraction data- driven model for extracting an applicable parameter range from the experimental data, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, wherein the extraction data-driven model is configured to follow task instructions, and select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s). This allows to preselect meaningful parameter values with respect to the tuneable parameters. For example, parameter values within a predefined range may allow to operate chemical production facilities safely and / or in a controlled manner. Hence, reducing the search space of the tuneable parameter values enables to preselect workable parameter values and make use of promising synthesis routes already developed. This saves time and resources for experimentation while increasing the accuracy of the operation data-driven model built upon the preselected parameter values. Ultimately, this further reduces the number of experimental cycles needed in the development of chemical synthesis routes.

[0072] In an embodiment, any one of the methods may further comprise providing an applicable parameter range based on an inventory of a target chemical production facility for obtaining the target chemical product and / or one or more setting(s) related to the target chemical production facility and selecting at least a part of the experimental data according to the applicable parameter range. The experimental data provided to the extraction data-driven model may correspond to the selected part of the experimental data. Additionally or alternatively, the part of the experimental data may be selected according to the one or more target chemical reaction(s). The experimental data may be indicative of the one or more target chemical reaction(s), e.g. by comprising an indication of the one or more target chemical reaction(s). Additionally or alternatively, the indication of the one or more target chemical reaction(s) may be provided and / or received. The part of the experimental data may be selected according to the indication of the one or more target chemical reaction(s). Selecting the part of the experimental data according to the one or more target chemical reaction(s) may comprise matching the chemical reactions associated with the experimental data and the one or more target chemical reaction(s) e.g. according to the indication of the one or more target chemical reaction(s). Additionally or alternatively, the one or more parts of the experimental data may be mapped to a numerical representation of the one or more parts of the experimental data and at least one part of the experimental data may be selected by determining a distance between the numerical representation of the one or more parts of the experimental data and the indication of the one or more target chemical reaction(s), in particular a numerical representation of the one or more target chemical reaction(s). The applicable parameter range may comprise at least the subset of available and / or used parameter values. This allows to preselect meaningful parameter values with respect to the tuneable parameters. For example, parameter values within a predefined range may allow to operate chemical production facilities safely and / or in a controlled manner. Hence, reducing the search space of the tuneable parameter values enables to preselect workable parameter values and make use of promising synthesis routes already developed. This saves time and resources for experimentation while increasing the accuracy of the operation data-driven model built upon the preselected parameter values. Ultimately, this further reduces the number of experimental cycles needed in the development of chemical synthesis routes.

[0073] In an embodiment, any one of the methods may further comprise determining an applicable parameter range from the experimental data by the extraction data-driven model. The applicable parameter range may be indicative of a range of tuneable parameter values associated with at least one tuneable parameter. The experimental data provided to the extraction data-driven model may correspond to the selected part of the experimental data. The extraction task instructions may further comprise one or more instruction(s) for triggering the extraction data-driven model to provide the applicable parameter range. The experimental data may be indicative of the applicable parameter range. The experimental data may comprise a subset of available and / or used parameter values, i.e. the tuneable parameter values, associated with the at least one tuneable parameter. The applicable parameter range may comprise at least the subset of available and / or used parameter values. This allows to preselect meaningful parameter values with respect to the tuneable parameters. For example, parameter values within a predefined range may allow to operate chemical production facilities safely and / or in a controlled manner. Hence, reducing the search space of the tuneable parameter values enables to preselect workable parameter values and make use of promising synthesis routes already developed. This saves time and resources for experimentation while increasing the accuracy of the operation data-driven model built upon the preselected parameter values. Ultimately, this further reduces the number of experimental cycles needed in the development of chemical synthesis routes.

[0074] In an embodiment, any one of the methods may further comprise providing verification experimental data indicative of at least one verification parameter value associated with the at least one target parameter. The verification experimental data may be obtained during the one or more target chemical reaction(s) controlled according to the one or more suggestion parameter value(s) and providing a trigger for adapting the operation data-driven model in response to determining that the operation data-driven model may be updated upon the verification experimental data. Any one of the methods may further comprise determining if the operation data- driven model may be updated based on the verification experimental data. Determining if the operation data- driven model may be updated based on the verification experimental data may include determining a deviation between the one or more parameter values associated with the target parameter range and at least one verification parameter value. Determining the deviation may comprise determining a deviation score, in particular a numerical value. For example, the deviation score may allow to rank deviations between parameter values associated with the target parameter range and verification parameter values on a scale. Additionally or alternatively, the deviation score may be a measure for the deviation related to a standard deviation associated with the proposed experimental data and / or the suggestion parameter values. In particular, determining the deviation may comprise determining a distance between the one or more parameter value(s) associated with the target parameter range and at least one verification parameter value. A measure for the distance may be related to a standard deviation associated with the proposed experimental data and / or the suggestion parameter values. Optionally, the parameter value associated with the target parameter may be determined from the verification experimental data.

[0075] In an embodiment, any one of the methods may further comprise providing verification experimental data for verifying the operation data-driven model. Verifying the operation data-driven model may comprise providing verification experimental data indicative of at least one verification parameter value associated with the at least one target parameter.

[0076] In an embodiment, the request may further comprise verification experimental data comprising a parameter value associated with the target parameter and a corresponding parameter value associated with the one or more tuneable parameter(s), wherein the parameter value associated with the target parameter is obtained by controlling the one or more target chemical reaction(s) according the corresponding parameter value, and wherein the corresponding parameter value is determined according to a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter and / or determined by an operation data-driven model configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and wherein the operating input data triggers the at least one selected operating engine to update the operation data-driven model based on the verification experimental data and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the updated operation data-driven model.

[0077] In an embodiment, the request may further comprise verification experimental data comprising a parameter value associated with the target parameter and a corresponding parameter value associated with the one or more tuneable parameter(s), wherein the parameter value associated with the target parameter is obtained by controlling the one or more target chemical reaction(s) according the corresponding parameter value, and wherein the corresponding parameter value is determined according to a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter and / or determined by an operation data-driven model configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and wherein the operating input data triggers the at least one selected operating engine to determine a model performance indicator associated with a deviation between the parameter value associated with the verification experimental data and the corresponding parameter value, determine if the operation data-driven model is to be updated according to the verification experimental data based on the model performance indicator, determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the operation data-driven model.

[0078] The operation data-driven model may be updated if the model performance indicator may be within a predefined performance range. Determining if the operation data-driven model may be updated may comprise determining if the model performance indicator may be within a predefined performance range. A trigger for adapting and / or updating the operation data-driven model may provided upon determining that the operation data-driven model is to be updated according to the verification experimental data. The model performance indicator may be obtained by determining a deviation, in particular a difference between the parameter value associated with the verification experimental data and the corresponding parameter value. The model performance indicator may be a deviation score.

[0079] In an embodiment, the operation data-driven model may comprise a combination of two or more data-driven model(s) configured to relate parameter values associated with the at least one tuneable parameter and the at least one target parameter based on the experimental data. The combination of the two or more data-driven models may obtain parameter values by relating the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter via the two or more data-driven models, preferably by relating the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter per data-driven model and weighting the parameter values obtained per data-driven model. Preferably, the parameter values obtained per data-driven model may be weighted according to a confidence score associated with the two or more data-driven models. The confidence score may be indicative of an accuracy associated with the two or more data-driven models. Preferably, two or more confidence scores may be associated per data-driven model, in particular per range of parameter values and per data-driven model. The two or more confidence scores may be associated with different ranges of parameter values per data-driven model. The operation data-driven model may relate the two or more data-driven models via two or more weighting factors obtained based on the confidence scores associated with the data-driven models, and optionally associated with a subset of the parameter values. For example, the first data-driven model may be associated with a first range of parameter values and a second range of parameter values. The first data-driven model may be associated with a first accuracy within the first range of parameter values and a second accuracy different from the first accuracy within the second range of parameter values. Where the confidene score associated with the first range of parameter values and the first data-driven model may be within a predefined confidence range, the weighting factor for weighting the influence of the two or more data-driven models may be higher in the first range of parameter values with respect to the first data-driven model than the weighting factor in the first range of parameter values with respect to the second data-driven model. In an embodiment, the two or more data-driven models may comprise a sub model and the extraction data-driven model. The sub model may be trained and / or determined analogous to determining and / or training the operation data-driven model. Additionally or alternatively, the operation data-driven model may comprise a combined function. The combined function may be obtained by relating two or more function(s) associated with the two or more data- driven models via two or more weighting factors associated with the two or more data-driven models. At least one of the two or more data-driven models may be configured according to the experimental data, in particular the verification experimental data and independent of proposed experimental data. By doing so, insights into the relation between the target parameter and the tuneable parameter can be obtained from different models, e.g. with a different focus and / or with different advantages and disadvantages. Thereby, the best from both models can be used to provide the suggestion parameter values with a higher accuracy and / or requiring less verifications via verification experimental data. At least one of the two or more data-driven models may be trained and / or configured based on historical experimental data, in particular historical experimental data obtained from sensor data, e.g. by transforming sensor data via one or more mathematical operation(s). Additionally or alternatively, at least one of the two or more data-driven models may be trained and / or configured based on historical proposed experimental data.

