Product data-driven monitoring and / or controlling chemical production

Abstract

A computer-implemented method for generating chemical product production and / or processing data related to a production and / or processing of a chemical product, the method comprising: providing chemical product data related to the production and / or processing of the chemical product, providing multiple production and / or processing data template indicative of a structure associated with at least a part of the chemical product data and / or associated element(s), selecting at least one production and / or processing data template based on at least a part of the chemical product data and / or associated elements, generating contextualized chemical product data by combining at least a part of the chemical product data and the at least one selected production and / or processing data template, providing the contextualized chemical product data to a data-driven model for generating the chemical product production and / or processing data, wherein the data-driven model is parametrized and / or trained to provide the chemical product production and / or processing data in response to being provided by the contextualized chemical product data, providing the chemical product production and / or processing data.

Description

PRODUCT DATA-DRIVEN MONITORING AND / OR CONTROLLING CHEMICAL PRODUCTIONTECHNICAL FIELDThe disclosure relates to the technical field of controlling and / or monitoring of production of chemical products and / or processing of produced chemical products. The disclosure relates to methods and apparatuses for generating chemical product production data associated with a production of a chemical product, a method for generating chemical product processing data associated with a processing of a produced chemical product, uses of chemical product production and / or processing data and / or computer elements.TECHNICAL BACKGROUNDChemical production is complex and consumes a large amount of resources such as energy, water and raw materials. As the scale for chemical production is usually in the scale of tons, even small relative changes constitute a major improvement. Hence, it is desired to improve chemical production.SUMMARYIn another aspect, the disclosure relates to a computer-implemented method for generating chemical product production and / or processing data related to a production and / or processing of a chemical product, the method comprising: providing chemical product data related to the production and / or processing of the chemical product, selecting at least one production and / or processing data template based on at least a part of the chemical product data and / or associated elements, generating contextualized chemical product data by combining at least a part of the chemical product data and the at least one selected production and / or processing data template, providing the contextualized chemical product data to a data-driven model for generating the chemical product production and / or processing data, wherein the data-driven model is parametrized and / or trained to provide the chemical product production and / or processing data in response to being provided by the contextualized chemical product data, providing the chemical product production and / or processing data.In another aspect, it relates to a computer-implemented method for generating chemical product production data related to a production of a chemical product or generating chemical product processing data related to a processing of the chemical product, the method comprising: receiving the chemical product production data, or, receiving the chemical product processing data, receiving a production data template indicative of a structure associated with at least a part of the chemical product production data, or, receiving a processing data templateindicative of a structure associated with at least a part of the chemical product processing data, generating contextualized chemical product data by combining at least a part of the chemical product production data and the production data template, or, generating contextualized chemical product data by combining at least a part of the chemical product production data and the processing data template, generating chemical product processing data by providing the contextualized chemical product data to a data-driven model, or, generating chemical product production data by providing the contextualized chemical product data to the data-driven model, wherein the data- driven model is parametrized and / or trained to provide the chemical product production data in response to being provided by the contextualized chemical product data and / or to provide the chemical product processing data in response to being provided by the contextualized chemical product data based on a training data set comprising historical data related to the chemical product production data and chemical product processing data, providing the chemical product processing data, or, providing the chemical product production data.In another aspect, it relates to a use of production and / or processing data related to a production and / or processing of a chemical product as obtained by any one of the methods as described herein for controlling and / or monitoring a production and / or processing of the chemical product.In another aspect, it relates to a computer-implemented method for generating chemical product processing data related to a processing of the chemical product, the method comprising: receiving the chemical product processing data, receiving a processing data template indicative of a structure associated with at least a part of the chemical product processing data, generating contextualized chemical product data by combining at least a part of the chemical product production data and the processing data template, generating chemical product production data by providing the contextualized chemical product data to the data-driven model, wherein the data- driven model is parametrized and / or trained to provide the chemical product processing data in response to being provided by the contextualized chemical product data based on a training data set comprising historical data related to the chemical product production data and chemical product processing data, providing the chemical product processing data.In another aspect, it relates to a computer-implemented method for generating chemical product production data related to a production of a chemical product, the method comprising: receiving the chemical product production data, receiving a production data template indicative of a structure associated with at least a part of the chemical product production data, generating contextualized chemical product data by combining at least a part of the chemical product production data and the production data template, generating chemical product processing data by providing the contextualized chemical product data to a data-driven model, wherein the data-driven model is parametrized and / or trained to provide the chemical product production data in response to being provided by the contextualized chemical product data based on a training data set comprising historical data related to thechemical product production data and chemical product processing data, providing the chemical product production data.In another aspect, it relates to use of chemical product production data related to a production of a chemical product as obtained by any one of the methods as presented herein.In another aspect, it relates to a device and / or a system 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 presented herein.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.EMBODIMENTSAny 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.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.Chemical products are usually at the beginning of a diverse plurality of supply chains. Depending on the consumer of chemical products, the performance requirements, the quantity and the quality of the chemical product vary strongly. Hence, tailored chemical products are desired for the different intended uses. Furthermore, the production of chemical products consumes large amounts of energy, water and raw materials while emitting carbon dioxide and toxic waste. Followingly, increasing the improving the management between the production and the processing of chemical products is key for saving resources such as energy, water and raw materials while reducing the carbon dioxide emissions and the amount of toxic waste. This can be achieved by tailoring the production of chemical products to the need of the chemical products for processing. Furthermore, providing accurate information on the available chemical products to a chemical product processor and / or chemical product producer helps to match the need for chemical products to the available chemical products. For this purpose, a data-driven model can be deployed to generate chemical product production data and / or chemical productprocessing data. Using this data-driven model enables fast and reliable tailoring of chemical production towards the need for processing the chemical products. As the data-driven model is provided with input generated based on production data templates and processing data templates, users are assisted towards a successful operation and time is saved for generating answers. More than that, this enables the use of a corresponding tool for all users not limited to technical experts. Hence, the invention provides a barrier-free possibility to generate chemical product processing data and chemical product production data. Followingly, more informed decisions increasing the suitability of measures in relation to the processing and the production of chemical products while reducing the risk of errors are achieved. Ultimately, this contributes to the goal of improving the processing and production efficiency in relation to chemical products, thereby reducing the amounts of energy, water and raw materials used while producing less carbon dioxide and less toxic waste.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 invention.In an embodiment, chemical product may refer to a product obtained by means of a chemical production process. Chemical production process may refer to a process including one or more chemical reaction(s). The chemical product may be characterized by at least one functional group. The functional group may be at least one of alkyl group, alkenyl group, alkynyl group, phenyl group, carbonyl group, ketone group, aldehyde group, hydroxyl group, haloformyl group, ester group, carboxylate group, halo group, carboxyl group, peroxy group, carboalkoxy group, hydroperoxyl group, ether group, acetal group, hemiacetcal group, hemiketal group, ketal group, carboxylic anhydride group, carboxamide group, amidine group, amine group, ketamine group, aldimine group, imide group, cyante group, azo group, nitrite group, nitrate group nitro group, nitrile group, sulfide group, thiol group, sulfinyl group, sulfonyl group, sulfo group, thiocyanate group, thionoester group, thiolester group, phosphino group, phosphono group, phosphate group or any combination thereof.In an embodiment, chemical product data may be related to and / or indicative of the production and / or processing of the chemical product. Chemical product data may comprise and / or may be chemical product production data and / or chemical product processing data.In an embodiment, chemical product data may be indicative of a production and / or processing of to the chemical product. The chemical product data may comprise chemical product producer data, chemical product production instructions, potential chemical product processing data and / or potential chemical product production data, one or more material properties associated with the chemical product, chemical product processor data, chemical product processing instructions, potential chemical product production data, potential chemical product processing data, one or more material properties associated with a product based on the chemical product, one or more input products the product based on the chemical product for producing the product based on the chemical product, oneor more chemical compounds the chemical product is based on or a combination thereof. A product based on the chemical product may refer to a product resulting from processing the chemical product.Chemical product producer data may be indicative of the producer with respect to the chemical product and / or of the production facilities associated with the production of the chemical product. For example, the chemical product producer data may be indicative of one or more producers with respect to the chemical product, quantitative production data, qualitative production data or a combination thereof. Qualitative production data may be indicative of the chemical structure associated with the chemical product to be produced. For example, the qualitative production data may be indicative of a name of the chemical product, the quality of the chemical product, the one or more chemical compounds associated with the chemical product, one or more material properties associated with the chemical product, one or more chemical compounds the chemical product is based on, or a combination thereof. Quantitative production data may be indicative of a quantity of the chemical product.Potential chemical product production data may be indicative of an expected production with respect of the chemical product based on historical production and / or production associated with alternative products to the chemical product. For example, the potential chemical product production data may be indicative of a historical production associated with the chemical product, alternatives to the chemical product, a product to be produced based on the chemical product, a field of application with respect to the chemical product or a combination thereof. In an embodiment, chemical product production data may be received in an audio format and / or a text format.Chemical product processor data may be indicative of the chemical product processor with respect to the chemical product and / or of the processing facilities associated with the processing of the chemical product. For example, the chemical product processor data may be indicative of one or more chemical product processors with respect to the chemical product, quantitative processing data, qualitative processing data, a field of application with respect to the chemical product, the product to be produced based on the chemical product, a field of application of the product to be produced based on the chemical product or a combination thereof. Qualitative processing data may comprise a name of the chemical product, the quality of the chemical product, one or more target material properties associated with the chemical product, one or more chemical compounds being comprised in the chemical product or a combination thereof. Quantitative processing data may be indicative of quantities associated with one or more components associated with the chemical product. Chemical product processing instructions may be indicative of the processing instructions associated with the processing of the chemical product. For example, the chemical product processing instructions may be related to processing conditions such as temperature, pressure, reaction rate, catalyst or the like.Potential chemical product processing data may be indicative of an expected processing with respect to the chemical product based on historical processing and / or processing associated with alternative products to the chemical product. For example, the potential chemical product processing data may be indicative of a historical processing associated with the chemical product, alternatives to the chemical product or a combination thereof.In an embodiment, chemical product data may be received and / or provided in an audio format and / or a numerical format and / or text format and / or digital format. In an embodiment, the chemical product data may comprise one or more elements, preferably production and / or processing parameter values. The one or more elements, preferably the production and / or processing parameter values may be suitable for being combined with the production and / or processing data template. Preferably, the one or more elements may be inserted into the production and / or processing data template preferably where the production and / or processing data template specifies production and / or processing parameters. The production and / or processing parameters may be placeholders indicating where to insert at least a part of the chemical product data, preferably the production and / or processing parameter values.In an embodiment, chemical product processing data may be indicative of a processing with respect to the chemical product. The chemical product processing data may comprise chemical product processor data, chemical product processing instructions, potential chemical product production data and / or potential chemical product processing data.Chemical product processor data may be indicative of the chemical product processor with respect to the chemical product and / or of the processing facilities associated with the processing of the chemical product. For example, the chemical product processor data may be indicative of one or more chemical product processors with respect to the chemical product, quantitative processing data, qualitative processing data, a field of application with respect to the chemical product, the product to be produced based on the chemical product, a field of application of the product to be produced based on the chemical product or a combination thereof. Qualitative processing data may comprise a name of the chemical product, the quality of the chemical product, one or more target material properties associated with the chemical product, one or more chemical compounds being comprised in the chemical product or a combination thereof. Quantitative processing data may be indicative of quantities associated with one or more components associated with the chemical product. Chemical product processing instructions may be indicative of the processing instructions associated with the processing of the chemical product. For example, the chemical product processing instructions may be related to processing conditions such as temperature, pressure or the like.Potential chemical product processing data may be indicative of an expected processing with respect to the chemical product based on historical processing and / or processing associated with alternative products to the chemical product. For example, the potential chemical product processing data may be indicative of a historical processing associated with the chemical product, alternatives to the chemical product or a combination thereof.In an embodiment, chemical product processing data may be received in an audio format and / or a text format. In an embodiment, the chemical product processing data may comprise one or more elements, preferably processing parameter values. The one or more elements, preferably the processing parameter values may be suitable for being combined with the processing data template. Preferably, the one or more elements may be inserted into the processing data template where the processing data template specifies processing parameters. The processing parameters may be placeholders indicating where to insert at least a part of the chemical product processing data, preferably the processing parameter values.In an embodiment, chemical product production data may be indicative of a production with respect to the chemical product. The chemical product production data may comprise chemical product producer data, chemical product production instructions, potential chemical product processing data and / or potential chemical product production data. Chemical product producer data may be indicative of the producer with respect to the chemical product and / or of the production facilities associated with the production of the chemical product. For