Chemical Production

Abstract

The present teachings relate to a method for monitoring and / or controlling a production process for manufacturing at least one industrial product by processing at least one input material in an industrial plant comprising at least one equipment, the method including receiving real-time process data from the equipment via an input interface, determining a subset of the real-time process data via a computing unit, and providing the subset of the real-time process data as output data. The present teachings also relate to a system, a method of use, and a software product.

Description

[Technical field]

[0001] Technical Field The present teachings relate generally to computer-aided chemical production. [Background technology]

[0002] Background technology In industrial plants, input materials are processed to produce one or more products, the properties of the produced products thus being dependent on the production parameters, and it is usually desirable to correlate at least some properties of the products with the production parameters to ensure product quality or production stability.

[0003] In a process industry or industrial plant, such as a chemical manufacturing plant or a biological production plant, one or more input materials are processed using a production process to produce one or more chemical or biological products. The production environment in a process industry can be complex, so that the properties of the product may change depending on the variations in the production parameters that affect said properties. Usually, the dependency of the properties on the production parameters may be complex and intertwined with further dependencies on one or more combinations of specific parameters. In some cases, the production process may be divided into multiple stages, which may further exacerbate the problem. Therefore, it may be difficult to produce chemical or biological products with consistent and / or predictable quality.

[0004] To ensure consistent quality of chemical products, quality control may be performed. Quality control typically involves taking one or more samples of the chemical product after or during the production process. The samples may then be analyzed and corrective actions may be implemented if necessary. To be effective, samples may need to be taken periodically and should be representative of the statistical variability of the chemical product. Depending on the frequency of variations that occur in the production process, the frequency of quality control may need to be consistent. As such, quality control may be costly and time consuming.

[0005] Furthermore, chemical or biological processes, such as continuous, campaign, or batch processes, as opposed to discrete processes, can provide vast amounts of time series data that can be difficult to integrate and provide meaningful data for integration or use in machine learning and / or for plant operators.

[0006] Therefore, there is a need for approaches that could ideally help improve quality and production stability throughout the entire value chain, from barrel to final product. Summary of the Invention

[0007] overview At least some of the problems inherent in the prior art are indicated to be solved by the subject matter of the attached independent claims. At least some of the further advantageous alternatives are outlined in the dependent claims.

[0008] Viewed from a first aspect, there is provided a method for monitoring and / or controlling a production process for producing a chemical product in an industrial plant, the industrial plant comprising at least one equipment, the product being produced by processing at least one input material using a production process via the equipment, the method being at least partly executed via a computing unit, the method comprising: - receiving real-time process data from a device via an input interface; - determining, via the computing unit, a subset of real-time process data, the subset of real-time process data indicative of process parameters and / or equipment operating conditions as the input material is processed; and - Providing a subset of the real-time process data as output data via the output interface A method may be provided that includes:

[0009] By doing so, applicants have realized that a subset of real-time process data indicative of process parameters and / or equipment operating conditions related to the particular input material being processed may be provided via the output interface. Thus, real-time process data that is not related to the input material being processed is not provided as output data. The output data is preferably provided, e.g., transmitted, in real time, but in some cases may be provided with a delay and / or in parts, as discussed in this disclosure.

[0010] The advantages of this approach are even greater when the equipment comprises multiple physically separated equipment zones. In such cases, the output data may track the input material as it progresses through the production process. In this manner, the output data may dynamically track the input material in the production process, for example as a time-dependent data stream. This may have several advantages, as discussed in this disclosure.

[0011] According to one aspect, the subset of real-time process data is determined using a zone presence signal. The zone presence signal may indicate the presence of an input material at a particular location in the equipment during the production process. For example, the location may be a particular equipment zone within the equipment.

[0012] The zone presence signal may be generated by performing, via the computing unit, a zone-to-time transformation that maps at least one characteristic associated with the input material to a specific location or equipment zone. For example, the characteristic associated with the input material may be the weight of the input material, such that knowledge of the production process, e.g., via real-time process data, may determine the presence of the input material or a derivative of the input material produced during the production process. As an example, if an input material located in an upstream equipment zone and having a certain weight is moved to a downstream equipment zone during the production process, a weight measurement in the downstream zone, e.g., at or within a predetermined time, may be used to generate a zone presence signal in the downstream zone. Similarly, if the input material or a derivative of the input material ... process A flow rate value, e.g., mass flow rate or volumetric flow rate, as it moves through a zone may be the characteristic used to generate a zone presence signal. As a further example, the speed or velocity of an input material as it moves along a location or equipment zone may be used to determine the space or location in which the input material or its corresponding derivative material is present at a given time. Alternatively or additionally, other non-limiting examples of characteristics associated with an input material include volume, fill value, level, color, etc.

[0013] The applicant has found that in a production environment it is advantageous to generate a zone presence signal by mapping real-time process data, which is time-dependent data (e.g., time series data), to spatial data to map the real production flow using digital flow elements representing the input material. For example, the digital flow rate of the input material may be tracked via one or more object identifiers, and occurrences in the time-dependent real-time process data may be used to identify the location of the material along the production process. Object identifiers are discussed in detail in this disclosure. In this way, the material is tracked or located via the already measured time and the real-time process data, i.e., by using the time dimension of the process data in relation to the time dimension of the flow rate of the input material along the production chain.

[0014] The zone presence signal may be intermittent, for example generated via calculation at fixed or irregular times, or may be generated continuously. This may have the advantage that the material, for example the material associated with the corresponding object identifier, may be located continuously or substantially continuously in the production chain, thus allowing for the addition of relevant data to the material and its transformation into a chemical product. Calculations at fixed or irregular times may be made, for example, to confirm the presence of the material at a particular checkpoint in the production chain. This may be complemented by occurrences in real-time process data, for example by one or more sensors as outlined below.

[0015] In chemical production, the zone-to-time transformation can be a simple mapping on the time scale, since the operating parameters related to the time dimension, such as residence time and flow rate, are known. Alternatively, more complex models based on process simulations can be used to match the time scale of the material flow rate with the real-time process data. In either case, the time scale of the process data may be finer than the material flow rate in order to more finely attribute the process data parameters to the material flow rate.

[0016] Additionally or alternatively, the zone presence signal may be provided at least in part via a sensor associated with a particular location or zone. For example, weight and / or image sensors may be used to detect the presence of input or derived materials in a space or at a particular location or equipment zone.

[0017] The input interface and the output interface may be the same component or different components.

[0018] According to one aspect, the method comprises: - calculating, via a computing unit, at least one performance parameter of a chemical product associated with the input material, the calculation being based on or performed using a subset of the real-time process data and the historical process data. Includes.

[0019] In this manner, the subset of real-time process data that is the relevant process data may be further utilized to calculate at least one performance parameter that may be expected for the chemical product obtained from the input material based on the subset of relevant real-time process data that is at least partially responsible for the conversion of the input material to the chemical product. Thus, the determination of the quality of the chemical product via the calculation of the at least one performance parameter may be improved. The calculation may also utilize historical process data, e.g., historical process data from processing one or more previous input materials, to further improve the calculation of the at least one performance parameter. In accordance with the present teachings, at least one performance parameter indicative of the quality of the chemical product may be calculated. One It will be appreciated that the performance parameters may be determined essentially while the input material is being processed. In this way, a quality indicator of the chemical product may be assessed on the fly via the at least one performance parameter. In case of multiple equipment zones, the at least one performance parameter may further include zone-specific performance parameters, i.e., performance parameters related to a particular equipment zone.

[0020] According to one embodiment, the output data may also include at least one performance parameter, which may enable the inclusion of quality parameters in the highly relevant output data, further advantages of which will be further appreciated in the following discussion.

[0021] Additionally or alternatively, the method comprises: - providing, via the interface, an object identifier including material input data, the material input data indicating one or more characteristics of the material input; may include.

[0022] This provides an object identifier that includes data related to the input material, which can have several advantages, for example, the input data can be used for better calculation of at least one performance parameter that may use the input material data. Thus, the characteristics of a particular input material used to produce a chemical product can be used for more accurate calculation of at least one performance parameter related to said chemical product.

[0023] The interface may be the same component as the input interface and / or the output interface, or may be a different component.

[0024] According to one aspect, the method comprises: - appending at least a portion of the subset of real-time process data to the object identifier; Includes.

[0025] This allows the relevant process data to be attached, for example as metadata, to a specific object identifier associated with a specific input material. This synergistically allows for the encapsulation of the relevant process data together with the input material data that is at least partially responsible for the transformation of the input material into a chemical product. Thus, essentially on the fly, the relevant data is also captured via the object identifier. Output data can be provided before, simultaneously with, or after the attachment of the subset of real-time process data to the object identifier.

[0026] For example, the output data may further be provided or generated via the object identifier, i.e. the subset is first added to the object identifier and then the output data is provided via the object identifier. This may have the advantage that the computing unit may be relieved of the task of providing the output data. The output data may be provided directly from the object identifier and / or via an intermediate device or agent. In this case, the output data may be provided continuously or intermittently at regular or irregular time intervals. For example, the output data stream may possibly be chunks or strings of data. The provision of the data chunks to the output data may depend on the object identifier and / or on the state of the production process. For example, the provision of the output data via the object identifier may be triggered based on one or more of the following: the completion of a production step, the size of the data chunk reaching a given value, a time span, material reaching a certain state or location, etc. This may have advantages in certain processes, e.g. slow processes, where meaningful data is first accumulated and then provided as output data. Thus, the output data may be provided on an event basis.

[0027] Similarly, the object identifier may be appended with the subset after providing the output data. The data appended to the object identifier may further be derived from the output data. For example, the output data may be transmitted with a specific identifier or ID, e.g., included as metadata, that may be used to determine which portion of the subset of real-time process data is appended to the object identifier. In this way, the data appended to the object identifier may be further optimized for memory storage and processing requirements. Thus, the usefulness of the data appended to the object identifier may be optimized.

[0028] It will also be appreciated that object identifiers that have been tagged with at least a portion of a subset of real-time process data, i.e., tagged object identifiers, may be extremely useful for machine learning ("ML") applications.

[0029] According to one aspect, the method comprises: - attaching at least one performance parameter to the object identifier; Includes.

[0030] Thereby, at least one performance parameter is also captured with at least a portion of the subset of process data that is at least partially responsible for the transformation of input materials into chemical products. Thus, the object identifier can more fully encapsulate the variables that are responsible for the resulting chemical products, which can be useful for essentially tracking a particular chemical throughout the production process. Furthermore, the data set encapsulated in the object identifier can also become more relevant for quality control and ML applications.

[0031] According to one aspect, the historical process data includes data from at least one historical object identifier. The data from the at least one historical object identifier may include one or more subsets of the historical process data determined in a manner similar to the subset of the real-time process data. Additionally or alternatively, the data may include one or more historical or past process parameters.

[0032] Thus, the historical process data may include data from one or more historical object identifiers associated with previously processed input materials. Preferably, at least a portion of the historical object identifiers relate to past input materials processed on the equipment. In some cases, the one or more historical object identifiers may be associated with similar production processes in which the past input materials were processed. process Since the object identifiers may be from other devices or zones according to the present invention, historical object identifiers from such zones may also be available.

[0033] Thus, relevant process data from one or more past or historical object identifiers may be leveraged to improve the calculation of at least one performance parameter.

[0034] At least one of the historical object identifiers may also be assigned one or more historical performance parameters. The historical performance parameters may have been calculated based on a subset of past or historical process data and / or may have been determined via physical testing or analysis performed on the corresponding chemical product or a sample thereof. Thus, historical testing or sampling data may also be utilized to calculate the performance parameters of the input materials.

[0035] Thus, the one or more historical object identifiers may also include a subset of process data that may be leveraged to correlate the corresponding historical input material characteristics with their relationships to performance parameters.

[0036] It will be appreciated that the object identifiers disclosed herein may enable at least certain material properties to be associated with corresponding previously determined performance parameters or parameters via their input material data, such that the object identifier data may be leveraged in a reusable and traceable manner, for example, to calculate at least one performance parameter for subsequent processing of the input material.

[0037] It will also be understood that at least one historical object identifier, more preferably some historical object identifiers, even more preferably most historical object identifiers, and more preferably each historical object identifier is loaded with or encapsulates process data from processing a corresponding previous input material to produce or process a corresponding chemical product. Thus, the historical data disclosed herein is a relevant yet concise data set that can be used to perform calculations of one or more performance parameters during production. This may thus not only improve the traceability of chemical products, but also simplify the quality control or monitoring of chemical products. This will be discussed in more detail in this disclosure.

[0038] Thus, via the object identifier, relevant process data may be captured along with the input data, also in the object identifier, such that the relationship of the chemical product with the input properties may also be captured along with the relevant process data captured as a subset or optimized portion of the real-time process data. This may provide a more complete relationship between various dependencies that may affect any one or more properties of the chemical product. Another advantage may be that the combination of various interdependencies that may exist between the input properties and / or process parameters may also be captured within the object identifier. In this way, the appended object identifier is enriched with information that may be used to track the specific process data that caused the chemical product to result, and not just its specific components, such as the chemical product and / or input materials. As a result, object identifiers, such as each of the historical object identifiers, may be more easily integrated for machine learning ("ML") and such purposes. Thus, the object identifier may also be used as a historical object identifier for future production.

[0039] Because data from object identifiers can provide a highly focused overview of the transformation of input materials into chemical products, such datasets are extremely concise, which can allow for faster processing and less computational power. Advantages include faster training times for ML models, faster data integration, and suitability for edge processing and cloud computing applications.

[0040] It will be appreciated that the performance parameters may relate directly to one or more properties of the chemical product and / or to the performance parameters of derived materials or products produced during the production process. For example, if an input material is converted to a derived material during the production process, it may be necessary to track the quality or performance of such derived material as well. It will be appreciated that if a derived material is an intermediate material resulting from an input material, said derived material is subsequently used to produce a chemical product. As the chemical product also depends on the derived material, it may be necessary to test and track the derived material. Thus, the term "input material" is used to encompass not only the starting material, but any derived or intermediate processed material.

[0041] Thus, according to one embodiment, at least one of the performance parameters is related to one or more properties of the derived material.

[0042] According to one aspect, the equipment comprises a plurality of physically separated equipment zones such that the output data includes a subset of real-time process data from each of the equipment zones and / or at least one performance parameter calculated in each of the equipment zones.

[0043] Thus, as the input material moves from one zone to another, the output data dynamically changes depending on the location of the input material. The output data may be understood as a stream of related data that follows the input material along the production process. Where the output data includes one or more performance parameters, these one or more performance parameters may also be adapted depending on the progression of the input material. It will be appreciated that once a subset of process data in multiple zones is adapted using the zone presence signal, the output data is also adapted.

[0044] In a production process where multiple batches of input materials are ongoing, there may furthermore be a separate output data or stream for each batch. The output data may therefore form a time-dependent data stream. Here, the object identifier may provide a synergistic advantage in that the desired stream or output data may follow via the corresponding object identifier of the batch. In a sense, the stream may be "tuned" by using the corresponding object identifier of the corresponding input material. This makes it easy to select or switch to a desired stream of output data.

[0045] The output data or the time-dependent data stream may be provided in a data store, e.g., a database and / or a distributed data store, for capturing and processing the output data stream in real time. The data store may be a memory storage element or at least one database as described above, or may be a different system. Thus, the object identifier may further be provided directly in the data store or in a memory different from the data store. Thus, the output data may be provided directly in the data store or the output data may be provided via a memory storage or database external to the data store.