[0080] In an embodiment, the experimental data may be indicative of the one or more target chemical reaction(s) and / or any one of the methods may further comprise providing the indication of the one or more target chemical reaction(s). Experimental data may be provided. A part of the experimental data may be selected according to the indication of the one or more target chemical reaction(s). The experimental data provided to the extraction data-driven model may correspond to the selected part of the experimental data. The operating input data may trigger the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and select at least a part of the experimental data according to the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s). By doing so, a preselection of the experimental data to the relevant part of the experimental data. This allows to focus the attention of the data- driven model to the relevant part of the experimental data and hence, improves extracting of the parameter values by the extraction data-driven model. Ultimately, this improves building the operation data-driven model and hence improve the suggestion parameter values. Thereby, less experimentation cycles are needed to arrive at the target specifications.

[0081] In an embodiment, the request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s) may be a request for receiving an applicable parameter range, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, and wherein the operating input data triggers the at least one selected operating engine to: provide extraction task instructions including experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions to an extraction data- driven model for extracting an applicable parameter range from the experimental data, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, wherein the extraction data-driven model is configured to follow task instructions, and select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s). Additionally or alternatively, the request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s) may further comprise an applicable parameter range based on an inventory of a target chemical production facility for performing the one or more target chemical reaction(s) and / or one or more setting(s) for performing the one or more target chemical reaction(s) by the target chemical production facility. The operating input data may trigger the at least one selected operating engine to select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s).

[0082] The applicable parameter range may comprise at least the subset of available and / or used parameter values. This allows to preselect meaningful parameter values with respect to the tuneable parameters. For example, parameter values within a predefined range may allow to operate chemical production facilities safely and / or in a controlled manner. Hence, reducing the search space of the tuneable parameter values enables to preselect workable parameter values and make use of promising synthesis routes already developed. This saves time and resources for experimentation while increasing the accuracy of the operation data-driven model built upon the preselected parameter values. Ultimately, this further reduces the number of experimental cycles needed in the development of chemical synthesis routes.

[0083] In an embodiment, any one of the methods may further comprise providing a plurality of operation data-driven models, wherein the plurality of operation data-driven models is configured to map parameter values associated with a plurality of tuneable parameter(s) to parameter values associated with the a plurality of parameters, wherein the plurality of parameter(s) includes the target parameter, select at least one operation data-driven model from the plurality of available operation data-driven models based on the target parameter, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the selected operation data-driven model. At least one of the plurality of operation data-driven models may be configured to map the parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter. Optionally, the parameter value(s) associated with the one or more tuneable parameter(s) may be retrieved, e.g., from a parameter database comprising a plurality of different parameter value(s) associated with a plurality of different parameters including the one or more tuneable parameter(s). This allows to select a well-suited model with respect to the provided request from a plurality of available models. Thereby, no resources for building new models are invested. Thus, resource invest for determining the parameter values is decreased. In an embodiment, the operating input data may trigger the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and optionally provide extraction task instructions including experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions to an extraction data-driven model for extracting an applicable parameter range from the experimental data, or wherein the request includes the applicable parameter range, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, wherein the extraction data-driven model is configured to follow task instructions, and optionally select at least a part of the experimental data according to the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s) and optionally the applicable parameter range, wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s), and optionally determine proposed experimental data according to a distribution of the parameter values associated with the at least one target parameter and the at least one tuneable parameter within the experimental data, and select at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter from at least a part of the experimental data and optionally the proposed experimental data according to the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s) and optionally the applicable parameter range, and determine an operation data-driven model based on the at least one selected set of parameter values, wherein the operation data-driven model is configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the operation data-driven model.

[0084] In an embodiment, the extraction data-driven model may be a generative data-driven model. In an embodiment, the extraction data-driven model may be a language model, in particular a large language model.

[0085] In an embodiment, determining the operation data-driven model may comprise training the data-driven model and / or providing, in particular generating the operation data-driven model by the extraction data-driven model.

[0086] In an embodiment, a task instruction may refer to a prompt. Any one of the methods may further comprise controlling the one or more target chemical reaction(s) according to the one or more parameter value and / or providing the one or more target chemical reaction(s) according to the one or more parameter value to a control engine associated with controlling the one or more target chemical reaction(s).

[0087] In an embodiment, at least one of the operating engine(s) may be configured to provide parameter values upon receiving operating input data, preferably associated with an data structure suitable for being received by the at least one operating engine.

[0088] BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0089] In the following, the present disclosure is further described with reference to the enclosed figures. The same reference numbers in the drawings and this disclosure are intended to refer to the same or like elements, components, and / or parts.

[0090] FIG. 1 illustrates an embodiment of producing chemical products 116 by one or more chemical production facilities 102.

[0091] FIG. 2A illustrates an embodiment of determining one or more parameter value(s) associated with one or more parameter(s) characeterizing one or more target chemical reaction(s).

[0092] FIG. 2B illustrates an embodiment of determining one or more parameter value(s) associated with one or more parameter(s) characeterizing one or more target chemical reaction(s).

[0093] FIG. 3A illustrates an embodiment of a method for obtaining a target chemical product by one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing one or more target chemical reaction(s).

[0094] FIG. 3B illustrates an embodiment of a method for obtaining a target chemical product by one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing one or more target chemical reaction(s).

[0095] FIG. 4A illustrates an embodiment of a method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing the one or more target chemical reaction(s).

[0096] FIG. 4B illustrates an embodiment of a method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing the one or more target chemical reaction(s).

[0097] FIG. 5 illustrates an operating system of a chemical production entity 118 for providing parameter values.

[0098] FIG. 6 illustrates an embodiment of the input and output data associated with the allocation model 604.

[0099] FIG. 7 illustrates an embodiment of the input and output data associated with the structure model. FIG. 8 illustrates an embodiment of the input and output data associated with the validation model.

[0100] FIG. 9 illustrates an embodiment of a user interface 902 for providing parameter values for controlling one or more chemical reaction(s) according to the parameter values.

[0101] FIG. 10 illustrates evolution of a parameter values of a target parameter in relation to a number of iterations including conducting experiments.

[0102] FIG. 11 illustrates an embodiment of a data-driven model.

[0103] DETAILED DESCRIPTION

[0104] The following embodiments are mere examples for implementing the method, the system or application device disclosed herein and shall not be considered limiting.

[0105] FIG. 1 illustrates an embodiment of producing chemical products 116 by one or more chemical production facilities 102.

[0106] Chemical products may be produced by the one or more chemical production facilities 102. The chemical products 116 produced by the one or more chemical production facilities 102 may be associated with one or more target parameter value(s) 112. The chemical products 116 may be processed by one or more product processing facilities 114. For processing the chemical products 116 by the one or more product processing facilities 114 the chemical products 116 may be desired to have to one or more target parameter value(s) 112. The one or more target parameter value(s) 112 may be necessary for producing end products with a desired quality. Said end products may be versatile and may range from parts of cars to packaging. End products with a lower quality may become waste immediately as such end products may not be suited for the intended application or at least faster than the end products with the desired quality due to faster degradation. Hence, ensuring sufficient quality of the chemical products produced by the one or more chemical production facilities 102 is important to reduce resource waste. Thereby, the waste of chemical production is further lowered, i.e. the efficiency of producing chemical products 116 is increased, while the production of the chemical products by the one or more chemical production facilities 102 may be tailored to the desired properties of the end products. The challenge in chemical production is to select the target reaction conditions for producing target chemical products associated with target property values. Said target reaction conditions may be obtained as described in the context of FIG. 2A and FIG. 2B.

