Chemical manufacturing control

Abstract

The present teachings relate to a method for controlling a manufacturing process for producing a chemical product, the method including providing an upstream object identifier including input material data and at least one desired performance parameter associated with the chemical product, determining a set of process and / or operating parameters based on the upstream object identifier and the at least one desired performance parameter, determining zone-specific control settings for each equipment zone based on the determined set of process and / or operating parameters and historical data, and providing the zone-specific control settings for controlling the production of the chemical product associated with the upstream object identifier. The present teachings also relate to systems for controlling manufacturing processes, uses of control settings, and software products for performing the method steps disclosed herein.

Description

Technical Field

[0004] , , , ,

[0003]

[0001] Technical Field The present teachings generally relate to computer assisted chemical production.

Background Art

[0002] Background Art In an industrial plant, input materials are processed to produce one or more products. Thus, the characteristics of the products produced depend on the manufacturing parameters. Usually, it is desirable to correlate the manufacturing parameters with at least some of the characteristics of the products in order to ensure product quality or manufacturing stability.

[0003] In an industrial plant such as a process industry or a chemical or biological manufacturing plant, one or more input materials are processed using a manufacturing process for producing one or more chemical or biological products. The manufacturing environment in the process industry can be complex, and thus the characteristics of the products may vary according to variations in the manufacturing parameters that affect said characteristics. Usually, the dependence of the characteristics on the manufacturing parameters is complex and may be intertwined with a further dependence on one or more combinations of specific parameters. In some cases, the manufacturing process may be divided into multiple stages, which may further exacerbate the problem. Thus, it may be difficult to produce chemical or biological products with consistent and / or predictable quality. ​​Quality control may be implemented to maintain consistent quality in chemical products. Quality control typically involves collecting one or more samples of the chemical product after or during the manufacturing process. The samples are then analyzed, and corrective actions may be taken as necessary. Depending on the frequency of variation occurring in the manufacturing process, it may be required to increase the frequency of quality control, which may not be practical. Furthermore, such quality control can be expensive and time-consuming. Therefore, multiple similar chemical products manufactured at different points in time may have associated statistical variations or tolerance ranges, within which their characteristics or performance may lie. The size of the variation usually depends on the cost of manufacturing the chemical product. For example, lower manufacturing costs may necessitate wider variation between multiple products, while higher manufacturing costs may be required for more consistent performance.

[0005] Furthermore, in contrast to discrete processing, chemical or biological processes such as continuous campaigns or batch processes can provide vast amounts of time-series data. However, machine learning through traditional time-series approaches has proven to be impractical, mainly because integrating data according to the need for horizontal integration across value chains can be challenging. In particular, easy and meaningful data exchange or standardization can present significant problems.

[0006] Therefore, ideally, an approach is needed that can improve control and manufacturing stability across the value chain from barrel to final product. [Overview of the Initiative] [Means for solving the problem]

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

[0008] From a first perspective, a method for controlling a manufacturing process for producing a chemical product in an industrial plant, wherein the industrial plant comprises a plurality of physically separated equipment zones, the product is produced by processing at least one input material using a manufacturing process through the plurality of equipment zones, the method is performed at least partially via a computing unit, and the method is - Provide an upstream object identifier via an interface that includes input material data and at least one desired performance parameter related to a chemical product, wherein the input material data represents one or more properties of the input material. - Determining a set of process and / or operational parameters based on an upstream object identifier and at least one desired performance parameter via a computing unit, - Determining zone-specific control settings for each equipment zone based on the determined set of process and / or operating parameters and historical data via the computing unit, - A method can be provided that includes providing a zone-specific control setting for controlling the production of a chemical product associated with an upstream object identifier via an output interface.

[0009] The applicant has realized that by doing so, at least one desired zone-specific performance parameter related to a desired quality of a chemical product may be used to control how a particular input material having the relevant properties is processed in the upstream equipment zone and / or zones downstream of the upstream equipment zone. Thus, by using at least one desired performance parameter, the computing unit may determine a set of process and / or operating parameters required to achieve the at least one desired performance parameter for an input material, the details of which are provided via the input material data. The set of process and / or operating parameters is then used to determine a zone-specific control setting, i.e., such a control setting that the manufacturing process may be executed in response to achieve a desired performance or quality of a chemical product, the quality of which is specified via at least one desired performance parameter. In addition, historical data can be synergistically utilized to find the optimal control setting. Upstream object identifiers may, for example, be when the input material is in the upstream equipment zone, or even earlier. Upstream object identifiers may also be triggered by certain events or event signals, such as zone location, some non-limiting examples of which will be described later.

[0010] Historical data may include historical process data and / or quality control data that associates at least one zone-specific control setting with at least one performance parameter, and / or some process and / or operating parameters with at least one performance parameter. For example, at least one performance parameter may be derived from quality control data such as laboratory analysis or result values.

[0011] According to one embodiment, the historical data includes data from one or more historical upstream object identifiers related to previously processed input material, wherein at least one of the historical upstream object identifiers is further augmented with at least a portion of the process data indicating process parameters and / or equipment operating conditions under which the previously processed input material was processed, for example, in an upstream equipment zone.

[0012] In some cases, the historical object identifier may be from another upstream zone with a similar manufacturing process in which the past input materials were processed, and therefore such historical object identifiers from such zones may be available.

[0013] Therefore, historical object identifiers may not encapsulate only the relevant portion of the process data in which each preceding input material was processed to manufacture or process each chemical product. Thus, the historical data disclosed herein may be a highly relevant but concise dataset that can be used to determine zone-specific control settings for each equipment zone as a target to achieve desired performance specified via at least one desired performance parameter. Upstream object identifiers may also be used as historical object identifiers for subsequent manufacturing.

[0014] In one embodiment, each or some of the historical object identifiers include at least one zone-specific performance parameter related to one or more properties of the relevant chemical product manufactured. Thus, each or some of the historical object identifiers may have at least one zone-specific performance parameter added to them, or provide at least one zone-specific performance parameter.

[0015] Therefore, the object identifiers proposed in the context of this instruction can not only improve the traceability of chemical products but can also be used to ensure that the manufacturing process is controlled to obtain more consistent quality of chemical products. Rather than relying on a universal control setting that may result in wider variability in multiple chemical products manufactured at different points in time, the manufacturing chain or equipment zone can be controlled in a more adaptable manner with the goal of achieving desired performance. Thus, variability in input materials and / or process parameters and / or equipment operating conditions can be compensated at least partially while providing zone-specific control settings for manufacturing chemical products.