[0046] The data store may allow publishing one or more different streams of output data and / or subscribing to one or more different streams of output data. Additionally or alternatively, the data store may further store output data from one or more batches in the order in which they are generated. Additionally or alternatively, the data store may process output data from one or more batches in real time. The data store may further be used to build real-time streaming data pipelines and applications that fit the output data streams. The data store may combine messaging, storage, and output data processing to allow for storage and analysis of both historical and real-time data. The data store may further provide data, for example in the form of a stream, to one or more applications. The application may be any application that consumes a stream of data. Non-limiting examples include applications for tracking one or more performance parameters, for example to optimize product quality in real time. Additionally or alternatively, any portion of highly relevant process data may be tracked and / or optimized. The data store may include one or more interfaces, such as an API. For example, a Producer API may be used to publish a stream of output data to a data store. Additionally or alternatively, a Consumer API may be used to subscribe to an output data stream and / or a playback stream of historical data (e.g., from one or more historical object identifiers). Furthermore, there may be a Streams API that allows an application to act as a stream processor. Such an application may ingest one of the output streams and convert it into an application output stream. Thus, after ingesting the output data, such an application may create a stream of processed data as an application output stream, which may be used for real-time optimization and / or monitoring and / or control of the plant.Optimization means improving a production process in some way. For simplicity, we will use the term “time-dependent data streams.” 」 For example The word is It is understood that the term "data store" refers to data, output data, or application output streams. The data streams may be continuous, intermittent, or even event-based, or a combination thereof. Additionally, there may be a Connector API for adding or connecting applications or data systems to the data store.

[0047] Thus, the data stored in and / or provided from the data stores may be highly focused, relevant data that may be used to improve production processes and / or operations at different plants or locations. Data from one plant may be leveraged in another plant, thereby synergistically improving the production processes at each plant. It will be appreciated that this may occur simultaneously as new output data is generated, automatically encapsulated via object identifiers, tracked with respect to corresponding input materials, provided as historical data, etc.

[0048] Such output data or time-dependent data streams disclosed in the present teachings may also be further advantageously provided to, for example, a human machine interface ("HMI") system or device.

[0049] The HMI system may at least partially be or may comprise a display device. The display device may be any suitable device with a screen for displaying the output data or stream in a meaningful way. For example, the display device may be a computer monitor or a screen on which an image is projected or displayed in any suitable way. The display device may further be in the form of a mobile device such as a smartphone or a tablet. This allows the operator to keep track of any batches being produced. The output data may provide highly relevant data that may enable a faster response from the operator in case of an anomaly somewhere in the output data. To further assist the operator in this regard, the historical data, or preferably data and / or expectations from the historical object identifiers, may be overlaid on the corresponding parts of the output data to easily capture any abnormal conditions. This is because the data from the historical object identifiers may provide a highly focused overview of the transformations that the input materials undergo in the production process with fewer computational resources. Thus, the system may enable the operator to take more appropriate corrective measures to address the anomaly. Any anomalies, such as when any of the output data exceed their corresponding thresholds, may be communicated, for example, using an alarm and / or a different color on the display. An operator may also be enabled to more quickly switch between different output streams so that an overview can be maintained across multiple materials being processed at various stages of the production process. Another advantage may be that the output data automatically tracks materials when tracking the same batch, rather than the operator having to manually switch outputs to different zones.

[0050] Additionally or alternatively, the HMI system comprises a mixed reality ("MR") device, such as an augmented reality ("AR") and / or virtual reality ("VR") device. For example, the MR device may be a head-mounted device for an operator or user. The MR device may comprise a number of sensors and hardware, which may include a computer processor, a camera, and a projection lens. The MR device may further comprise at least one visor or lens, allowing the user to view the real-world surroundings. The visor or lens may further comprise one or more display elements that provide a computer-generated image to the user. Thus, the visor or lens may enable the user to simultaneously view the real-world surroundings and the computer-generated image, for example as a hologram. In some cases, the computer-generated image may also be overlaid or overlapped with real-world elements visible through the visor or lens. The sensors and hardware may adapt the image that the MR device provides to the user depending on the position of the user's orientation, e.g., the direction in which the user's head and / or eyes are facing. The image may further be adapted according to a gesture or movement of the user, for example a swipe gesture or a pointing gesture or even a tap gesture. Providing the output data or a time-dependent data stream to the MR device may enable the user to virtually visualize the input material through the production process even if said material is not physically visible, for example inside an enclosed piece of equipment. Thus, material flowing through an enclosure such as a duct or pipe may be tracked along the production process. According to one aspect, at least a part of a subset of real-time process data and / or at least one performance parameter is automatically displayed for the input and / or derived material, for example for the input and / or derived material within the field of view of the MR device. Object identifier references of said material may further be displayed so that the user can note or recognize references to specific materials that require further confirmation or investigation. This may simplify sampling and quality control.Any abnormal batches may be indicated, for example, as a hologram, in a different color or flashing, as described in connection with the display device. For example, good batches or batches with normal parameters may be indicated in one color (e.g., green), while bad batches, e.g., batches with abnormal performance parameters, may be indicated in another color (e.g., red). As discussed, historical data, or preferably data and / or expectations from historical object identifiers, may be overlaid on the corresponding portion of the output data to easily capture any abnormal conditions. Other levels may also be possible, for example, an average batch may be indicated in another different color (e.g., yellow). Colors are mentioned as non-limiting examples without departing from the generality of the present teachings.

[0051] Additionally or alternatively, the batch of material as viewed in the open state, i.e. through the visor or lens, may be shown with a computer-generated overlay or fill, tags or pointers to indicate any one or more of the options discussed above, i.e. performance parameters, object identifier references, process parameters, etc. It will be appreciated that the virtual overlay or fill may be shown, for example, by at least partially overlaying a computer-generated image on the physical material as viewed by the user through the visor of the MR device. The MR device may further display active elements, such as images or holograms that change over time, for example to indicate the movement of material on the equipment, and / or dashboards, and / or tags, showing portions of the output data that are continuously or intermittently updated. As mentioned above, the images or holograms may be overlaid on enclosed portions of the real-world equipment, or on a virtual model of the equipment, thereby indicating the flow or location of the material in production, and / or may be overlaid at least partially with the real-world material as viewed through the visor or lens. This may, for example, help the user to identify the correct material. For example, if a material does not meet certain performance parameters, this may be indicated in the MR system with a different overlay image or tag, which may help the operator locate the material (e.g., take the material for sampling).

[0052] Additionally or alternatively, the HMI system comprises an audio device. The audio device may be unidirectional or bidirectional. For example, the audio device may accept audio input from a user, e.g., performance parameter "X" of object "A", or the current mix temperature of zone "21". The device may then return the data requested by the user as audio and / or video output to the display device and / or MR device. The audio device may also provide data requested by the user via a mouse click or a tap on the screen as audio output.

[0053] In either case, it will be appreciated that the output data may be provided automatically or on demand, for example, on request from a user or a server. Any suitable standard or protocol may be used for this purpose, for example, the OPC Unified Architecture ("OPC UA"). For example, data The output data to the store may be provided automatically, for example continuously as it is generated. For example, data to a MR device may be provided in response to a user selection or in response to the user's location. User The location of may be detected, for example, via the MR device itself, by performing detection such as image recognition, and / or via positioning signals (e.g., geolocation systems) and / or beacons (e.g., radio beacons or signals), and / or near field communication ("NFC") and / or Bluetooth. Some non-limiting examples of user selection have already been provided, such as clicking, tapping, or voice query.

[0054] "Equipment" may refer to one or more pieces of equipment in an industrial plant. As non-limiting examples, equipment may refer to any one or more of the following, or any combination thereof: computing units or controllers, such as programmable logic controllers ("PLCs") or distributed control systems ("DCSs"); sensors, actuators, end effector units, transport elements, such as conveyor systems; heat exchangers, such as heaters; furnaces, cooling units, reactors, mixers, grinders, choppers, compressors, slicers, extruders, dryers, sprayers, pressure or vacuum chambers, tubes, bins, silos, and any other type of equipment used directly or indirectly for or during production in an industrial plant. Preferably, equipment refers specifically to equipment, devices, or components that are directly or indirectly involved in the production process. More preferably, equipment refers to equipment, devices, or components that may affect the performance of a chemical product. Equipment may or may not have a buffer. Furthermore, equipment may or may not involve mixing, and may or may not involve separation. Non-limiting examples of equipment without mixing and without buffers include conveyor systems or belts, extruders, pelletizers, and heat exchangers. Non-limiting examples of equipment without mixing and with buffers include buffer silos, bins, etc. Non-limiting examples of equipment with mixing and with buffers include silos with mixers, mixing vessels, cutting mills, double cone blenders, curing tubes, etc. Non-limiting examples of equipment without buffers with mixing include static mixers or dynamic mixers, etc. Non-limiting examples of equipment with separation and with buffers include columns, separators, extractors, thin film evaporators, filters, sieves, etc. Equipment may also be or include storage or packaging elements such as octabin fillers, drums, bags, tank trucks, etc. In some cases, a combination of two or more pieces of equipment may also be considered as equipment.

[0055] "Equipment zone" refers to physically separate zones, which may be part of the same equipment or may be separate facilities used to manufacture a chemical product. As such, each zone is physically located in a non-identical location. The locations may be horizontally and / or vertically non-identical geographic locations. Thus, input materials start at an upstream equipment zone and move downstream toward one or more equipment zones downstream of the upstream equipment zone. As such, various steps of a production process may be distributed among the zones.

[0056] In this disclosure, the terms "instrument" and "instrument zone" may be used interchangeably.

[0057] "Equipment operating conditions" refers to characteristics or values ​​that represent the state of an equipment, such as any one or more of the following: set points, controller outputs, production sequences, calibration status, equipment-related warnings, vibration measurements, speeds, temperatures, and fouling values ​​(filter differential pressure, maintenance dates, etc.).

[0058] The term "upstream" shall be understood to mean a direction facing away from the production flow. For example, the very first equipment zone where the production process begins is the upstream equipment zone. However, the term "upstream" is used in a relative sense within the meaning of this disclosure. For example, an intermediate equipment zone between the first and last equipment zone may be referred to as an upstream zone with respect to the last equipment zone and a "downstream" equipment zone with respect to the first equipment zone. Thus, the last equipment zone is a downstream zone with respect to both the first equipment zone and the intermediate equipment zone. Similarly, both the first equipment zone and the intermediate equipment zone are upstream of the last equipment zone.

[0059] An "industrial plant" or "plant" may refer to any technological infrastructure used for industrial purposes, including, but not limited to, the manufacture, production, or processing of one or more industrial products, i.e., the manufacturing or production process or processing performed by the industrial plant. The industrial product may be any physical product, e.g., chemical, biological, pharmaceutical, food, beverage, textile, metal, plastic, semiconductor, etc. Additionally or alternatively, the industrial product may also be a service good, e.g., recovery or waste treatment, such as recycling, chemical processing, such as disintegration or dissolution into one or more chemical products. Thus, the industrial plant may be one or more of a chemical plant, a process plant, a pharmaceutical plant, a fossil fuel processing facility, such as an oil well and / or a natural gas well, a refinery, a petrochemical plant, a fractionation plant, etc. The industrial plant may also be a distillery, a processing plant, or a recycling plant. The industrial plant may also be a combination of any of the above examples, or a similar one thereof.

[0060] The infrastructure may include equipment or process units such as any one or more of: heat exchangers, columns such as fractionation towers, furnaces, reaction chambers, cracking units, storage tanks, extruders, pelletizers, precipitators, blenders, mixers, cutters, curing tubes, vaporizers, filters, sieves, pipelines, stacks, filters, valves, actuators, mills, transformers, conveying systems, shutoff switches, machines (e.g., large rotating equipment such as turbines, generators, pulverizers, compressors, industrial fans, pumps, etc.), transport elements such as conveyor systems, motors, etc.

[0061] Furthermore, industrial plants typically include a number of sensors and at least one control system for controlling at least one parameter related to a process in the plant, i.e., the process parameter. Such control functions are usually performed by a control system or controller in response to at least one measurement signal from at least one of the sensors. The controller or control system of the plant may be implemented as a distributed control system ("DCS") and / or a programmable logic controller ("PLC").

[0062] Thus, at least some of the equipment or process units of the industrial plant may be monitored and / or controlled to produce one or more of the industrial products. The monitoring and / or control may further be done to optimize the production of the one or more products. The equipment or process units may be monitored and / or controlled via a controller, such as a DCS, in response to one or more signals from one or more sensors. In addition, the plant may further comprise at least one programmable logic controller ("PLC") for controlling a portion of the process. An industrial plant may typically comprise multiple sensors that may be distributed in the industrial plant for monitoring and / or control purposes. Such sensors may generate large amounts of data. The sensors may or may not be considered part of the equipment. Thus, the production of chemical products and / or service goods, etc. may be a data-heavy environment. Thus, each industrial plant may generate large amounts of process-related data.

[0063] Those skilled in the art will appreciate that industrial plants may typically include instrumentation that may include different types of sensors. Sensors may be used to measure one or more process parameters and / or equipment operating conditions or parameters associated with equipment or process units. For example, sensors may be used to measure process parameters such as flow rate in a pipeline, level in a tank, temperature in a furnace, chemical composition of gas, and some sensors may be used to measure vibrations in a grinder, speed of a fan, opening of a valve, corrosion in a pipeline, voltage across a transformer, and the like. The differences between these sensors are not only based on the parameters they sense, but may also be the sensing principles that the corresponding sensors employ. Some examples of sensors based on the parameters they sense include temperature sensors, pressure sensors, radiation sensors such as optical sensors, flow sensors, vibration sensors, displacement sensors, chemical sensors such as those that detect specific substances such as gases, and the like. Examples of sensors that employ different sensing principles include impedance sensors such as piezoelectric sensors, piezoresistive sensors, thermocouples, capacitance sensors, and resistance sensors, and the like.

[0064] An industrial plant may also be part of a plurality of industrial plants. As used herein, the term "multiple industrial plants" is a broad term and is to be given its ordinary and customary meaning to those skilled in the art, and is not limited to any special or customized meaning. The term may specifically refer to, but is not limited to, a composite of at least two industrial plants having at least one common industrial purpose. Specifically, a plurality of industrial plants may include at least two, at least five, at least ten, or even more industrial plants that are physically and / or chemically combined. The plurality of industrial plants may be combined such that the industrial plants forming the plurality of industrial plants may share one or more of the value chains, extracts, and / or products. A plurality of industrial plants may also be referred to as a composite, composite site, Verbund, or Verbund site. Furthermore, the value chain production of the plurality of industrial plants through various intermediate products to the final product may be distributed in various locations, such as various industrial plants, or integrated in a Verbund site or chemical park. Such a Verbund site or chemical park may be or may contain one or more industrial plants, where the products produced in at least one industrial plant may serve as feedback to another production plant.

[0065] "Production process" refers to any industrial process that, when used or applied to an input material, provides a chemical product. Thus, a chemical product is provided through a production process by converting an input material, either directly or through one or more derived materials, into a chemical product. Thus, a production process can be any manufacturing or treatment process, or a combination of processes, used to obtain a chemical product. A production process can further include packaging and / or stacking of chemical products. Thus, a production process can be a combination of chemical and physical processes.

[0066] The terms "manufacture," "produce," or "process" are used interchangeably in reference to a production process. These terms can encompass any type of application of an industrial process, including chemical processes, to input materials that results in one or more chemical products.

[0067] In this disclosure, a "chemical product" may refer to any industrial product, such as a chemical product, a pharmaceutical product, a nutritional product, a cosmetic product, or a biological product, or any combination thereof. A chemical product may be composed solely of natural components, or may include at least in part one or more synthetic components. Non-limiting examples of chemical products include organic or inorganic compositions, monomers, polymers, foams, pesticides, herbicides, fertilizers, feed, dietary supplements, precursors, pharmaceuticals, or treatment products, or any one or more of their components or active ingredients. In some cases, the chemical product may further be a product that can be used by an end user or consumer, such as a cosmetic or pharmaceutical composition. The chemical product may further be a product that can be used to make one or more further products, for example, the chemical product may be a synthetic foam that can be used to make shoe soles or a coating that can be used for the exterior of an automobile. The chemical product may be in any form, such as a solid, semi-solid, paste, liquid, emulsion, solution, pellets, granules, beads, particles such as thermoplastic polyurethane ("TPU") particles, or powder.