[0107] FIG. 2A illustrates an embodiment of determining one or more parameter value(s) associated with one or more parameter(s) characterizing one or more target chemical reaction(s).

[0108] For example, the chemical reaction such as a SN2 reaction of two educts for forming two products.

[0109] Parameters characterizing one or more target chemical reaction(s) may include reaction condition(s) and / or one or more chemical(s) participating in the one or more target chemical reaction(s). For example, the one or more chemical(s) may include at least one of one or more educt(s), one or more product(s), one or more catalyst(s), one or more solvent(s), one or more substrate(s) or a combination thereof. The one or more target chemical reaction(s) may be conducted to obtain and / or produce one or more target chemical product(s). Target parameter values associated with target parameters may characterize targets in relation to the one or more target chemical product(s). Target parameter values may be desired for processing chemical products towards end products as described in the context of FIG. 1.

[0110] Typically, target parameter values are obtained by a plurality of iterations including gathering historical experimental data 202 by a chemical expert 204, defining a target parameter range 218 by the chemical expert 204 and suggestion new experiments 206 to approach the target parameter values. This may require a lot of time and slow down development time of chemical products. Further, chemicals and electricity are required for conducting experiments. In turn, capacities for conducting other experiments are blocked towards other experiments. Hence, it is desired to reduce the number of experiments conducted per development of a chemical product. This comes with the benefit of saving materials, electricity and time while accelerating material research by freeing up capacities for conducting further experiments. Fol lowingly , reducing the number of experiments allows for a more than linear increase in speed of developing new materials. A difference between a traditional chemical research as shown in FIG. 2A can be seen in FIG. 2B and the following Figures.

[0111] FIG. 2B illustrates an embodiment of determining one or more parameter value(s) associated with one or more parameter(s) characterizing one or more target chemical reaction(s).

[0112] FIG. 2B illustrates a workflow that allows to reduce the number of experiments significantly. In comparison to the traditional workflow of FIG. 2A, the presented workflow in FIG. 2B allows to reduce the number of experiments to a third or a quarter of the experiments required by the traditional workflow while arriving at better results, e.g. better maximization of a target parameter as it can be seen in FIG. 10.

[0113] To do so, historical experimental data 202 may be gathered and provided together with a target parameter range 218 to an extraction data-driven model 214. From the provided data, the extraction data-driven model 214 may extract sets of parameter values associated with at least one target parameter and at least one parameter other than the target parameter. These sets may be indicative of a relation between the target parameter and the at least one parameter other than the target parameter. In particular, the sets may be indicative of an influence of the at least one parameter other than the target parameter on the target parameter. Based on these sets of parameter values, an operation data-driven model 216 may be determined. The operation data-driven model 216 may be configured to relate the parameter values associated with the target parameter to the parameter values associated with the at least one parameter other than the target parameter. Hence, the suggestion model may be indicative and / or may comprise one or more mathematical relation(s) between the parameter values associated with the target parameter and the parameter values associated with the at least one parameter other than the target parameter. From the operation data-driven model 216, a suggestion for improving the parameter values associated with the target parameter towards the target parameter range may be obtained, e.g. by determining parameter values associated with the parameter other than the target parameter corresponding to a parameter value associated with the target parameter closer to and / or within the target parameter range according to the operation data-driven model 216. The parameter other than the target parameter may be a tuneable parameter. The tuneable parameter may be influence the one or more target chemical reaction(s). Adapting the tuneable parameter values may result in adapting the parameter values associated with the target parameter. The parameter values associated with the target parameter may be adapted indirectly by adapting one or more tuneable parameter values. By the operation data-driven model 216, a different configuration of the tuneable parameter may be suggested. A new experiment may be conducted according to the suggested tuneable parameter value. From this validation experimental data may be obtained. The validation experimental data may be indicative of parameter values associated with the target parameter in relation to the suggested tuneable parameter value. The operation data-driven model 216 may be updated based on the updated parameter values associated with the target parameter and the suggested tuneable parameter value. This may increase the accuracy for determining the parameter values associated with the target parameter in relation to tuneable parameter values. Hence, the operation data-driven model 216 may be improved over a few amount of iterations. The so-obtained operation data-driven model 216 allows to arrive closer to the target range than the traditional workflow as described in the context of FIG. 2A while requiring less resource and time intensive iterations. Further details with regard to the processing of the historical experimental data 202 and the target parameter range 218, in particular by the extraction data-driven model 214 and / or the operation data-driven model 216 may be described in the following Figures.

[0114] FIG. 3A illustrates an embodiment of a method for obtaining a target chemical product by one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing the one or more target chemical reaction(s).

[0115] A target parameter range may be provided 302. The target parameter range may be indicative of one or more target parameter value(s) associated with a target parameter characterizing one or more target chemical reaction(s). The parameter values associated with the target parameter may be adapted by adapting one or more tuneable parameter value(s). Hence, it may be desired to determine one or more tuneable parameter value(s) related to one or more target parameter value(s). The target parameter range may be provided via an interface such as a user interface. This may allow for tuning the parameters of a target chemical reaction to arrive at target parameter values. For example, it may be desired to minimize the amount of byproduct produced in a target chemical reaction. In the example, increasing the temperature may increase the conversion rate for obtaining the target chemical product over the byproduct until a threshold temperature may be reached. Meanwhile, increasing the temperature above the threshold temperature may increase the conversion rate of another chemical product than the target chemical product and the byproduct. Hence, a balance between increasing the temperature for increasing the conversion rate associated with the target chemical product and decreasing the temperature for decreasing the conversion rate associated with the other chemical product, i.e., finding the threshold temperature, may be desired, i.e. maximizing the conversion rate associated with obtaining the target chemical product. The target parameter range may specify one or more target parameter values for obtaining the target chemical product via one or more target chemical reaction(s) to allow for efficient production and / or processing of the target chemical product.

[0116] Experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions may be provided 304. At least a part of the plurality of parameters may be tuneable parameters and / or may have an influence on the target parameter. In particular, adapting at least a part of the plurality of parameters may result in adapting the parameter values associated with the target parameter. The experimental data may be obtained by one or more sensor(s) associated with the plurality of chemical reactions. The experimental data may be provided by a database, i.e. a database may store the experimental data. In the above-described example, the experimental data may be indicative of one or more temperature curve(s) in relation to a concentration of the target chemical product at a predefined point in time. The experimental data may comprise numerical data, in particular tabular data, string data and / or image data, in particular image data indicative of a plot of one or more tuneable parameter values against the property values associated with the target parameter. The experimental data may be indicative of a plurality of parameter values of parameters characterizing a plurality of chemical reactions. The experimental data may be indicative of one or more tuneable parameters characterizing the target chemical reaction.

[0117] An applicable parameter range may be received and / or provided 306. The applicable parameter range may be received and / or provided via an interface such as an user interface. The applicable parameter range may be provided based on an inventory of a target chemical production facility for obtaining the target chemical product and / or one or more setting(s) related to the target chemical production facility. This allows to set limitations with regard to available means for obtaining the target chemical product. For example, where machinery may be operated within a predefined pressure range, obtaining the chemical product outside of the predefined pressure range may be a safety risk and / or may be impossible. Followingly, setting limitations allows to focus on feasible chemical reactions. Furthermore, this limits the search space for tuneable parameter values and in turn, improves the accuracy of the operation data-driven model. Thereby, less iterations for adapting the operation data-driven model are required and hence, material and time can be saved.