[0016] Therefore, the method is, - This also includes executing the manufacturing process using zone-specific control settings.

[0017] The manufacturing process may be carried out by at least some of the zone-specific control settings entered into a plant control system operably coupled to the equipment zone. In addition, or alternatively, the manufacturing process may be carried out by at least some of the zone-specific control settings automatically provided to the plant control system. The zone-specific control settings may be transmitted directly to the plant control system by a computing unit, or provided in a memory location operably coupled to the computing unit, from which the plant control system may read or fetch the control settings. In some cases, the computing system may be at least partially part of the plant system, thereby allowing the computing system to directly use the zone-specific control settings to at least partially control the manufacturing process. The zone-specific settings enable control of the manufacturing process in each zone. Thus, finer granularity and flexibility of control can be achieved to achieve the performance of the chemical product according to at least one desired performance parameter.

[0018] According to one aspect, the method further, - The computing unit includes receiving real-time process data from one or more of the equipment zones, the real-time process data including real-time process parameters and / or equipment operating conditions.

[0019] Therefore, the computing unit may be coupled to an equipment zone or equipment in a communicative and / or operational manner.

[0020] In another aspect, the method is - This includes determining a subset of real-time process data via a computing unit based on upstream object identifiers and zone presence signals, where zone presence signals indicate the presence of input materials in a specific equipment zone during the manufacturing process.

[0021] Therefore, a subset of real-time process data for an upstream equipment zone may be determined in response to a zone presence signal indicating that the input material is in the upstream equipment zone. Thus, the computing unit can select process data associated with an upstream object identifier. Alternatively, the associated data, or a subset of real-time process data, may be selected based on where the material is located in the manufacturing chain, or by using the zone presence signal.

[0022] In another aspect, the method is - This includes calculating at least one zone-specific performance parameter for a chemical product associated with an upstream object identifier based on a subset of real-time process data and historical data via a computing unit.

[0023] As can be recognized, process data may not be consistent overall and may have variability associated with one or more of the data components. For example, two different batches of materials mixed by the same mixer at different times may be mixed in non-identical forms. Similar variability may also exist with other parameters and / or operating conditions. The variability between individual components may be random and independent or may be partially independent from those of other components. Furthermore, such combinations of variability and / or other interdependencies may result in variability in the performance or quality of chemical products. Thus, as indicated above, depending on a subset of real-time process data, a computing unit may be configured to calculate at least one zone-specific performance. Thus, at least one zone-specific performance parameter indicating the quality of a chemical product may be determined essentially while the input materials are being processed in an upstream equipment zone. The deviation from at least one zone-specific performance parameter and / or its corresponding desired performance parameter may be displayed to an operator, for example, via a human machine interface ("HMI"). The operator may then adjust the manufacturing process so that each or some of the at least one zone-specific performance parameters can become the same value as or close to the associated value of the desired performance parameter.

[0024] Alternatively, or in addition, the method - includes adding at least one zone-specific performance parameter to an upstream object identifier.

[0025] Zone-specific performance parameters may be added, for example, as metadata to the upstream object identifier. Thus, the upstream object identifier also encapsulates at least one zone-specific performance parameter calculated during the manufacturing process. Thus, this can not only improve the traceability of chemical products but also simplify the quality control for chemical products. The upstream object identifier can also be used later as a historical object identifier, which can provide insights into the performance obtained through specific input materials.

[0026] Alternatively, or in addition, the method - includes controlling the manufacturing process via a computing unit such that the difference between each value of at least one of the zone-specific performance parameters and the desired performance parameter is minimized.

[0027] Thus, the calculated performance value can track the desired performance parameter value such that the difference between at least one of the zone-specific performance parameters and their respective or associated desired performance parameter values is minimized. Thereby, the granularity of the control of the manufacturing process can be further improved on a finer scale. Such control can at least partially compensate for variability in various process parameters and / or operating conditions. Potentially, each equipment zone may be automatically controlled such that the resulting chemical product can have more consistent performance or quality.

[0028] Alternatively, or in addition, the method - includes adding a subset of real-time process data to the upstream object identifier.

[0029] Therefore, relevant portions of real-time process data may be captured and packaged or encapsulated in upstream object identifiers along with input material data so that all relationships between the properties of the input materials and the chemical product are captured in a traceable format. This can provide a more complete relationship between various dependencies that may affect any one or more properties or performance of the chemical product. Another advantage is that combinations between various interdependencies that may exist between input material properties and / or process parameters may also be captured within the upstream object identifier. Thus, the upstream object identifier is enhanced with information that can be used not only to track chemical products and / or their specific components such as input materials, but also to track specific real-time process data that contributed to the production of the chemical product. As a result, object identifiers such as each historical object identifier can be more easily integrated for any machine learning ("ML") and such purposes. As already described, upstream object identifiers can also be used as historical object identifiers for future manufacturing, which can provide insights into the performance obtained when a particular input material is processed under specific process conditions specified via a subset of real-time process data in such cases.

[0030] It will be recognized that desired performance parameters may be directly related to one or more properties of a chemical product and / or related to one or more properties of derivative materials produced during the manufacturing process. For example, when input materials are converted into derivative materials during the course of a manufacturing process, it may sometimes be required to track and / or control the quality or performance of such derivative materials as well. In such cases, it will be understood that the derivative material is an intermediate material resulting from the input material, which is then used to manufacture the chemical product. Since the chemical product also depends on the derivative material, it may sometimes be required to track and control the derivative material as well.

[0031] Therefore, according to one embodiment, at least one of the desired performance parameters is related to one or more properties of the derived material.

[0032] In one embodiment, a zone presence signal may be generated via a computing unit by performing a zone-time transformation that maps at least one characteristic related to the input material to a specific equipment zone. For example, the characteristic related to the input material may be the weight of the input material, thereby allowing the presence of the input material or derivative material produced during the manufacturing process to be determined by knowledge of the manufacturing process, for example, via real-time process data. As an example, if an input material with a certain weight in an upstream equipment zone crosses to a downstream equipment zone during the manufacturing process, a weight measurement in the downstream zone, for example, at or within a given time, can be used to generate a zone presence signal for the downstream zone. Similarly, the flow value that the input material or its derivative material crosses during manufacturing, for example, the mass flow rate or volume flow rate, can be a characteristic and can be used to generate a zone presence signal. Furthermore, as an example, the speed or velocity at which the input material crosses along the equipment zone can be used to determine the space or position where the input material or its corresponding derivative material is at any given time. Alternatively or additionally, other non-limiting examples of characteristics related to the input material include volume, fill value, level, color, etc.