[0068] Therefore, chemical products may be difficult to trace or track, especially in the production process. During production, materials such as input materials may be mixed with other materials, and / or the input materials may be split into different parts further down the production chain, for example to be processed in different ways. Input materials may be transformed multiple times, for example into one or more derived materials, before being transformed into a chemical product. In some cases, chemical products may be split and packaged into different packages. In some cases, it may be possible to label a packaged chemical product or a portion thereof, but it may be difficult to attach details of the production process responsible for the production of that particular chemical product or portion thereof. In many cases, input materials and / or chemical products may be in a form that is difficult to physically label. Thus, the present teachings may also provide ways in which one or more object identifiers may be used to overcome such limitations.

[0069] A production process may be continuous in a campaign or may be a batch chemical production process, for example, if based on a catalyst that requires recovery. One main difference between these production types is the frequency of occurrence of data generated during production. For example, in a batch process, the production data spans the different batches produced in the run, from the start of the production process to the last batch. In a continuous setting, the data is more continuous, with the possibility of shifts in production operation and / or downtime due to maintenance. The present teachings have been found to be particularly advantageous in batch processes or processes similar to batch processes.

[0070] "Process data" refers to data including values, e.g., numerical or binary signal values, measured, e.g., via one or more sensors, during a production process. The process data may be time series data of one or more of the process parameters and / or equipment operating conditions. Preferably, the process data includes temporal information of the process parameters and / or equipment operating conditions, e.g., the data includes time stamps for at least some of the data points related to the process parameters and / or equipment operating conditions. More preferably, the process data includes spatio-temporal data, i.e., temporal data and data related to one or more equipment zones that are physically located apart such that a location or a spatio-temporal relationship can be derived from the data. The spatio-temporal relationship can be used, for example, to calculate the location of an input material at a given time.

[0071] "Real-time process data" refers to process data that is measured or essentially transient while a particular input material is being processed using a production process. For example, real-time process data of an input material is process data from or at approximately the same time as the processing of the input material using a production process. Here, approximately the same time means that there is little or no time delay. The term "real-time" is understood in the computer and instrumentation arts. As a specific, non-limiting example, the time delay between the occurrence of production when a production process is being performed on an input material and the process data being measured or read is less than 15 seconds, specifically 10 seconds or less, more specifically 5 seconds or less. For high throughput processing, the delay is less than 1 second, or less than a few milliseconds, or even less. Thus, real-time data can be understood as a stream of time-dependent process data or time-series data that is generated during the processing of the input material.

[0072] "Process parameter" may refer to any variable associated with a production process, such as any one or more of temperature, pressure, time, level, etc.

[0073] "Input material" may refer to at least one raw material or raw material used to produce a chemical product. The input material may be any organic or inorganic material, or a combination thereof. Thus, the input material may also be a mixture or may be composed of multiple organic and / or inorganic components in any form. In some cases, the input material may also be an intermediate processing material, for example, from an upstream processing facility or plant.

[0074] Some non-limiting examples of input materials include any one or more of polyether alcohols, polyether diols, polytetrahydrofuran, polyester diols such as those based on adipic acid and butane-1,4-diol, isocyanates, fillers (either organic or inorganic materials) such as wood flour, starch, flax, hemp, ramie, jute, sisal, cotton, cellulose or aramid fibers, silicates, barytes, glass spheres, zeolites, metals or metal oxides, talc, chalk, kaolin, aluminum hydroxide, magnesium hydroxide, aluminum nitrite, aluminum silicate, barium sulfate, calcium carbonate, calcium sulfate, silica, quartz flour, aerosil, clay, mica or wollastonite, iron powder, glass spheres, glass fibers, or carbon fibers.

[0075] As a further non-limiting example, the input material may be methylene diphenyl diisocyanate ("MDI") and / or polytetrahydrofuran ("PTHF"), which undergoes at least a portion of the production process to obtain a thermoplastic polyurethane. Thus, it will be understood that the input material is chemically processed in one or more equipment zones to obtain a thermoplastic polyurethane, which may in some cases be a derived material. The derived material is further processed to obtain a chemical product. For example, a thermoplastic polyurethane ("TPU") may be further processed in one or more additional equipment zones to obtain an expanded thermoplastic polyurethane ("ETPU"). An ETPU may be, for example, a chemical product. However, in some cases, the TPU itself may be a chemical product that is further sent to a downstream customer or facility for further processing.

[0076] "Input material data" refers to data relating to one or more properties or characteristics of an input material. Thus, the input material data may include any one or more of values ​​indicative of a characteristic such as a quantity of the input material. Alternatively or additionally, the value indicative of the quantity may be the fill level and / or mass flow rate of the input material. The value is preferably measured via one or more sensors operably coupled to or included in the device. Alternatively or additionally, the input material data may include sample / test data associated with the input material. Alternatively or additionally, the input material data may include values ​​indicative of any physical and / or chemical properties of the input material such as any one or more of density, concentration, purity, pH, composition, viscosity, temperature, weight, volume, etc. Alternatively or additionally, the input material data may include performance data associated with the input material. In some cases, if the input material is produced in such a way that there is already a pre-generated object identifier associated with the input material, the input material data may include a portion of the data from the pre-generated object identifier. For example, the input data may then include a reference or link to the pre-generated object identifier, or even at least a portion of the process data from the pre-generated object identifier.

[0077] It should be noted that the input materials processed by the processing equipment of the underlying chemical production environment are divided into physical or real-world packages (hereinafter referred to as "packaging objects" (or "physical packages" or "product packages", respectively). The package size of such packaging objects may be fixed, for example by a weight or a quantity of material, or may be determined based on a weight or quantity, such that relatively constant process or equipment operating parameters may be provided by the processing equipment. Such packaging objects may be created from liquid and / or solid input raw materials by supply units.

[0078] Subsequent processing of such package objects is governed by a corresponding data object containing the above-mentioned object identifier, which is assigned to each package object via a computing unit coupled to or even part of the above-mentioned equipment, the data object containing the corresponding object identifier of the underlying package object being stored in an element of the memory storage of the computing unit.

[0079] The data objects may be generated in response to trigger signals provided via the equipment, preferably in response to the output of corresponding sensors located in each of the equipment units. As mentioned above, the underlying industrial plant may comprise different types of sensors, for example, sensors for measuring one or more process parameters and / or equipment operating conditions or parameters associated with the equipment or process units.

[0080] The above-mentioned "object identifier" more particularly refers to a digital identifier of the input material. The object identifier is preferably generated by the computing unit. The provision or generation of the object identifier may be triggered by the equipment or, for example, in response to a triggering event or signal from the equipment. The object identifier is stored in a memory storage element operatively coupled to the computing unit. The memory storage may comprise or be part of at least one database. Thus, the object identifier may further be part of the database. In this way, the object identifier is carried forward or at least referenced with respect to the chemical product produced using the input material. Similarly, a historical upstream object identifier corresponds to a particular historical input material previously processed. It will be appreciated that the object identifier may be provided via any suitable manner, for example, the object identifier may be transmitted, received or generated.

[0081] A "computing unit" may comprise or be a processing means or computer processor, such as a microprocessor, microcontroller, etc., having one or more processing cores. In some cases, the computing unit may be at least partially part of the equipment, e.g., a process controller, such as a programmable logic controller ("PLC") or a distributed control system ("DCS"), and / or at least partially part of a remote server. Thus, the computing unit may receive one or more input signals from one or more sensors operably connected to the equipment. If the computing unit is not part of the equipment, the computing unit may receive one or more input signals from the equipment. Alternatively or additionally, the computing unit may control one or more actuators or switches operably coupled to the equipment. The one or more actuators or switches may further , machine It can be part of the vessel.

[0082] Thus, the computing unit may be capable of manipulating one or more parameters related to the production process, for example via manipulating one or more of the equipment operating conditions, by controlling any one or more of the actuators or switches and / or end effector units, the control being preferably in response to one or more signals obtained from the equipment.

[0083] "Memory storage" may refer to a device for storing information in the form of data on a suitable storage medium. Preferably, the memory storage is a digital storage suitable for storing information in a machine-readable digital form, e.g., digital data readable via a computer processor. Thus, the memory storage may be realized as a digital memory storage device readable by a computer processor. More preferably, the memory storage on the digital memory storage device may also be operated via a computer processor. For example, any part of the data recorded on the memory storage digital device may be written and / or erased and / or overwritten with new data, partially or entirely, by the computer processor.

[0084] In this context, an "end effector unit" or "end effector" refers to a device that is part of an instrument and / or operably connected to the instrument and thus controllable via the instrument and / or computing unit for the purpose of interacting with the environment surrounding the instrument. As some non-limiting examples, an end effector may be a cutter, a gripper, a sprayer, a mixing unit, an extruder tip, etc., or corresponding parts thereof designed to interact with the environment (e.g., input materials and / or chemicals).

[0085] The one or more "characteristics" with respect to an input material may refer to any one or more of the quantity, batch information, values ​​specifying the quality of the input material, such as purity, concentration, or any property of the input material.

[0086] An "interface" may be a hardware and / or software component that is at least partially part of the device or part of another computing unit (e.g., a computing unit through which an object identifier is provided). For example, the interface may be an application programming interface ("API"). In some cases, the interface may also connect to at least one network, for example, to interface two hardware components and / or protocol layers within the network. For example, the interface may be an interface between the device and a computing unit. In some cases, the device may be communicatively coupled to a computing unit via a network. Thus, the interface may further be or comprise a network interface. In some cases, the interface may further be or comprise a connection interface.

[0087] "Network interface" refers to a device or group of one or more hardware and / or software components that enables an operative connection to a network.

[0088] What is a "Connection Interface"? , faith Number or data Transfer or exchange ofIt refers to a software and / or hardware interface for establishing communication. The communication may be wired or wireless. The connection interface is preferably based on or supports one or more communication protocols. The communication protocol may be a wireless protocol, e.g. a short-range communication protocol such as Bluetooth, WiFi, or a long-range communication protocol such as a cellular or mobile network, e.g. a second generation cellular network ("2G"), 3G, 4G, Long Term Evolution ("LTE"), or 5G. Alternatively or additionally, the connection interface may further be based on a proprietary short-range or long-range protocol. The connection interface may support any one or more standard and / or proprietary protocols. The connection interface and the network interface may be the same unit or different units.

[0089] A "network" as discussed herein may be any suitable type of data transmission medium, wired, wireless, or a combination thereof. A particular type of network does not limit the scope or generality of the present teachings. Thus, a network may refer to any suitable interconnection between at least one communication endpoint and another communication endpoint. A network may include one or more distribution points, routers, or other types of communication hardware. Network interconnections may be formed by physical wired wiring, optical and / or wireless radio frequency methods. A network may specifically be or include a physical network made entirely or partially by wiring, such as a fiber optic network, or a network made entirely or partially by conductive cables, or a combination thereof. A network may include, at least in part, the Internet.

[0090] As discussed, at least a portion of the subset of process data is attached to an object identifier. In this manner, a snapshot of real-time process data associated with processing an input material at an equipment is made available or linked to an upstream object identifier.

[0091] A "performance parameter" may be or be indicative of any one or more characteristics of a chemical product. Thus, a performance parameter is a parameter that should satisfy one or more predefined criteria that indicate the suitability or degree of suitability of a chemical product for a particular application or use. It will be understood that in some cases, a performance parameter may indicate the lack of suitability or degree of non-suitability of a chemical product for a particular application or use. As non-limiting examples, a performance parameter may be any one or more of strength, such as tensile strength, color, concentration, composition, viscosity, stiffness, such as Young's modulus value, purity or impurity, such as parts per million ("ppm") value, failure rate, such as mean time to failure ("MTTF"), or any one or more values ​​or value ranges (e.g., determined via testing with predefined criteria). Thus, a performance parameter is representative of the performance or quality of a chemical product. The predefined criteria may be, for example, one or more reference values ​​or ranges to which the performance parameter of a chemical product is compared to determine the quality or performance of the chemical product. The predefined criteria may be determined using one or more tests, thereby defining requirements for performance parameters for a chemical product to be suitable for one or more particular uses or applications.

[0092] As non-limiting examples, the performance parameters may be any one or more of strength, such as tensile strength, hardness, such as Shore hardness, density, such as bulk density, color, concentration, composition, age, such as melt viscosity, melt flow value ("MFV"), stiffness, such as Young's modulus value, purity or impurity, such as parts per million ("ppm") value, failure rate, such as mean time to failure ("MTTF"), or any one or more values ​​or value ranges (e.g., determined via testing using predefined criteria). Thus, the performance parameters are representative of the performance or quality of the chemical product. The predefined criteria may be, for example, one or more reference values ​​or ranges against which the performance parameters of the chemical product are compared to determine the quality or performance of the chemical product. The predefined criteria may be determined using one or more tests, such as clinical trials, reliability tests, or wear tests, thereby defining requirements for the performance parameters for the chemical product to be suitable for one or more specific uses or applications. In some cases, the performance parameters may be related to or measured from properties of the derived material.

[0093] One or more of the performance parameters may further be zone-specific. It will be understood that "zone-specific" here refers to belonging to a particular equipment zone (e.g., an upstream equipment zone). Thus, at least one of the zone-specific performance parameters may be calculated for the upstream equipment zone, and / or one or more of them may be calculated by the computing unit for one or more equipment zones downstream of the upstream equipment zone. Thus, the predicted parameters may be directly related to the chemical product and / or related to one or more derived materials that are converted into the chemical product.

[0094] Typically, the performance parameters are determined from one or more samples of the chemical product and / or derived materials taken during and / or after production. The samples may be taken to a laboratory and analyzed to determine the performance parameters. It will be appreciated that the entire activity of taking, processing or testing samples and analyzing the test results may take significant time and resources. As such, there may be a significant delay between taking the sample and making adjustments to the input materials and / or process parameters and / or equipment operating conditions. This delay or lag may result in the production of chemical products with suboptimal or reduced quality or, in the worst case, production may have to be stopped until samples are analyzed and corrective action taken by adjusting the input materials and / or process parameters and / or equipment operating conditions.

[0095] The proposed computing of at least one performance parameter may be used as a solution to at least mitigate the effects of time lags in sampling techniques for adjusting input materials and / or process parameters and / or equipment operating conditions.

[0096] According to one aspect, the calculation of at least one of the performance parameters is made using a model that is at least in part an analytical computer model. Additionally or alternatively, the model may be at least in part one or more machine learning ("ML") models. The ML model may be trained using historical data, for example, using historical data from one or more historical upstream object identifiers. Thus, the term ML model is understood in this disclosure to refer to a model that is at least in part one or more machine learning ("ML") models.

[0097] More specifically, in the context of the present teachings, an ML model may be or may include a predictive model that, when trained using historical data, may result in a data-driven model. A "data-driven model" refers to a model that is derived at least in part from data, in this case from historical data. In contrast to a strict model derived purely using physicochemical laws, a data-driven model may allow describing relationships that cannot be modeled by physicochemical laws. Using a data-driven model may allow describing relationships without solving equations based on physicochemical laws. This may reduce computational power and / or improve speed.

[0098] The data-driven model may be a regression model. The data-driven model may be a mathematical model. The mathematical model may describe the relationship between the provided input and the output as a function. For example, when the subset is provided as an input to the ML model, the model calculates at least one of the performance parameters as an output by applying the function.

[0099] Thus, a data-driven model, preferably a data-driven machine learning ("ML") model, or simply a data-driven model, as used herein, refers to a trained mathematical model that is parameterized according to a corresponding training data set, such as historical process data, to reflect a kinetic or physicochemical process associated with the corresponding production process. An untrained mathematical model refers to a model that does not reflect a kinetic or physicochemical process, e.g., an untrained mathematical model is not derived from physical laws that provide scientific generalizations based on empirical observations. Thus, kinetic or physicochemical properties may not be inherent to an untrained mathematical model. An untrained model does not reflect such properties. Feature engineering and training with a corresponding training data set allows the parameterization of the untrained mathematical model. The result of such training is simply a data-driven model, preferably a data-driven ML model, that reflects a kinetic or physicochemical process associated with the production process as a result of the training process, preferably as a result of the training process only.