example, the chemical product producer data may be indicative of one or more producers with respect to the chemical product, quantitative production data, qualitative production data or a combination thereof. Qualitative production data may be indicative of the chemical structure associated with the chemical product to be produced. For example, the qualitative production data may be indicative of a name of the chemical product, the quality of the chemical product, the one or more chemical compounds associated with the chemical product, one or more material properties associated with the chemical product, one or more chemical compounds the chemical product is based on, or a combination thereof. Quantitative production data may be indicative of a quantity of the chemical product. Name of the chemical product may refer to a name under which the chemical product may be sold and / or a name specifying the one or more chemical compounds associated with the chemical product.Potential chemical product production data may be indicative of an expected production with respect of the chemical product based on historical production and / or production associated with alternative products to the chemical product. For example, the potential chemical product production data may be indicative of a historical production associated with the chemical product, alternatives to the chemical product, a product to be produced based on the chemical product, a field of application with respect to the chemical product or a combination thereof. In an embodiment, chemical product production data may be received in an audio format and / or a text format.In an embodiment, the chemical product production data may comprise one or more elements, preferably production parameter values. The one or more elements, preferably the production parameter values may be suitable for being combined with the production data template. Preferably, the one or more elements may be inserted into the production data template where the production data template specifies production parameters. The production parameters may be placeholders indicating where to insert at least a part of the chemical product production data, preferably the production parameter values.In an embodiment, contextualized chemical product data may refer to a combination of input data and at least one production and / or processing data template. Contextualized chemical product data may comprise contextualized chemical product data and / or contextualized chemical product data. Contextualized chemical product data may be second contextualized chemical product data.In an embodiment, data-driven model may comprise one or more machine-learning architectures and model parameters. The one or more machine-learning architectures may be at least one of one or more convolutional layers, one or more fully connected layers, one or more pooling layers, one or more transformer encoders, one or more classification layers, one or more transformer decoders, one or more feed forward layers, one or more linear layers, one or more transformer encoder-decoders or a combination thereof.The data-driven model may be trained and / or parametrized based on a training data set. The training data set may comprise historical data related to the chemical product data and chemical product production and / or processing data. Further, the training data set may comprise at least one data set comprising chemical product data and corresponding chemical product production and / or processing data. The training data may comprise one or more sequences of elements. Preferably, at least one data set within the training data set may comprise a sequence of elements specifying chemical product data and corresponding chemical product production and / or processing data. The data-driven model may be trained and / or parametrized based on the training data set to provide the chemical product production and / or processing data in response to being provided by the contextualized chemical product data. The data-driven model may be a generative model. The generative model may be trained and / or parametrized based on the training data set to generate chemical product production and / or processing data, preferably based on being provided by the contextualized chemical product data. Preferably, the training data set may comprise numerical and / or sentence-based historical data related to the chemical product production and / or processing data and chemical product data. Numerical historical data may refer to historical data comprising one or more numbers. Sentence-based historical data may refer to historical data comprising at least a part of a sentence. The data-driven model may be trained and / or parametrized to generate the chemical product productionand / or processing data sequentially, in particular based on being provided with the contextualized chemical product data.The data-driven model may be trained and / or parametrized to generate the chemical product production and / or processing data sequentially, in particular based on being provided with the contextualized chemical product data may refer to the data-driven model may be trained and / or parametrized to generate the chemical product production and / or processing data comprising a sequence of elements by generating an element of the sequence of elements, preferably the first element of the sequence of elements, based on the contextualized chemical product data and generating the further elements, preferably the elements following the first element in the sequence, based on the contextualized chemical product data and the previously generated elements in the sequence. By taken previously generated elements into account, the data-driven model may generate the chemical product production and / or processing data more accurately and better linked to the other elements within the sequence. Ultimately, this enables a tailored production with respect to quantity and / or quality associated with chemical product, improves the efficiency of processes associated with respect to processing and producing the chemical product and helps to use resources as efficient as possible.In an embodiment, the data-driven model may be a pre-trained data-driven model. The pre-trained data-driven model may be trained based on general data to provide output data based on being provided by input data. In an embodiment, the data-driven model may be a fine-tuned data-driven model. The fine-tuned data-driven model may be trained based on general data to provide output data based on being provided by input data, preferably in a first training process. The fine-tuned data-driven model may be further trained, preferably in a second training based on historical data chemical product data to provide chemical product production and / or processing data based on being provided by contextualized chemical product data. General data may comprise historical input data and / or historical output date. Input data and / or output data may refer to data processable by the data-driven model, preferably by the data-driven model as parametrized and / or initialized. The input data and / or the output data may comprise and / or may represent one or more sequences of elements, wherein a sequence of elements comprises two or more elements. In particular, the sequence of elements may be indicative of a sequence of the two or more elements of the sequence.Training the data-driven model my refer to and / or the training process may be a process of building the data- driven model, in particular determining and / or updating parameters of the data-driven model. During the training process, the data-driven model may adjust to achieve best fit with the training data set, e.g. relating the at least on input value with best fit to the at least one target output value. For example, if the neural network is a feedforward neural network such as a convolutional neural network (CNN), a backpropagation-algorithm may be applied for training the neural network. In case of a recurrent neural network (RNN), a gradient descent algorithm may beemployed for training purposes. Gradient descent algorithm may use gradient for updating parameters. Gradient may indicate the degree of change for a parameter of the data-driven model. The gradient may be obtained by backpropagation. Thus, gradient descent algorithm may be based on backpropagation. A training process may be terminated when a deviation of the output generated by the data-driven model in comparison to a target output specified by the training data set falls within a predetermined range. The determining and / or updating of parameters of the data-driven model may be terminated when the training process may be terminated. The output generated by the data-driven model may be chemical product production and / or processing data, historical data related to the chemical product production and / or processing data or a combination thereof. The target output specified by the training data set may be chemical product production and / or processing data, historical data related to the chemical product production and / or processing data or a combination thereof. During the training process and / or the training of the data-driven model the training data set may comprise one or more sequences of elements and the one or more sequences of elements may be provided to the data-driven model sequentially and / or the data-driven model may generate the output sequentially. Hence, the data-driven model may generate a first element of the one or more sequences of elements based on the contextualized chemical product data and may generate further elements such as a second element of the one or more sequences based on one or more previously generated elements and the contextualized chemical product data. A second training process following a first training process may be advantageous to tailor the data-driven model to the use cases of the production and processing of the chemical product. This helps to increase the accuracy of the data-driven model further.In an embodiment, the transformer encoder may comprise an encoder input, one or more encoder blocks and / or an encoder output. The encoder input may generate embedded contextualized chemical product data based on receiving contextualized chemical product data. The one or more encoder blocks may generate production and / or processing data context tensor based on receiving the embedded contextualized chemical product data. The encoder output may generate chemical product production and / or processing data based on receiving the production and / or processing data context tensor. The transformer decoder may comprise a decoder input, one or more decoder blocks and / or a decoder output. The decoder input may generate embedded contextualized chemical product data based on being receiving with contextualized chemical product data. The one or more decoder blocks may generate production and / or processing data context tensor based on receiving the embedded contextualized chemical product data. The decoder output may generate chemical product production and / or processing data based on receiving the production and / or processing data context tensor. The transformer encoder-decoder may comprise a transformer encoder comprising an encoder input and one or more encoder blocks and a transformer decoder comprising a decoder input, one or more decoder blocks and a decoder output.In an embodiment, generating the chemical product production and / or processing data by providing the contextualized chemical product data to the data-driven model may include receiving the contextualized chemicalproduct data, e.g. at an encoder input and / or a decoder input, applying input embedding to the contextualized chemical product data, e.g. to result in embedded contextualized chemical product data, applying positional encoding to the contextualized chemical product data and / or the embedded contextualized chemical product data, applying self-attention to the contextualized chemical product data and / or the embedded contextualized chemical product data, e.g. to result in production and / or processing data context tensor, applying a softmax function to the contextualized chemical product data.In an embodiment, the method may further comprise embedding the contextualized chemical product data by the data-driven model, preferably in response to being provided by the contextualized chemical product data. Embedding the contextualized chemical product data may refer to applying input embedding and / or positional encoding to the contextualized chemical product data. Embedding the contextualized chemical product data may comprise transforming the contextualized chemical product data into machine-processable contextualized chemical product data such as a tensor. The data-driven model may process the machine-processable contextualized chemical product data into chemical product production data. By doing so, the contextualized chemical product data may be received in user language and accurate, robust and efficient processing of the user input may be processed by the data-driven model. This enables a barrier-free usage of the data-driven model to generate chemical product production data. In turn, providing access to all users provides the opportunity to all users to deploy the methods and systems as presented herein. This will increase the number of requests treated by the methods and systems resulting in more tailored processing and production of the chemical product while lowering the amount of errors in processing and production of the chemical product. Hence, resources for processing and production of the chemical product are used more efficiently and with less errors.Additionally or alternatively, embedding the contextualized chemical product data may comprise passing the contextualized chemical product data through an embedding layer. The embedding layer may be suitable for transforming the contextualized chemical product data into a machine-processable format. The machine- processable format may refer to a number-based, in particular tensor-based representation of the contextualized chemical product data. Embedding the contextualized chemical product data may result in embedded contextualized chemical product data. The embedded contextualized chemical product data may comprise a tensor representing the contextualized chemical product data. The tensor-based representation of the contextualized chemical product data may be referred to as embedded contextualized chemical product data. The embedded contextualized chemical product data may be referred to as machine-processable format of the contextualized chemical product data.In an embodiment, the method may further comprise processing the embedded contextualized chemical product data to production and / or processing data context tensor by the data-driven model. Processing the embeddedcontextualized chemical product data to chemical product processing data to production and / or processing data context tensor may refer to transforming the embedded contextualized chemical product data to production and / or processing data context tensor. Preferably the embedded contextualized chemical product data may be transformed into production and / or processing data context tensor by forming one or more tensor products associated with the embedded contextualized chemical product data and / or one or more representations associated with the embedded contextualized chemical product data. The one or more representations associated with the embedded contextualized chemical product data may be obtained by applying one or more mathematical operations to the embedded contextualized chemical product data. One or more mathematical operation may include for example, summing, subtracting, dividing, integrating, forming a derivative, multiplying, normalizing or a combination thereof.In an embodiment, the embedded contextualized chemical product data may be sequence specific. The sequencespecific embedded contextualized chemical product data may refer to a representation of the embedded contextualized chemical product data taking the sequence of data points associated with the embedded contextualized chemical product data into account. The sequence-specific embedded contextualized chemical product data may be obtained by applying input embedding and / or positional encoding to the contextualized chemical product data. For example, applying positional encoding may refer to adding and / or multiplying the one or more parts of the embedded contextualized chemical product data by a positional factor indicative of the position of the one or more parts of the embedded contextualized chemical product data within the embedded contextualized chemical product data. The sequence-specific embedded contextualized chemical product data may be processed, in particular transformed, analogous to the embedded contextualized chemical product data.In an embodiment, the received chemical product production data may be sentence-based. Hence, the production and / or processing data template indicative of structure associated with at least a part of the chemical product data and / or the contextualized chemical product data may be sentence-based. Follow! ngly , the data-driven model may be a natural language processing model. A natural language processing model may be a model suitable for processing sentence-based input such as sentence-based chemical product data and / or chemical product data comprising one or more elements. The sentence-based contextualized chemical product data may be embedded resulting in the embedded contextualized chemical product data. The sentence-based contextualized chemical product data may be embedded by means of input embedding, preferably word embedding. Word embedding may refer to transforming the sentence-based contextualized chemical product data into a machine-processable format.In an embodiment, the determined chemical product production and / or processing data may comprise two or more parts, specifically a first part of chemical product production and / or processing data and a second part of chemicalproduct production and / or processing data. Where the chemical product production and / or processing data may be sentence-based, the two or more parts may refer to words, numbers and / or parts of a word. First part of the chemical product production and / or processing data may refer to the first part in the sequence of the chemical product production and / or processing data and second part of the chemical product production and / or processing data may refer to the second part in the sequence of the chemical product production and / or processing data. The first part may be determined at a first time step and the second part may be determined at a second time step.In the first-time step, the first chemical product production and / or processing data may be determined based on the embedded contextualized chemical product data. In the second time step, the second chemical product production and / or processing data may be determined based on the embedded contextualized chemical product data and an embedded first chemical product production and / or processing data. Embedded first chemical product production and / or processing data may refer to first chemical product production and / or processing data embedded analogous to the contextualized chemical product data. Fol lowingly, the chemical product production and / or processing data may be generated based on the contextualized chemical product data and at least partially based at least a part of the generated chemical product production and / or processing data, preferably by further providing at least a part of the generated chemical product production and / or processing data to the data-driven model, in particular providing at least a first part of the generated chemical product production and / or processing data to the data-driven model.In an embodiment, material property may refer to a physical property, to a chemical property, a biological property or a combination thereof. Chemical property may be a property that can be established only by changing one or more chemical structures associated with the at least one chemical product. Examples for chemical properties may be acidity, oxidation state or reactivity. 