[0033] The applicant found it advantageous to map time-dependent data, such as real-time process data which is time-series data, to spatial data in a manufacturing environment, thereby generating zone presence signals by mapping real-life manufacturing flows using digital flow elements representing input materials. For example, the digital flow of input materials can be tracked via upstream object identifiers, and occurrences in time-dependent real-time process data can be used to locate materials along the manufacturing process. Thus, materials are tracked or located via already measured time and real-time process data, i.e., using the time dimension of process data correlated with the time dimension of the flow of input materials along the manufacturing chain.

[0034] Zone presence signals may be intermittent, generated through calculations at regular or irregular intervals, or continuously generated. This has the advantage that materials associated with each object identifier can be placed continuously or essentially continuously within the manufacturing chain, thereby enabling the addition of highly relevant data for their conversion into materials and chemical products. Calculations at regular or irregular intervals may be performed, for example, to check for the presence of materials at certain checkpoints in the manufacturing chain. This may be supplemented by occurrences in real-time process data, for example, by one or more sensors, as outlined below.

[0035] In chemical manufacturing, since operating parameters related to time dimensions such as residence time and flow rate are known, zone-time transformation can be a simple mapping on a time scale. Alternatively, more complex models based on process simulations may be used to align the time scale of the material flow with real-time process data. In either case, the time scale of the process data may be finer than that of the material flow in order to attribute the process data parameters more finely to the material flow.

[0036] Therefore, a subset of real-time process data, or even its components, such as each or some of the process parameters and / or equipment operating conditions, can be further optimized or simplified according to the time the material spends in a particular subpart of the equipment zone. For example, if an equipment zone, such as an upstream equipment zone, includes a mixer and a subsequent heater, the subset of real-time data might include process parameters and / or equipment operating conditions related to the mixer only for the time the input material was in the mixer. Similarly, process parameters and / or equipment operating conditions related to the heater might include, for example, only from the time the material was exposed to the heater when it left the mixer. In this way, the relevance of the dataset can be further and constantly managed and optimized according to the relevance for a particular material. An alternative example, as understood, might be that a subset of process data includes all process parameters and / or equipment operating conditions related to the upstream equipment zone from the time the input material enters the upstream equipment zone to the time the input material exits the upstream equipment zone. While this alternative already has the advantage of providing highly relevant data for upstream object identifiers, the subset of real-time process data can be further optimized within the zone itself by further specifying the individual components of the process data as described, and the relevance of the data encapsulated within each object identifier can be further improved.

[0037] Additionally or alternatively, zone presence signals may be provided, at least in part, via sensors associated with a particular zone. For example, weight sensors and / or image sensors may be used to detect the presence of input or derived materials in space or in a particular equipment zone.

[0038] "Equipment" may refer to any one or more assets within an industrial plant. In non-limiting examples, equipment may refer to any one or more, or any combination thereof, of controllers or distributed control systems ("DCS") such as computing units or programmable logic controllers ("PLCs"), conveying elements such as sensors, actuators, end-effector units, conveyor systems, heat exchangers such as heaters, furnaces, cooling units, evaporators, extractors, reactors, mixers, milling machines, choppers, compressors, slicers, extruders, dryers, sprayers, pressure or vacuum chambers, tubes, bins, silos, and any other types of equipment used directly or indirectly for or during manufacturing in an industrial plant. Preferably, equipment refers in particular to assets, equipment or components directly or indirectly involved in the manufacturing process. More preferably, it refers to assets, equipment or components that can affect the performance of a chemical product. Equipment may or may not be buffered. Furthermore, equipment may or may not involve mixing, separation, or other processes. Some non-limiting examples of unbuffered equipment without mixing include conveyor systems or belts, extruders, pelletizers, and heat exchangers. Some non-limiting examples of buffered equipment with mixing include buffer silos, bins, etc. Some non-limiting examples of buffered equipment with mixing include silos with mixers, mixing vessels, cutting mills, double-cone blenders, and hardening tubes, etc. Some non-limiting examples of unbuffered equipment with mixing include static or dynamic mixers, etc. Some non-limiting examples of buffered equipment with separation include columns, separators, extractors, thin-film vaporizers, filters, and sieves, etc. Equipment may also be, or may include, storage or packaging elements such as octabine fillings, drums, bags, and tank trucks. Sometimes, a combination of two or more pieces of equipment may be considered equipment.

[0039] An "equipment zone" refers to a physically separated zone that is part of the same piece of equipment, or a zone may be a different piece of equipment used to manufacture a chemical product. Zones are therefore physically located in non-identical locations. Locations may be geographically non-identical in the lateral and / or vertical directions. Thus, input materials start from an upstream equipment zone and traverse downstream toward one or more equipment zones downstream of the upstream equipment zone. Various steps of the manufacturing process may therefore be distributed between zones.

[0040] In this disclosure, the terms “equipment” and “equipment zone” may be used interchangeably.

[0041] The term "equipment operating conditions" refers to one or more of the following characteristics or values ​​that represent the state of the equipment, for example, a particular zone, such as setpoints, controller outputs, manufacturing sequences, calibration status, any equipment-related warnings, vibration measurements, speed, temperature, fouling values ​​such as filter differential pressure, maintenance dates, etc.

[0042] The term “upstream” is understood to mean the opposite direction to the manufacturing flow. For example, the very first equipment zone where the manufacturing process begins is the upstream equipment zone. However, the term is used in a relative sense within its meaning in this disclosure. For example, an intermediate equipment zone between the first equipment zone and the last equipment zone may be called the upstream zone relative to the last equipment zone, and the “downstream” equipment zone relative to the first equipment zone. Thus, the last equipment zone is the downstream zone relative to the first equipment zone and the intermediate equipment zone. Similarly, the first equipment zone and the intermediate equipment zone are upstream of the last equipment zone.

[0043] The term “industrial plant” or “plant” may, without limitation, refer to any technical infrastructure used for the industrial purpose of manufacturing, producing or processing one or more industrial products, i.e., any manufacturing or production process or processing carried out by an industrial plant. Industrial products can be any physical product, such as chemical, biological, pharmaceutical, food, beverage, textile, metal, plastic, or semiconductor. Additionally or alternatively, industrial products can also be service products, such as recovery or disposal processes, including recycling, or chemical processes, such as decomposition or dissolution into one or more chemical products. Thus, an industrial plant may be one or more of the following: a chemical plant, a process plant, a pharmaceutical plant, a fossil fuel processing facility such as oil and / or natural gas, a refinery, a petrochemical plant, a fractionating distillery, etc. An industrial plant may also be any of the following: a distillery, a processing plant, or a recycling plant. An industrial plant may also be any combination of the above examples or similar thereto.