[0100] The model may also be a hybrid model. A hybrid model may refer to a model that includes a first-principles part, an analytical model or so-called white box, and a data-driven part, so-called black box, as described above. The model may include a combination of a white box model and a black box model and / or a grey box model. The white box model may be based on physicochemical laws. The physicochemical laws may be derived from first principles. The physicochemical laws may include one or more of chemical kinetics, conservation of mass, momentum and energy, and particle populations of any dimension. The white box model may be selected according to the physicochemical laws governing the corresponding production process or part thereof. The black box model may be based on historical data, for example from one or more historical object identifiers. The black box model may be constructed using one or more of machine learning, deep learning, neural networks, or other forms of artificial intelligence. The black box model may be any model with a good fit between the training data set and the test data. The grey box model is a model that combines partial theoretical structures and data to complete the model.

[0101] As used herein, the term "machine learning" or "ML" may refer to statistical techniques that allow a machine to "learn" a task from data without being explicitly programmed. Machine learning techniques may include "traditional machine learning" (workflows in which features are selected manually and a model is trained). Examples of traditional machine learning techniques include decision trees, support vector machines, and ensemble learning. In some examples, the data-driven model may include a data-driven deep learning model. Deep learning is a subset of machine learning that loosely models the neural circuits of the human brain. Deep layers refer to multiple layers between the input layer and the output layer. In deep learning, algorithms automatically learn what features are useful. Examples of deep learning techniques include convolutional neural networks ("CNNs"), recurrent neural networks such as long short-term memory ("LSTM"), and deep Q-networks.

[0102] In this disclosure, the terms "ML model" and "trained ML model" may be used interchangeably, although it will be apparent or obvious to one of ordinary skill in the art what type of data a particular ML model has been trained with to enable it to perform its intended function.

[0103] As discussed, chemical production can be a data-rich environment generating a large amount of data from various sources such as equipment. It will also be appreciated that the proposed teachings make the implementation of a quality control method or system more efficient and suitable for edge computing in industrial plants, particularly chemical plants. Thus, monitoring such as safety and / or quality control can be achieved essentially on-the-fly, while reducing computational resources such as processing power and / or memory requirements, since the object identifiers provide a highly targeted data set of relevant data for the calculation of performance parameters. Also, the computational latency can be reduced, which may allow sufficient time for computationally intensive algorithms without slowing down the production process. Also, the training process of ML models can be made faster and more efficient.

[0104] For similar reasons, the data sets can be compact and efficient, making the present teachings suitable for cloud computing environments or infrastructures: many cloud service providers operate on a pay-per-use model based on utilization of computing resources, thus reducing costs and / or making efficient use of computing power.

[0105] Thus, according to one embodiment, a model, at least in part at least one ML model, may be trained using data from one or more historical object identifiers from one or more equipment zones. The data used to train the ML model may also include historical and / or current laboratory test data, or data from past and / or recent samples of chemical products and / or derived materials. For example, quality data from one or more analyses, such as image analysis, inspection equipment, or other measurement techniques, may be used.

[0106] In this manner, at least one ML model trained with data from the historical object identifiers may be used to predict one or more of the performance parameters associated with the chemical product, thereby eliminating at least a portion of the manual sampling and testing requirements, thereby saving time and resources.

[0107] Thus, an ML model trained using historical data to calculate at least one zone-specific performance parameter may receive as input at least a portion of the subset of real-time process data and, optionally, input material data, such that the ML model may provide at least one of the performance parameters as a calculated value, such that such an ML model may be used to monitor the production process and provide early attention to any quality control issues.

[0108] In some cases, the same model or another model, such as an ML model, may be used by the computing unit to determine which portions or components of the real-time process data have the most dominant impact on the chemical product. Thus, the computing unit is enabled to filter out process parameters and / or equipment operating conditions that have a negligible impact on at least one zone-specific performance parameter. Thus, the subset of process data may be further optimized in terms of computational resources. Thus, the relevance of real-time process data attached to a particular chemical product may be improved in terms of their corresponding object identifiers.

[0109] According to one aspect, the equipment comprises multiple equipment zones that are physically separated such that an input material progresses from an upstream equipment zone to a downstream equipment zone during a manufacturing or production process. In some cases, the input material may be split or reduced (e.g., reduced in volume) before reaching the downstream equipment zone. Thus, according to a further aspect, a downstream object identifier is provided to at least a portion of the input material in the downstream equipment zone. It will also be understood that in some cases, at least a portion of the input material may be referred to as a derived material. As discussed, the zone presence signal may be used to detect or calculate when the input material or derived material is in the downstream equipment zone, such that the computing unit may determine another subset of real-time process data based on the downstream object identifier and the zone presence signal. Thus, the computing unit may calculate another at least one performance parameter of the chemical product associated with the downstream identifier based on another subset of real-time process data and another historical data, for example including data from one or more historical downstream object identifiers associated with previously processed input materials in the downstream equipment zone. Any one, some, or each of the historical downstream object identifiers may be added to at least a portion of the process data indicative of the process parameters and / or equipment operating conditions under which a previously processed input material was processed, for example in a downstream equipment zone. In this manner, the downstream object identifier may be associated with at least one other zone-specific performance parameter. To It can be added.

[0110] Similar to what was discussed above with respect to the equipment, a model such as an ML model may also be applied to any one or more of the equipment zones.

[0111] Those skilled in the art will appreciate that the term "appending" or "to append" may mean to include or to attach different data elements, such as metadata, in adjacent or different locations of a database or memory storage, for example in the same database or in the same memory storage element. The term may further mean to link one or more data elements, packages, or streams in the same or different locations in such a way that the data packages or streams can be read and / or fetched and / or combined when needed. At least one of the locations may be part of a remote server or even at least partially part of a cloud service.

[0112] A "remote server" refers to one or more computers or one or more computer servers that are located away from the plant. Thus, a remote server may be located several kilometers or more away from the plant. A remote server may also be located in a different country. A remote server may also be implemented, at least in part, as a cloud service or platform, for example, as a platform as a service ("PaaS"). The term may also refer collectively to multiple computers or servers located in different locations. A remote server may be a data management system.

[0113] It will be appreciated that in some cases, the input material after passing through an initial equipment zone, e.g., an upstream equipment zone, may have substantially different properties than when the input material entered the upstream equipment zone. Thus, at the time the material enters a subsequent zone, such as a downstream zone, the input material may have been transformed into a derived material or intermediate processing material. However, for simplicity and without loss of generality of the present teachings, the term input material may be used in this disclosure to refer to an input material during a production process when it is transformed into such intermediate processing or derived material. For example, a batch of input material in the form of a mixture of chemical components may have passed through an upstream zone on a conveyor belt where the batch is heated to induce a chemical reaction. As a result, when the input material enters a downstream zone immediately after leaving the upstream zone, or after having traveled through other zones as well, the material may be a derived material with different properties than the input material. However, as described above, such derived material may still be referred to as an input material, at least because the relationship between such intermediate processing material and the input material is defined and determined through the production process. Moreover, in other cases, the input material may still maintain essentially similar properties even after passing through the upstream zone or even other zones, for example, if the upstream zone simply dries the input material or filters it to remove trace amounts of unwanted materials. Thus, one skilled in the art will appreciate that the input material in the intermediate zone may or may not be converted into derived materials.

[0114] In some cases, there may be one or more intermediate zones between the upstream and downstream zones, but such zones are not provided with separate object identifiers. The applicant has found it advantageous to generate downstream object identifiers when the input or derived material is combined with other materials, or when the input or derived material is split or fragmented into multiple parts. Alternatively, more generally, after providing the object identifier, the generation of the downstream object identifier or any further object identifiers may only be in zones where the mass flow rate of the material changes. The change in mass flow rate may be a change in mass that is the result of the addition or mixing of new material to the input or derived material, and / or the removal or splitting of material from the input or derived material or intermediate processing material. For example, a change in mass due to the removal of moisture or due to the release of gas resulting from a chemical reaction during production may in some cases be excluded from the generation that triggers the second or further object identifier. In particular in zones where there is no substantial change in the mass of the input material, further object identifiers may not be provided. It is not essential to specify a range of "substantial change" in mass here, as one of ordinary skill in the art will appreciate that this may depend, among other factors, on the type of input material and / or chemical product being produced. For example, in some cases a change in mass of 20% or more may be considered substantial, while in other cases the percentage value may be 5% or more, or even 1% or more, or even less. For example, in the case of a valuable product, a smaller change may be considered significant compared to other less valuable products.

[0115] As some examples, the decision to provide or generate an object identifier in an equipment zone, for example an equipment zone after an upstream equipment zone, may be based on any of: not providing a new object identifier if the degree of backmixing in the equipment zone is less than or approximately the same as the size of a package in the zone preceding said equipment zone; providing a new object identifier if the degree of backmixing in the equipment zone is greater than the size of a package in the zone preceding said equipment zone; not providing a new object identifier in an equipment zone that is merely a transport zone including one or more transport systems or elements; providing a new object identifier for one or more components if the equipment zone includes separation of materials in said zone and one or more components are separated components of the material; and providing at least one new object identifier in an equipment zone if the equipment zone includes filling or packaging of materials into at least one package and each package includes one or more chemical products.

[0116] As discussed, when samples of input materials, derived materials, or chemical products are taken for analysis, such samples may also be provided with a sample object identifier. The sample object identifier may in principle be similar to the object identifiers discussed in this disclosure, and thus be appended with associated corresponding process data as discussed. Thus, the sample may also be digitally appended with an accurate snapshot of the production process related to the characteristics of said sample. This may further improve analysis and quality control. Furthermore, the production process may be synergistically improved, for example, based on improved training of one or more ML models.

[0117] According to another aspect, if the production process includes that the input material is physically transported, flowed or moved within or between zones using a transport element such as, for example, a conveyor system, the real-time process data may also include data indicative of the speed of the transport element and / or the speed at which the input material is transported during the production process. The speed may be provided directly via one or more of the sensors and / or may be calculated via a computing unit based on the time of movement type measurements, for example, via the real-time process data, using, for example, the time of entering and leaving the zone or the time of entering another zone following the zone. In this way, the object identifier may be further enriched with aspects of the processing time in the zone, in particular those that may be affecting one or more performance parameters of the chemical product. Furthermore, by using time stamps of entry and exit or entry into a subsequent zone, speed measurement sensors or devices for the transport element may not be necessary.

[0118] According to another aspect, each object identifier includes a unique identifier, preferably a globally unique identifier ("GUID"). By attaching a GUID to each virtual package of a chemical product, at least chemical product tracking may be enhanced. Through the GUID, data management of process data, such as time series data, may also be reduced, allowing direct correlation of virtual / physical packages, production history, and quality control history.

[0119] As discussed with respect to the ML model, according to one embodiment, the ML model may be trained using data from the object identifier, and the training data may also include past and / or current laboratory test data, or data from past and / or recent samples of derived materials and / or chemical products.

[0120] In addition to the advantages of previous ML models discussed above, having a trained model based on zones in a production line may allow for more detailed tracking of materials and their respective performance parameters, or even prediction of performance parameters of chemical products.

[0121] In some production scenarios, such as batch production, such models can be used on the fly to signal quality control issues not only for the produced chemical products, but also for derived materials.

[0122] Thus, any or each of the equipment zones may be monitored and / or controlled via an individual ML model, which is trained based on data from a corresponding object identifier from that zone.

[0123] According to one aspect, the provision of a corresponding object identifier for a zone may occur or be triggered in response to any one or more of the values ​​indicative of the characteristics of the input material and / or any one or more of the values ​​from the equipment operating conditions and / or any one or more of the values ​​of the process parameters reaching, meeting or exceeding a predefined threshold. Such values ​​may be measured via one or more sensors and / or switches. For example, the predefined threshold may be associated with a weight value of the input material introduced at the equipment. Thus, a trigger signal may be generated when a quantity, such as a weight of the input material accepted at the equipment, reaches a predefined quantity threshold, such as a weight threshold. Particular examples of events or occurrences that trigger the provision of an object identifier are also discussed above in this disclosure. An object identifier may be provided in response to a trigger signal or directly in response to the quantity or weight reaching a predefined weight threshold. The trigger signal may be a separate signal or may simply be an event, such as a particular signal meeting a predefined criterion, such as a threshold detected via a computing unit and / or equipment. Thus, it will be appreciated that an object identifier may also be provided in response to the quantity of the input material reaching a predefined quantity threshold. The quantity may be measured as weight, as described in the examples above, and / or may be any one or more other values, such as level, fill level, or volume, and / or by summing the mass flow rates of the input materials or by applying an integral to the mass flow rates of the input materials.

[0124] Thus, for example, the upstream object identifier may be provided in response to a triggering event or signal, said event or signal being preferably provided via the equipment or an upstream equipment zone. This may be done in response to the output of any one or more sensors and / or switches operably coupled to the upstream equipment. The triggering event or signal may be related to a quantity value of the input material, for example the occurrence of the quantity value reaching or meeting a predefined quantity threshold. Said occurrence may be detected via the computing unit and / or the upstream equipment, for example using one or more weight sensors, level sensors, fill sensors or any suitable sensors capable of measuring or detecting the quantity of the input material.

[0125] An advantage of using quantities as a trigger for providing object identifiers, as described in the present teachings, may be that any change in the quantity of a material during the production process may be used as a trigger for providing one or more further object identifiers. The applicant has recognized that in an industrial environment for processing or producing one or more chemical products, it may provide an optimal way to partition the generation of different object identifiers so that the input materials, any derived materials, and finally the chemical products can be tracked while taking into account the quantities or mass flows, essentially throughout the entire production chain and at least in some cases beyond. By providing object identifiers only at points where the amount or quantity of material changes (e.g. when new materials are introduced or input) or where materials are split, the number of object identifiers may be minimized while preserving the traceability of materials not only at the end of production but also during production. In equipment or production zones where new materials are not added or materials are not split, knowledge of the process in such zones may be used to maintain the observability in two adjacent object identifiers.

[0126] Viewed from another perspective, there may also be provided a use of output data generated in any one of the methods disclosed herein for monitoring and / or controlling and / or optimizing and / or improving a production process of an industrial plant.

[0127] Viewed from another perspective, there may also be provided a system for monitoring and / or controlling a production process, the system being configured to perform any of the methods disclosed herein. Alternatively, there may also be provided a system for monitoring and / or controlling a production process for manufacturing a chemical product in an industrial plant by processing at least one input material through at least one piece of equipment, the system comprising a computing unit, the system being configured to perform any of the methods disclosed herein.

[0128] A system for monitoring and / or controlling a production process for producing a chemical product in an industrial plant, for example by processing at least one input material through at least one device, the system comprising a computing unit, the system comprising: - Receive real-time process data from the equipment via the interface, - determining, via the computing unit, a subset of real-time process data, the subset of real-time process data indicative of process parameters and / or equipment operating conditions as the input material is processed; - Providing a subset of the real-time process data as output data via the output interface A system may be provided that is configured to:

[0129] Viewed from another perspective, a computer program may also be provided that includes instructions that, when executed by a suitable computing unit, cause the computing unit to perform any of the methods disclosed herein. A non-transitory computer readable medium may also be provided that stores a program that causes a suitable computing unit to perform any of the method steps disclosed herein.

[0130] For example, a computer program, or a non-transitory computer readable medium storing a program, comprising instructions that, when executed by a suitable computing unit operatively coupled to at least one piece of equipment for producing a chemical product in an industrial plant by processing at least one input material using a production process, cause the computing unit to perform any of the methods disclosed herein, the instructions causing the computing unit to: - Receive real-time process data from the device via the interface, - determining a subset of real-time process data, the subset of real-time process data indicative of process parameters and / or equipment operating conditions as the input material is processed; - Provide a subset of the real-time process data as output data via the output interface A computer program, or a non-transitory computer readable medium storing the program, may be provided.