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 properties may refer to toxicity, biological activity, biodegradability, bioaccumulation or the like.In an embodiment, processing data template is indicative of a structure associated with at least a part of the chemical product processing data. The processing data template may be indicative of a sequence of one or more elements associated with at least a part of the chemical product processing data. Preferably, the processing data template may be indicative of a sequence of the one or more elements associated with at least a part of the chemical product processing data and one or more elements comprised in the processing data template.The processing data template may be indicative of one or more processing parameters and one or more elements associated with a relation between the one or more processing parameters, in particular one or more processing parameter values. The processing parameter may specify where the processing parameters values may be inserted. The one or more processing parameters and the one or more elements associated with a relation between the one or more processing parameters, in particular one or more processing parameter values, may constitute a sequence of two or more elements.Hence, the elements associated with the relation between the one or more processing parameters may specify a part of a sequence of elements associated with the contextualized chemical product data. Additionally or alternatively, the elements associated with the relation between the one or more processing parameters may specify one or more parts of a sequence next to, preferably before, after, and / or in between, which the processing parameters values may be inserted.In an embodiment, the processing data template may include at least a part of the chemical product processing data. The processing data template may comprise one or more of chemical product processor data, chemical product processing instructions potential chemical product processing data or a combination thereof.In an embodiment, production and / or processing data template may refer to production data template and / or a processing data template. Production and / or processing data template may be indicative of a structure associated with at least a part of the input data, chemical product production data and / or chemical product processing data.In an embodiment, production data template is indicative of a structure associated with at least a part of the chemical product production data. The production data template may be indicative of a sequence of one or more elements associated with at least a part of the chemical product production data. Preferably, the production data template may be indicative of a sequence of the one or more elements associated with at least a part of the chemical product production data and one or more elements comprised in the production data template. The production data template may be indicative of one or more production parameters and one or more elements associated with a relation between the one or more production parameters, in particular one or more production parameter values. The production parameter may specify where the production parameters values may be inserted. The one or more production parameters and the one or more elements associated with a relation between the one or more production parameters, in particular one or more production parameter values, may be constitute a sequence of two or more elements. Hence, the elements associated with the relation between the one or more production parameters may specify a part of a sequence of elements associated with the contextualized chemical product data. Additionally or alternatively, the elements associated with the relation between the one ormore production parameters may specify one or more parts of a sequence next to, preferably before, after, and / or in between, which the production parameters values may be inserted.In an embodiment, the production data template may include at least a part of the chemical product production data. The production data template may comprise one or more of chemical product producer data, chemical product production instructions and / or potential chemical product production data or a combination thereof.In an embodiment, the chemical product data may comprise one or more elements associated with the chemical product data and wherein the production and / or the multiple production and / or processing data template may comprise one or more elements.In an embodiment, element may refer to at least a part of a word, at least a part of a number, a part of a table or a combination thereof. Additionally or alternatively, the sequence of two or more elements may refer to at least a part of a sentence, at least a part of a number sequence, at least a part of a table or a combination thereof. A part of a table may refer to a row number, a column number and / or a table entry. Further, the contextualized chemical product data may comprise two or more elements being equal to the one or more elements associated with the chemical product data and / or to the one or more elements of the at least one production and / or processing data template. Further, contextualized chemical product data may comprise a sequence of the two or more elements being equal to the one or more elements associated with the chemical product data and / or to the one or more elements of the at least one production and / or processing data template. Additionally or alternatively, the chemical product data may comprise one or more elements associated with the chemical product data. Further, at least one of the multiple production and / or processing data template may comprise one or more elements.In an embodiment, the method may further comprise receiving second chemical product production data, in particular based on the chemical product processing data, or, receiving second chemical product processing data, in particular based on the chemical product production data, and, optionally receiving a second production data template, or, receiving a second processing data template, and, generating second contextualized chemical product data based on the second chemical product production data and the production data template, optionally being the second production data template, or, generating the second contextualized chemical product data based on the second chemical product processing data and the processing data template, optionally being the second processing data template, and, generating second chemical product processing data based on the second contextualized chemical product data, or, generating second chemical product production data based on the second contextualized chemical product data, and, providing the second chemical product processing data or the second chemical product production data. Additionally or alternatively, receiving second contextualized chemical product data based on at least a part of the chemical product processing data or receiving second contextualizedchemical product data based on at least a part of the chemical product production data and generating second chemical product processing data based on the second contextualized chemical product data, or, generating second chemical product production data based on the second contextualized chemical product data, and, providing the second chemical product processing data or the second chemical product production data. The second chemical product production data, the second chemical product processing data, the second contextualized chemical product data, the second contextualized chemical product data or a combination thereof may be received via a user interface. This allows for obtaining tailored solutions to the specific situation of the user. Further, second chemical product production data, e.g. from the user, can be obtained to further improve the generated data or to react to changes of conditions involving the production and / or processing of the chemical product while building upon previously generated data. This is resource-efficient and allows to cover use cases with everchanging conditions.In an embodiment, the method may further comprise providing an indication for selecting the data-driven model from a plurality of data-driven models and selecting the data-driven model from the plurality of data-driven models based on the indication. Preferably, the indication of selecting the data-driven model may be provided via a user interface, preferably from a user. Two or more data-driven models may be available. The two or more data-driven models may be differentiated by the intended use. For example, a first data-driven model of the two or more data- driven models may be more accurate than a second data-driven model of the two or more data-driven models. On the other hand, the second data-driven models may generate the chemical product processing data and / or the chemical product production data faster than the first data-driven model. In a situation where the accuracy of the generated data is valued higher than the speed while generating the data, the corresponding first data-driven model may be selected. In a situation where the speed of generating data is valued higher than a high accuracy of the generated data, the second data-driven model may be selected. Additionally or alternatively, one or more of the two or more data-driven models may be more resource efficient than other data-driven models of the two or more data-driven models. Thus, the best-suited model for the respective use case can be selected. This enables tailor-made chemical product processing data and chemical product production data while saving resources where possible. The indication may be provided by a user. Additionally or alternatively, the indication may be based on the one or more elements associated with the chemical product data and / or the indication may be provided by providing the chemical product data. Hence, the indication may comprise the one or more elements associated with the chemical product data. The data-driven model may be selected based on the chemical product data, preferably based on the one or more elements associated with the chemical product data.In an embodiment, the multiple production and / or processing data templates may comprise multiple types of production and / or processing data templates. Selecting at least one production and / or processing data template based on at least a part of the chemical product data and / or associated elements may refer to selecting the atleast one production and / or processing data template based on the type of production and / or processing data template. Further, the type of the production and / or processing data template may be selected based on determining that the type of the production and / or processing data template corresponds to the chemical product data, in particular the one or more elements associated with the chemical product data. The chemical product data may comprise one or more elements associated with the chemical product data. A type of the production and / or processing data template may be associated with one or more elements associated with the chemical product data. Hence, selecting the at least one production and / or processing data template may refer to selecting the at least one production and / or processing data template where the type of the at least one production and / or processing data template may correspond to the one or more elements associated with the chemical product data. The type of the production and / or processing data template specifies that the chemical production and / or processing data comprises at least one of chemical product producer data, chemical product production instructions, chemical product processor data, chemical product processing instructions, one or more material properties associated with the chemical product, one or more material properties associated with a product based on the chemical product. By doing so, the interaction with the data-driven model may be tailored to the target use case resulting in more accurate chemical product production and / or processing data. This enables higher resource efficiency in the production and processing of the chemical product.In an embodiment, the method may further comprise selecting the data-driven model from a plurality of data- driven models based on the chemical product data. Preferably, the data-driven model may be selected from a plurality of data-driven models based on the at least one production and / or processing data template, in particular the type of the at least one production and / or processing data template. By doing so, the interaction with the data- driven model may be tailored to the target use case resulting in more accurate chemical product production and / or processing data. This enables higher resource efficiency in the production and processing of the chemical product.In an embodiment, the multiple production and / or processing data template may be provided via a database and wherein selecting at least one production and / or processing data template based on at least part of the chemical product data and / or associated elements may comprise generating embedded chemical product data and generating one or more embedded production and / or processing data templates and selecting the production and / or processing data template based on determining that a tensor product between a tensor associated with the embedded chemical product data and a tensor associated with at least one of the one or more production and / or processing data templates may be within a predefined numerical range.Generating embedded chemical product data may refer to providing the production and / or processing data to an encoder and receiving embedded chemical product data from the encoder. Generating one or more embedded production and / or processing data templates may refer to providing the production and / or processing datatemplates to an encoder and receiving one or more embedded production and / or processing data templates from the encoder. The encoder may be configured for transforming the chemical product data and the production and / or processing data templates into a machine-readable format, e.g. a tensor. Hence, the encoder may reduce the dimensionality of the chemical product data and the production and / or processing data templates. An example for such an encoder may be the embedding layer of the continuous bag of words (CBOW) model as described within the context of FIG. 8. A plurality of encoders for embedding data are available such as word2vec, GloVe, FastText or the like. The tensor product between the embedded chemical product data and the embedded production and / or processing data templates may refer to determining the degree of similarity between the embedded chemical product data and the one or more embedded production and / or processing data templates. Selecting the at least one production and / or processing data template based on the tensor product may refer to selecting the at least one production and / or processing data template being the most similar to the chemical product data. Searching for the processing production and / or processing data template based on embedded chemical product data allows to search beyond categorization but enables to compare the content. In an example, chemical compounds are usually associated with a plurality of names such as UIPAC nomenclature and historically developed trivial name. Thus, when referring to ethylene and ethene the same chemical compound is meant, but the name is different. Searching via embedded data enables to link these two distinct words to the same chemical compound. Hence, the search quality is strongly increased by using an embedded database.In an embodiment, the chemical product production data may comprise one or more elements associated with the chemical product production data and the chemical product processing data may comprise one or more elements associated with the chemical product processing data. The one or more elements may be part of a sequence of elements, in particular a sequence as specified by the contextualized chemical product data and / or the contextualized chemical product data.In an embodiment, the contextualized chemical product data may comprise a sequence of two or more elements associated with the contextualized chemical product data and / or wherein the contextualized chemical product data may comprise a sequence of two or more elements associated with the contextualized chemical product data.In an embodiment, an element may refer to a word and / or a part of a word. In an embodiment, the sequence of two or more elements may be a sentence and / or a part of a sentence.By doing so, the contextualized chemical product data, the chemical product production data, the chemical product processing data and the contextualized chemical product data may be received in user language. User and accurate, robust and efficient processing of the user input may be processed by the data-driven model. This enables a barrier-free usage of the data-driven model to generate chemical product production data and chemicalproduct processing data. In turn, providing access to all users provides the opportunity to all users to deploy the methods and systems as presented herein. This will increase the number of requests treated by the methods and systems resulting in more tailored processing and production of the chemical product while lowering the amount of errors in processing and production of the chemical product. Hence, resources for processing and production of the chemical product are used more efficiently and with less errors.In an embodiment, the system may further comprise one or more databases configured for providing the processing data template and / or the production data template. Storing the processing data template and the production data template in a database enables robust retrieval of the processing data template and the production data template while being readily available when needed.In an embodiment, the system may further comprise a user interface, wherein the user interface is configured for receiving the chemical product processing data and / or the chemical product production data, in particular from a user, and / or is configured for providing the chemical product processing data and / or the chemical product production data, in particular to the processor. The user interface may allow the user to interact with the data- driven model. Hence, tailored chemical product production data and chemical product processing data is generated under the control of the user.In an embodiment, processor may refer to an arbitrary logic circuitry configured to perform basic operations of a computer or system, and / or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processor, or computer processor may be configured for processing basic instructions that drive the computer or system. It may be a semi-conductor based processor, a quantum processor, or any other type of processor configures for processing instructions. As an example, the processor may be or may comprise a Central Processing Unit ("CPU"). The processor may be a (“GPU”) graphics processing unit, (“TPU”) tensor processing unit, ("CISC") Complex Instruction Set Computing microprocessor, Reduced Instruction Set Computing ("RISC") microprocessor, Very Long Instruction Word ("VLIW") microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing means may also be one or more special-purpose processing devices such as an Application-Specific Integrated Circuit ("ASIC"), a Field Programmable Gate Array ("FPGA"), a Complex Programmable Logic Device ("CPLD"), a Digital Signal Processor ("DSP"), a network processor, or the like. The methods, systems and devices described herein may be implemented as software in a DSP, in a micro-controller, or in any other side-processor or as hardware circuit within an ASIC, CPLD, or FPGA. It