[0044] Infrastructure may include equipment or process units such as heat exchangers, columns including fractionation columns, furnaces, reaction chambers, fractionation units, storage tanks, extruders, pelletizers, dust collectors, blenders, mixers, cutters, curing tubes, vaporizers, filters, sieves, pipelines, stacks, valves, actuators, mills, transformers, conveying systems, breakers, machinery, such as heavy-duty rotating equipment, such as turbines, generators, crushers, compressors, industrial fans, pumps, conveying elements such as conveying systems, and motors. Sometimes, a combination of two or more of these may also be considered equipment.

[0045] Furthermore, an industrial plant typically includes multiple sensors and at least one control system for controlling at least one parameter related to a process in the plant or a process parameter. Such control functions are usually performed by the control system or controller in response to at least one measurement signal from at least one of the sensors. The plant's controller or control system may be implemented as a distributed control system ("DCS") and / or a programmable logic controller ("PLC").

[0046] Therefore, at least some of the equipment or process units in an industrial plant may be monitored and / or controlled to manufacture one or more industrial products. Monitoring and / or control may further be performed to optimize the manufacture of one or more products. 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 include at least one programmable logic controller ("PLC") for controlling some of the processes. An industrial plant may typically include multiple sensors that may be distributed throughout the industrial plant for monitoring and / or control purposes. Such sensors may generate large amounts of data. Sensors may or may not be considered part of the equipment. Therefore, manufacturing, such as chemical and / or service manufacturing, can be a data-heavy environment. Therefore, an industrial plant may generate large amounts of process-related data.

[0047] Those skilled in the art will acknowledge that industrial plants may typically include instrumentation that can contain different types of sensors. Sensors may be used to measure one or more process parameters and / or equipment operating conditions or parameters related to 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, furnace temperature, and chemical composition of a gas, and some sensors can be used to measure crusher vibration, fan speed, valve opening, pipeline corrosion, and voltage in a transformer. The differences between these sensors cannot be based solely on the parameters they sense, but may also be based on the sensing principle that each sensor uses. Some examples of sensors based on the parameters they sense may include radiation sensors such as temperature sensors, pressure sensors, and light sensors, flow sensors, vibration sensors, displacement sensors, and chemical sensors such as sensors for detecting specific substances such as gases. Examples of sensors that differ in terms of the sensing principle they use may include impedance sensors such as piezoelectric sensors, piezoresistive sensors, thermocouples, and capacitive sensors, and resistance sensors.

[0048] An industrial plant may also be part of multiple industrial plants. The term “multiple industrial plants” as used herein is a broad term, given to those skilled in the art in its ordinary and idiomatic meaning, and not limited to any special or customized meaning. The term may, in particular, refer to a complex of at least two industrial plants having at least one common industrial purpose. Specifically, multiple industrial plants may include at least two, at least five, at least ten, or even more industrial plants that are physically and / or chemically linked. Multiple industrial plants may be linked in such a way that the industrial plants forming the multiple industrial plants may share one or more of their value chains, extracts, and / or products. Multiple industrial plants may also be referred to as a compound, compound site, verbund, or verbund site. Furthermore, the value chain manufacturing of multiple industrial plants through various intermediate products to the final product may be dispersed across various locations, such as in different industrial plants, or integrated into a verbund site or chemical park. Such a verbund site or chemical park may be one or more industrial plants or may include one or more industrial plants, and products manufactured in at least one industrial plant may be available as raw materials for other industrial plants.

[0049] A “manufacturing process” refers to any industrial process that provides a chemical product when used in or applied to input materials. Therefore, chemical products are provided by transforming input materials directly or through one or more derived materials via a manufacturing process to produce a chemical product. Thus, a manufacturing process can be any manufacturing or treatment process, or combination of processes used to obtain a chemical product, that involves at least one or more chemical processes. A manufacturing process may further include packaging and / or stacking of the chemical product. Therefore, a manufacturing process may be a combination of chemical and physical processes.

[0050] The terms “manufacture,” “produce,” or “process” are interchangeable in the context of manufacturing processes. These terms may encompass all types of industrial processes that involve chemical processes on input materials to produce one or more chemical products.

[0051] In this disclosure, “Chemical Products” may refer to any industrial product, such as a chemical, pharmaceutical, nutritional, cosmetic, or biological product, or any combination thereof. Chemical Products may consist entirely of natural components or may contain at least one or more synthetic components in part. Some non-limiting examples of Chemical Products are one or more of the following: organic or inorganic compositions, monomers, polymers, foams, insecticides, herbicides, fertilizers, feeds, nutritional products, precursors, pharmaceutical or therapeutic products, or components or active ingredients thereof. In some cases, Chemical Products may also be products usable by end-users or consumers, such as cosmetic or pharmaceutical compositions. Chemical Products may also be products usable to make one or more further products, for example, Chemical Products may be synthetic foams usable to manufacture shoe soles, or coatings usable for automotive exteriors. Chemical Products may be in any form, such as solids, semi-solids, pastes, liquids, emulsions, solutions, pellets, granules, beads, particles such as thermoplastic polyurethane ("TPU") or expanded thermoplastic polyurethane ("ETPU") particles, or powders.

[0052] Therefore, chemical products can be difficult to trace or track, especially during their manufacturing process. During manufacturing, materials such as input materials may be mixed with other materials, and / or input materials may be divided into different parts downstream in the manufacturing chain, for example, to be processed in different ways. Input materials may be converted into one or more derived materials, for example, two or more times before being converted into a chemical product. Sometimes, chemical products may be divided and packaged in different packages. In some cases, it may be possible to label the packaged product or part thereof, but it may be difficult to attach details of the manufacturing process that resulted in the production of that particular chemical product or part thereof. Often, input materials and / or chemical products may be in forms that make it physically difficult to label them. Therefore, this teaching provides a method in which one or more object identifiers can also be used to overcome such limitations.

[0053] Manufacturing processes can be continuous, or they can be batch chemical manufacturing processes, for example, based on catalysts that require recovery in a campaign. One key difference between these manufacturing types is the frequency that occurs in the data generated during manufacturing. For example, in a batch process, manufacturing data extends across different batches manufactured in that run, from the start of the manufacturing process to the last batch. In a continuous setting, the data is more continuous and includes potential shifts and / or maintenance-driven downtime in the operation of manufacturing.