[0131] The computer-readable data medium or carrier includes any suitable data storage device having stored thereon one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. Also, the instructions may reside, completely or at least partially, within the main memory and / or within the processor during execution thereof by the computing unit, the main memory, and the processing device, which may constitute computer-readable storage media. The instructions may further be transmitted or received over a network via a network interface device.

[0132] A computer program for implementing one or more of the embodiments described herein may be stored on and / or distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but also in other forms, such as via the Internet or other wired or wireless telecommunications systems, however, the computer program may also be presented via a network, such as the World Wide Web, and downloaded from such a network into the working memory of a data processor.

[0133] Furthermore, a data carrier or data storage medium for making a computer program product available for download may also be provided, the computer program product being configured to perform a method according to any of the aspects disclosed herein.

[0134] Viewed from another perspective, there may also be provided a computing unit including computer program code for performing the methods disclosed herein, and a computing unit operably coupled to a memory storage including computer program code for performing the methods disclosed herein.

[0135] It should be apparent to one of ordinary skill in the art that two or more components are "operably" coupled or connected. Without limitation, this means that there may at least be a communication connection between the coupled or connected components, for example, via an interface or other suitable interface. The communication connection may be fixed or removable. Furthermore, the communication connection may be one-way or two-way. Furthermore, the communication connection may be wired and / or wireless. In some cases, the communication connection may also be used to provide control signals.

[0136] As used herein, a "parameter" refers to any physical or chemical property and / or its measurement, such as temperature, direction, position, quantity, density, weight, color, moisture, speed, acceleration, rate of change, pressure, force, distance, pH, concentration, and composition. A parameter may also refer to the presence or absence of a particular property.

[0137] "Actuator" refers to any component that serves, directly or indirectly, to move or control a mechanism associated with equipment such as a machine. An actuator may be a valve, a motor, a drive, etc. An actuator may be operable either electrically, hydraulically, pneumatically, or a combination thereof.

[0138] A "computer processor" refers to any logic circuitry configured to perform basic operations of a computer or system, and / or generally, a device configured to perform calculations or logical operations. In particular, the processing means or computer processor may be configured to process basic instructions that run the computer or system. By way of example, the processing means or computer processor may include at least one arithmetic logic unit ("ALU"), at least one floating point unit ("FPU"), such as a numeric coprocessor or numeric coprocessor, a number of registers, particularly registers configured for providing operands to the ALU and storing operation results, and memory, such as L1 and L2 cache memories. In particular, the processing means or computer processor may be a multi-core processor. In particular, the processing means or computer processor may be or comprise a central processing unit ("CPU"). The processing means or computer processor may be a Complex Instruction Set Computing ("CISC") microprocessor, a Reduced Instruction Set Computing ("RISC") microprocessor, a Very Long Instruction Word ("VLIW") microprocessor, or a processor implementing other instruction sets, or a processor 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, etc. The methods, systems, and devices described herein may be implemented as software in a DSP, a microcontroller, or any other side processor, or as hardware circuitry in an ASIC, a CPLD, or an FPGA. It should be understood that the term processing means or 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.

[0139] A "computer-readable data medium" or carrier includes any suitable data storage device or computer-readable memory having stored thereon one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. Also, the instructions may reside, completely or at least partially, within the main memory and / or within the processor during execution thereof by the computing unit, the main memory, and the processing device, which may constitute a computer-readable storage medium. The instructions may further be transmitted or received over a network via a network interface device.

[0140] Brief description of some figures of the drawing Certain aspects of the present teachings will now be described by way of example with reference to the following figures which illustrate said aspects. The drawings may not be to scale as the generality of the present teachings does not depend on them. Certain features shown in the drawings may be logical features shown together with physical features for the sake of understanding and without affecting the generality of the present teachings. [Brief description of the drawings]

[0141] [Figure 1] 1 illustrates certain aspects of a system according to the present teachings. [Diagram 2] 1 illustrates a method embodiment according to the present teachings. [Diagram 3] A first embodiment of a system and corresponding method according to the present teachings is illustrated in a composite block / flow diagram. [Figure 4] A second embodiment of a system and corresponding method according to the present teachings is illustrated in a combined block / flow diagram. [Diagram 5] A third embodiment of a system and corresponding method according to the present teachings is illustrated in a combined block / flow diagram. [Figure 6] 1 illustrates a first embodiment of a graph-based database configuration representing a topological structure of an industrial plant or cluster of plants that includes multiple equipment devices and corresponding multiple equipment zones through which input materials progress during a manufacturing or production process. [Figure 7] 7 illustrates a second embodiment of the graph-based database configuration shown in FIG. 6. [Figure 8] Another embodiment of a system and corresponding method according to the present teachings is illustrated in a composite block / flow diagram using a cloud computing platform, in which a machine learning (ML) process is implemented. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0142] Detailed Description FIG. 1 illustrates an example of a system 168 for monitoring and / or controlling a production process for manufacturing a chemical product 170 in an industrial plant. At least some method aspects should also be understood from the following discussion. The industrial plant includes at least one equipment or multiple equipment zones for manufacturing or producing a chemical product 170 using a production process. The chemical product 170 can be in any form, such as a pharmaceutical product, a foam, a dietary supplement, an agricultural product, or a precursor. For example, the chemical product 170 can be a thermoplastic polyurethane in granular form. The chemical product 170 can also be in batches, such as packages of 10 kg each. Due to the nature of such chemical products, it can be difficult to track the chemical products in the production chain. However, it can be important to ensure that each component, such as each unit or package, or even parts inside, has a consistent desired characteristic or quality. At least some aspects of the present teachings can make this possible.

[0143] In FIG. 1, the equipment is shown with an equipment zone, for example as a hopper or mixing pot 104 that may be part of an upstream equipment zone. The mixing pot 104 receives an input material, which may be a single material or may include multiple components, for example, methylene diphenyl diisocyanate ("MDI") and / or polytetrahydrofuran ("PTHF"). Here, the input material is shown to be received in two portions and fed to the mixing pot 104 via a first valve 112a and a second valve 112b, respectively. The first valve 112a and the second valve 112b may also belong to an upstream equipment zone.

[0144] Optionally, an object identifier, or in this case an upstream object identifier 122, is provided for the input material 114. The upstream object identifier 122 may be a unique identifier, preferably a globally unique identifier ("GUID"), that is distinguishable from other object identifiers. The GUID may be provided according to the specifications of the particular industrial plant, and / or the chemical product 170 being produced, and / or the time and date specifications, and / or the particular input material being used. The upstream object identifier 122 is shown to be provided in a memory storage 128. The memory storage 128 is operably coupled to the computing unit 124. The memory storage 128 may further be part of the computing unit 124. The memory storage 128 and / or the computing unit 124 may be at least partially part of a cloud service. Optionally, the memory storage 128 may further be a data store, and the memory storage 128 may provide the output data to the data store.

[0145] The computing unit 124 is operably coupled to the upstream equipment zone or to equipment belonging to the upstream equipment zone via a network 138, which may be, for example, any suitable type of data transmission medium. The computing unit 124 may further be part of the equipment in the plant, for example, at least partially part of the upstream equipment zone. The computing unit 124 may further be, at least partially, a plant control system, such as a DCS and / or a PLC. The computing unit 124 may receive one or more signals from one or more sensors operably coupled to the equipment of the upstream equipment zone. For example, the computing unit 124 may receive one or more signals from the fill sensor 144 and / or one or more sensors associated with the transport elements 102a-b, which are also part of the upstream equipment zone. The computing unit 124 may further at least partially control the upstream equipment zone or some parts thereof. For example, the computing unit 124 may control the valves 112a,b (e.g., via corresponding actuators) and / or the heater 118 and / or the transport elements 102a-b. The transport elements 102a, b etc. in the example of FIG. 1 are shown as conveyor systems which may comprise one or more motors and a belt driven via said motors to move such that input material 114 is transported via the belt in the direction of belt movement 120.

[0146] Other types of transport elements may be used instead of or in combination with the conveyor system without affecting the scope or generality of the present teachings. In some cases, any type of equipment with a flow of material, e.g., one or more materials entering and one or more materials leaving, may be referred to as a transport element. Thus, in addition to conveyor systems, belts, conduits, or rails, equipment such as extruders, pelletizers, heat exchangers, buffer silos, silos with mixers, mixers, mixing vessels, cutting mills, double cone blenders, curing tubes, columns, separators, extractors, thin film evaporators, filters, sieves, etc. may also be referred to as transport elements. Thus, it will be understood that the presence of a transport system as a conveyor system may be optional, at least in some cases, since materials may move directly from one equipment to another equipment via mass flow or may move as a normal flow from one equipment to another equipment. For example, materials may move directly from a heat exchanger to a separator, or even further, such as to a column or beyond. Thus, in some cases, one or more transport elements or systems may be internal to the device.

[0147] The object identifier, here denoted as upstream object identifier 122, may be provided in response to a trigger signal or event, which may be a signal or event related to a quantity of the input material. For example, the fill sensor 144 may be used to detect at least one quantity value, such as a fill level and / or a weight of the input material. When the quantity reaches a predefined threshold, the computing unit 124 may automatically provide the first upstream object identifier 122 in the memory storage 128. The upstream object identifier 122 includes data related to the input material, i.e., input material data. The input material data is indicative of one or more characteristics of the input material.

[0148] In some cases, the computing unit 124 may receive process data from all equipment or equipment zones in the industrial plant via an input interface. The computing unit 124 may determine a subset of the real-time process data based on, for example, a zone presence signal and / or an upstream object identifier. The subset of the real-time process data may be provided as output data via an output interface. The output data may be provided to a data store. In some cases, the data store may be at least partially the same system as the memory storage 128. Alternatively, the data store may be provided with the output data via the memory storage 128.

[0149] The trigger signal or event may also be used to generate a zone presence signal for an upstream equipment zone. The zone presence signal may then be used to determine not only process parameters and / or equipment operating conditions associated with the processing of the input material 114 in the upstream equipment zone, but also the temporal aspects of such process parameters and / or equipment operating conditions that are included in the real-time process data.

[0150] Optionally, the computing unit 124 may calculate at least one performance parameter related to the chemical product 170 associated with the upstream object identifier 122. The calculation is based on the subset 126 of real-time process data, which in this case is shown to be optionally attached to the upstream object identifier 122. The calculation is also based on historical data, which may include data from one or more historical upstream object identifiers. Each historical upstream object identifier is associated with a corresponding input material that was previously processed in an upstream equipment zone, etc. The at least one historical upstream object identifier may be attached to at least a portion of the process data indicative of process parameters and / or equipment operating conditions under which the previously processed input material was processed in a downstream equipment zone, etc. The at least one performance parameter may be attached to the upstream object identifier 122 and / or the at least one performance parameter is included in the output data.

[0151] At least one zone-specific performance parameter is shown to be added, for example as metadata, to the upstream object identifier 122, thereby enriching the upstream object identifier 122 with performance parameters associated with the quality of the chemical product 170. In this way, the quality control process may be simplified and improved, for example by combining the resulting chemical product 170 with quality-related data, while improving traceability.

[0152] The subset 126 of real-time process data from the upstream equipment zone may be data within a time window when the input material 114 was in the upstream equipment zone, or the time window may be shorter, only during the time when the input material 114 was processed through the mixing pot 104. The real-time process data may be used to determine the time window. Thus, the upstream object identifier 122 may be enriched with relevant data by using the time dimension of the real-time process data. Thus, the object identifier may not only be used to track materials in the production process, but may also encapsulate high-quality data that may make edge computing and / or cloud computing more effective. The object identifier data may be highly suitable for fast training and retraining of machine learning models. Also, data encapsulated in the object identifier may be more compact than traditional datasets, simplifying data integration. Thus, the output data may also be relevant data suitable for streaming and ingestion, such as for data analysis in one or more applications or devices. The output data may be provided to, for example, an HMI system. The HMI system may include at least one display device and / or at least one audio device and / or at least one mixed reality device.

[0153] The subset of real-time process data 126 is indicative of process parameters and / or equipment operating conditions, i.e., operating conditions of the mixing pot 104 and valves 112a-b, as the input material is processed in the upstream equipment zones, such as any one or more of the following: inlet mass flow rate, outlet mass flow rate, fill level, temperature, moisture, timestamps, i.e., time in, time out, etc. The equipment operating conditions in this case may be control signals and / or setpoints of the valves 112a,b and / or the mixing pot 104. The subset of real-time process data 126 may be or may include time series data, i.e., time-dependent signals that may be obtained via one or more sensors (e.g., the output of the fill sensor 144). The time series data may include continuous signals or any of the time series data may be intermittent signals at regular or irregular time intervals. The subset of real-time process data 126 may further include one or more timestamps from the mixing pot 104, such as time in and / or time out. Thus, a particular input material 114 may be associated with a subset 126 of real-time process data related to that input material 114 via an upstream object identifier 122. The upstream object identifier 122 may be appended to other object identifiers downstream in the production process such that particular process data and / or equipment operating conditions may be associated with a particular chemical product. Other important advantages have been discussed elsewhere in this disclosure (e.g., in the Overview section).

[0154] The conveyor system comprising the transport elements 102a,b and associated belts may be considered an intermediate equipment zone downstream of the upstream equipment zone. The intermediate equipment zone in this example comprises a heater 118 used to apply heat to the input material moving on the belt. The conveyor system may further comprise any one or more sensors, such as speed sensors, weight sensors, temperature sensors, or any other type of sensor, for measuring or detecting process parameters and / or characteristics of the input material 114 in the intermediate equipment zone. The output of any or all of the sensors may be provided to a computing unit 124.

[0155] As the input material 114 travels along the movement direction 120, heat is applied via the heater 118. The heater 118 may be operably coupled to the computing unit 124, i.e., the computing unit 124 may receive signals or real-time process data from the heater 118. Furthermore, the heater 118 may be further controllable via the computing unit 124, for example, via one or more control signals and / or set points. Similarly, the conveyor system comprising the transport elements 102a,b and the associated belts may also be operably coupled to the computing unit 124, i.e., the computing unit 124 may receive signals or process data from the transport elements 102a,b. The coupling may be, for example, via a network. Furthermore, the transport elements 102a,b may be further controllable via the computing unit 124, for example, via one or more control signals and / or set points provided via the computing unit 124. For example, the speed of the transport elements 102a,b may be observable and / or controllable by the computing unit 124. Optionally, no further object identifier may be provided for the intermediate equipment zones, since the quantity of the input material 114 is constant or nearly constant in the intermediate equipment zones. Thus, process data from the intermediate equipment zones, i.e., the heater 118 and / or the transport elements 102a,b, may also be added to the object identifier of the previous or preceding zone, i.e., the upstream object identifier 122. Here, it will be understood that the subset of real-time process data refers to the real-time data from the intermediate equipment zones when the input material is being processed in the intermediate equipment zones, so that the output data dynamically tracks the input material along the production process. In this way, the output data forms a time-dependent stream of process data related to the conversion of the corresponding input material into their corresponding chemical products. Similarly, the performance parameters may also be dynamically changed. The advantages of such output data and streams are also discussed in detail in the overview section.

[0156] Further, the augmented subset 126 of real-time process data may therefore be augmented to further indicate process parameters and / or equipment operating conditions from the intermediate equipment zone, i.e., the operating conditions of the heater 118 and / or the transport elements 102a,b when the input material 114 is processed in the intermediate equipment zone, such as any one or more of the following: inlet mass flow rate, outlet mass flow rate, one or more temperature values ​​from the intermediate zone, time in, time out, speed of the transport elements 102a,b and / or belt, etc. The equipment operating conditions in this case may be the control signals and / or setpoints of the transport elements 102a,b and / or the heater 118. It is clear that the subset 126 of real-time process data is primarily related to the time period during which the input material 114 is present in the corresponding equipment zone. Thus, an accurate snapshot of the relevant process data for a particular input material 114 may be provided via the upstream object identifier 122. Further observability of the input material 114 may be derived via knowledge of a particular part or part of the production process, such as a chemical reaction, within the intermediate equipment zone. Alternatively or additionally, the velocity at which the input material 114 passes through the intermediate equipment zones may be used to derive further observability via the computing unit 124. In conjunction with a subset 126 of real-time process data having specific timestamps or time series data and / or times at which the input material 114 entered and / or exited the intermediate equipment zones, more granular details of the conditions under which the input material 114 was processed in the intermediate equipment zones may be obtained from the upstream object identifiers 122.