is to be understood that the term processor may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified. The processor may also be an interface to a remote computer system such as a cloud service. Theprocessor may include or may be a secure enclave processor (SEP). An SEP may be a secure circuit configured for processing the spectra. A "secure circuit" is a circuit that protects an isolated, internal resource from being directly accessed by an external circuit. The processor may be an image signal processor (ISP) and may include circuitry suitable for processing images, in particular images with personal and / or confidential information.In an embodiment, memory may be a physical system memory which may be volatile, non-volatile, or a combination thereof. The memory may include non-volatile mass storage such as physical storage media. The memory may be a computer-readable storage media such as RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, non-magnetic disk storage such as solid- state disk or any other physical and tangible storage medium which can be used to store target program code means in the form of computer-executable instructions or data structures and which can be accessed by the computing system. Moreover, the memory may be a computer-readable media that carries computer- executable instructions (also called transmission media). Further, upon reaching various computing system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computing system RAM and / or to less volatile storage media at a computing system. Thus, it should be understood that storage media can be included in computing components that also (or even primarily) utilize transmission media.In an embodiment, the multiple production and / or processing data template may be provided via a database and wherein selecting at least one production and / or processing data template based on at least part of the chemical product data and / or associated elements comprises providing a query based on the production and / or processing data to the database and / or receiving the at least one production and / or processing data template based on the query. The query may define categories associated with searching the production and / or processing data template. This allows for a foreseeable, robust and human-relatable search. More than that, it allows for the user to define and refine the search if needed.In an embodiment, wherein providing the chemical product data comprises retrieving the chemical product data from a data source by providing a request for receiving the chemical product data to the data source and receiving the chemical product data from the data source in response to providing the request. The data source may be configured to provide requested data upon receiving a request for receiving the data. By doing so, real-time data can be retrieved. Thereby, latest data related to the production and / or processing of chemical products can be retrieved. This improves reliability of the chemical production and / or processing data generated based on the chemical product data. Ultimately, this improves producing and / or processing of chemical products.In an embodiment, any one of the methods may further comprise retrieving further chemical product data from a data source by providing a request for receiving the further chemical product data to the data source and receiving the chemical product data from the data source in response to providing the request. The data source may be configured to provide requested data upon receiving a request for receiving the data. The further chemical product data may be related to the chemical product data. For example, the chemical product data may comprise a first part of related datapoints and / or the further chemical product data may comprise a second part of the chemical product data, in particular a plurality of datapoints corresponding to the chemical product data. The contextualized chemical product data may be generated by combining at least a part of the chemical product data, at least a part of the further chemical product data and the at least one selected production and / or processing data template.In an embodiment, a digital identifier relating to one or more datapoint(s) associated with the chemical product data and / or the further chemical product data may be provided, e.g. via a user interface and / or together with the contextualized chemical production and / or processing data. The digital identifier may lead to the one or more datapoint(s) associated with the chemical product data and / or the further chemical product data. For example, the digital identifier may comprise a link and / or may point to one or more dataset(s) associated with a data source. The chemical product data and / or the further chemical product data may be accessed based on the digital identifier. By doing so, the original data stored in a database or on a website can be accessed to check the chemical production and / or processing data generated by the data-driven model. This allows to evaluate the quality of an answer from the data-driven model. Thereby, the user is of full control over where the data was obtained and how the data was processed by the data-driven model. Consequently, the user can interact with the data-driven model upon detecting errors. This increases the accuracy of the chemical production and / or processing data generated by the data-driven model. Ultimately, this improves production and / or processing of chemical products.In an embodiment, the contextualized chemical product data may be suitable for triggering the data-driven model to generate the chemical product production and / or processing data.In an embodiment, the chemical product production and / or processing data may be provided for monitoring a production of the chemical product and / or for producing the chemical product. In particular, the method for generating chemical product production and / or processing data related to a production and / or processing of a chemical product may be a method for producing and / or processing the chemical product.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGSIn 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.FIG. 1 illustrates the production of a chemical product and the processing of the chemical product.FIG. 2 illustrates an embodiment of chemical production system.FIG. 3 illustrates a system for generating chemical product production and / or processing data related to the production and / or processing of a chemical product.FIG. 4A illustrates an embodiment for selecting at least one production and / or processing data template.FIG. 4B illustrates another embodiment for selecting at least one production and / or processing data template.FIG. 5 illustrates an embodiment of a method for generating chemical product production and / or processing data related to the production and / or processing of a chemical product.FIG. 6 illustrates another embodiment of a method for generating chemical product production and / or processing data related to the production and / or processing of a chemical product.FIG. 7A illustrates an embodiment of contextualized chemical product data and production and / or processing data.FIG. 7B illustrates another embodiment of contextualized chemical product data and production and / or processing data.FIG. 8 illustrates an embodiment of training an embedding layer.FIG. 9A illustrates an embodiment of a transformer encoder architecture.FIG. 9B illustrates an embodiment of a transformer decoder architecture.FIG. 90 illustrates an embodiment of a transformer encoder-decoder architecture.FIG. 10 illustrates an embodiment of training and / or deploying the transformer encoder, the transformer decoder and / or the transformer encoder-decoder.FIG. 11 illustrates an embodiment of input embedding for embedding numbers and / or tables.FIG. 12 illustrates an embodiment of a Mamba architecture.DETAILED DESCRIPTIONThe following embodiments are mere examples for implementing the method, the system or application device disclosed herein and shall not be considered limiting.FIG. 1 illustrates the production of a chemical product and the processing of the chemical product. The chemical product may be produced by the chemical product producer 104. Usually, several steps and / or synthesis may be necessary to generate a chemical product from raw material provided by the input material supplier 102 or recycled material based on the chemical product as provided by the recycling system operator 1 14. Hence, the chemical product may be produced within a chemical production network including a plurality of plants. Then, the chemical product may be supplied to the chemical product user 106. The chemical product user 106 may process the chemical product into an intermediate product being a product based on the chemical product. The intermediate product may be further supplied and / or processed until the end product producer 108 may process one or more intermediate products into an end product. This end product may be based on the chemical product. The end product may be provided to the end product user 110. Once the end product becomes waste, it may be provided to the waste collector and / or sorter 112. The waste may be separated based on the waste fraction to enable recycling. The fractions may be provided to the recycling system operator 114. Still, the waste and its fractions may be based on the chemical product. Followi ngly , the processing of a chemical product 118 may include all acts performed on the chemical product, the product based on the chemical product and the waste and waste fraction based on the chemical product.FIG. 2 illustrates a distributed production environment such as one or more chemical plants.A distributed production environment may comprise equipment and sensors 204 generating one or more sensor related data flows. The distributed production environment may produce one or more products as defined above, wherein properties of said one or more products may be measured, extracted or calculated generating one ormore product related data flows. Plant data 228 (plant-based data) may comprise data obtained from each of said one or more data flows.Equipment may be any equipment of a distributed production environment such as pumps, heat exchangers, valves, reaction tanks, separation chambers and / or alike.Sensors may be any kind of sensors of a distributed production environment such temperature sensor, flow sensor, pressure sensor and / or alike.One or more products produced by the distributed production environments may be any type of products as described above. Properties of the products may be measured by, for example, gas chromatography.Plant data 228 may be stored in a database, e.g., as historic data. Plant data 228 may be provided to the preprocessing engine that pre-process the data and provides plant-based input data to an analytics engine 230 that may analyze the plant-based input data and generate machine-readable instructions 236 for control and / or monitoring engine 232. Additionally or alternatively, the analytics engine 230 may analyze and / or receive the production and / or processing data 234 and generate machine-readable instructions 236 for control and / or monitoring engine 232. Generation of the machine-readable instructions 236 may be automatic (i.e., without involving an operator) by the analytics engine 230 based on the analysis of the input plant data. For example, the analytics engine 230 may continuously receive input plant data and may analyze said data in a continuous mode. When an anomaly occurs, the analytics engine may generate machine-readable instructions 236 for the control and / or monitoring engine to identify and / or remove the anomaly. The analytics engine may identify solutions for improving production efficiency by analyzing plant-based input data on the background of production processes and may send machine-readable instructions 236 to the control and / or monitoring engine for improving production. The control and / or monitoring engine 232 may display a push notification to an operator to review the machine- readable instructions 236 and based on the review may generate machine-readable instructions 236. The control and / or monitoring engine 232, based on the machine-readable instructions 236, may change the operating parameters of one or more pieces of equipment and sensors 204. Reviewing said machine-readable instructions 236 may allow for safe integration of the trained transformer-based model in the distributed production environment such as chemical production.Alternatively, or in addition to the automatic generation of machine-readable instructions 236 by the analytics engine 230, an operator may prompt the analytics engine 230 to provide machine-readable instructions 236 based on a prompt and / or a query. The prompt and / or the query may be indicative of a context associated operation.Machine-readable instructions 236 may be used by an operator for controlling and / or monitoring of one or more production operations of the distributed production environment.Machine-readable instructions 236 may comprise operating instructions for production such as machine-readable instructions for controlling equipment and sensors 204 and / or machine-readable instructions for monitoring equipment and sensors 204. Machine-readable instructions 236 may comprise operating instructions for an operator for controlling and / or monitoring the distributed production environment, wherein the operator may be a human based operator, computer-based operating system or a hybrid system comprising a human operator and a computer-based assistance system.An operator may review the machine-readable instructions 236 and, based on the review, may generate machine- readable instructions 236 for controlling equipment and sensors 204. An operator may prompt the analytics engine 230 to provide operating instructions (machine-readable instructions 236) based on a prompt, a query and / or a context.An operator may be a human operator reviewing the machine-readable instructions 236. An operator may be a human operator having a computer-based assisted system for reviewing the machine-readable instructions 236. An operator reviewing the machine-readable instructions 236 may be a computer-based system based on a computer program comprising a set of instructions for reviewing machine-readable instructions 236.The control system of the distributed production environment may comprise one or more computing units that may be able to manipulate one or more process parameters related to the production process by controlling one or more of the actuators or switches and / or end effector units, for example via manipulating one or more of the equipment operating conditions. The controlling is typically done in response to the one or more signals retrieved from the equipment.The control and / or monitoring engine 232 may comprise one or more computer processors for revising machine- readable instructions 236 and generating machine-readable instructions 236 for the control system of the distributed production. The control system of the distributed production, based on the machine-readable instructions 236, may adjust the equipment operating conditions such that the adjusted process parameters and / or equipment operating conditions result in a controlled product (such as a chemical product) that has one or more required or pre-determined properties or performance parameters. Production can thus be controlled on-the-fly whilst ensuring that the equipment operating conditions are adapted to undesired variations in the process parameters.FIG. 3 illustrates a system for generating chemical product production and / or processing data related to the production and / or processing of a chemical product.The system may comprise a user interface 306 for receiving chemical product data 308 and / or for providing the chemical product production and / or processing data 310. The one or more production and / or processing data templates may be received based on the chemical product data 308. Hence, the one or more production and / or processing data templates may be selected from a plurality of production and / or processing data templates. Production and / or processing data templates may refer to production data templates and / or processing data templates. The plurality of production and / or processing data templates may be comprised by a database 302. The one or more production and / or processing data templates may be received from the database 302 as described within the context of FIG. 4A and FIG. 4B. The contextualized chemical product data may be generated based on receiving the chemical product data 308 and the one or more production data templates or the one or more processing data templates. The contextualized chemical product data may be provided to the data-driven model 304 via an application programming interface (API) 312. The data-driven model 304 may provide chemical product production and / or processing data based on receiving the contextualized chemical product data. The chemical product production and / or processing data may be generated by the data-driven model 304 may be provided via an API 314. API 312 may be the same or a different API than 314. The received chemical product production and / or processing data may be provided via the user interface 306 in response to being received and / or provided via the API 314.FIG. 4A illustrates an embodiment for selecting at least one production and / or processing data template.The at least one production and / or processing data template 406 may be received from a database comprising production and / or processing data templates 418. In FIG. 4A, the chemical product data 402 may point to and / or may be linked to one or more production and / or processing data templates 418. Specifically, the chemical product data 402 may point to and / or may be linked to at least one query associated with the chemical product data 404. Preferably, the query associated with the chemical product data 404 may point to and / or may be linked to one or more production and / or processing data templates 418. The query associated with the chemical product data 404 may be suitable for retrieving one or more production data template and / or processing data template from the databased comprising a plurality of production and / or processing data templates 418. Based on the query associated with the chemical product data 404 one or more production data templates or processing data templates 406 may be received. Hence, receiving at least one production and / or processing data template 406 may comprise providing a request for receiving one or more at least one production and / or processing data template 406, wherein the request may comprise a query associated with the chemical product data 404 to a database comprising the plurality of production and / or processing data templates 418. Further, receiving at leastone production and / or processing data template 406 may comprise receiving at least one production and / or processing data template 406 from the database based on having provided the request for receiving at least one production and / or processing data template 406. This is advantageous since low computational resources are needed for receiving the at least one production and / or processing data template 406.FIG. 4B illustrates another embodiment for selecting at least one production and / or processing data template.The at least one production and / or processing data template 412 may be received from a database comprising a plurality of production and / or processing data templates 414. For receiving the at least one production and / or processing data template 412, the chemical product data 408 may be embedded via an embedding layer as described within the context of FIG. 8. Embedding the chemical product data 408 may result in embedded chemical product data 410. The embedded chemical product data 410 may be a lower dimensional representation than the chemical product data 408 and may be machine-processable. The production and / or processing