[0118] Additionally or alternatively, the applicable parameter range may be determined by an extraction data-driven model and / or based on the experimental data. The experimental data may be indicative of a plurality of parameter values of parameters characterizing a plurality of chemical reactions including the one or more target chemical reaction(s). The experimental data may be indicative of a subset of parameter values characterizing a plurality of chemical reactions. The subset of parameter values may correspond to the applicable parameter range. Hence, by evaluating the experimental data, the applicable parameter range may be determined. In an embodiment, the experimental data may be provided to the extraction data-driven model for extracting the applicable parameter range. The extraction data-driven model may be configured to extract and / or suitable for extracting parameter values from provided input data, i.e. the experimental data. Examples of extraction data-driven models may be described in further detail the context of

[0119] - FIG. 16. The extraction data-driven model may be provided with the experimental data. Further, the extraction data-driven model may be provided with range extraction task instructions. The range extraction task instructions may trigger the extraction data-driven model to extract the applicable parameter range from the experimental data. The range extraction task instruction may be indicative of instructions to the extraction data-driven model. The extraction data-driven model may be configured to follow task instructions provided to the extraction data-driven model. For example, the extraction data-driven model may be a large language model and / or the taks instructions may comprise string data indicative of the instructions to be carried out by the extraction data-driven model. By determining the applicable parameter range from the experimental data, a meaningful parameter space can be defined according to knowledge gained from previous experiments or experiments performed by other entities. Further, this enables to convey insights into chemical phenomena from historical experiments to improve efficiency of future experiments.

[0120] At least a part of the experimental data may be seleted according to the applicable parameter range and / or the one or more target chemical reaction(s) 308. The experimental data may be associated at least in parts with parameters outside of the applicable parameter range. At least the part of the experimental data may be selected based on the inventory of a target chemical production facility for obtaining the target chemical product and / or one or more setting(s) related to the target chemical production facility. This allows to set limitations with regard to available means for obtaining the target chemical product. For example, where machinery may be operated within a predefined pressure range, obtaining the chemical product outside of the predefined pressure range may be a safety risk and / or may be impossible. Fol lowingly, setting limitations allows to focus on feasible chemical reactions. Furthermore, this limits the search space for tuneable parameter values and in turn, improves the accuracy of the operation data-driven model 216. Thereby, less iterations for adapting the operation data-driven model are required and hence, material and time can be saved. Further, experimental data may apply to a subset of chemical reactions including the target chemical reactions. Hence, selecting at least the part of the experimental data according to the one or more chemical reaction(s) allows to use the applicable part of the experimental data and exclude non-meaningful experimental data from data analysis. In an embodiment, selecting at least the part of the experimental data may comprise retrieving at least the part of the experimental data by providing a query based on the applicable parameter range and / or the one or more target chemical reaction(s), in particular to the database comprising the experimental data. Additionally or alternatively, selecting at least the part of the experimental data may trigger at least a part of available sensor(s) to record experimental data associated with the one or more target chemical reaction(s).

[0121] Additionally or alternatively, selecting at least the part of the experimental data may comprise determining if parameter values associated with the experimental data may be within the applicable parameter range and selecting the part of the experimental data associated with the parameter values within the applicable parameter range. Additionally or alternatively, selecting the part of the experimental data may comprise determining a numerical representation of the experimental data and determining a distance score indicative of a distance between the numerical representation of the target parameter range and the numerical representation of the experimental data. The numerical representation(s) may be obtained by one or more embedding layer(s) as described in the context of FIG. 11. In an example, the target parameter range may comprise string data, numerical data such as tabular data, image data or the like. The numerical representation of the target parameter range and / or the experimental data may allow to select the part of the experimental data according applicable parameter range in a computationally low-cost manner even if the target parameter range may not allow for direct comparison with the experimental data, e.g. because of a different modality of the target parameter range than the experimental data.

[0122] Proposed experimental data may be determined according to the selected part of the experimental data 310. The proposed experimental data may be determined from at least two parameter values associated with at least two different parameters. The at least two different parameters may be related to each other. Preferably, at least one of the two different parameters may be the target parameter and the at least one other parameter may be the parameter other than the target parameter, in particular the at least one tuneable parameter. Determining the proposed experimental data may comprise determining at least two proposed parameter values associated with the at least two different parameters from the at least two parameter values associated with the selected part of the experimental data. A cumulative distribution function may be determined from the selected part of the experimental data. The cumulative distribution function may be indicative of a distribution of parameter values, in particular a probability of an appearance of parameter values associated with the at least two different parameters. The cumulative distribution function may be divided into a number of parts equal to a number of corresponding parameter values and / or a number of sets of parameter values. For example, where two parameter values may be associated with two different parameters, one further set of two parameter values associated with the two different parameters may be determined. Where three sets of parameter values, i.e., three parameter values associated with a first parameter and three parameter values associated with a second parameter different from the first parameter, may be used for determining the proposed experimental data, the cumulative distribution function may be divided into three parts. A set of parameter values, i.e. at least two parameter values, may be determined per part of the cumulative distribution function. This may be known as latin hypercube sampling. Additionally or alternatively, the proposed experimental data may be determined by monte carlo sampling. In general, a plurality of sampling methods may be available to obtain proposed experimental data from measured experimental data, i.e., the selected part of the experimental data.

[0123] Additionally or alternatively, the proposed experimental data may be determined by providing an extraction task instruction for extracting proposed experimental data including the selected part of the experimental data to a data-driven model. The data-driven model may be configured to follow task instructions, in particular may be the extraction data-driven model. The data-driven model, in particular the extraction data-driven model may be a model as described in the context of FIG. 11. The data-driven model may provide the proposed experimental data in response to receiving the extraction task instruction. The data-driven model may be suitable for and / or configured to extract proposed experimental data from the selected part of the experimental data. In particular, the selected part of the experimental data may be associated with and / or indicative of one or more relations between two or more parameters. Hence, the data-driven model may obtain the proposed experimental data from a relation between two or more parameters, in particular upon receiving the extraction task instruction. The extraction task instruction may further comprise the applicable parameter range. This may guide the data-driven model to generate the proposed experimental data within the applicable parameter range. The relation between the two or more parameters may be suitable for determining at least one parameter value associated with at least one of the two or more parameters from another parameter value associated with at least another one of the two or more parameters. Further, the relation between the at least two parameters may be obtained by retrieving the relation between the least two parameters from a data storage such as a database. The proposed experimental data may be obtained by applying the extracted and / or retrieved relation.

[0124] By doing so, more sample points can be used for building a operation data-driven model. This improves the performance, in particular the precision and the accuracy, of said operation data-driven model. Overall, this result in reducing the number of experiments to be carried out, hence reducing resource invest for obtaining a target chemical product.

[0125] The proposed experimental data and / or the selected part of the historical experimental data may be provided to an extraction data-driven model for extracting at least one set of parameter values associated with at least one target parameter and at least one parameter other than the target parameter 312. The extraction data- driven model may be suitable for extracting and / or configured to extract at least one set of parameter values associated with at least one target parameter and at least one parameter other than the target parameter. The extraction data-driven model may be configured to follow task instructions. The task instruction may trigger the extraction data-driven model to extract the at least one set of parameter values. The task instruction may include the selected part of the experimental data and / or the proposed experimental data. In particular, the at least one target parameter and the at least one parameter other than the target parameter may be related, i.e. adapting the at least one parameter other than the target parameter may result in adapting the target parameter. This may allow for determining parameter values of the target parameter in relation to a tuneable parameter associated with a target chemical reaction. Usually, a plurality of tuneable parameters may be available. The experimental data may be indicative of a plurality of relations, e.g. by providing a plurality of sets of parameter values. The extraction data-driven model may be suitable for selecting the relevant tuneable parameters from the data input by determining parameter values of the target parameter according to the data input in relation to at least one tuneable parameter. In other words, the extraction data-driven model can extract not explicetly disclosed parameter values by obtaining relations between parameters indicated by experimental data. This allows to extract more information from already available experimental data without the need for generating new experimental data. Hence, the extraction data-driven model may interpolate and / or extrapolate from measured experimental data to determine parameter values of the target parameter in relation to a tuneable parameter. By using more infromation from already available sources, the number of newly conducted experiments can be lowered significantly. Ultimately, this allows to reduce material, energy, water and time used for experimentation in order to obtain target chemical products.