[0157] Data from the upstream object identifier 122 may be used to train one or more ML models for monitoring the production process and / or particular portions thereof, such as portions of the production process in an upstream equipment zone and / or an intermediate equipment zone. The ML models and / or the upstream object identifier 122 may further be used to associate one or more performance parameters of the chemical product with the specifications of the production process in one or more zones.

[0158] It will be appreciated that as the input material 114 progresses along the direction of travel 120, the input material 114 may change its properties and be transformed or converted into the derived material 116. For example, a heater 118 may heat the input material 114, resulting in the derived material 116. Those skilled in the art will appreciate that for simplicity and ease of understanding, the derived material 116 may also be referred to as an input material in the present teachings, e.g., in relation to the equipment zone or component being discussed, in this manner making clear which phase the input material is in within the production process discussed in the present embodiment description.

[0159] An example of a zone in which a material is split into multiple portions will now be discussed. Figure 1 shows such a zone as a downstream equipment zone comprising a cutting mill 142 and a second transport element 106a,b. The resulting material 116 moving along a moving direction 154 is split or fragmented using the cutting mill 142, resulting in multiple portions, which in this example are shown as a first split material 140a and a second split material 140b.

[0160] Thus, according to one aspect of the present teachings, an individual object identifier may be provided for each portion. However, in some cases, instead of providing an individual object identifier for each portion, an object identifier may be provided only for one of the portions, or for some of the portions. This may be the case, for example, when there is no interest in tracking any of the portions. For example, an object identifier may not be provided for discarded portions of the derived material 116. Now, referring back to FIG. 1, a first downstream object identifier 130a is provided for the first split material 140a, and a second downstream object identifier 130b is provided for the second split material 140b. Such splitting of the material may also trigger splitting of the output data, for example a separate stream for each object identifier. Thus, in this example, two streams may be provided, which may be selectable or subscribable, for example via unique object identifiers.

[0161] Optionally, the first downstream object identifier 130a may be appended with a first subset of downstream real-time process data 132a, and the second downstream object identifier 130b may be appended with a second subset of downstream real-time process data 132b. The first subset of downstream real-time process data 132a may be a copy of the second subset of downstream real-time process data 132b, or may be partially the same data. For example, if the first divided material 140a and the second divided material 140b undergo the same process, i.e., are substantially at the same place and time, the process data appended to the downstream object identifier 130a and the second downstream object identifier 130b may be the same or similar. However, if the downstream object identifier 130a and the second downstream object identifier 130b should be treated differently within the downstream equipment zone, the first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b may be different from each other.

[0162] However, those skilled in the art will appreciate that in some cases, optionally only one object identifier may be provided at the cutting mill 142, and then multiple object identifiers may be provided after the cutting mill 142 if the material processed through the cutting mill 142 is split into multiple parts. Thus, depending on the specifications of a particular production process, the cutting mill may or may not be a separation device. Similarly, in some cases, a new object identifier may not be provided for the cutting mill, such that process data from this zone is added to the preceding object identifier. Thus, a new object identifier may be provided in the zone where the material is split and / or in the zone where the material is combined. For example, in some cases, the downstream object identifier 130a and the second downstream object identifier 130b may be provided after the cutting mill 142, for example, when entering a different zone following the cutting mill 142.

[0163] In this embodiment, the downstream equipment zone also comprises an imaging sensor 146, which may be a camera or any other type of optical sensor. The imaging sensor 146 may also be operatively coupled to the computing unit 124. The imaging sensor 146 may be used to measure or detect one or more properties of the derived material 116 before it enters the downstream equipment zone. This may be done, for example, to sort or divert material that meets a predetermined quality standard, e.g., at least one of the performance parameters determined in the upstream equipment zone and / or the intermediate equipment zone. When the mass flow rate of the material is changed in the downstream equipment zone, according to one aspect of the present teachings, another object identifier (not shown in FIG. 1) may be provided before the downstream object identifier 130a and the second downstream object identifier 130b.

[0164] The provision of the downstream object identifier 130a and the second downstream object identifier 130b may be triggered in response to the derived material 116 passing a quality standard via the imaging sensor 146. By correlating data from adjacent zones or data from object identifiers, such as mass flow rates from an intermediate equipment zone and mass flow rates to a downstream equipment zone, the computing unit 124 may determine which particular input material 114 or derived material 116 is associated with entering a subsequent zone. Alternatively or additionally, two or more of the timestamps may be associated between zones, such as a timestamp of exiting an intermediate equipment zone and a timestamp of detection via the imaging sensor 146 and / or entering a downstream equipment zone. The speed of the transport elements 102a,b, either directly measured via a sensor output or determined from two or more timestamps, may also be used to establish a relationship between a particular packet or batch of input material and its object identifier. As such, it may also be determined where a particular chemical product 170 was in the production process at a given time, thus building a spatio-temporal relationship. Some or all of these aspects may be usable to not only improve traceability of chemical product 170 from input materials to final product, but also to monitor and improve the production process, making it more adaptable and controllable.

[0165] As discussed, the first downstream object identifier 130a and the second downstream object identifier 130b may be populated with a first subset 132a and a second subset 132b of downstream real-time process data from the downstream equipment zone, respectively. The first subset 132a and the second subset 132b of downstream real-time process data may further be linked or populated with the upstream object identifier 122. Similar to the upstream object identifier 122 discussed above, the first subset 132a and the second subset 132b of downstream real-time process data may indicate any one or more of process parameters and / or equipment operating conditions, i.e., output of the imaging sensor 146, operating conditions of the cutting mill 142 and the second transport element 106a,b as the derived material 116 is processed in the downstream equipment zone, such as inlet mass flow rate, outlet mass flow rate, fill level, temperature, optical properties, time stamp, etc. The equipment operating conditions in this case may be control signals and / or set points of the cutting mill 142 and / or the second transport element 106a,b. The first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b may include time series data, i.e., may include a time-dependent signal, which may be obtained via one or more sensors, such as the output of the imaging sensor 146 and / or the speed of the second transport element 106a,b.

[0166] As the derived material 116 progresses after encountering the imaging sensor 146, it moves in a moving direction 154 driven by the second transport elements 106a,b toward the cutting mill 142. The second transport elements 106a,b are shown in this example as part of a second conveyor belt system separate from the conveyor system that comprises the transport elements 102a,b. It will be understood that the second conveyor belt system may also be part of the same conveyor system that comprises the transport elements 102a,b. Thus, the downstream equipment zone may comprise some of the same equipment used in another zone.

[0167] 1, the first divided material 140a and the second divided material 140b can be individually traced or tracked to the remainder of the production process and possibly beyond by their corresponding object identifiers, i.e., downstream object identifier 130a and second downstream object identifier 130b, even though they take different paths in later production. Similarly, the output data also tracks the corresponding materials.

[0168] Thus, the second divided material 140b is transported for curing in a third equipment zone comprising a curing device 162 and a third transport element 108a,b. The illustrated transport elements 108a,b are thus non-limiting examples as described above. It will be understood that the third equipment zone is downstream of the upstream equipment zone and the downstream equipment zone.

[0169] As the second divided material 140b is moved in the moving direction 156 via the belt, it undergoes a hardening process via the hardening device 162, resulting in a hardened second divided material 160. Since no substantial mass change occurs, according to one embodiment, a new object identifier may not be provided for the third equipment zone. However, the output data corresponding to the second divided material 140b dynamically changes depending on the production process in the third equipment zone, e.g., streams data from the hardening device 162 from ongoing processing. Thus, as discussed above, process data from the third equipment zone may also be added to the second downstream object identifier 130b. Similar to above, the enriched second subset 132b of downstream real-time process data may thus be augmented to further indicate process parameters and / or equipment operating conditions from the third equipment zone, i.e., the operating conditions of the curing device 162 and / or transport elements 108a,b as the second divided material 140b is processed in the third equipment zone, such as any one or more of the inlet mass flow rate, the outlet mass flow rate, one or more temperature values ​​from the third zone, the time in, the time out, the speed of the transport elements 108a,b and / or the belts, etc. 8a, b and / or the control signals and / or set points of the curing device 162.

[0170] Similarly, the first split material 140a proceeds to a fourth equipment zone comprising an extruder 150, a temperature sensor 148, and a fourth transport element 110a,b. Again, since no substantial mass change occurs, according to one embodiment, a new object identifier may not be provided for the fourth equipment zone. Thus, as discussed above, process data from the fourth equipment zone may also be included in the output data and may also optionally be appended to the downstream object identifier 130a. As above, the appended first subset 132a of downstream real-time process data may therefore include process parameters and / or equipment operating conditions from the fourth equipment zone, i.e., the first split material 140a is fed to the fourth equipment zone. 4 The extruder 150 and / or the temperature sensor 148 and / or the transport element when processed in the second equipment zone. 110 The operating conditions of a and b are, for example, the inlet mass flow rate, the outlet mass flow rate, 4 The equipment operating conditions may be augmented to further indicate any one or more of the following: temperature values ​​from the first zone, time in, time out, speed of the transport elements 110a,b and / or belts, etc. 110 a, b and / or extruder 150 control signals and / or set points. Thus, the properties and dependencies of the conversion of first portion material 140a to extruded material 152 may also be included in downstream object identifier 130a. It will be appreciated that the fourth equipment zone is also downstream of the upstream equipment zone and the downstream equipment zone.

[0171] As can be appreciated, the number of individual object identifiers can be reduced while improving oversight of materials and products throughout the production process.

[0172] The extrusion material 152 is a transport element. 110Moving further in the travel direction 158 occurring through a, b, the extruded material 152 may be collected in a collection zone 166. The collection zone 166 may be a storage unit or a further processing unit for applying further steps of the production process. In the collection zone 166, additional materials may be combined, for example, the hardened second divided material 160 may be combined with the extruded material 152. Thus, a new object identifier may be provided as discussed above. Such an object identifier is shown as the last downstream object identifier 134. The last downstream object identifier 134 may be appended with a subset 136 of the real-time process data of the last zone, which subset 136 may include all or a portion of the downstream object identifier 130a and the second downstream object identifier 130b. In this manner, the last downstream object identifier 134 is provided with process parameters and / or equipment operating conditions from the collection zone 166 in a manner similar to that discussed in detail in this disclosure. Depending on the function or further processing, if any, performed in the recovery zone 166, data such as any one or more of the inlet mass flow rate, the outlet mass flow rate, one or more temperature values ​​from the recovery zone 166, time in, time out, velocity, etc. may be included as real-time process data 136 for the last zone. Corresponding output data is adapted as discussed above.

[0173] In some cases, individual lots from the recovery zone 166 may be sorted, packaged, and / or stored. Such sorted lots are shown as product collection bins 164a. Again, because the quantities are partitioned, individual object identifiers may be provided for each of the silos such that the individual object identifiers for the chemical products 170 within that silo, i.e., the product collection bins 164a, may be associated with process data or conditions to which the chemical products 170 are exposed. Additionally, performance parameters associated with such multiple products may be attached to the individual object identifiers for the product collection bins 164a.

[0174] As will be appreciated, each of the object identifiers may be a GUID. Each may contain or be linked to all or part of the data from the previous object identifier. In this manner, the associated quality data may be attached as a snapshot or traceable link to a particular chemical product 170.

[0175] As also discussed, one or more ML models may be used to calculate or predict one or more performance parameters. Each or some of the ML models may also be configured to provide a confidence value indicating the confidence for at least one zone-specific performance parameter. An alert may be generated, for example, as a warning signal to initiate physical testing of the sample for laboratory analysis if the confidence in predicting the performance parameter is lower than a predetermined limit value. A sampling object identifier may also be automatically provided via the interface in response to the confidence in the prediction falling below an accuracy threshold. The sampling object identifier may be provided as well, and the computing unit 124 may add to the sampling object identifier a subset of the associated process data for the material with which the sampling object identifier is associated, shown here as sample material 172. The computing unit 124 may also add to the sampling object identifier at least one zone-specific performance parameter with which the confidence was lower. In this manner, the sample material 172 may be taken and verified and / or analyzed to further improve quality control using the object identifier. It will also be appreciated that the sampling bin 164b may be a target zone of the sample material 172. This also improves the reliability of sampling and sample recovery.

[0176] FIG. 2 shows a flow chart 200 or routine illustrating a method aspect of the present teachings, in this example, from the perspective of a first equipment zone.

[0177] In block 202, real-time process data is received from the equipment or from one or more of the equipment zones, e.g., from an upstream equipment zone, via an input interface. The real-time process data includes process parameters and / or equipment operating conditions. In block 204, a subset of the real-time process data is determined. The subset of the real-time process data is indicative of process parameters and / or equipment operating conditions as the input material 114 is processed. In block 206, the subset of the real-time process data is provided as output data via an output interface.

[0178] The subset determination may be made using a zone presence signal that indicates the presence of input material 114 in a particular equipment zone during the production process.

[0179] Optionally, at block 208, at least one performance parameter of a chemical product associated with the input material 114 or an upstream object identifier is calculated based on the subset of real-time process data and the historical process data. The historical process data may include data from one or more historical object identifiers associated with previously processed input materials, preferably in the same equipment, e.g., upstream equipment zones. Preferably, each historical object identifier is populated with at least a portion of the process data indicative of the process parameters and / or equipment operating conditions under which the previously processed input material was processed, e.g., in the same equipment zone.

[0180] Optionally, at block 210, at least one performance parameter is included in the output data.

[0181] The output data according to the present teachings can be a dynamic stream of data tracking corresponding materials during production. The output data can be particularly suitable for integration, streaming, cloud computing, edge computing, and real-time monitoring and / or control and / or optimization or improvement of production processes in any suitable industrial plant.

[0182] As the input material progresses to a subsequent zone, it may be determined whether or not a separate object identifier should be provided. If not, the process data from the subsequent zone may also be added to the same object identifier. If it is determined that a separate object identifier should be provided, the process data from the subsequent zone is added to the separate object identifier. Details of each of these options, such as intermediate equipment zones and downstream equipment zones, are discussed in detail in this disclosure, for example, in the Overview section with reference to FIG. 1. Additionally, examples of target zones are discussed.

[0183] The block diagram shown in Figure 3 represents a portion of a product production system of an industrial plant, which in this embodiment comprises ten product processing devices or units 300-318 (i.e., technical equipment, respectively) arranged along the entire illustrated product processing line. In this embodiment, one of these processing units (processing unit 308) includes three corresponding equipment zones 320, 322, 324 (see also more detailed embodiments in Figures 3 and 5).

[0184] In this embodiment, chemical products as input materials are produced based on raw materials fed to the processing lines via a liquid raw material reservoir 300, a solid raw material reservoir 302 and a recycle silo 304. The recycle silo 304 recycles any chemical products or intermediate products, e.g. with insufficient material / product properties or insufficient material / product quality. The corresponding raw materials fed to the processing lines 306-318 are processed through corresponding processing equipment, namely a feeding unit 306, a subsequent heating unit 308, subsequent processing units including a material buffer 310 and a subsequent sorting unit 312. Downstream of the processing equipment 306-312, a transport unit 314 is arranged to transport materials that need to be recycled, e.g. due to insufficient quality of the produced materials, from the sorting units to the recycle silo 304. Finally, the materials sorted by the sorting unit 312 are transferred to first and second packaging units 316, 318 which package the appropriate materials into material containers for shipment, such as material bags in the case of bulk materials, or bottles in the case of liquid materials.

[0185] The production systems 300-318 in this embodiment provide data interfaces of computing units (both not shown in this block diagram) through which data objects containing data on the corresponding input materials and changes thereto resulting from processing are provided, and the entire production process is controlled, at least in part, through the computing units.