data templates 414 may be embedded via an embedding layer as described within the context of FIG. 8. Embedding the production and / or processing data templates 414 may result in embedded production and / or processing data templates 416. The embedded chemical product data 410 and embedded production and / or processing data templates 416 may be compared in terms of similarity. The more similar the embedded chemical product data 410 and the embedded production and / or processing data templates 416 are the closer may the tensors associated with the embedded chemical product data 410 and the embedded production and / or processing data templates 416 may be. Hence, for selecting the one or more of the production and / or processing data templates 414 corresponding to the chemical product data 408, the embedded chemical product data 410 and the embedded production and / or processing data templates 416 are compared e.g. by calculating the dot product between the two or more tensors associated with the embedded production and / or processing data templates 416 and the embedded chemical product data 410. By selecting the at least one production and / or processing data template 412 from the production and / or processing data templates 414 via similarity between the embedded chemical product data 410 and the embedded production and / or processing data templates 416, at least one production and / or processing data template 412 may be received independent of categories e.g. as specified by the query associated with the chemical product data 404 but solely on the content associated with chemical product data 408. Therefore, using embeddings for retrieving data from a database enhances the accuracy of retrieving at least one production and / or processing data template 412 matching the chemical product data 408.FIG. 5 illustrates an embodiment of a method for generating chemical product production and / or processing data related to the production and / or processing of a chemical product.Chemical product data may be received 502, e.g. via a user interface as described within the context of FIG. 3. A user may select the chemical product data and / or may input the chemical product data via the user interface. For example, the user may select the chemical product data from data obtained during the production and / or processing of the chemical product, in particular from historical data obtained during the production and / or processing of the chemical product.Further, production and / or processing data template may be selected 506 as described within the context of FIG. 4A and FIG. 4B. For this purpose, multiple production and / or processing data templates may be provided 504 prior to selecting and at least one production and / or processing data template may be selected from the multiple production and / or processing data templates.Two or more data-driven models may be available for being provided with contextualized chemical product data. The two or more data-driven models may comprise different model parameters resulting in different output and / or different times associated with providing the chemical product production and / or processing data. For example, the first data-driven model of the two or more data-driven models may comprise fewer model parameters than a second data-driven model of the two or more data-driven models. Followingly, the first data-driven model may provide the chemical product production and / or processing data faster than the second data-driven model. On the other hand, the second data-driven model may provide more accurate chemical product production and / or processing data than the first data-driven model. Depending on the target quality of the output, a data-driven model may be selected, preferably from a plurality of data-driven models. For this purpose, a selection of one or more data-driven models may be received 508, e.g. by the user via the user interface.Further, the contextualized chemical product data may be generated by combining at least a part of the production and / or processing data template and the chemical product data 510 as described within the context of FIG. 6.The contextualized chemical product data may be provided to a data-driven model for generating the chemical product data 512. The data-driven model may be trained and / or parametrized as described within the context of FIG. 6 and FIG. 10. The data driven model may process the contextualized chemical product data to the chemical product production and / or processing data as described within the context of FIG. 6 to FIG. 11 .The chemical product production and / or processing data may be provided 514, e.g. via a user interface as described within the context of FIG. 3, preferably in response to receiving the chemical product production and / or processing data from the data-driven model.FIG. 6 illustrates another embodiment of a method for generating chemical product production and / or processing data related to the production and / or processing of a chemical product.The received chemical product data 602 is used to generate the contextualized chemical product data 606 based on the selected production and / or processing data template. The production and / or processing data template may be indicative of structure associated with at least a part of the chemical product data 602. For example, the production and / or processing data template may specify one or more parts of one or more sequences and one or more production and / or processing parameters next to the one or more parts of the one or more sequences. The production and / or processing parameters may be placeholders for production and / or processing parameter values to be inserted. For generating the contextualized chemical product data at least a part of the chemical product data, preferably the production and / or processing parameters values comprise and / or related to the chemical product data, may be inserted into the production and / or processing data template. The generated contextualized chemical product data may be input data into the data-driven model 624. The data-driven model may be and / or may comprise one or more architectures as described within the context of FIG. 9A - FIG. 9C and / or FIG. 11 . The data-driven model may be trained as described within the context of FIG. 10.The generated contextualized chemical product data 606 is provided to the data-driven model 624 and / or the generated contextualized chemical product data 606 is received by the data-driven model 624. The data-driven model may be trained to embed the received contextualized chemical product data 606. For example, the contextualized chemical product data 606 may comprise one or more sequences such as at least a part of a sentence, one or more numbers or a combination thereof. The at least part of the sentence may be received via the user interface e.g. in text form, audio form or the like.For processing the contextualized chemical product data 606 by the data-driven model 624, the data-driven model 624 may transform the contextualized chemical product data 606 into embedded contextualized chemical product data 618. The embedded contextualized chemical product data 618 may be a second rank tensor. The embedded contextualized chemical product data 618 may represent the contextualized chemical product data 606 in a machine-processable format and / or may be a machine-processable format. Where the contextualized chemical product data 606 may comprise at least a part of a sentence, the words, numbers and / or parts of words associated with the contextualized chemical product data 606 may be embedded. The words, numbers and / or parts of words associated with the contextualized chemical product data 606 may be an example for elements associated with the input data. Further examples for elements associated with the input data may be numbers and / or at least a part of a table including e.g. row and / or column numbering. The embedded contextualized chemical product data 618 may comprise a one or more data points. Embedding the contextualized chemical product data 606 enables the computational processing of the contextualized chemical product data 606.Furthermore, the embeddings, in particular word embeddings, are an efficient representation of data using less storage and requiring less computational resources for processing of the respective data.In particular, where the contextualized chemical product data 606 may comprise at least a part of a sequence, the position of one or more elements associated with the contextualized chemical product data 606 may be taken into account as described within the context of FIG. 9A - FIG. 9C via positional encoding. By applying the positional encoding to the contextualized chemical product data 606 the sequence-specific embedded contextualized chemical product data 618 may be obtained.In an embodiment, the contextualized chemical product data 606 may be embedded in parts. For example, a first part of the contextualized chemical product data 606 may be embedded resulting in first embedded contextualized chemical product data 618 and a second part of the contextualized chemical product data 606 may be embedded resulting in second embedded contextualized chemical product data 618. The first embedded contextualized chemical product data 618 and the second embedded contextualized chemical product data 618 may be combined resulting in the embedded contextualized chemical product data 618. The first embedded contextualized chemical product data 618 and the second embedded contextualized chemical product data 618 may be combined by concatenating the first vector associated with the first embedded contextualized chemical product data 618 and the second vector associated with the second embedded contextualized chemical product data 618. Concatenating the first embedded contextualized chemical product data 618 and the second embedded contextualized chemical product data 618 may result in the embedded contextualized chemical product data 618. Additionally or alternatively, the first contextualized chemical product data 606 and the second contextualized chemical product data 606 may be embedded together in a second rank tensor.The embedded contextualized chemical product data 618 may be a second rank tensor representing the contextualized chemical product data 606. The second rank tensor may be combined in tensor products with itself and / or transformations of itself resulting in a production and / or processing data context tensor 620 as described within the context of FIG. 9A - FIG. 9C and FIG. 10. The production and / or processing data context tensor 620 may comprise a second rank tensor and / or may represent the relation between one or more elements associated with the embedded contextualized chemical product data 606.Where the contextualized chemical product data 606 is sentence-based, the production and / or processing data context tensor 620 may represent at least a part of a word and / or number and the relation between the word associated with the production and / or processing data context tensor 620. Analogously, for numerical input as described within the context of FIG. 11 . By doing so, the relation between two or more components of the contextualized chemical product data 606 is taken into account when generating the chemical product productionand / or processing data 612. This enables a more accurate determination of chemical product production and / or processing data 612.The production and / or processing data context tensor 620 may be transformed via one or more machine learning architectures e.g. linear layers, classification layers into the chemical product production and / or processing data 612. In particular, a first part of the chemical product production and / or processing data 612, herein referred to as first chemical product production and / or processing data 612, may be generated based on the embedded contextualized chemical product data 618. A second chemical product production and / or processing data 612 being a second part of the chemical product production and / or processing data 612, in particular being a second part of the sequential chemical product production and / or processing data 612, may be generated and / or provided based on the embedded contextualized chemical product data 618 and the embedded first chemical product production and / or processing data 612 as described within the context of FIG. 10. Embedded first chemical product production and / or processing data 612 may refer to first chemical product production and / or processing data 612 being embedded analogous to the embedded contextualized chemical product data 618.The embedded contextualized chemical product data 618 and the embedded first chemical product production and / or processing data 612 may be combined resulting in the production and / or processing data context tensor 620 at a second timestep for generating the second chemical product production and / or processing data 612, e.g. by concatenating. The second chemical product production and / or processing data 612 may be generated based on the production and / or processing data context tensor 620 at a second timestep. This may be repeated for three or more parts of chemical product production and / or processing data 612. Where the chemical product production and / or processing data 612 may be sentence-based, a part of the chemical product data 602 and / or the contextualized chemical product data 606 may refer to a word and / or a part of the word, in particular a token associated with a word. Parts of the chemical product production and / or processing data 612 may be generated based on previous parts of the chemical product production and / or processing data 612 until the complete chemical product production and / or processing data 612 is generated, typically indicated by an end token.In an embodiment, the embedded contextualized chemical product data 618 may be transformed into a first production and / or processing data context tensor 620 and a second production and / or processing data context tensor 620. The first production and / or processing data context tensor 620 and the second production and / or processing data context tensor 620 may be combined e.g. by concatenating into the production and / or processing data context tensor 620. The production and / or processing data context tensor 620 may be processed as described above to result in the chemical product production and / or processing data 612. By taking two or more production and / or processing data context tensor 620 sets into account, less computational resources are and / or less time is needed for generating the chemical product production and / or processing data 612.The data-driven model 624 may comprise one or more of transformer decoder, transformer encoder, transformer encoder-decoder architectures or the like. This data-driven model 624 may be trained self-supervised based on the training data set. The training data set may comprise historical data related to the chemical product data and chemical product production and / or processing data. The data-driven model may be trained to generate the chemical product production and / or processing data 612 sequentially based on the chemical product data 602 and previously generated parts of the chemical product production and / or processing data 612. For this purpose, the data-driven model may be provided by contextualized chemical product data 606 for generating the first part of the chemical product production and / or processing data 612 and by contextualized chemical product data 606 and the first part of the chemical product production and / or processing data 612 for generating the first part of the chemical product production and / or processing data 612 as specified by the training data set. During training, the data-driven model may be provided with the ground truth of previous parts of the chemical product production and / or processing data 612 as specified by the training data set. This self-supervised training enables the humanindependent learning of the data-driven model resulting in efficient embeddings requiring no human interaction. Followingly, time is saved by providing an efficient learning while achieving highly accurate results associated with the chemical product production and / or processing data 612.FIG. 7A illustrates an embodiment of contextualized chemical product data and production and / or processing data.A user may desire to identify a supplier with respect to a chemical product for a specific application by providing chemical product data via a user interface. Corresponding production and / or processing data template may be selected. Chemical product data may be inserted into the production and / or processing data template and may result in contextualized chemical product data 704. Chemical product production and / or processing data 706 may be generated by the data-driven model in response to being provided with the contextualized chemical product data 704. The user may desire further information and provide second contextualized chemical product data 708. In this example, the second contextualized chemical product data may comprise a further inquiry. Based on being provided with second contextualized chemical product data, the data-driven model may provide second chemical product production and / or processing data.FIG. 7B illustrates another embodiment of contextualized chemical product data and production and / or processing data.A user may desire to identify a potential consumer for the chemical products of company B via a user interface. Hence, chemical product data may be received. Corresponding production and / or processing data template may be received. Inserting the chemical product data into the production and / or processing data template may result incontextualized chemical product data 712. Chemical product production and / or processing data 714 may be generated by the data-driven model in response to being provided with the contextualized chemical product data. The user may desire further information and provide second contextualized chemical product data 716. Based on being provided with second contextualized chemical product data, the data-driven model may provide second chemical product production and / or processing data 710.FIG. 8 illustrates an embodiment of obtaining an embedding layer. The embedding layer may be obtained by training for example a continuous bag of words model (CBOW) or a skip-gram model.The embedding layer may be suitable for generating embedded input data based on input data. Generating embedded input data may refer to embedding input data. Embedding input data may result in a representation associated with the input data. Thus, the embedded input 814 may be the representation associated with the input data. The input data may comprise a one or more elements. The one or more elements may be represented by the input vector 806. In particular, the embedded input 814 and / or the input vector 806 may be machine- readable and / or processable by a processor. For this purpose, the embedded input 814 and / or the input vector 806 may be a tensor, in particular a first-rank tensor. Specifically, the input vector 806 may be a one-hot vector or a summation of a plurality of one-hot vectors. A one-hot vector may be a vector with one entry unequal to zero. Examples for one-hot vectors may be 808, 810 and 812. The entries unequal to zero in the one-hot vector and / or in the input vector 806 may indicate the element. For example, a look up table may define the relation between the position of the entries unequal to zero and the element indicated by the one-hot vector. The look up table may specify a plurality of different elements. The number of different elements may be equal to the number of entries in the one-hot vector. The number of different elements may be referred to as vocabulary size. In an example, the elements may be represented by tokens and a sequence of elements may refer to at least a part of a sentence. The at least a part of the sentence may be represented by a plurality of tokens. A token may represent at