[0126] An operation data-driven model may be determined based on the at least one extracted set of parameter values 314. Determining the operation data-driven model may comprise configuring the operation data-driven model to map parameter values associated with parameters other than the target parameter to parameter values associated with the target property. The operation data-driven model may be a data-driven model, i.e. may obtain a relation between the target parameter and at least one parameter other than the target parameter from the extracted set of parameter values. A plurality of different data-driven models may be available to relate parameters. For example, the operation data-driven model may include a neural network, a regression model such as linear regression, use bayesian optimization or the like. Determining the operation data-driven model may comprise updating model parameters associated with the operation data-driven model to decrease a distance between the parameter values provided by the operation data-driven model and the parameter values indicated by at least a part of the extracted set of parameter values, i.e. the parameter values associated with the target parameter. In the case of a neural network, determining the operation data- driven model may include initializing model parameter values indicative of a structure, i.e. a number of nodes and one or more connection(s) between the nodes, determining a parameter value associated with the target parameter by the operation data-driven model from at least one parameter value associated with the parameter other than the target parameter, determining a deviation between the determined parameter value and the extracted parameter value associated with the target parameter. Said parameter values may be related to the at least one parameter value associated with the parameter other than the target parameter. The deviation may be determined by a cost function associated with the operation data-driven model. Depending on the deviation, a measure for updating the model parameters may be determined via backpropagation of the deviation. The measure for updating the model parameters may be determined per model parameter by adjusting a cost function associated with the operation data-driven model via gradient descent. In the case of bayesian optimization, a plurality of functions including at least a part of the experimental data may be determined. The operation data-driven model may comprise a mean function representing a mean between the plurality of functions determined. The plurality of functions may be determined within a predefined range from the mean function, i.e. a standard deviation. In the case of linear regression, model parameters of a linear predictor function may be updated to decrease a deviation between the determined parameter value and the extracted parameter value associated with the target parameter.

[0127] The operation data-driven model may be provided and / or deployed 316. Providing the operation data-driven model may include for example to provided the operation data-driven model from a model providing engine 514 to a suggestion providing engine 516. This may allow for use of the operation data-driven model. Using the operation data-driven model may comprise updating the operation data-driven model by updating one or more model parameter(s) of the operation data-driven model. Using, adapting and / or deploying the operation data-driven model may be described further in FIG. 3B. FIG. 3B may continue the method as described in the context of FIG. 3A.

[0128] In an embodiment, the experimental data may comprise text data, numerical data, in particular tabular data, image data or a combination thereof. The data-driven model used for processing the experimental data may be associated with different embeddings schemas as described in the context of FIG. 11.

[0129] FIG. 3B illustrates an embodiment of a method for obtaining a target chemical product by one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing one or more target chemical reaction(s).

[0130] The operation data-driven model may be obtained as described in the context of FIG. 3A. FIG. 3B may show deploying, using and / or updating the operation data-driven model.

[0131] Producing target chemical products may require developing production conditions, i.e., chemical reaction parameters, for producing the target chemical product associated with a target parameter value. For example, known conversion rates may be too slow for large-scale chemical prodcution, chemical product may not be suited for a target application because the parameter value of the target parameter may be outside of a range indicated by a target parameter range, i.e. the chemical product may not be associated with a required performance, or share of the target chemical product produced by a chemical reaction may be low and consequently a large amount of byproducts, i.e. waste, may be produced. All of these examples require adaption of parameters of the production process such as composition, catalyst, reaction conditions or the like to arrive at the target chemical product associated with the target parameter value of the target parameter. The operation data-driven model may be used to approach the target parameter value. Nevertheless, confirming experimentation may be necessary to confirm the operation data-driven model or to identify potential for adaption. As described in the context of FIG. 2B, the operation data-driven model may be updated upon newly generated experimental data. For conducting further experiments, i.e. providing further experimental data, to confirm and / or adapt the operation data-driven model, suggestion parameter, it is beneficial to use an educated guess provided by the operation data-driven model. This allows to arrive faster with less experiments at a better operation data-driven model as currently available as it can be seen in FIG. 10.

[0132] One or more suggestion parameter value(s) related to at least one target parameter may be determined by the operation data-driven model 318. The suggestion parameter value(s) may be related to the at least one target parameter value by the operation data-driven model. Hence, the operation data-driven model may determine the suggestion parameter value from the at least one target parameter. In layman's words, the operation data- driven model provides tuneable parameters of a target chemical reaction for the parameter value of the target parameter to be within the target parameter range.

[0133] Upon determining the suggestion parameter value(s), an experiment may be conducted for confirming the suggestion from the operation data-driven model. The experiment may be conducted according to the suggestion parameter value. While conducting the experiment, experimental data indicative of at least one parameter value associated with the at least one target parameter may be obtained. The obtained experimental data may be verification experimental data. The verification experimental data may allow for verifying the suggestion by the operation data-driven model. The verification experimental data may be provided 320.

[0134] In an embodiment, the operation data-driven model may comprise the extraction data-driven model and a suggestion sub model. The suggestion sub model may be determined as the operation data-driven model as described in the context of 314. The operation data-driven model may be a combination of two or more data- driven models. Determining the suggested parameter values by the operation data-driven model may include determining the suggested parameter values by two or more data-driven models, in particular the extraction data-driven model and the suggestion sub model, and relating the determined suggested parameter values via two or more weighting factors associated with the two or more models. Additionally or alternatively, the operation data-driven model may comprise a combined function. The combined function may be obtained by relating two or more function(s) associated with the two or more data-driven models via two or more weighting factors associated with the two or more data-driven models. The combination of the two or more data-driven models may be obtained by transfer learning. Hence the operation data-driven model may be obtained by combining the weights associated with the two or more data-driven models.

[0135] It may be determined if the operation data-driven model may be updated according to the verification experimental data 322. This may include comparing the parameter value associated with the target parameter determined by the operation data-driven model with the parameter value associated with the target parameter provided with and / or indicated by the verification experimental data. Optionally, the parameter value associated with the target parameter may be determined from the verification experimental data. This parameter value may be the verification parameter value. If the parameter value associated with the target parameter determined by the operation data-driven model correspond to the verification parameter value, the operation data-driven model may be confirmed and / or an indication of a confirmation of the operation data- driven model by the verification experimental data may be provided. Otherwise, the operation data-driven model may be updated based on the verification experimental data 326. Updating the operation data-driven model based on the verification experimental data may include the steps of determining the operation data- driven model by starting from the operation data-driven model and updating the model parameters of the operation data-driven model according to the verification experimental data. Hence, updating the operation data-driven model may not include initializing the operation data-driven model. Updating the operation data- driven model may initialize and / or trigger repeating 318 to 322 until the parameter value associated with the target parameter determined by the operation data-driven model correspond to the verification parameter value. Parameter values corresponding to each other may comprise refer to the parameter value associated with the target parameter determined by the operation data-driven model being within a predefined distance from the verification parameter value. Once, the parameter value associated with the target parameter determined by the operation data-driven model corresponds to the verification parameter value, the operation data-driven model may be provided and / or deployed analogous to 316.

[0136] By following this approach, the operation data-driven model is improved iteratively. This allows to assess whether further experiments are needed per iteration cycle. Hence, only a number of experiments required to fulfill the set targets is conducted. Consequently, resources are applied efficiently and waste of water, material, energy and time can be circumvented.

[0137] FIG. 4A illustrates an embodiment of a method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing the one or more target chemical reaction(s).

[0138] A request for receiving one or more parameter value(s) may be provided 402 analogous to providing the target parameter range. The request may comprise an indication of the one or more target chemical reaction(s) and one or more target parameter value(s) associated with a target parameter characterizing one or more target chemical reaction(s). The indication may be a digital representation, ie a smiles code and / or a denotation, of the one or more target chemical product(s) obtained by the one or more target chemical reaction(s). In an embodiment, the request may further comprise an indication of a selection of experimental data from available experimental data.

[0139] Functional specification data related to one or more functions of one or more operating engine(s) may be provided 404. At least one of the operating engine(s) may be configured to provide the requested parameter value(s). Examples for operating engine(s) may be further described in the context of FIG. 5.

[0140] One or more model instruction(s) for triggering an allocation engine to select at least one operating engine according to the request may be provided 406. The model instruction(s) may be provided together or separate from the functional specification data. In an embodiment, the model instruction(s) and the functional specification data may be comprised in an initial prompt.

[0141] Allocation task instructions for determining one or more suggestion parameter value(s) associated with the one or more parameter(s) comprising the indication of the one or more target chemical reaction(s), the one or more target parameter value(s), the functional specification data and the one or more model instruction(s) for triggering an allocation engine to select at least one operating engine may be generated according to the request 408. This may include merging, the model instruction(s), the functional specification data, the indication of the one or more target chemical reaction(s) and the one or more target parameter value(s), e.g. as specified by the target parameter range.