[0054] "Process data" refers to data including values ​​measured during the manufacturing process via one or more sensors, e.g., numerical or binary signal values. Process data may be one or more time-series data of process parameters and / or equipment operating conditions. Preferably, process data includes time information of process parameters and / or equipment operating conditions, for example, the data includes timestamps for at least some of the data points related to process parameters and / or equipment operating conditions. More preferably, process data includes time-space data, i.e., time data and location or data related to one or more physically separated equipment zones, so that a time-space relationship can be derived from the data. The time-space relationship can be used, for example, to calculate the position of input material at any given time.

[0055] "Real-time process data" essentially refers to process data that is measured or in a transitional state while a particular input material is being processed using a manufacturing process. For example, real-time process data for an input material is process data from or around the same time as the processing of the input material using the manufacturing process. Here, "around the same time" means that there is little to no time delay. The term "real-time" is understood in the technical fields of computer and instrumentation. As a specific, non-limiting example, the time delay between manufacturing occurrences during the manufacturing process performed on the input material and the process data measured or read is less than 15 seconds, especially 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 lower. Thus, real-time data can be understood as a stream of time-dependent process data generated during the processing of an input material.

[0056] "Process parameters" may refer to one or more of the manufacturing process-related variables, such as temperature, pressure, time, and level.

[0057] "Input materials" may refer to at least one raw material or unprocessed material used to manufacture a chemical product. Input materials may be any organic or inorganic substance or even a combination thereof. Thus, input materials may further be a mixture or may contain multiple organic and / or inorganic components in any form. In some cases, input materials may further be derived materials or intermediate materials, for example, received or transported from an upstream equipment zone. Some non-limiting examples of input materials may be one or more of the following: polyether alcohols, polyether diols, polytetrahydrofurans, polyester diols based on adipic acid and butane-1,4-diol, isocyanates, filler materials - organic or inorganic materials, such as wood powder, starch, flax, hemp, ramie, jute, sisal, cotton, cellulose or aramid fibers, silicates, barite, glass spheres, zeolites, metals or metal oxides, talc, chalk, kaolin, aluminum hydroxide, magnesium hydroxide, aluminum nitride, aluminum silicate, barium sulfate, calcium carbonate, calcium sulfate, silica, quartz powder, aerosil, clay, mica or wollastonite, iron powder, glass spheres, glass fibers or carbon fibers.

[0058] As a further non-limiting example, the input material may be methylenediphenyl diisocyanate ("MDI") and / or polytetrahydrofuran ("PTHF"), which are subjected to at least part of the manufacturing process to obtain thermoplastic polyurethane. Thus, it will be observed that the input material is chemically treated in one or more equipment zones to obtain thermoplastic polyurethane, which may in some cases be a derived material. The derived material is further treated to obtain a chemical product. For example, thermoplastic polyurethane may be further treated in one or more further equipment zones to obtain expandable thermoplastic polyurethane. Expandable thermoplastic polyurethane may, for example, be a chemical product. However, in some cases, the thermoplastic polyurethane itself may be a chemical product that is sent to a downstream customer or facility for further processing.

[0059] "Input material data" refers to data relating to one or more properties or characteristics of the input material. Therefore, input material data may include one or more values ​​indicating properties such as the quantity of the input material. Alternatively or in addition, the quantity-indicating value may be the fillness and / or mass flow rate of the input material. The values ​​are preferably measured via one or more sensors operably coupled to or incorporated into the equipment. Alternatively or in addition, input material data may include sample / test data relating to the input material. Alternatively or in addition, input material data may include values ​​indicating any physical and / or chemical properties of the input material, such as one or more of density, concentration, purity, pH, composition, viscosity, temperature, weight, and volume. If an upstream equipment zone is downstream of a preceding equipment zone, input material data may include some data from the object identifier of the preceding equipment zone; for example, input material data may therefore include a reference or link to the object identifier of the preceding zone, or in some further cases, at least some process data from the preceding object identifier.

[0060] The input materials being processed by the processing equipment in the underlying chemical manufacturing environment are divided into physical or real-world packages, hereafter referred to as “package objects” (or “physical packages” or “product packages,” respectively). The package size of such package objects can be fixed, for example, by material weight or quantity, or determined based on weight or quantity that the processing equipment can provide with fairly constant process parameters or equipment operating parameters. Such package objects can be formed from input liquid and / or solid raw materials by a dosing unit.

[0061] The subsequent processing of such package objects is managed by corresponding data objects containing a so-called "object identifier" assigned to each package object via a computing unit that is connected to or even part of the equipment mentioned. The data objects containing the corresponding "object identifiers" of the underlying package objects are stored in the memory storage elements of the computing unit.

[0062] Data objects can be generated in response to trigger signals provided through the equipment, preferably in response to the output of corresponding sensors located in each of the equipment units. As described above, the underlying industrial plant may include different types of sensors, for example, sensors for measuring one or more process parameters and / or equipment operating conditions or parameters related to the equipment or process unit.

[0063] The “object identifier” referred to more specifically refers to a digital identifier for each of its input materials. For example, an upstream object identifier is provided for an input material. Similarly, a historical upstream object identifier corresponds to a specific historical input material that was processed earlier. Object identifiers are preferably generated by a computing unit. The provision or generation of object identifiers may be triggered by equipment or in response to a trigger event or signal, for example, from an upstream equipment zone. Object identifiers are stored in a memory storage element operably coupled to the computing unit. The memory storage may include or be part of at least one database. Thus, object identifiers may also be part of a database. Object identifiers may be provided through any suitable form, for example, by being transmitted, received, or generated.

[0064] The “Computing Unit” may include, or may be, a processing means or computer processor, such as a microprocessor or microcontroller, having one or more processing cores. In some cases, the Computing Unit may be at least partially part of the device, and may be a process controller, such as a programmable logic controller ("PLC") or a distributed control system ("DCS"), and / or at least partially a remote server. Thus, the Computing Unit may receive one or more input signals from one or more sensors operably connected to the device. If the Computing Unit is not part of the device, the Computing Unit may receive one or more input signals from the device. Alternatively or in addition, the Computing Unit may control one or more actuators or switches operably coupled to the device. One or more actuators or switches may, operationally, further be part of the device.

[0065] "Memory storage" may refer to a device for storing information in a data format on a suitable storage medium. Preferably, memory storage is digital storage suitable for storing information in a digital format, such as digital data, which is machine-readable and readable via a computer processor. Thus, memory storage may be implemented as a computer processor-readable digital memory storage device. Memory storage may be implemented, at least partially, in a cloud service. More preferably, memory storage in a digital memory storage device may be operated via a computer processor. For example, any portion of the data recorded in a digital memory storage device may be written to and / or erased and / or partially or entirely overwritten with new data by a computer processor.