least a part of the element and / or word. For example, where one element would be associated with only one word, words such as “embeddings", “embedding” or “embed” would constitute different elements. A first token may represent the stem “embed” and the endings, typically appearing in a plurality of word, may be represented by a second token, a third token and a fourth token. The second token, the third token and the fourth token may be used for representing other words such as “look”, “looking” or the like, preferably together with a fifth token representing the stem “look”. Ultimately, this tokenization of elements associated with a plurality of stems and a plurality of endings results in less tokens to be used for representing a plurality of elements and thus, uses less computational resources.A look up table specifying a subset of the vocabulary size e.g. of the English language may comprise 10,000 words or more. The embedded input 814 may be a lower-dimensional representation than the input vector 806.For example, typical embedded inputs 814 may comprise some hundreds of different entries. Followingly, the embedded inputs 814 constitute a densified representation of one or more elements using less computational resources. More than that, the embedded input 814 may represent a relation between two or more elements. For example, the words “Italy” and “Germany” may be similar or may be more closely related since they both define European countries, whereas the word “embodiment” may be very different from the two respective words. The smaller the dot product between two embedded inputs 814 may be the more similar the two elements associated with the embedded inputs 814 may be. Hence, the embedded inputs 814 may represent one or more elements accurately and lead to accurate results based on processing the embedded inputs 814.For transforming the input vector 806 into the embedded input 814, the embedding layer may comprise a number of neurons equal to the number of entries in the embedded input 814. Based on the embedded inputs 814, the output layer may generate the output vector 816. The output vector may be a vector and / or may indicate one or more elements. The output vector 816 may indicate one or more elements different from the input vector 806 and / or the one-hot vectors associated with the input vector 806. For this purpose, the output layer may comprise a number of neurons equal to the number of entries of the input vector 806 and / or the output vector 816. The output layer may apply a softmax function to the embedded inputs 814. By doing so, the output vector may comprise the probabilities associated with the elements associated with the entries of the output vector 816 unequal to zero. Hence, from the output vector 816 one or more elements may be obtained with a corresponding probability. Where the input vector 806 may specify one or more sequence(s) of elements, the output vector 816 may specify one or more elements corresponding to the sequence(s) of elements specified by the input vector 806. In the example of FIG. 8, the element associated with vector 818 may correspond to the input vector with a probability of 71 %. Additional or alternative elements may correspond to the input vector as indicated by the output vector with lower probability. By defining a threshold to which the probability may be compared, the selection of the corresponding elements may be tailored to the needs of the user. The elements generated by the model comprising the embedding layer 802 and the output layer 804 may refer to the most probable elements indicated by the output vector 816. Hence, the model depicted in FIG. 8 may generate the element associated with the vector 818 with a confidence score of 71 %.The model of FIG. 8 may be continuous bag of words (CBOW) model. The CBOW model may be trained based on a training data set comprising a plurality of input vectors and corresponding output vectors. As the training data set may not be labeled, the training of the CBOW model may be referred to as self-supervised. Before training of the CBOW model, the CBOW model may be initialized with random values assigned to the weights of the neurons. During the training of the CBOW model, the input vectors may be passed through the initialized embedding layer and the output layer and a loss may be determined by comparing the output vector obtained by passing the input vector 806 through the model to the output vector corresponding to the input vector 806 as specified by thetraining data set. Based on the determined loss, backpropagation may be applied to determine the gradients associated with the neurons of the embedding layer 802 and the output layer 804 to lower the loss. According to the determined gradients, the weights of the neurons may be updated by using a gradient descent algorithm. If a predetermined loss may be achieved by the CBOW model, the training may be terminated and a trained CBOW model may be obtained. From the trained CBOW model, the embedding layer 802 may be suitable for embedding input data comprising one or more elements. This embedding layer 802 may be used in other machine-learning architectures requiring an embedding layer 802 such as a transformer encoder, transformer decoder or transformer encoder decoder architecture as described within the context of FIG. 9A, FIG. 9B and FIG. 9C. For training these architectures, a trained embedding layer 802 may be required. Hence, a model such as a CBOW model may be trained prior to training the transformer encoder, transformer decoder or transformer encoder decoder architecture.FIG. 9A illustrates an embodiment of a transformer encoder architecture. The transformer encoder comprises an encoder input 978, one or more encoder blocks 974, 914 and an encoder output.The transformer encoder architecture may be derived from the transformer encoder-decoder architecture as known in the art and shown in FIG. 9C. In particular, the transformer encoder may be referred to as X-former. The transformer encoder architecture may correspond to the encoder architecture associated with the transformer encoder-decoder architecture with an additional encoder output instead of connecting the encoder block directly to the decoder of the transformer encoder-decoder architecture. 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 978. The input data may be contextualized chemical product data. The encoder input 978 may apply an input embedding 902. Applying the input embedding 902 may refer to passing the input data through an embedding layer e.g. as described within the context of FIG. 8. Applying the input embedding 902 to the contextualized chemical product data may result in embedded contextualized chemical product data. Applying the input embedding 902 to the contextualized chemical product data may result in embedded contextualized chemical product data. This may be further described within the context of FIG. 6.The encoder input 978 may apply positional encoding 904. Applying positional encoding 904 may refer to adding a positional factor to the embedded input obtained via input embedding. Preferably, the input data may specify a sequence of elements. The positional factor Pp°smay be indicative of the position of the elements within the sequence. For example, the positional factor Pp° may be obtained based on the following equation:where pos may refer to the position of the element within the sequence, I may refer to the dimension associated with the input embedding and d may refer to the dimension of the model, e.g. transformer decoder, transformer encoder or transformer encoder-decoder. This may be referred to as absolute positional embeddings.Alternatively, the positional encoding may be based on rotary positional embeddings (RoPE). Positional encoding is beneficial since it enables the processing of sequential data without requiring further dimensions indicating the position of each element. Followingly, the positional encoding 904 reduces the computational resources needed for embedding the input data.By passing the input data through the encoder input, the input data may be transformed into a second-rank tensor representing the sequence of elements. This second-rank tensor may be referred to as embedded input data. The embedded input data may be processed by the encoder block. The embedded input data may be provided to the layer normalization 908 by a residual connection. Multi-head self-attention 906 may be applied to the embedded input data. Multi-head self-attention 906 may comprise the two components multi-head and self-attention. Selfattention may be understood as being a filter applied to the embedded input data. By applying the filter to the embedded input data, the elements associated with the embedded input data contributing to the to be generated output data may be identified for generating the output data. Hence, the filter may represent the degree of contributing to the to be generated output data by the elements associated with the embedded input data. Applying the filter may be referred to as weighting the elements associated with the embedded input data. This is advantageous specifically regarding long sequences of elements. The filter may be learned and improved during the training by learning to identify the contribution of elements associated with the embedded input data. For example, in the partial sentence “I went to the bakery to buy a” the last word may be generated by the data-driven model such as the transformer encoder. The self-attention may focus the transformer encoder to attend to the word “bakery” and “buy” mostly to generate the word “bread”, self-attention may refer to attention generated based on the input data. Hence, the filter may be determined based on the input data, preferably 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. The self-attention may refer to attention based on the received input data. Hence, the filter may be calculated based on the following formula by inserting the respective tensors based on the embedded input data:where dk corresponds to the dimension of the key.For improving the efficiency of the transformer encoder further, the multiple heads are used to apply the filter resulting in the multi-head self-attention 906. Multi-head self-attention 906 may comprise applying the filter to twoor more parts of the embedded input data. Hence, the tensor may be split into two or more parts and the filter may be applied to the two or more parts separately by two or more heads according to the following equation:head i =Attention QWiQ, KWiK, VWiV) with parameter matricesW,KE Rdxd‘, W,-vG Rdxdywhere I may refer to the number of heads,may refer to the dimensions of the value, key and query.The result of the two or more head may be concatenated according to the following equation: MultiHead(Q, K, V) = Concat(head 1, . . . , headh) W° where ye^hdvxd and h may refer to the number of heads.The embedded input data may be transformed via the multi-head self-attention 906 into a context tensor. The context tensor may represent the sequence of elements and the relation between two or more elements of the input data. Transforming the embedded contextualized chemical product data may result in processing data context tensor 622. Transforming the embedded contextualized chemical product data 618 may result in production and / or processing data context tensor 620. Hence, the context tensor may be production and / or processing data context tensor 620. The context tensor may be a second rank tensor and / or may comprise one or more first rank tensor(s). After the multi-head self-attention 906 layer normalization 908 may be applied based on the context tensor and / or the embedded input data from the residual connection. Applying layer normalization 908 may refer to normalizing the context tensor.Normalizing the context tensor may lower the values of the entries of the context tensor. This reduces the computational cost associated with processing the context tensor. Layer normalization 908 may be followed by passing the context tensor to a feed forward layer 910 again followed by layer normalization 912 based on the residual connection to the context tensor and / or the output of the feed forward layer 910. The feed forward layer 910 may be a feed-forward neural network. The feed-forward neural network may comprise of a plurality of fully connected neurons. Passing the context tensor through the feed-forward neural network may result in transforming the context tensor linearly. Additionally or alternatively, the neural network may comprise one or more activation functions such as a rectified linear unit (ReLU). Hence, the neural network may be configured for performing one or more non-linear operations to the context tensor and / or transforming the context tensor non- linearly. After the context tensor has been transformed and / or normalized by the feed forward layer 910 and the layer normalization 912, the context tensor may be provided to one or more further encoder blocks 914. Having passed the context tensor through the feed forward layer 910 may adapt the context tensor for the processing by a further attention layer of the one or more further encoder blocks 914 for applying a self-attention filter, preferablymulti-head self-attention 906. The context vector after being transformed by the layer normalization 912 and the feed forward layer 910 may be referred to as hidden state.The encoder output 976 comprises of a linear layer 916 and a softmax layer 918. The linear layer 916 may transform the context vector into a logits vector. The linear layer may be fully-connected. The logits vector obtained by passing the context tensor through the linear layer 916 may be passed through the softmax layer 918. Passing the logits vector through the softmax layer 918 may refer to applying the softmax function to the logits vector. Applying the softmax function to the logits vector may result in a probability distribution of one or more elements corresponding to the sequence of elements in the input data. From the probability distribution based on predefined selection criteria, one or more elements may be chosen. The one or more chosen elements may be referred to as the one or more elements generated by the transformer encoder. The one or more generated elements may be provided to the encoder input for generating further one or more elements corresponding to the sequence of the input data and the one or more elements generated by the transformer encoder as described within the context of FIG. 10.The output data from the encoder output 976 may be chemical product production and / or processing data. Hence, the result of transforming the production and / or processing data context tensor 620 may be chemical product production and / or processing data.FIG. 9B illustrates an embodiment of a transformer decoder architecture.Input data, embedded input data, context tensor and / or output data may be as defined within the context of FIG. 9A.The transformer decoder comprises a decoder input 984, one or more decoder blocks 980, 932 and a decoder output 992. The transformer decoder architecture may be derived from the transformer encoder-decoder architecture as known in the art and shown in FIG. 9C. The transformer decoder may be referred to as X-former. The transformer decoder architecture may correspond to the decoder architecture associated with the transformer encoder-decoder architecture independent of receiving one or more hidden states from the encoder of the transformer encoder-decoder. A plurality of transformer decoder architectures are available in the art such as the generalized pretrained transformers (GPT).The decoder input 984 may apply input embedding 920 and positional encoding 922 analogous to analogous to the input embedding 902 and the positional encoding 904 as described within the context of FIG. 9A.The decoder block 980 may comprise the layer normalizations 926, the masked multi-head self-attention 924, the feed forward layers 928 and / or the layer normalization 930. The embedded input data resulting from passing the input data through the decoder input 984 may be provided to the layer normalization 926 via a residual connection. Further, masked multi-head self-attention 924 may be applied to the embedded input data. Masked multi-head self-attention 924 corresponds to the multi-head self-attention 906 as described within the context of FIG. 9A with 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. Thus, the transformer decoder may be suitable for generating a subsequent element to a sequence, whereas the transformer encoder may be suitable for generating a missing element in within one sequence and / or between two or more sequences. Therefore, the transformer encoder may be configured for classification tasks. The transformer decoder may be configured for text generation.Similar to the transformer encoder as described within the context of FIG. 9A, a context tensor may be generated by applying the masked multi-head self-attention 924 and the layer normalization 926. The context tensor may be provided to the layer normalization 930 via a residual connection. Further, the feed forward layer 928 and the layer normalization 930 may be analogous to the feed forward layer 910 and the layer normalization 912 as described within the context of FIG. 9A. The context tensor may be provided to one or more further decoder blocks 932.The decoder output 992 may comprise of a linear layer 934 and a softmax layer 936. The linear layer 934 and the softmax layer 936 may be analogous to the linear layer 916 and the softmax layer 918 as described within the context of FIG. 9A.FIG. 9C illustrates an embodiment of a transformer encoder-decoder architecture.Input data, embedded input data, context tensor and / or output data may be as defined within the context of FIG. 9A.The transformer encoder-decoder may comprise the encoder input 988, the one or more encoder blocks 986, 964, the decoder input 994, the decoder block 990 and the decoder output 992. The encoder input 988 may correspond to the encoder input 978 of FIG. 9A. The one or more encoder block 986, 964 may correspond to the one or more encoder blocks 974, 914 of FIG. 9A. The decoder input 994 may correspond to the decoder input 984 of FIG. 9B.The decoder block 990 may comprise a masked multi-head self-attention 970, a layer normalization 972, a feed forward layer 938 and a layer normalization 940 analogous to the masked multi-head self-attention 924, the layer normalization 926, the feed forward layer 928 and the layer normalization 930 as described within the context of FIG. 9B. The decoder block 990 may further comprise a multi-head self-attention 950 and a layer normalization 948. Analogous to the description of FIG. 9B, the context tensor may be obtained from the masked multi-head self-attention 970 and the layer normalization 972. Multi-head self-attention 950 analogous to the multi-head selfattention 906 of FIG. 9A may be applied to the context vector obtained from the layer normalization 972 and the hidden states of the one or more encoder blocks 986, 964. Layer normalization 948 may be applied to the context vector obtained from the multi-head self-attention 950 and the context vector obtained from the layer normalization 972 provided via a residual connection. The context vector resulting from the layer normalization 948 may be processed via the feed forward layer 938 and the layer normalization 940 analogous to the description of FIG. 9B. The context vector resulting from the layer normalization 940 may be provided to further decoder blocks 942 analogous to the decoder block 990. The context vector obtained from the one or more decoder blocks 990, 942 may be provided to the decoder output 992. The decoder output 992 may correspond to the decoder output 982 of FIG. 9B.With the above-described architecture, the transformer encoder-decoder may receive and process input data at the encoder input 988 and the one or more encoder blocks 986, 964 and the decoder block 990 and the decoder output 992. Based on the input data, the transformer encoder-decoder may generate output data part by part or sequentially. The sequentially generated