[0142] The allocation task instructions may be provided to an allocation model for selecting the at least one operating engine according to the request 410. The allocation model may be configured to follow task instructions. The allocation model may be a data-driven model as described in the context of FIG. 11. The allocation engine may comprise the allocation model. The allocation model may provide an indication of the at least one selected operating engine in response to receiving the allocation task instruction. In response to providing the indication of the at least one selected operating engine, operating input data associated with the at least one selected operating engine may be generated and / or provided. The operating input data may comprise structured data. This may allow to control engines that requires structured inputs based on e.g. unstructured requests. Thereby, users can control the generating of the parameter values by human-interpretable language. This improves the machine-human interaction significantly by reliefing the user from sticking to a predefined data structure. Consequently, even requests with wrong syntax can be processed.

[0143] One or more input data structure related to operating input data suitable for being provided to the one or more operating engine(s) may be provided 412. The one or more input data structure(s) may define the data structure required by the one or more operating engine(s) for receiving input data.

[0144] Structuring task instructions for generating operating input data from the one or more input data structure(s), an indication of the at least one selected operating engine, the indication of the one or more target chemical reaction(s), the one or more target parameter value(s) and one or more model instruction(s) for triggering the structure engine to generate operating input data may be generated 414. The model instructions may be provided together or separate from the input data structure(s). This may include merging, the model instruction(s), the one or more input data structure(s), the indication of the one or more target chemical reaction(s), the indication of the at least one selected operating engine and the one or more target parameter value(s), e.g. as specified by the target parameter range. The structuring task instructions may be provided to a structure engine. The structure engine may comprise a structure model. The structure model may be configured to follow task instructions. The structure model may be a data-driven model as described in the context of FIG. 11. The structure model may provide operating input data associated with a data structure corresponding to the data structure associated with the at least one selected operating engine. In response to providing the operating input data, the operating input data may be validated as described in the context of FIG. 4B.

[0145] FIG. 4B illustrates an embodiment of a method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more parameter(s) characterizing the one or more target chemical reaction(s).

[0146] In response to providing the operating input data, the operating input data as described in the context of FIG. 4A may be validated. Validating the data structure of the operating input data allows to further reduce errors when operating the operating engines. Validation increases the accuracy of providing processable operating input data. Hence, processing efficiency is increased leading to less data processing due to errors in handling. Overall, this improves the human-machine interaction. Validating the operating input data may comprise generating validation task instructions 418 and providing the validation task instructions to a validation engine 420. Upon validating the operating input data, the validated operating input data to the at least one selected operating engine 422. The at least one selected operating engine may provide one or more parameter value(s), e.g. as described in the context of FIG. 5 and FIG. 3A and FIG. 3B. The one or more parameter value(s) may be provided 424.

[0147] An example of validation task instructions may be seen in FIG. 8. The validation task instructions may comprise one or more input data structure(s), the operating input data and one or more model instruction(s) for triggering a validation engine to validate the data structure associated with the operating input data. The model instruction(s) may comprise natural language. The model instruction(s) may be retrieved e.g. from an instruction providing engine such as an instruction database and / or provided by an instruction providing engine.

[0148] The validation engine may comprise a validation model. The validation model may be configured to follow model instruction(s) provided to the validation model. The validation model may perform one or more task instructed by the model instruction(s) on the provided operating input data and the provided one or more input data structure(s), i.e. context provided to the validation model. The validation model may be a natural language model. The validation model may be configured, trained and / or parametrized as described in the context of FIG. 11. The validation model may be the same model as the structure model and / or the allocation model. Additionally or alternatively, the validation model may be different from the structure model and / or the allocation model. The validation model may be trained, in particular further trained, based on historical validation task instructions and corresponding indications on a validation of the historical operating input data provided by providing the historical validation task instructions. The validation model may provide the indication on a validation of the operating input data in response to receiving the validation task instructions. Providing the indication on the validation of the operating input data may trigger providing the validated operating input data to the at least one selected operating engine for determining the one or more parameter value(s). The one or more parameter value(s) may be provided to a control engine control engines of a chemical production entity 518. Providing the one or more parameter value may result in and / or may trigger controlling one or more target chemical reaction(s) according to the one or more parameter value(s).

[0149] FIG. 5 illustrates an operating system of a chemical production entity 118.

[0150] The operating system of a chemical production entity 118 may be connected to a control engine of a chemical production entity 518. The operating system of a chemical production entity 118 may provide parameter values for controlling the one or more target chemical reaction(s) to the control engine of a chemical production entity 518. The control engine of a chemical production entity 518 may be configured to control the chemical product entity according to the provided parameter values. The operating system of a chemical production entity 118 may be configured to determine the parameter values, e.g. by performing the method according to FIG. 3B and FIG. 3A. For this purpose, the operating system of a chemical production entity 118 may comprise an intake interface 504, an experimental data proposing engine 508, a filtering engine 506, a database 520, an extraction engine 510, a suggestion engine 512, an allocation engine 522 and / or an output interface 502. In an embodiment, the suggestion engine 512, the filtering engine 506, the experimental data proposing engine 508, the database 520, the extraction engine 510, the intake interface 504 and / or the output interface 502 may be separate operating engine(s) and / or may be combined to one operating engine. Where the suggestion engine 512, the filtering engine 506, the experimental data proposing engine 508, the database 520, the extraction engine 510, the intake interface 504 and / or the output interface 502 may be separate operating engine(s), the operating input data may be the input data to the suggestion engine 512, the filtering engine 506, the experimental data proposing engine 508, the database 520, the extraction engine 510, the intake interface 504 and / or the output interface 502 may be separate operating engine(s).

[0151] The intake interface 504 may be configured to receive the request as described in the context of FIG. 4A and FIG. 4B. From the request the allocation task instruction 626 may be generated as described in the context of FIG. 4A and FIG. 4B. The allocation task instruction 626 may be provided to the allocation engine 522. The allocation engine 522 may comprise an allocation model. The allocation model may be a data-driven model as described in the context of FIG. 11. The allocation model may select at least one operating engine as described in the context of FIG. 4A and FIG. 4B. Operating input data may be generated as described in the context of FIG. 4A and FIG. 4B. The structuring engine and the validation engine may not be depictured in FIG. 5. The data structure associated with the operating input data may depend on the at least one selected operating engine. For example, where the operating engine may be the database 520, the operating input data may be a structured query for retrieving parameter values for the one or more target chemical reaction(s), e.g. SQL queries. In another example, the operating engine may be the suggestion engine 512. The operating input data may trigger selection of at least one suggestion model configured to relate suggestion parameter values and target parameter values associated with one or more target parameters. The operating input data may comprise one or more target parameter value(s), in particular at least one threshold target parameter value. Additionally or alternatively, the operating input data may comprise historical experimental data. The threshold target parameter value may be provided to the experimental data proposing engine 508 and / or the filtering engine 506. In an embodiment, only proposed experimental data, only a selected part of the experimental data and / or a combination of the proposed experimental data and the selected part of the experimental data may be used for determining the operation data-driven model. The experimental data may be stored by a database 520. The database 520 may be configured to provide the experimental data to the experimental data proposing engine 508 and / or the filtering engine 506, in particular upon providing a request for retrieving the experimental data to the database 520. The filtering engine 506 may be configured to select at least a part of the experimental data as described in the context of FIG. 3A. The experimental data proposing engine 508 may be configured to determine the proposed experimental data as described in the context of FIG. 3A. The proposed experimental data and the selected part of the experimental data may be provided to the extraction engine 510. The extraction engine 510 may be configured to extract at least one set of parameter values associated with at least one target parameter and at least one parameter other than the target parameter as described in the context of FIG. 3A. The experimental data proposing engine 508 may comprise the extraction engine 510 and optionally the database 520 for providing the experimental data. The extracted parameter values may be provided to the suggestion engine 512 for determining the operation data- driven model and providing suggestion parameter values by the operation data-driven model. The suggestion engine 512 may comprise a model providing engine 514, a model performance evaluation engine 524 and a suggestion providing engine 516. The model providing engine 514 may be configured to determine the operation data-driven model as described in the context of FIG. 3A. Further, the model providing engine 514 may be suitable and / or may be configured to update the operation data-driven model. The model performance evaluation engine 524 may be configured to determine if the operation data-driven model may be updated according to the verification experimental data as described in the context of FIG. 3A and FIG. 3B. The model performance evaluation engine 524 may provide a trigger for adapting the suggestion model to the model providing engine 514. The model providing engine 514 may update the operation data-driven model upon receiving the trigger for adapting the operation data-driven model. In an embodiment, the model providing engine 514 may comprise a plurality of operation data-driven models. The model providing engine 514 may be further configured to select the at least one operation data-driven model according to the operating input data. Additionally or alternatively, the operating input data may comprise a selection of at least one operation data- driven model. Hence, the functional specification data may further comprise a functional specification associated with the plurality of operation data-driven models and the model instruction(s) may be further suitable for triggering the allocation model to select at least one operation data-driven model from the plurality of operation data-driven models. The suggestion providing engine 516 may be configured to determine the suggestion parameter value(s) as described in the context of FIG. 3B. The model providing engine 514 may provide the operation data-driven model 216 to the suggestion providing engine 516. The suggestion parameter values may be provided to the output interface 502. The output interface 502 may provide the suggestion parameter values to the control engine of a chemical production entity 518 for controlling the production of the target chemical product. At least one of the operating engine(s), in particular the at least one selected operating engine may be configured to perform any one of the steps of the method as described in the context of FIG. 3A and FIG. 3B.