[0066] A “computation unit” may include, or may be, a processing means or computer processor, such as a microprocessor or microcontroller, having one or more processing cores. In some cases, a computation unit may be at least partially part of a device, for example, a process controller such as a programmable logic controller ("PLC") or a distributed control system ("DCS"), and / or at least partially a remote server and / or cloud service. Thus, a computation unit may receive one or more input signals from one or more sensors operably connected to a device or multiple device zones. If a computation unit is not part of a device, it may receive one or more input signals from a device or device zone. Alternatively or in addition, a computation unit may control one or more actuators or switches operably coupled to a device. One or more actuators or switches may, operably, further be part of a device. The computation unit is operably coupled to a device or multiple device zones.

[0067] Therefore, the computing unit may be able to manipulate one or more parameters related to the manufacturing process by controlling one or more actuators or switches and / or end effector units, for example, by manipulating one or more of the equipment operating conditions. The control is preferably performed in response to one or more signals retrieved from the equipment.

[0068] In this context, “end effector unit” or “end effector” refers to a device that is part of and / or operably connected to the equipment, and therefore controllable via the equipment and / or computing unit, with the purpose of interacting with the environment surrounding the equipment. In some non-limiting examples, an end effector may also be a cutter, gripper, sprayer, mixing unit, extruder tip, or any part thereof, designed to interact with the environment, e.g., input materials and / or chemicals.

[0069] In the case of input materials, "properties" may refer to one or more of the quantities of the input material, batch information, or one or more values ​​that specify the quality of the input material, such as purity, concentration, viscosity, or any other characteristic.

[0070] An "interface" may be a hardware and / or software component, at least partially part of a device, or part of another computing unit that provides an object identifier; for example, an interface may be an application programming interface ("API"). In some cases, an interface may also connect to at least one network to interface with two pieces of hardware components and / or a protocol layer in a network; for example, an interface may be an interface between a device and a computing unit. In some cases, a device may be communicably coupled to a computing unit over a network; therefore, an interface may further be a network interface or may include a network interface. In some cases, an interface may further be a connectivity interface or may include a connectivity interface.

[0071] The terms "output interface" and "interface" may refer to the same component or to different components. Similar to the descriptions of interfaces, an output interface may be a hardware and / or software component, at least partially part of a device, or part of another computing unit that provides an object identifier. For example, an output interface may be an application programming interface ("API"). In some cases, an output interface may also be connected to at least one network to interface with two pieces, for example, a hardware component and / or the protocol layer in a network. For example, an output interface may be an interface between a device and a computing unit. An output interface may be or include a network interface. In some cases, an output interface may be or include a connectivity interface.

[0072] A "network interface" refers to a device or a group of one or more hardware and / or software components that allows for an operational connection to a network.

[0073] "Connectivity interface" refers to a software and / or hardware interface for establishing communication, such as transmission or exchange or signals or data. Communication may be wired or wireless. The connectivity interface is preferably based on or supports one or more communication protocols. The communication protocols may be wireless protocols, such as short-range communication protocols like Bluetooth® or WiFi, or long-range communication protocols like cellular or mobile networks, such as second-generation cellular networks (i.e., "2G"), 3G, 4G, Long-Term Evolution ("LTE"), or 5G. Alternatively or in addition, the connectivity interface may further be based on a proprietary short-range or long-range protocol. The connectivity interface may support any one or more standard and / or proprietary protocols. The connectivity interface and network interface may be the same unit or different units.

[0074] As described herein, “Network” may be any suitable type of data transmission medium, wired, wireless, or a combination thereof. Certain types of networks are not limited to the scope or generality of this teaching. Therefore, a network can refer to any suitable interconnection between at least one communication endpoint and another. A network may include one or more distribution points, routers, or other types of communication hardware. Network interconnections may be formed by physically hard wiring, optical, and / or wireless radio frequency methods. A network may, in particular, be or include physical networks formed entirely or partially by wiring, such as fiber optic networks or networks formed entirely or partially by conductive cables, or a combination thereof. A network may include, at least partially, the Internet.

[0075] As explained, in some cases, each subset of process data is added to the object identifier. For example, a subset of real-time process data in which input material is processed by an upstream equipment zone may be included in the upstream object identifier as a whole, or a portion of it may be added to or saved. Thus, a snapshot of the real-time process data that was relevant to processing the input material in the upstream equipment zone is made available or linked to the upstream object identifier. Whether all or part of the real-time process data is saved may be based, for example, on a calculation unit-mediated determination of which parts of the process data subset should be added to the object identifier.

[0076] In lieu of, or in addition to, or in addition to, the previously described, the decision may be based, for example, on the most dominant process parameters and / or equipment operating conditions that do not affect the desired properties of the chemical product. This can be advantageous in some cases, particularly when the volume of relevant real-time process data is large rather than adding a large amount of data to the upstream object identifier, and the computing unit may determine which subset of the real-time process data is added. Thus, the portion of real-time process data added to the object identifier may be determined via the computing unit. Furthermore, the determination may be based on one or more ML models. Such models are described in more detail below in this disclosure.

[0077] In one further aspect, the upstream object identifier may also include process-specific data. This data may include: Process-specific data may be one or more of the following: Enterprise Resource Planning ("ERP") data, e.g., order quantities and / or production codes and / or manufacturing process recipes and / or batch data, recipient data, and digital models related to the conversion of input materials into chemical products. ERP data may be received from an ERP system associated with the industrial plant. Digital models may be one or more machine-readable mathematical models representing one or more physical and / or chemical changes related to the conversion of input materials into chemical products. Recipient data may be, for example, data related to one or more customer orders and / or specifications. Batch data may relate to data related to a batch in production and / or previous products produced through the same equipment. In doing so, the traceability of chemical products can be further improved by bundling the relevant process-specific data. More specifically, batch data can be used to more optimally sequence the production of chemical products that are at least partially produced through the same equipment, but which have one or more different characteristics or specifications. For example, the production of such chemical products can be coordinated and / or ordered in a way that subsequent batches are least affected by previous batches. For instance, if two or more chemical products are different colors, the order of production may be determined via a calculation unit so that, with respect to the color traces from previous products, later-produced products are least affected by previously produced chemical products.