output data may be provided to and / or may be processed by the decoder input 994, the one or more decoder blocks 990, 942 and the decoder output 992. Preferably, a sequence may be provided to the encoder input 988 and after having generated at least a part of the output data, the decoder input 994 may be provided with at least the part of the elements of the output data already generated. By doing so, the next elements of the output data may be generated with a higher accuracy by taking the input data and the generated output data into account since more data is received by the transformer encoder-decoder may be received over time.Because of the transformer encoder-decoder architecture, the transformer encoder-decoder may be configured for transforming a sequence into another representation of the sequence. An example for transforming one sequence into another representation may be translation of one sentence into another language. A plurality of transformer encoder-decoders are available in the art such as BART, T5 or the like.In an embodiment, the layer normalization 908, 912 may be applied prior to the masked multi-head self-attention 924, multi-head self-attention 906 and / or the feed forward layer 910 in the transformer decoder, the transformer encoder and / or the transformer encoder-decoder. By doing so, the computational resources for applying the multi-head self-attention 906 and / or the feed forward layer 910 to the embedded input data and / or the context tensor may be decreased as the entries of the respective tensors may be lower after normalization.In an embodiment, the decoder output 992 may comprise of a classification neural network, further feedforward layers, convolutional layers, fully connected layers or the like. For example, the transformer encoder-decoder may be configured for choosing between a plurality of options. For this purpose, the transformer encoder-decoder may be provided with three different input data sets and may classify the context vectors obtained from the one or more decoder blocks 990 via one or more linear layers. Followingly, the architecture may be extended depending on the use case to be solved.FIG. 10 illustrates an embodiment of training and / or deploying the transformer encoder, the transformer decoder and / or the transformer encoder-decoder.The encoder / decoder / encoder-decoder architecture 1002 may correspond to the transformer decoder, the transformer encoder and / or the transformer encoder-decoder as describe within the context of FIG. 9A- FIG. 9C. Input data, embedded input data, context tensor and / or output data may be as defined within the context of FIG. 9A.The output data generated by the encoder / decoder / encoder-decoder architecture 1002 may comprise of one or more elements, in particular a sequence of elements. The previously generated elements of the output data may be provided as input for generating the next element in the sequence of the output data.In the example of FIG. 10, the input data may comprise of N elements, in particular input tokens. An input token may be a token dedicated to be inputted into a data-driven model such as the transformer decoder, the transformer encoder or the transformer encoder-decoder. The output data to be generated may comprise of M elements. The encoder / decoder / encoder-decoder architecture 1002 may generate one element of the output data based on receiving the input data and optionally previously generated elements of the output data at a timestep. Hence, for generating M elements M time steps are required. A time step comprises of providing input 1010, 1012, 1014 to the encoder / decoder / encoder-decoder architecture 1002 and receiving output data 1004, 1008, 1006 from the encoder / decoder / encoder-decoder architecture 1002. In a first timestep, the input 1010 may comprise of N input tokens. The N input tokens may be associated e.g. with N words, stems or endings. Preferably, the N input tokens may specify a question. One or more input tokens may specify the beginning of the sequence of tokens and / or the end of the sequence of tokens. The input 1010 may be processed by the encoder / decoder / encoder- decoder architecture 1002. Based on the input 1010 at least a part of the output data 1004 may be generated. The at least a part of the output data may comprise a first output token. In the next timestep, the generated first outputtoken may be provided together with the input 1012. Specifically, where the input 1012 may be received by a transformer encoder-decoder the input tokens may be received at the encoder input 988 and the first output token may be received at the decoder input 994. Where the input 1012 may be received by the transformer encoder, the input 1012 may be received by the encoder input 978 and analogously regarding the transformer decoder and the decoder input 984. Based on the input 1012, the output data 1008 comprising the first output token and a second output token may be generated. Generating the output data 1008 based on the input 1012 may refer to generating the second token based on the first token and the N input tokens, wherein the first token may have been generated based on the N input tokens. This process may be repeated until the last token in the sequence of the output data 1006 may be generated. Preferably, the last token may be an end token. The end token may terminate the generation of a further output token.Similarly, to the data processing during deployment of the encoder / decoder / encoder-decoder architecture 1002, the encoder / decoder / encoder-decoder architecture 1002 may be trained. The training data set may comprise a plurality of sequences comprising a plurality of elements. The sequences may be associated with the input data and / or the output data. Additionally or alternatively, the sequences may be independent of the input data and / or the output data. For example, where the input data and the output data may refer to chemical compositions represented via text, the training data set may comprise sequential text data independent of chemical compositions. In this example, the training data set may comprise sequences of words originating from a conversation. In an embodiment, the training data set may comprise at least partially input data sets and / or output data sets.The training may be initialized by initializing the encoder / decoder / encoder-decoder architecture 1002. In an embodiment, the parameters associated with the encoder / decoder / encoder-decoder architecture 1002 may be initialized randomly. Additionally or alternatively, the input embedding of the encoder / decoder / encoder-decoder architecture 1002 may be obtained by training a CBOW model or a skip gram model as described within the context of FIG. 8. The trained embedding layer may be used during training. The parameters associated with the embedding layer may be kept constant and / or may be updated after a predefined number of training epochs. By doing so, the number of parameters to be updated is lower enabling a faster and less computational resources- consuming training. Further, the accuracy associated with the embedding layer may be constant and / or may be increased by avoiding error compensation in relation to the just initialized encoder / decoder / encoder-decoder architecture 1002.During the training of the encoder / decoder / encoder-decoder architecture 1002, at least a part of the sequences of the training data set may be provided to the encoder / decoder / encoder-decoder architecture 1002 one by another and one or more elements may be generated based on the sequences of the training data set one by another. Theelements generated based on the sequences may follow the elements of the parts of sequences the encoder / decoder / encoder-decoder architecture 1002 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 encoder / decoder / encoder-decoder architecture 1002 as specified by the training data set. Hence, during the training the encoder / decoder / encoder-decoder architecture 1002 may generate a guess on the next element and the guess on the next element in a sequence may be compared to the ground truth specifying the actual next element according to the training data set. Based on the guess on the next element and the ground truth a loss may be determined. The loss may define the similarity between the guess on the next element and the ground truth. The loss may be determined by forming a vector dot product between the token associated with the one or more elements and the token associated with the ground truth. A loss unequal to zero may result in updating the parameters associated with encoder / decoder / encoder-decoder architecture 1002. Preferably the parameters associated with the encoder / decoder / encoder-decoder architecture 1002 may be independent of the embedding layer. For example, the parameters associated with the encoder / decoder / encoder-decoder architecture 1002 may be weights of the neurons of the encoder / decoder / encoder-decoder architecture 1002.Based on the determined loss, backpropagation may be applied to determine the gradients associated with the parameters of the parameters associated with encoder / decoder / encoder-decoder architecture 1002 to lower the loss. According to the determined gradients, the parameters associated with the encoder / decoder / encoder- decoder architecture 1002, preferably the weights of the neurons associated with the encoder / decoder / encoder- decoder architecture 1002, may be updated by using a gradient descent algorithm.The training data set may be unlabeled. The sequences of elements within the training data set may inherently comprise the ground truth for determining the loss with respect to the one or more elements generated during the training of the encoder / decoder / encoder-decoder architecture 1002. Hence, the encoder / decoder / encoder- decoder architecture 1002 may be trained self-supervised. This is advantageous since time and resources for creating a labeled training data set may be saved. Furthermore, this enables the usage of large training data sets associated with a size of several tera bytes. Consequently, the data-driven model may be accurate in generating elements of a sequence. In addition, the large training data set enables few shot predictions or even zero shot predictions. Hence, the data-driven models trained as described above are versatile contributing to saving resources needed for training and / or hosting a plurality of purpose-driven models such as CNNs. The training described above may be referred to as pretraining. The data-driven model may be configured for performing few shot or even zero shot predictions with respect to a plurality of use cases after pretraining. The performance of the data-driven model may be increased further by additional training referred to as finetuning.FIG. 1 1 illustrates an embodiment of input embedding for embedding numbers and / or tables. Input data, embedded input data, context tensor and / or output data may be as defined within the context of FIG. 9A.Where the sequence of elements associated with the input data, preferably comprised in the input data, may be of one type, the input embedding 902, 920, 952, 966 as described within the context of FIG. 9A - 9C may be used. For example, a type of input data may be text where the elements may be associated with at least a part of a word, a punctuation character, a start token specifying the beginning of one or more sequences associated with the input data and / or the end token. In another example, the input data may be at least partially numerical. Hence, the input data may comprise a plurality of numbers. Numerical input data may be for example tabular data. Tabular data may specify one or more rows and / or one or more columns. Hence, the tabular data may comprise one or more cells, wherein the cells may be associated with one or more numerical values.Numerical input data may require a different embedding than text input data. Input embeddings for numerical input data may comprise a token embedding, a positional embedding, a column embedding, a row embedding or a combination thereof.Applying a token embedding to one or more elements, in particular tokens associated with the input data may result in a machine-processable representation associated with the one or more elements, in particular tokens. Applying the token embedding to one or more elements may refer to passing the one or more elements through the embedding layer, e.g. as described within the context of FIG. 8. Hence, token embeddings may specify the one or more elements, in particular tokens in a machine-processable representation. For example, the token embedding may transform a numerical value into a vector. This is advantageous since this representation can be enriched by further information such as the position of the token within the sequence and / or within a table associated with the sequence of tokens. The positional embedding may be analogous to the positional embedding as described within the context of FIG. 8, FIG. 9A - 9C. Where the input data may be tabular data, column embedding may be applied. Applying a column embedding to one or more elements, in particular tokens associated with the input data may result in a machine-processable representation specifying the location of the one or more elements within a table 1 102, preferably within the columns of the table 1102. Applying the column embedding may refer to adding a column factor to the input data embedded via token embeddings, in particular the embedded input data. The column factor may be the same for elements associated with the same column and / or may differ between two or more elements associated with different columns. Analogous, row embeddings may be applied where the input data may be tabular data. Applying a row embedding to one or more elements, in particular tokens associated with the input data may result in a machine-processable representation specifying the location of the one or more elements within a table 1 102, preferably within the rows of the table 1102. Applying the row embedding may refer to adding a column factor to the input data embedded via token embeddings, inparticular the embedded input data. The row factor may be the same for elements associated with the same row and / or may differ between two or more elements associated with different rows.In an embodiment, input data may be at least partially numerical and at least partially text. Hence, the input data may comprise two or more types of data. A type of data may refer to a modality. Followingly, different embeddings may be applied to the input data. To parts of the input data comprising text the input embedding referred to in FIG. 8, FIG. 9A - 9C may be applied. To parts of the input data being numerical token embeddings, positional embeddings, column embeddings and row embeddings may be applied. Further, segment embeddings may be applied to the input data independent of the type of input data. The segment embedding may specify the type of input data one or more elements may be associated to. For example, if the input data comprises of text and numbers, the input data may comprise of two types of input data. Applying the segment embedding to the input data may refer to adding a segment factor to the input data, preferably the embedded input data and / or the input data after having applied the token embedding. The segment factor may specify the type of data associated with the one or more elements. The segment factor may be the same for one or more elements associated with the same type of input data and / or may differ between two or more elements associated with different types of input data.Applying the token embedding, the positional embedding, the segment embedding, the column embedding, the row embedding, or a combination thereof may result in embedded input data and / or may be the output of any one of the encoder input 978, 984, 988 or decoder input 984, 994. The data obtained by applying the token embedding, the positional embedding, the segment embedding, the column embedding, the row embedding or a combination thereof may be processed by the encoder block 974, 986, decoder block 980, 990, encoder output 976, decoder output 992, 982.FIG. 12 illustrates an embodiment of a Mamba architecture. The mamba architecture may be used as data-driven model. A Mamba architecture may enhance inference speed in relation to a transformer based model.The Mamba architecture with its layered structure may be similar to the transformer decoder architecture discussed in relation to FIG. 12. However, instead of decoder blocks mamba blocks 1232, 1204 are stacked. Mamba block 1232 may be based on a selective space state sequence model (S6).An input token may be linearly projected via linear layer 1212, 1220 into an expanded latent space (which may allow to capture more information during processing in the selective state space layer 1210), followed by a convolution via a convolutional layer 1214 and a non-linear function (e.g. a sigmoid linear unit (SiLu) or swish activation function). The convolution before the selective state space layer 1210 may prevent independent tokencalculations. The selective state space layer 1210 performs a selective state space operation. Further, a learnable skip connection may be provided via linear layer 1220, this may use a linear transformation to map the input to the output, similar to a residual connection in a transformer model this may help to mitigate vanishing gradient effects. A selective state space layer 1210 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 1210 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, e.g. a matrix D, used in a learnable skip connection.The state equation for a hidden state may be (in discretized form): hk= Ahk~i + BxkThe output may be expressed by (in discretized form): yk= ChkThis discretized space state model may be unfolded into a recurrent form similar to a recurrent network, exemplifying that a selective state space model may be or comprise a linear recurrent model. However, here matrices A, B, and C may also be used as a kernel of a convolution of the state space model. Kernel K for this may e.g. be:which may allow to determine an output:So, in this representation of the state space model training may be performed in a parallel manner like in convolutional neural networks.Matrix A may be a matrix that represents recent tokens well and decays older tokens and may be initialized usingwhere every entry below the diagonal is set to 0. This may allow to create a long-term memory for the selective state space model.For a Mamba block 1232, the matrices B and C as well as the step size A used for discretization of the matrices may be dependent on the input token and may be trained during training, so that for each input token different matrices B and C are determined, which may enhance the content-awareness and may act similar to a multi-head self-attention in a transformer model. However, unlike in space state models with fixed matrices A, B, and C, here the convolutional representation may not be easily determined. Hence, to operate the selective state space layer 1210 in convolutional mode a selective scan may be applied utilizing associative properties of the hidden states calculation, allowing parallel determination of the sequence in parts and iteratively combining them, so that parallel training may be used. Further reading and writing operations may be decreased by using kernel fusion of the described step size, the selective scan, and the multiplication with C.Linear layer 1202 may project the generated output back into the same dimension as the input.Mamba blocks may be used together with transformer decoder blocks or mixture of expert blocks (e.g. decoder blocks wherein the feed-forward layer is exchanged for a gating network and a number of parallel feed-forward layers, wherein the gating network switches between the feed-forward layers depending on the input), which may allow leveraging advantages of the different architectures.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.The publication Prior Art Disclosure; Issue 684; paragraphs