[0152] FIG. 6 illustrates an embodiment of the input and output data associated with the allocation model 604.

[0153] FIG. 7 illustrates an embodiment of the input and output data associated with the structure model.

[0154] FIG. 8 illustrates an embodiment of the input and output data associated with the validation model.

[0155] FIG. 9 illustrates an embodiment of a user interface 902 for providing parameter values for controlling one or more chemical reaction(s) according to the parameter values.

[0156] FIG. 10 illustrates evolution of a parameter values of a target parameter in relation to a number of iterations including conducting experiments.

[0157] In the example, the target parameter corresponds to a discharge capacity of a battery material. The tuneable parameter may be a composition of the battery material and / or reaction conditions for combining the components of the battery material. An iteration may include analyzing experimental data, providing suggested parameter values for controlling the one or more target chemical reaction(s), conducting one or more experiment(s) for verifying the suggested parameter values and verifying if the suggested parameter value resulted in at least one target parameter value within the target parameter range. Upon determining that the suggested parameter value did not result in the at least one target parameter value within the target parameter range, at least another iteration may be necessary to arrive at the at least one target parameter value. From FIG. 10 it can be concluded that random guessing results in less performant battery materials than compared to the methods as described herein, in particular in the context of FIG. 2B, FIG. 3A and FIG. 3B.

[0158] FIG. 11 illustrates an embodiment of a data-driven model, i.e. a transformer encoder comprising an encoder input 1188, one or more encoder block(s) 1186 and an encoder output 1176 and / or a transformer decoder comprising a decoder input 1194, one or more decoder block(s) 1190 and a decoder output 1192 and / or a transformer encoder-decoder.

[0159] The transformer encoder comprises an encoder input 1178, one or more encoder blocks 1174, 1114 and an encoder output. A plurality of transformer encoder architectures are available in the art such as the bi- directional encoder representations from transformers (BERT). The input data may be received at the encoder input 1178. The input data may comprise at least one of text data, numerical data, tabular data, image data or the like. Where the input data may comprise one of text data, numerical data, tabular data, image data or the like, input embedding of a type corresponding to the type of input data may be applied. Hence, the input embedding may be configured to map text data, numerical data, tabular data, image data or a combination thereof to a numerical representation of the input data. An example of input embedding associated with text data may be a continuous bag-of-words-model (CBOW). Additionally or alternatively, Word2Vec may be used for representing input data. Upon receiving the input data, the input data may be tokenized via a vocabulary associated with a preselection of elements of expected input data. Applying the input embedding may comprise mapping the input data, in particular the two or more elements of the input data to a numerical represenation of the input data, preferably of a predefined size e.g. via padding. Further, the encoder input 1178 may apply positional encoding 1104. For example, the positional factor pposmay be obtained based on the following equation: pos

[0160] Ppos (2i) = Sin( - ) lOOOO d- pos ppos(2i + l) = cos ( - -) lOOOOd- where pos may refer to the position of the element within the sequence, / may refer to the dimension associated with the input embedding and d may refer to the dimension of the data-driven model. Alternatively, the positional encoding may be based on rotary positional embeddings (RoPE). The embedded input data may be processed by the encoder block. The embedded input data may be provided to the layer normalization 1108 by a residual connection. Multi-head self-attention 1106 may be applied to the embedded input data. The embedded input data may serve as query Q, key K and value V with respect to the self-attention operation. For improving the efficiency, multiple heads are used to apply the filter according to the following equation: head i = Attenti.on(QWQ, KWK, VWiV~) with parameter matrices number of heads, dv, dKand dQmay refer to the dimensions of the value, key and query.

[0161] The result of the two or more head may be concatenated according to the following equation: MultiHead Q, K, P) = Concat(head 1, . . . , headh~)W0where Woe and h may refer to the number of heads. This may result in a context tensor. After the multi-head self-attention 1106 layer normalization 1108 may be applied based on the context tensor and / or the embedded input data from the residual connection. The so-obtained tensor may be passed to a feed-forward layer 1110 again followed by layer normalization 1112 based on the residual connection to the context tensor and / or the output of the feed-forward layer 1110. The encoder output 1176 may comprise a linear layer 1134 and a softmax layer 1136. The encoder output may be configured to map the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data. Additionally or alternatively, a decoding model may be used to map the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data. The decoding model may be trained to relate a numerical represenation of data of a type according to the input data.

[0162] The transformer decoder comprises a decoder input 1184, one or more decoder blocks 1180, 1128 and a decoder output 1192. A plurality of transformer decoder architectures are available in the art such as the generative pretrained transformers (GPT). In contrast to the transformer encoder, the transformer decoder may perform masked multi-head self-attention 1120 by additionally masking a part of the embedded input data associated with elements later in the sequence than the element to be generated. Additionally or alternatively, the part of the input data associated with elements later in the sequence than the element to be generated may not be received and / or transformed into the embedded input data.

[0163] The transformer encoder-decoder may comprise a combination of the transformer encoder and transformer decoder wherein the context tensor obtained from the encoder block may be used for the multi-head selfattention 1164 operation in at least one decoder block 1190.

[0164] Input data to the data-driven model may comprise image data. The encoder input and / or decoder input may comprise one or more linear projection layer(s) for a linear projection of a sequence of two or more partial images. This may result in changing the dimension of the one or more received images. Furthermore, positional embedding may be applied to the sequence, preferably by passing the sequence of one or more images and / or partial images through the one or more linear projection layer(s). Additionally or alternatively, the input data may comprise tabular data. Input embeddings for tabular input data may comprise a token embedding, a positional embedding, a column embedding, a row embedding or a combination thereof.

[0165] In an example, the data-driven model may comprise a Mamba block. A Mamba architecture may enhance inference speed in relation to a transformer based model. Mamba block may be based on a selective space state sequence model. A selective state space layer may be a linear recurrent network that selectively process data based on the input token, which may allow to focus on relevant data and discard irrelevant data. For instance in each step a separate weight vector may be determined based on the respective input token. The determined weight vector may then be used in a selective scan. A selective state space layer may be used in a convolutional mode e.g. for parallelizable training and a recurrent mode for near-constant time generation of output data. A state space operation may be based on solving the state and output equations, wherein a state equation may describe how a state changes based on how the input influences the state and an output equation may describe how the state is translated to the output. Further how the input influences the output may be represented by a learnable linear transformation used in a learnable skip connection. An example of the architecture of a mamba block may be found in "Mamba: Linear-Time Sequence Modeling with Selective State Spaces” by Albert Gu and Tri Dao arXiv:2312.00752v2 [cs.LG] 31 May 2024, , which is incorporated herein by reference.

[0166] In an example, any one of the data-driven models may further comrpise a mixture of experts block. The mixture of experts block may be decoder blocks wherein the feed-forward layer may be exchanged for a gating network and a number of parallel feed-forward layers, wherein the gating network may switch between the feed-forward layers depending on the input. This may allow leveraging advantages of the different architectures.