[0078] "Control setting" refers to any controllable setting and / or value that can be influenced by one or more plant control systems functionally or operablely coupled to an equipment zone, such that the setting and / or controllable value affects the manner in which input materials, and if applicable derived materials, are processed to manufacture a chemical product. Thus, a control setting determines the process parameters and / or operating conditions used in the manufacture of derived materials and / or chemical products. For example, a control setting may be a setpoint for one or more controllers in one or more plant control systems. A control setting may, for example, relate to a temperature setpoint that a controller should use for processing in an equipment zone. Another control setting may be a time period in which one or more materials should be mixed. Other non-limiting examples of control settings include quantities such as time, pressure, weight or volume, ratios, rates of change such as level, flow rate, rate and mass. In addition, or alternatively, a control setting may also determine the recipe for manufacturing a chemical product. For example, at least some of the zone-specific control settings may determine the amount or proportion of material to be used, such as the ratio in which two components should be mixed and / or the amount of additional dosing.

[0079] Therefore, “zone-specific control setting” refers to a control setting for a specific zone, for example, for an upstream equipment zone, i.e., any controllable setting and / or value.

[0080] "Performance parameters" may be, indicate, or be related to one or more properties of a chemical product. Therefore, performance parameters are one or more predetermined criteria indicating suitability for a chemical product for a particular application or use, or parameters that should meet a certain degree of suitability. In some cases, performance parameters may indicate a lack of suitability or degree of unsuitability for a particular application or use of a chemical product. In non-limiting examples, performance parameters may be strengths such as tensile strength, hardness such as Shore hardness, density such as bulk density, color, concentration, composition, viscosity, melt flow value ("MFV"), stiffness such as Young's modulus, 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 ranges of values ​​determined, for example, through testing using predetermined criteria. Thus, performance parameters represent the performance or quality of a chemical product. The specified criteria may be, for example, one or more reference values ​​or ranges against which the performance parameters of a chemical product are compared in order to determine the quality or performance of the chemical product. The specified criteria may be determined using one or more tests, such as clinical trials, reliability tests, or abrasion tests, and thus specify requirements for performance parameters for a chemical product that is suitable for one or more specific uses or applications. In some cases, the performance parameters may be related to or measured from the properties of derived materials.

[0081] The "desired performance parameter" may be one or more desired properties of a chemical product, or it may represent one or more desired properties of a chemical product. Therefore, the desired performance parameter may correspond to a desired value of a performance parameter.

[0082] In this context, "zone identification" can be understood to refer to a specific equipment zone, such as an upstream equipment zone.

[0083] Typically, performance parameters are determined from one or more samples of chemical products and / or derived materials collected during and / or after manufacturing. These samples may be transported to a laboratory and analyzed to determine the performance parameters. The results of the analysis, or the determined performance parameters, may be included in or added to the respective object identifier, thereby potentially being included in the historical data.

[0084] However, it will be acknowledged that the entire activity of collecting, processing or testing, and then analyzing the test results, can be quite time-consuming and financially demanding.

[0085] Therefore, a considerable delay may occur between the collection of samples and the implementation of any adjustments to input materials and / or process parameters and / or equipment operating conditions. This delay or lag may result in the production of suboptimal chemical products, or, in the worst case, production may be halted until the samples are analyzed and any corrective actions are taken by adjusting input materials and / or process parameters and / or equipment operating conditions.

[0086] As a solution to at least reduce variability in the performance of chemical products, this teaching can be used to more precisely control the manufacturing process via historical data, and in some cases via at least one zone-specific performance parameter which may be added to at least some of the historical object identifiers. Thus, the need for manual sampling can be reduced.

[0087] In one embodiment, the calculation of at least one zone-specific performance parameter is performed using a model that is at least partially an analytical computer model. In addition or alternatively, the model may be at least partially one or more machine learning ("ML") models.

[0088] In addition, or alternatively, the determination of zone-specific control settings is made using a model or another model. Similar to the model, the other model may be at least partially an analytical computer model. Therefore, the other model may also be at least partially one or more machine learning ("ML") models. An ML model may be trained, for example, using historical data from one or more historical upstream object identifiers.

[0089] In the context of these instructions, 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 at least partially derived from data, in this case, historical data. In contrast to rigorous models that are purely derived using physiological and chemical laws, data-driven models can enable the description of relationships that cannot be modeled by physiological and chemical laws. The use of data-driven models can enable the description of relationships without solving mathematical equations from physiological and chemical laws. This can reduce computational power and / or increase speed.

[0090] A data-driven model may be a regression model. A data-driven model may be a mathematical model. A mathematical model may describe the relationship between the given performance characteristics and the determined performance characteristics as a function.

[0091] Therefore, in this context, a data-driven model, preferably a data-driven machine learning ("ML") model, or simply a data-driven model, refers to a trained mathematical model parameterized according to its respective training dataset, such as upstream or downstream historical data, to reflect the reaction rate or physiochemical processes associated with its respective manufacturing process. An untrained mathematical model refers to a model that does not reflect the reaction rate or physiochemical processes; for example, an untrained mathematical model is not derived from physical laws that provide scientific generalizations based on empirical observations. Therefore, mechanical or physiochemical properties may not be inherent to an untrained mathematical model. An untrained model does not reflect such properties. Feature engineering and training with its respective training dataset enables the parameterization of an untrained mathematical model. The result of such training is a simple data-driven model, preferably a data-driven ML model, which reflects the reaction rate or physicochemical processes associated with the manufacturing process as a result of the training process, preferably only as a result of the training process.

[0092] Models and / or other models may be hybrid models. 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, a so-called black box, as described earlier. Models may include combinations of white-box and black-box models, as well as / or gray-box models. White-box models may be based on physiological and chemical laws. Physiological and chemical laws may be derived from first principles. Physiological and chemical laws may include one or more of reaction kinetics, the law of conservation of mass, momentum and energy, and particle populations in arbitrary dimensions. White-box models may be selected according to physiological and chemical laws governing their respective manufacturing processes or parts thereof. Black-box models may be based on historical data from, for example, one or more historical object identifiers. Black-box models may be constructed by using one or more of machine learning, deep learning, neural networks, or other forms of artificial intelligence. A black-box model may be any model that produces a good fit between the training dataset and the test data. A gray-box model is a model that combines a partial theoretical structure with data to complete the model.