[0186] The input materials processed by the processing equipment 306-312 are divided into physical or real-world so-called "packaging objects" (hereinafter also referred to as "physical packages" or "product packages"), which are handled or processed by each of the processing units 306-312. The package size of such packaging objects may be fixed, for example, by a weight of material (e.g., 10 kg, 50 kg, etc.) or a quantity of material (e.g., 1 decimeter, 1 / 10 cubic meter, etc.), or may even be determined by weight or quantity, so that relatively constant process or equipment operating parameters may be provided by the processing equipment.

[0187] The supply unit 306 first creates such packaged objects from liquid and / or solid input raw materials and / or recycled materials provided by the recycle silo 304. Having created the packaged objects, the supply unit then distributes these objects to the heating Transport to Unit 308. heating The unit 308 is configured to pack the material of the package object. heating i.e. for example, treating a treated liquid material and a solid material, or two liquid or solid materials. heating After the heating process, the heating unit 308 transports the suitably heated packaged object to a processing unit 310 that transforms the input packaged object's materials into a different physical and / or chemical state, for example by heating, drying or humidifying, or by a specific chemical reaction. The suitably transformed packaged object is then transported to one or more of three downstream packaging units 316, 318 or the transport unit 314 mentioned above.

[0188] Subsequent processing of the real-world package objects is governed by corresponding data objects 330, 332, 334 (or, respectively, previously described "object identifiers") that are assigned to each package object via a computing unit operatively coupled to or part of the equipment 306-312 and stored in a memory storage element of the computing unit. According to this embodiment, the three data objects 330-334 are generated in response to a trigger signal provided via the equipment 306-312, i.e. in response to the output of a corresponding sensor located in each of the equipment units 306-312 or in response to an appropriate switch, respectively, such sensor being operatively coupled to the equipment units 306-312. As mentioned above, an industrial plant may comprise different types of sensors, for example sensors for measuring one or more process parameters and / or for measuring equipment operating conditions or parameters related to the equipment or process units. In this embodiment, sensors for measuring the flow rate and / or level of bulk and / or liquid materials processed inside the equipment units 306-312 are arranged in these units.

[0189] The three exemplary data objects 330, 332, 334 shown in FIG. 3 relate to different ones of the three equipment zones 320, 322, 324 of the overall product production process based on processing units 306-312 and 314-318, respectively, in this embodiment.

[0190] The first two data objects 330, 332 include product package objects that contain process data. The process data includes the processes / processing information that the associated physical package underwent as it resided / processed in some processing unit. The process data may be aggregated data, such as the calculated average temperature during the residence time of the underlying physical package in the associated processing unit, and / or may be time series data of the underlying production process.

[0191] The first data object 330 is a first type of package (referred to as "A package" in FIG. 3), which in this embodiment is assigned to a physical package that has been transported through two processing units, the supply unit 306 and the heating unit 308. The first data object 330 contains relevant data for both units at each stop at the current point in time of processing. The first data object includes a corresponding "Product Package ID."

[0192] The heating unit 308 includes several equipment zones, in this embodiment three equipment zones 320, 322, 324 ("Zone 1", "Zone 2", "Zone 3"). These different equipment zones are utilized as filtering groups to filter or select relevant process data. Such filtering may help to obtain only data for package objects from the relevant equipment zone that is relevant to the processing of the underlying physics package within a corresponding time while the relevant physics package is in this equipment zone. However, in this embodiment, the material composition of the physics package does not change in both processing units 306, 308.

[0193] When the A packages 330 arrive at the next processing unit 310 (in this embodiment, the "buffered processing unit"), the material composition of each physics package changes because this processing unit 310 does not just transport the physics packages in a plug-flow mode. Moreover, such physics packages have a defined degree of backmixing because the corresponding physics packages contain a buffer volume that is larger than the original package size. As a result, each physics package that leaves this processing unit 310 is a different type of physics package, called a "B package" in FIG. 3.

[0194] A corresponding second data object 332 ("B package") also includes a corresponding "Product Package ID." Data object 332 further includes a defined percentage of data from a defined number of previous data objects (in this example, data objects 330 designated as "A packages"), so-called "aggregated data from related A packages." The appropriate aggregation scheme or algorithm depends, for example, on the underlying processing unit, the size of the underlying physics package, the material mixing capacity of the underlying physics package, and the residence time of the underlying physics package in the underlying processing unit or corresponding equipment zone of the processing unit.

[0195] Once the processed physical (product) packages are packed into a discrete physical package by one of the two packaging units 316, 318, e.g. by packing the processed physical packages into a container, drum, or octabin container, etc., the corresponding packed physical package is handled or tracked via another data object 334, called "Physical Package" in this embodiment. This data object 334 includes the related previous physical packages (such as "A Package" and "B Package" in this scenario) that were packed inside. Instead of using the complete data object, specifying the corresponding "Product Package ID" is sufficient, e.g. for tracking purposes, since such product package IDs can be easily linked together in later data processing, e.g. data processing performed by an external "Cloud Computing" platform.

[0196] The first data object (or “object identifier”) 330 contains, among other things, the following information: - The "Product Package ID" for the underlying package; and - general information about the underlying package, such as information or specifications about the processing material on which the package is based; - the current location of the underlying package within the overall processing lines 306-318; - process data, i.e. aggregate values ​​of temperature and / or weight of the processing material of the underlying package; - Time series data of the underlying production process; - a connection to a sample from a basic package, where the product package passes through a sample station and, at a defined moment, an operator takes a sample from this product package and provides it to the laboratory; In this sample, a sample object (see FIG. 6, reference numbers 634 and 638) is generated and linked to the associated product package (see FIG. 6, reference numbers 626 and 630). This sample object includes, among other things, corresponding quality control (QC) data from a laboratory and / or performance data from an appropriate testing machine.

[0197] The second object identifier 332 may additionally include: - aggregate data from the associated A-packages generated by the buffered processing unit 310; Includes.

[0198] A third object identifier 334 is generated by the two packaging units 316, 318 using the designation and timestamp "Physical Package 1976-02-06 19:12:21.123" and includes the following information: - again with the appropriate package or object identifier ("package ID"), - the names of the products to be packed in the two material containers for shipment, as shown in Figure 3; - the order number for ordering the products to be so packaged, and - the lot number of the product so packaged; Includes.

[0199] The package general information of the first and second object identifiers 330, 332 includes material data for the input raw material, which in this embodiment indicates chemical and / or physical properties of the input material, or the process material, respectively, such as the temperature and / or weight of the material, and in this embodiment also includes the above-mentioned laboratory sample or test data associated with the input material, such as historical test results.

[0200] 3, process data is collected from the entire equipment via the interfaces described above, where the process parameters indicate processing parameters, such as the temperature and / or weight of the material being processed described above, and in this embodiment also indicate equipment operating conditions under which the input material is being processed, such as the heater temperature and / or applied feed parameters described above. In this embodiment, only a portion of the collected process data, in this embodiment, such as aggregate data from the associated A package, is added to the second object identifier 332.

[0201] As explained above, the three object identifiers 330-334 are used in this embodiment to associate or map the referenced input material data and / or specific process parameters and / or equipment operating conditions to at least one performance parameter of a chemical product, said performance parameter being or indicative of any one or more properties of the underlying material (e.g., each appropriate chemical product).

[0202] According to the present embodiment shown in Fig. 3, the collected process data (as aggregate values) included in the two object identifiers 330, 332 include numerical values ​​indicative of process parameters and additionally include numerical values ​​indicative of equipment operating conditions measured during the production process. In addition, the object identifiers 330, 332 include process data provided as time series data of one or more of the process parameters and / or equipment operating conditions. The equipment operating conditions may be any characteristic or value representing the state of the equipment, which in this embodiment may be the setpoint of the production machine, the controller output, and the warnings related to the equipment (e.g., based on vibration measurements). Additionally, the speed, temperature, and fouling values ​​(filter differential pressure, maintenance date, etc.) of the transport elements may be included.

[0203] In the embodiment of the product production system shown in FIG. 3, during the production process, input raw materials 300-304 move along the entire processing line 306-318, which in this embodiment includes the plurality of three equipment zones 320-324 described above, such that the input raw materials progress from a first equipment zone 320 to a second equipment zone 322 and from the second equipment zone 322 to a third equipment zone 324. In such a production scenario, a first object identifier 330 is provided at the first equipment zone 320 and a second object identifier 332 is provided as the input material enters the second equipment zone 322 after being processed through the first equipment zone 320. The second object identifier 332 is appended with or includes at least a portion of the data or information provided by the first object identifier 330 and additionally includes the final data / information "aggregated data from associated A packages."

[0204] It is noteworthy that any or each of the object identifiers 330-334 may include a unique identifier, preferably a globally unique identifier ("GUID"), to enable reliable and secure assignment of the object identifier to the corresponding package throughout the production process.

[0205] In this product processing scenario, the process data mentioned above attached to the first object identifier 330 is at least a portion of the process data collected from the first equipment zone 320. Accordingly, the second object identifier 332 is attached with at least a portion of the process data collected from the second equipment zone 322, the process data collected from the second equipment zone 322 being indicative of the process parameters and / or equipment operating conditions under which the input raw materials 300-304 were processed in the second equipment zone 322.

[0206] In Table 1 below, another exemplary object identifier is also shown in tabular form, which contains much more information / data than the three object identifiers 330-334 described above.

[0207] This exemplary object identifier relates to a so-called "B Package" having an underlying date and time stamp "1976-02-06 18:31:53.401" as shown in FIG. 4, and which, as will be explained below, contains more data than is contained in FIG.

[0208] The unique identifier ("uniqueID") in this example includes a unique URL ("uniqueObjectURL"). The main details of the underlying package ("package details") are the date and time stamp of the package's creation ("creation timestamp"), which in this example has two values ​​"02.02.1976 18:31:53.401", and the type of package ("package type"), which in this example is package type "B". The package's current location along the underlying production line ("package location") is defined by the "package location link", which in this example is a transport link to "conveyor belt 1" of the production line.

[0209] The conveyor belt 1 is provided with a measurement device (see "Measurement Points" with exemplary processed data or values) for measuring an average temperature ("Average"), currently indicating a material temperature of 85°C, and a suitable description ("Description") of the underlying temperature zone, which in this example is "Temperature Zone 1". In addition, the measurement device may also include a sensor for detecting the date / time of entry ("Time In") of the package on the conveyor belt 1 ("02.02.1976 18:31:54.431" in this example) and a sensor for detecting the date / time of exit ("Time Out") of the package from the conveyor belt 1 ("02.02.1976 18:31:57.234" in this example). Finally, the measurement device includes a sensor device for detecting time series values ​​("Time Series Values") of the underlying time series information ("Time Series") regarding the production process.

[0210] In addition, the illustrated object identifier, in this embodiment, further includes information regarding a "Conveyor Belt 2" located downstream, a "Mixer 1" located downstream, and a "Silo 1" located downstream for intermediate storage of processed material.

[0211] [Table 1]

[0212] FIG. 4 shows a second embodiment of the process part of the product production system underlying an industrial plant, which in this second embodiment comprises six product processing devices 400, 402, 406, 410, 412, 416 (ie, technical equipment, respectively).

[0213] An "upstream process" 400 for processing package objects is connected to a "sorting unit" 402 for sorting the processed package objects. The upstream process 400 and the sorting unit 402 are managed by a first data object 404. This data object 404 relates to the already described "B package" and has an underlying date and time stamp "1976-02-06 18:51:43.431" indicating the date and time of its creation. The data object 404 contains the "package ID" (so-called "object identifier") of the currently processed package object. The data object 404 further contains n pre-described chemical and / or physical properties of the currently processed package object, which in this example are "property 1" and "property n".

[0214] Input material, i.e. the corresponding packaging objects input in the upstream process 400, is provided in this example by a "Recycle Silo" 406. The recycle silo 406 in turn obtains the underlying recycled material that needs to be recycled and has been properly sorted by the sorting unit 402 from a "Transport unit 1" 410 which transports the packaging objects to the recycle silo 406. The underlying transport process step 410 is managed by a second data object 408 which relates to the above mentioned "B package" and contains the above mentioned underlying date and time stamp "1976-02-06 18:51:43.431", the "package ID" of the currently processed packaging object and two chemical and / or physical properties "Property 1" and "Property n". However, due to the above-mentioned requirement to recycle the underlying sorted packaging object, the second data object 408 further includes another chemical and / or physical property of the underlying packaging object (in this embodiment "Property 2"), which in particular includes a corresponding performance indicator of that packaging object (in this embodiment "Low or Unsatisfactory Material or Product Performance").

[0215] The packaged objects processed by the upstream process 400 and not sorted by the sorting unit 402 are provided by the sorting unit 402 to either a first "packaging unit 1" 412 or a second "packaging unit 2" 416 depending on the performance value of the corresponding packaged object. The packaging units 412, 416 are used to pack the corresponding packaged objects into corresponding containers 414, 418. The packaging process performed by the two packaging units 412, 416 is governed by a third data object 420 and a fourth data object 422.

[0216] The two data objects 420, 422, both related to a "Physical Package", contain the same date "1976-02-06" as the "B Package" above, but contain a different last timestamp "19:12:21.123" than the "B Package" above. The two data objects 420, 422 also contain the "Package ID" of the underlying package object. However, the data objects 420, 422 further contain the performance indicators of the underlying end products, which in this example are "Medium Performance" for the product stored in the first container (or filling bag) 414 and "High Performance" for the product stored in the second container (or filling bag) 418. In addition, the two data objects 420, 422 contain the "Order Number" and "Lot Number" of the corresponding end products.

[0217] FIG. 5 shows a third embodiment of a part of an underlying chemical production process or system implemented in an industrial plant. 3 In this embodiment, nine product processing devices 500-516 (ie, technical equipment, respectively) are provided.

[0218] The product processing approach is based on two raw materials, namely a "feedstock liquid" 500 and a "feedstock solid" 502, for producing polymeric materials in a known manner. Similar to the production scenario according to figures 3 and 4 described above, the technical equipment comprises a "recycle silo" 504 for using recycled materials, as previously described.

[0219] The technical equipment further comprises a "feed unit" 506 for producing packaging objects based on the above mentioned input raw materials, which are processed by a "reaction unit" 508 which transports the packaging objects along the four illustrated polymer reaction zones ("zones 1-4") 510, 512, 514, 516 for processing the packaging objects, and by a "curing unit" 518 for curing the polymer material (i.e. the corresponding packaging object) being produced in the reaction unit 508. The curing unit 518, in this embodiment, only includes a material buffer and does not have a backmixing device. The curing unit 518 also transports the appropriately processed packaged objects.

[0220] A "Transport unit 1" 520 transports the packaging objects being sorted for recycling by the recycling silo 504. The final processed, i.e. unsorted, units are similarly transported to a first "Packaging unit 1" 522 and a second "Packaging unit 2" 524. The two packaging units 522, 524 convert and transport the corresponding packaging objects into corresponding containers or filled bags 526, 528.

[0221] The production process shown in FIG. 5 is governed by a first data object 530 and a second data object 534 .

[0222] The first data object 530 relates to "A package" with creation date "1976-02-06" and creation time "18:31:53.401". The data object 530, in this production scenario, likewise contains a pre-described "Package ID", process information regarding the supply process being carried out by the supply unit 506 ("Supply Characteristics"), and further process information regarding the production of the polymeric material by the reaction unit 508 ("Reaction Unit Characteristics"). The Supply Characteristics contain information regarding the raw material quantities of each package object, i.e. "Percentage of Raw Material 1 (Liquid)", "Percentage of Raw Material 2 (Solid)", and the product temperature. The Reaction Unit Characteristics contain the temperatures of the four polymer reaction zones 510-516 ("Temperature Zone 1", "Temperature Zone 2", "Temperature Zone 3" and "Temperature Zone 4").