[0167] The data-driven model may generate one token per timestep upon processing the input data and optionally all output tokens already produced. The training data set may comprise a plurality of sequences comprising a plurality of elements. During the training of the data-driven model, sequences of the training data set may be provided to the data-driven model and one or more elements may be generated based on the sequences of the training data set one by another. The elements generated based on the sequences may follow the elements of the parts of sequences the data-driven model may have been provided with. The generated one or more elements may be compared to the one or more elements following the at least a part of the sequences provided to the data-driven model in order to adapt the parameters of the data-driven model depending on a deviation of the predefined token and the generated token.

[0168] The present disclosure has been described in conjunction with preferred embodiments and examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed subject-matter, from the studies of the drawings, this disclosure and the claims. Notably, in particular, the any steps presented can be performed in any order, i.e. the present disclosure is not limited to a specific order of these steps. Moreover, it is also not required that the different steps are performed at a certain place or at one node of a distributed system, i.e. each of the steps may be performed at different nodes using different equipment / data processing.

[0169] As used herein ..determining" also includes ..initiating or causing to determine", "generating" also includes ..initiating and / or causing to generate" and "providing” also includes "initiating or causing to determine, generate, select, send and / or receive”. "Initiating or causing to perform an action” includes any processing signal that triggers a computing node or device to perform the respective action.

[0170] In the claims as well as in the description the word "comprising” or "including” or similar wording does not exclude other elements or steps and shall not be construed limiting to the elements or steps lined out. The indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation or further elements may be included.

[0171] Providing in the scope of this disclosure may include any interface configured to provide data. This may include an application programming interface, a human-machine interface such as a display and / or a software module interface. Providing may include communication of data or submission of data to the interface, in particular display to a user or use of the data by the receiving entity.

[0172] Any disclosure and embodiments described herein relate to the methods, the systems, devices, the computer program element lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa.

Claims

CLAIMSWhat is claimed is:

1. A method for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more tuneable parameter(s) characterizing the one or more target chemical reaction(s), the method comprising: providing a request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s), wherein the request comprises an indication of the one or more target chemical reaction(s) and one or more target parameter value(s) associated with a target parameter characterizing one or more target chemical reaction(s), and wherein adapting the one or more parameter value(s) associated with one or more tuneable parameter(s) influences parameter values associated with the target parameter, providing functional specification data related to one or more functions of one or more operating engine(s), wherein at least one of the operating engine(s) is configured to provide parameter values, providing one or more input data structure related to operating input data suitable for being provided to the one or more operating engine(s) providing one or more model instruction(s) for instructing a data-driven model to generate operating input data for triggering at least one operating engine selected from a plurality of operating engines to provide the one or more parameter value(s) associated with one or more tuneable parameter(s), providing parameter task instructions comprising the indication of the one or more target chemical reaction(s), the one or more input data structure(s), the functional specification data, the one or more model instruction(s) and the one or more target parameter value(s) to a data-driven model for generating the operating input data, wherein the data-driven model is configured to follow task instructions, providing the operating input data to the at least one selected operating engine for generating the one or more parameter value(s) associated with one or more tuneable parameter(s), providing the one or more parameter value(s), in particular for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s).

2. The method of claim 1 , wherein the operating input data triggers the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and select at least a part of the experimental data according to the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneableparameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s).

3. The method of claim 1 or 2, wherein the operating input data triggers the at least one selected operating engine to: provide extraction task instructions including experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions to an extraction data-driven model for extracting an applicable parameter range from the experimental data, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, wherein the extraction data-driven model is configured to follow task instructions, and select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s).

4. The method of any one of claims 1 to 3, wherein the request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s) is a request for receiving an applicable parameter range, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, and wherein the operating input data triggers the at least one selected operating engine to: provide extraction task instructions including experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions to an extraction data-driven model for extracting an applicable parameter range from the experimental data, wherein the applicable parameter range is indicative of a range of applicable parameter values associated with the one or more tuneable parameter, wherein the extraction data-driven model is configured to follow task instructions, and select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s).

5. The method of any one of claims 1 to 4, wherein the request for receiving the one or more parameter value(s) associated with one or more tuneable parameter(s) further comprises an applicable parameter range based on an inventory of a target chemical production facility for performing the one or more target chemical reaction(s) and / or one or more setting(s) for performing the one or more targetchemical reaction(s) by the target chemical production facility, and wherein the operating input data triggers the at least one selected operating engine to select at least a part of the experimental data according to the applicable parameter range and the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s), wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s).

6. The method of any one of claims 1 to 5, wherein the operating input data triggers the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and determine proposed experimental data according to a distribution of the parameter values associated with the at least one target parameter and the at least one tuneable parameter within the experimental data.

7. The method of any one of claims 1 to 6, wherein the operating input data triggers the at least one selected operating engine to provide a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter, and determine proposed experimental data according to the relation, wherein the proposed experimental data comprises the one or more parameter value(s).

8. The method of any one of claims 1 to 7, wherein the operating input data triggers the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and select at least one set of parameter values associated with the at least one target parameter and the at least one tuneable parameter from the experimental data according to the indication of the one or more target chemical reaction(s) and / or the one or more target parameter value(s) and optionally according to the applicable parameter range, wherein the plurality of parameters comprises at least one target parameter and at least one tuneable parameter, and wherein the selected part of the experimental data comprises the one or more parameter value(s), and determine an operation data-driven model based on the at least one selected set of parameter values, wherein the operation data-driven model is configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the operation data-driven model.

9. The method of any one of claims 1 to 8, wherein the request further comprises verification experimental data comprising a parameter value associated with the target parameter and a corresponding parameter value associated with the one or more tuneable parameter(s), wherein the parameter value associated with the target parameter is obtained by controlling the one or more target chemical reaction(s) according the corresponding parameter value, and wherein the corresponding parameter value is determined according to a relation between the parameter values associated with the at least one target parameter and the parameter values associated with the at least one tuneable parameter and / or determined by an operation data-driven model configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and wherein the operating input data triggers the at least one selected operating engine to update the operation data-driven model based on the verification experimental data and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the updated operation data-driven model.

10. The method of any one of claims 1 to 9, wherein providing parameter task instructions comprises providing allocation task instructions comprising the indication of the one or more target chemical reaction(s), the functional specification data, the one or more target parameter value(s), and one or more model instruction(s) for triggering the allocation model to select the at least one operating engine from the plurality of operating engines to an allocation model, and wherein the allocation model is configured to follow the task instructions, and generating operating input data by providing structuring task instructions comprising the indication of the at least one selected operating engine, one or more input data structure(s) characterizing an input data structure associated with the at least one selected operating engine, the indication of the one or more target chemical reaction(s), the one or more target parameter value(s) and the one or more model task instruction(s) for instructing the structure model to generate the operating input data.

11. The method of any one of claims 1 to 10, wherein the operating input data triggers the at least one selected operating engine to provide experimental data indicative of a plurality of parameter values associated with a plurality parameters characterizing a plurality of chemical reactions, and provide model determining task instructions including the experimental data for generating an operation data-driven model to an extraction data-driven model, wherein the operation data- driven model is configured to map parameter values associated with the one or more tuneable parameter(s) to parameter values associated with the target parameter, and wherein the extraction data-driven model is configured to follow task instructions, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the operation data-driven model.

12. The method of any one of claims 1 to 11 , further comprising providing a plurality of operation data-driven models, wherein the plurality of operation data- driven models is configured to map parameter values associated with a plurality of tuneable parameter(s) to parameter values associated with the a plurality of parameters, wherein the plurality of parameter(s) includes the target parameter, select at least one operation data-driven model from the plurality of available operation data- driven models based on the target parameter, and determine the one or more parameter value(s) by providing the one or more target parameter value(s) to the selected operation data-driven model.

13. Use of parameter task instructions according to any one of the preceding claims for determining one or more parameter value(s) for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s).

14. Use of a data-driven model for generating operating input data for triggering at least one operating engine to determine one or more parameter value(s) for controlling the one or more target chemical reaction(s) according to the one or more parameter value(s).

15. An apparatus for controlling one or more target chemical reaction(s) according to one or more parameter value(s) associated with one or more tuneable parameter(s) characterizing the one or more target chemical reaction(s), the apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to perform the steps of any one of the methods of claim 1-12.

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