[1000] to

[8005] ; ISSN: 2198-4786; published: February 12, 2024 will be regarded as Reference RF1 , which is incorporated herein by reference in its entirety. Preferably, the (chemical) product is a product as described in Reference RF1 ; paragraphs

[1000] to

[8005] , Preferably, the method / process described herein is further a method / process for the production of a product.The converting step to obtain the product preferably comprises one or more step(s) as described below and can be performed by conventional methods well known to a person skilled in the art. The converting step preferably comprises one or more step(s) selected from: recycling, preferably depolymerizing, gasifying, pyrolyzing, and / or steam cracking; and / or purifying, preferably crystallizing, (solvent) extracting, distilling, evaporating, hydrotreating, absorbing, adsorbing and / or subjecting to ion exchanger; and / or assembling, preferably foaming, synthesizing, chemical conversion, chemically transforming, polymerizing and / or compounding; and / or forming, preferably foaming, extruding and / or molding; and / orfinishing, preferably coating and / or smoothing.In addition, the one or more step(s) are described in detail in Reference RF1 ; paragraphs

[1000] to

[8005] ,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, 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.The sequence of all method steps presented above is not mandatory, also alternative sequences may be possible. Nevertheless, the specific sequence of method steps shown as examples in the figures shall be considered as one possible sequence of method steps, e.g. for the respective embodiment described by the respective figure or an embodiment comprising at least some of the steps described by the respective figure.In the present specification, any presented connection in the described embodiments is to be understood in a way that the involved components are operationally coupled. Thus, the connections can be direct or indirect with any number or combination of intervening elements, and there may be merely a functional relationship between the components.As used herein ..determining" may also include ..initiating or causing to determine", “generating" may also include ..initiating and / or causing to generate", “providing” may also include “initiating or causing to determine, generate, select, send and / or transmit”, and "obtaining" may also include “initiating or causing to determine, generate, select, retrieve and / or receive”. “Initiating or causing to perform an action” may include any processing signal that triggers a computing node or device to perform the respective action.The term “comprising” or “including” is to be understood in an open sense, i.e. in a way that an object that “comprises an element A” may also comprise further elements in addition to element A. Further, the term “comprising” or “including” may be limited to “consisting of”, i.e. consisting of only the specified elements.The indefinite article “a” or “an” is not to be understood as “one”, i.e. use of the expression “an element” does not preclude that also further elements are present. 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 differentdependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation or further elements may be included.The expressions “A and / or B” and “at least one of: A or B” are considered interchangeable and meant to comprise any one of the following three scenarios: (I) A, (ii) B, (ill) A and B. More generally, the expression “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.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.Obtaining in the scope of this disclosure may include any interface configured to obtain or receive data. This may include an application programming interface, a human-machine interface such as a display and / or a software module interface. Obtaining may include communication of data or submission of data from the interface, in particular use of the data by the receiving entity. Any obtaining of data, data structures, data sets, or the like may comprise receiving the data, data structures, data sets, or the like from a server providing (e.g. hosting) a data base comprising the data, data structures, data sets, or the like.Various units, circuits, entities, nodes or other computing components may be described as “configured to” perform a task or tasks. Configured to shall recite structure meaning “having circuitry that” performs the task or tasks on operation. The units, circuits, entities, nodes or other computing components can be configured to perform the task even when the unit / circuit / component is not operating. The units, circuits, entities, nodes or other computing components that form the structure corresponding to “configured to” may include hardware circuits and / or memory storing program instructions executable to implement the operation. The units, circuits, entities, nodes or other computing components may be described as performing a task or tasks, for convenience in the description. Such descriptions shall be interpreted as including the phrase “configured to.” Any recitation of “configured to” is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation.In general, the methods, apparatuses, systems, computer elements, nodes or other computing components described herein may include memory, software components and hardware components. The memory can include volatile memory such as static or dynamic random-access memory and / or nonvolatile memory such as optical or magnetic disk storage, flash memory, programmable read-only memories, etc. The hardware components mayinclude any combination of combinatorial logic circuitry, clocked storage devices such as flops, registers, latches, etc., finite state machines, memory such as static random-access memory or embedded dynamic random-access memory, custom designed circuitry, programmable logic arrays, etc.In the present specification, any presented connection in the described embodiments is to be understood in a way that the involved components are operationally coupled. Thus, the connections can be direct or indirect with any number or combination of intervening elements, and there may be merely a functional relationship between the components.Moreover, any of the methods, processes and actions described or illustrated herein may be implemented using executable instructions in a general-purpose or special-purpose processor and stored on a computer-readable storage medium (e.g., disk, memory, or the like) to be executed by such a processor. References to a ‘computer- readable storage medium’ should be understood to encompass specialized circuits such as signal processing devices, and other devices.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.All terms and definitions used herein are understood broadly and have their general meaning if not indicated otherwise.It will be understood that all presented embodiments are only examples, and that any feature presented for a particular example embodiment may be used with any aspect on its own or in combination with any feature presented for the same or another particular example embodiment and / or in combination with any other feature not mentioned. In particular, the example embodiments presented in this specification shall also be understood to be disclosed in all possible combinations with each other, as far as it is technically reasonable and the example embodiments are not alternatives with respect to each other. It will further be understood that any feature presented for an example embodiment in a particular category (method / apparatus / computer program / system) may also be used in a corresponding manner in an example embodiment of any other category. It should also be understood that presence of a feature in the presented example embodiments shall not necessarily mean that this feature forms an essential feature and cannot be omitted or substituted.

Claims

CLAIMSWhat is claimed is:1 . A computer-implemented method for generating chemical product production and / or processing data related to a production and / or processing of a chemical product, the method comprising: providing chemical product data related to the production and / or processing of the chemical product, providing multiple production and / or processing data template indicative of a structure associated with at least a part of the chemical product data and / or associated element(s), selecting at least one production and / or processing data template based on at least a part of the chemical product data, generating contextualized chemical product data by combining at least a part of the chemical product data and the at least one selected production and / or processing data template, providing the contextualized chemical product data to a data-driven model for generating the chemical product production and / or processing data, wherein the data-driven model is parametrized and / or trained to provide the chemical product production and / or processing data in response to being provided by the contextualized chemical product data, providing the chemical product production and / or processing data.

2. The method of claim 1 , further comprising receiving second contextualized chemical product data based on at least a part of the chemical product chemical product production and / or processing data, and, providing the second contextualized chemical product data to the data-driven model for generating second chemical product production and / or processing data, and, providing the second chemical product production and / or processing data.

3. The method of claim 1 or 2, wherein the multiple production and / or processing data templates comprise multiple types of production and / or processing data templates and wherein selecting at least one production and / or processing data template based on at least a part of the chemical product data and / or associated elements refers to selecting the at least one production and / or processing data template based on the type of production and / or processing data template, wherein the type of the production and / or processing data template specifies that the chemical production and / or processing data comprises at least one of chemical product producer data, chemical product production instructions, chemical product processor data, chemical product processing instructions, one or more material properties associated with the chemical product, one or more material properties associated with a product based on the chemical product.

4. The method of any one of claims 1 to 3, wherein the data-driven model is a pre-trained data-driven model and wherein the pre-trained data-driven model is trained based on general data to provide output data based on being provided by input data.

5. The method of any one of claims 1 to 4, wherein the data-driven model is a fine-tuned data-driven model and wherein the fine-tuned data-driven model is a pre-trained data-driven model further trained, preferably in a second training based on historical data input data to provide chemical product production and / or processing data based on being provided by contextualized chemical product data, wherein the pre-trained data-driven model is trained based on general data to provide output data based on being provided by input data.

6. The method of any one of claims 1 to 5, further comprising selecting the data-driven model from a plurality of data-driven models based on the chemical product data and / or providing an indication for selecting the data-driven model from a plurality of data-driven models and selecting the data-driven model from a plurality of data-driven models based on the indication.

7. The method of any one of claims 1 to 6, wherein the multiple production and / or processing data template are provided via a database and wherein selecting at least one production and / or processing data template based on at least part of the chemical product data and / or associated elements may comprise providing a query based on the production and / or processing data to the database and / or receiving the at least one production and / or processing data template based on the query.

8. The method of any one of claims 1 to 7, wherein the multiple production and / or processing data template are provided via a database and wherein selecting at least one production and / or processing data template based on at least part of the chemical product data and / or associated elements comprises generating embedded chemical product data and generating one or more embedded production and / or processing data templates and selecting the production and / or processing data template based on determining that a tensor product between a tensor associated with the embedded chemical product data and a tensor associated with at least one of the one or more production and / or processing data templates is within a predefined numerical range.

9. The method of any one of claims 1 to 8, wherein the chemical product data comprises one or more elements associated with the chemical product data and wherein at least one of the multiple production and / or processing data templates comprise one or more elements.

10. The method of claim 9, wherein the contextualized chemical product data comprises two or more elements being equal to the one or more elements associated with the chemical product data and / or to the one or more elements of the at least one production and / or processing data template.1 1 . The method of claim 10, wherein the contextualized chemical product data comprises a sequence of the two or more elements being equal to the one or more elements associated with the chemical product data and / or to the one or more elements of the at least one production and / or processing data template.

12. The method of any one of claims 8 to 10, wherein the element refers to at least a part of a word, at least a part of a number, a part of a table or a combination thereof and / or wherein the sequence of two or more elements refers to at least a part of a sentence, at least a part of a number sequence, at least a part of a table or a combination thereof.

13. A device and / or system 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 any one of claims 1 -2, 4-10 or 12.

14. The system of claim 13 further comprising a user interface, wherein the user interface is configured for receiving the chemical product processing data and / or the chemical product production data, in particular from a user, and / or is configured for providing the chemical product processing data and / or the chemical product production data, in particular to the processor.

15. Use of production and / or processing data related to a production and / or processing of a chemical product as obtained by any one of the methods according to any one of claims 1 to 12 for controlling and / or monitoring a production and / or processing of the chemical product.

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