[0223] So, the first data object 530 contains the current location of the underlying packaging object along the processing line 506-524 ("current package location"). The current location of that packaging object is managed in this embodiment by a "package location link" and a corresponding "zone location". Finally, chemical and / or physical information about the underlying polymer reaction is included, i.e. the corresponding "reaction enthalpy / reactivity". Thereby, the processing unit 506-524 transporting a given packaging object permanently calculates and writes / manifests the reaction enthalpy value in the first data object 530. This is possible due to the existing information about the package location and the corresponding residence time, as well as the existing information about the appropriate process values ​​(e.g. the temperature of the package). Via a communication line 532 between the first data object 530 and the curing unit 518, the curing time parameter is adjusted based on the calculated value of the reaction enthalpy based on the current value of the reaction enthalpy and / or reactivity contained in the first data object 530.

[0224] The second data object 534 relates to a "Physical Package" being processed by one of the packaging units 522, 524 and includes corresponding creation date and time information "1976-02-06 19:12:21.123". The "Package ID", "Product" description / specifications, "Order Number", "Lot Number", and the above mentioned values ​​of calculated enthalpy and / or reactivity are included.

[0225] 6 illustrates a first embodiment of a graph-based database configuration that represents a hierarchical or topological structure of an underlying industrial plant 602 that is part of a cluster 600 of industrial plants and includes a number of equipment devices and corresponding equipment zones that are part of appropriate product processing lines 604. This topological structure allows visualization of functional relationships between different underlying parts of the industrial plant 602 (or underlying plant cluster 600) to enable improved processing or planning of the underlying product packages. The illustrated circular nodes of the graph-based database are linked via connecting lines, although different link types are possible.

[0226] The equipment device comprises material processing units 606, 614 connected via signal and / or data connections to sensors / actuators 608, 616 which in this embodiment are part of the processing units 606, 614, and to a number of input / output (I / O) devices 610, 612 and 618, 620.

[0227] In this embodiment, the first processing unit 606 is further connected to three exemplary product packages (product packages 1-3) 622, 624, 626, and the second processing unit 614 is further connected to three additional product packages (product packages 4-n) 628, 630, 632. By way of example only, "product package 3" 626 is connected to a product sample (sample 1) 634, and "product package 5" 630 is connected to another product sample (sample n) 638. "Sample 1" 634 is further connected to "inspection lot 1" 636, and "sample n" is further connected to "inspection lot n" 640. Finally, both inspection lots 636, 640 are connected to an "inspection order 1" unit 642, which serves as a specification on how to create the above-mentioned inspection lots and how to realize the analysis / quality control of the corresponding underlying samples 634, 638.

[0228] The topology structure shown in FIG. 6 advantageously allows for an intuitive and easy understanding of the functionality and processing of the illustrated chemical plant since the illustrated objects (nodes) are modeled very similarly to their corresponding real-world objects, thus providing a data structure that allows easy management of such complex production processes in a chemical plant or cluster of chemical plants by users, in particular machine / plant operators.

[0229] More specifically, this topology structure provides high-level situational information, based on which a user / operator may easily gather technical / material properties of each object. This additionally allows the user to perform somewhat complex queries, for example queries regarding relevant production-related connections or relationships between objects, especially between objects across several nodes or even topology / hierarchy levels. This allows the objects (nodes) shown in Fig. 6 to be easily extended with further properties and / or values ​​at run-time.

[0230] FIG. 7 illustrates a second embodiment of the graph-based database organization shown in FIG. 6, but for production line 700 ("Line 1") only.

[0231] The equipment devices, in this embodiment, include material processing units 702 "Unit 1" and "Unit n" 708 that are connected via signal and / or data connections to sensors / actuators "Sensor / Actuator 1" 704 and "Sensor / Actuator n" 710 that are connected to corresponding input / output (I / O) devices "I / O 1" 706 and "I / O n" 712. These I / O devices include connections to a PLC (not shown) for controlling the operation of the production line 700.

[0232] In this embodiment, the first processing unit ("Unit 1") 702 is further connected to three exemplary product packages (product portions 1-3) 714, 716, 718, and the second processing unit ("Unit n") 708 is further connected to two additional product packages ("product portions" 4 and n) 720, 722. By way of example only, 「 Product Package 3" 718 is connected to a product sample ("Sample 1") 724, 「 Product Package 」 722 is connected to another product sample (“Sample n”) 728 .

[0233] In contrast to the embodiment shown in Fig. 6, the first "sensor / actuator 1" 704 is also connected to a first product sample ("sample 1") 724, and the second "sensor / actuator n" 710 is also connected to a second product sample ("sample n") 728. These two additional connections have the advantage that it allows samples to be taken independently at independent times, or even simultaneously, at different sample stations. For example, the sensor / actuator 704 can be a push button located at the sample station, which is pressed by a user or operator at the moment a sample is to be taken.

[0234] Alternatively, such a sample may be a signal that may be automatically generated by the sampling machine. Such an automatically generated signal may arrive, for example, at the sensor / actuator object 704 via the illustrated I / O object 706, which receives the above-mentioned push button information from a PLC / DC (not shown). At the moment of taking a sample, a sample object 724 is created (for example) and linked to the product part that is located at the sampling station location at that moment.

[0235] Based on appropriately generated samples 724, 728, one or more inspection lots 726, 730 may be generated, even if only from identical samples. However, one or more samples may be generated independently or even simultaneously within one processing line.

[0236] Finally, similar to the embodiment shown in Fig. 6, "Sample 1" 724 is further connected to a first "Inspection Unit 1" 726, and "Sample n" is further connected to a second "Inspection Unit n" 730. Both inspection units 726, 730 are finally connected to a "Inspection Order 1" unit 732 which serves similarly as a specification as in the case of the "Inspection Order 1" unit 642 shown in Fig. 6, i.e. as a specification on how to create the above mentioned inspection lot and on how to realize the analysis / quality control of the underlying samples 724, 728. The "Inspection Order 1" unit 732 may be created independently or only once, using the inspection order 732 for more than one inspection lot, as shown in Fig. 7 by "Inspection Lot 1" 726 and the further "Inspection Lot n" 730.

[0237] FIG. 8 shows an abstraction layer 800 that includes an object database 801 and serves as an abstraction layer for pre-described production equipment and corresponding raw materials, and the pre-described product data may include related data of the pre-described physical package or product package, i.e., a suitable digital twin.

[0238] The abstraction layer 800, in this embodiment, provides a two-way communication link 802 with an external cloud computing platform 804. Furthermore, the abstraction layer 800 also communicates with a number of n production PLCs / DCs and / or machine PLCs 806, 808, either two-way 810, as in the case of "PLC / DC 1" 806, or one-way 812, as in the case of "PLC / DC n" 808. The cloud computing platform 804, in this embodiment, includes a two-way communication link 814 to a customer integration interface or platform 816, through which a customer of the owner of this production plant may communicate and / or deliver control signals to pre-described equipment units of the plant.

[0239] The object database 801 further includes other objects relevant to this specification, such as the above-mentioned samples, inspection lots, sample orders, sensors / actuators, devices, device-related documentation, users (e.g., machine or plant operators), corresponding user groups and user rights, recipes, orders, settings-parameter sets, or inbox objects of cloud / edge devices.

[0240] An artificial intelligence (AI) or machine learning (ML) system is implemented in the cloud computing platform 804 to find or create optimal algorithms that are deployed to Internet of Things (IoT) edge devices or components 820 via a dedicated deployment pipeline 818 to use the appropriately created or discovered algorithms to control the edge devices 820. The edge devices 820, in this embodiment, communicate 822 bidirectionally with the abstraction layer 800.

[0241] The abstraction layer 800 and the included object database 801 allow the creation of pre-described physical or product packages as described herein. The abstraction layer 800 may also connect to specific processing and / or AI (or ML) components in the cloud computing platform 804. This connection may use the known data streaming protocol "Kafka". This allows an initial empty data packet to be sent as a message around the time the underlying product package is created, in particular independently of the underlying time series data. Then, another message may be sent when the final product package has been processed. These messages contain the object identifier of the underlying package as data packet ID, so that related packets can be linked together later on the cloud platform side as well. This has the advantage that large data packets can be avoided when sending to the cloud, thereby minimizing the required transmission bandwidth or capacity.

[0242] Within the cloud computing platform 804, the streamed and received product data is used by the AI ​​or ML methods described above to find or create algorithms to obtain additional data related to the underlying product, such as predicted product quality control (QC) values. To perform this procedure within the cloud computing platform 804, additional data is required, such as QC data or measured performance parameters of the relevant product (or physical) package. This can be received from the object database 801 as well, in the form of sample objects and inspection lot objects (see also FIG. 6) that contain such information on the relevant product packages.

[0243] Also, such information may be received from systems other than the object database, where the other system sends QC and / or performance data along with sample / inspection lot IDs from the object database. Within the cloud computing platform 804, this data is combined and used to find ML-based algorithms / models, etc. This allows for efficient use of computing power within the cloud platform 804.

[0244] In this embodiment, a properly discovered algorithm or model is deployed to an edge device 820 via a deployment pipeline 818. The edge device 820 may be a component located close to the object database 801 of the abstraction layer 800 and therefore also close to the PLC / DC1-PLC / DCn 806, 808, i.e., close in terms of location and network security level that allows low network latency and direct and secure communication.

[0245] Because use of ML models does not require such computational power, edge device 820 uses the ML models to generate and provide the above-mentioned advanced information to object database 801. Thus, edge device 820 requires the same information, or a subset of information, used by cloud computing platform 804 to generate the ML-based algorithms or models, and object database 801 may provide this data to edge device 820 via an open network protocol for machine-to-machine communication, such as, for example, the well-known "Message Queuing Telemetry Transport" (MQTT) protocol.

[0246] This setup enables AI / ML-based advanced process control and autonomous manufacturing, resulting in machines that operate autonomously.

[0247] As shown in the embodiment depicted in Figure 8, based on data from pre-described data objects 330-334 (Figure 3), an AI / ML system or suitable AI / ML model is trained at the cloud computing platform 804 using such data as training data. Thus, the training data, in this embodiment, may include historical and current laboratory test data, in particular data indicative of historical chemical product performance parameters.

[0248] The AI / ML model may be used to predict one or more of the pre-described performance parameters, said prediction being preferably made via a computing unit. Additionally or alternatively, the AI / ML model may be used to at least partially control the production process, preferably via adjusting equipment operating conditions, more preferably said control being made via said computing unit. Additionally or alternatively, the AI / ML model may also be used, for example, by the computing unit to determine which of the process parameters and / or equipment operating conditions have a dominant effect on the chemical product, so that the dominant of the process parameters and / or equipment operating conditions is added to the data object, or to the object identifier as mentioned above, respectively.

[0249] Those skilled in the art will understand that the method steps, at least the method steps performed via the computing unit, may be performed in "real time" or "quasi-real time", each term being understood in the art of computers. As a specific example, the time delay between any two steps performed by the computing unit is 15 seconds or less, specifically 10 seconds or less, more specifically 5 seconds or less. Preferably, the delay is less than 1 second, more preferably less than a few milliseconds. Thus, the computing unit may be configured to perform the method steps in real time. Furthermore, the software product may cause the computing unit to perform the method steps in real time.

[0250] The method steps may be performed, for example, in the order as described in the examples or aspects. However, it should be noted that under certain circumstances, a different order may be possible. Furthermore, it is also possible to perform one or more of the method steps once or repeatedly. The steps may be repeated at regular or irregular time intervals. Furthermore, two or more of the method steps may be performed simultaneously or overlappingly, in particular, some of the method steps may be performed repeatedly. The method may include further steps not described.

[0251] The word "comprise" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality. A single processing means, processor or controller or other similar unit may fulfill the functions of several items recited in the claims. The mere fact that certain means are recited in mutually different dependent claims does not indicate that a combination of these means cannot be used to advantage. Any reference signs in the claims should not be interpreted as limiting the scope of the invention.

[0252] Furthermore, it should be noted that in this disclosure, the terms "at least one," "one or more," or similar language indicating that a feature or element may be present one or more times, are typically used only once when introducing each feature or element. Thus, in some cases, unless specifically stated otherwise, the language "at least one" or "one or more" will not be repeated when referring to each feature or element, despite the fact that each feature or element may be present one or more times.

[0253] Furthermore, the terms "preferably", "more preferably", "particularly", "more particularly", "particularly", "more particularly", or similar terms are used with any feature without limiting the possibility of substitution. Thus, the features introduced by these terms are optional features and do not limit the scope of the claims in any way. As will be understood by those skilled in the art, the present teachings may be implemented by using alternative features. Similarly, features introduced by "in one embodiment" or similar expressions are intended to be optional features without limitations on the alternative forms of the present teachings, without limitations on the scope of the present teachings, and without limitations on the possibility of combining the introduced feature with other optional or non-optional features of the present teachings.

[0254] The headings used herein are for convenience only and such headings should not be considered to have any limiting or restrictive effect on the subject matter.

[0255] Various embodiments have been disclosed above for the method for monitoring and / or controlling and / or improving a production process, the method of use, the system for implementing the methods disclosed herein, the system for monitoring and / or controlling and / or improving a production process, the software program, and the computing unit including the computer program code for implementing the methods disclosed herein. However, those skilled in the art will understand that changes and modifications can be made to these embodiments without departing from the spirit and scope of the appended claims and their equivalents. It will be further understood that aspects from the embodiments of the methods and products discussed herein can be freely combined.

Claims

1. 1. A method for monitoring and / or controlling a production process for producing a chemical product in an industrial plant, said industrial plant comprising at least one equipment, said product being produced by processing at least one input material using said production process via said equipment, said method being at least partially executed via a computing unit, said method comprising: receiving real-time process data from the device via an input interface; determining, via the computing unit, a subset of the real-time process data, the subset of the real-time process data indicative of process parameters and / or equipment operating conditions as the input material is processed; and providing the subset of the real-time process data as output data via an output interface; A method comprising:

2. The method further comprising: calculating, via the computing unit, at least one performance parameter of the chemical product associated with the input material, the calculation being performed based on the subset of real-time process data and historical process data; The method of claim 1 , comprising:

3. The method of claim 2 , wherein the output data comprises the at least one performance parameter.

4. The method further comprising: providing, via an interface, an object identifier including input data, the input data indicating one or more characteristics of the input; The method according to any one of claims 1 to 3, comprising:

5. The method further comprising: appending the subset of the real-time process data to the object identifier; The method of claim 4 , comprising:

6. The method further comprising: attaching said at least one performance parameter to said object identifier; The method of claim 4 , comprising:

7. 7. The method of any one of claims 1 to 6, wherein the input material for processing through the equipment is divided into at least two packages, the size of the packages being fixed or determined based on the weight or amount of the input material, so that relatively constant process or equipment operating parameters can be provided by the equipment.

8. The method of any one of claims 1 to 7, wherein processing of said at least two packages is governed by corresponding data objects, each of said data objects including at least an object identifier.

9. A method according to any preceding claim, wherein a data object is generated in response to a trigger signal being provided via the instrument.

10. The method of claim 9 , wherein the trigger signal is provided in response to an output of a corresponding sensor located on each of the equipment units of the equipment.

11. 11. The method of claim 1, wherein the equipment comprises a plurality of physically separated equipment zones such that the output data comprises a subset of the real-time process data from each of the equipment zones and / or at least one performance parameter calculated in each of the equipment zones.

12. A method according to any preceding claim, wherein the output data forms a time-dependent data stream.

13. A method according to any preceding claim, wherein the output data and / or the time dependent data stream are provided to a Human Machine Interface ("HMI") system.

14. The method of claim 13 , wherein the HMI system is at least in part a display device.

15. 15. The method of claim 13 or 14, wherein the HMI system is at least in part an augmented reality ("AR") and / or virtual reality ("VR") device.

16. The method according to any one of claims 13 to 15, wherein the HMI system is at least partly an audio device.

17. Use of output data generated according to any one of claims 1 to 16 for monitoring and / or controlling and / or improving a production process in an industrial plant.

18. A system for monitoring and / or controlling a production process, said system being configured to carry out the method of any one of claims 1 to 16.

19. A computer program comprising instructions, when said program is executed by a suitable computing unit, causing said computing unit to carry out the method steps of any one of the preceding claims.

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