[0093] Where used herein, the terms “machine learning” or “ML” may refer to statistical methods that enable machines to “learn” tasks from data without explicit programming. Machine learning techniques may include “traditional machine learning”—workflows that involve manually selecting features and then training a model. Examples of traditional machine learning techniques may include decision trees, support vector machines, and ensemble methods. In some examples, data-driven models may include data-driven deep learning models. Deep learning is a subset of machine learning that loosely models the neural pathways of the human brain. “Deep” refers to the numerous layers between the input and output layers. In deep learning, algorithms automatically learn which features are effective. Examples of deep learning techniques may include convolutional neural networks (“CNN”), recurrent neural networks such as long-short-term memory (“LSTM”), and deep Q networks.

[0094] In this disclosure, the terms “ML model” and “trained ML model” may be used interchangeably. However, it will be shown or become apparent to those skilled in the art which types of data, when used to train a particular ML model, enable it to perform its intended function.

[0095] Chemical manufacturing can be a data-heavy environment, generating a large amount of data from various devices. The proposed teachings will also be recognized as suitable and more efficient for implementing monitoring and / or control methods or systems for edge computing in industrial plants, particularly chemical plants. Thus, monitoring such as safety and / or quality control and / or control of the manufacturing process can be performed essentially on-site, for example, within each zone, and computational resources such as processing power and / or memory requirements are reduced, as object identifiers provide a highly targeted dataset of relevant data for calculating performance parameters. It may also be possible to reduce latency in calculations, thereby ensuring there is sufficient time for number crunching algorithms without slowing down the manufacturing process. Furthermore, the training process for ML models can be made faster and more efficient.

[0096] For similar reasons, this also makes this teaching suitable for cloud computing, because it allows for compact and efficient datasets. Many cloud service providers operate on a pay-per-use model based on the utilization of computing resources, thus reducing costs and / or allowing for more efficient use of computing power.

[0097] Therefore, according to one embodiment, at least one ML model as described above may be trained on data from one or more historical upstream object identifiers, or on historical data, to produce a data-driven model. The data used to train the ML model may include historical and / or current laboratory test data, or data such as performance parameters measured 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, laboratory instruments, or other measurement techniques, may be used. By including the analyzed performance parameters in the relevant historical object identifiers, a more complete relationship between the performance parameters and their corresponding process data is captured in an efficient format. Thus, costly and time-consuming laboratory results can be more accurately utilized to improve the quality of future chemical products. The scope of human error can also be reduced as the quality data is integrated with relevant snapshots of process data. In some cases, when a chemical product or derived material is analyzed, a sampling object identifier is automatically provided. This may be based on confidence values ​​or when the computing unit cannot minimize the difference between the calculated performance parameter and its corresponding desired value. Therefore, the results of analyses performed on the sample can be included in or added to the sampling object identifier, further reducing the scope of human error. Data from the sampling object identifier can also be included in the historical data.

[0098] Therefore, at least one ML model trained on data from historical upstream object identifiers can be used to determine at least some of the zone-specific control settings.

[0099] Therefore, an ML model trained using historical data to determine zone-specific control settings may accept input material data and at least one desired performance parameter as input. Thus, the trained ML model or data-driven model can provide zone-specific control settings as calculated values. As previously described, the calculated values ​​may be provided to the operator via an HMI and / or directly to the control system. Also, as described, the trained ML model can be used to automatically adapt the manufacturing process according to input material details obtained from input material data, desired performance obtained from at least one desired performance parameter, and a subset of real-time process data. The calculation unit can, for example, minimize the difference between each or some of the zone-specific performance parameters calculated via the trained ML model and their respective desired performance parameter values.

[0100] In another embodiment, the trained ML model may provide at least one confidence value indicating a zone-specific control setting. In some cases, the confidence value may be added to the upstream object identifier, for example, as metadata. If the confidence level of any prediction or calculation of a zone-specific control setting falls below an accuracy threshold, an alert may be triggered in the control system for manufacturing. The alert may be generated, for example, as a warning signal to start manufacturing using a default set of settings, and may be used to determine whether the ML model should be retrained.

[0101] In some cases, a retrained object identifier is automatically provided via the interface in response to the confidence level of any prediction or calculation of a zone-specific control setting falling below an accuracy threshold. The processing unit may add confidence values, input material data, and at least one desired performance parameter to the retrained object identifier. The retrained object identifier may be used to determine what insights are missing to control the manufacturing process with the set of variables included in the retrained object identifier. Thus, the retrained object identifier can be used via the calculation unit to further refine the historical data for future decisions. According to one embodiment, the chemical product manufactured in relation to the retrained object identifier may be sampled and analyzed. The analysis results, e.g., measured performance parameters, may be added to the retrained object identifier. Thus, the retrained object identifier can be included to generate historical data. In this way, complete traceability of the material can be maintained, accurate products can be sampled, and the historical data is efficiently enhanced even in cases where previous historical data did not fully cover the data. Therefore, this allows one or more precise samples to be collected from the manufacturing process through tracking provided by retrained object identifiers, and the samples may be analyzed together with data from the retrained object identifiers to find the cause of the decrease in confidence level. Thus, a better understanding of the complex relationships between various variables can be achieved, which can further improve the control process.

[0102] In some cases, the same or a different ML model may be used by the computing unit to determine which subset or component of the real-time process data has the most dominant effect on the chemical product. Thus, the computing unit is able to exclude process parameters and / or equipment operating conditions that have a negligible effect on at least one zone-specific performance parameter. Therefore, the relevance of the real-time process data added for a particular chemical product can be improved for their respective object identifiers.

[0103] According to one embodiment, a plurality of physically separated equipment zones also include a downstream equipment zone so that input materials advance from the upstream equipment zone to the downstream equipment zone during a manufacturing or production process. In some cases, the input materials may be divided or reduced before reaching the downstream equipment zone. Thus, according to yet another embodiment, a downstream object identifier is provided for at least a portion of the input materials in the downstream equipment zone. The downstream object identifier is augmented with at least a portion of the upstream object identifier. Thus, all or only a portion of the upstream object identifier may be encapsulated in the downstream object identifier. The portion may be, for example, a reference to the upstream object identifier, or a link that combines two object identifiers directly or via one or more other object identifiers which may be generated for the zone between the upstream and downstream equipment zones.

[0104] In some cases, it may be acknowledged that at least a portion of the input material may be called derived material. As described, the zone presence signal may be used to detect or calculate when input material or derived material is in a downstream equipment zone, so that the calculation unit may determine further zone-specific control settings for at least the downstream equipment zone. Further zone-specific control settings may also be generated for other zones downstream of the downstream equipment zone. Further zone-specific control settings may be generated based on data from upstream object identifiers and / or data from intermediate object identifiers related to intermediate equipment zones between the upstream and downstream equipment zones. For example, at least one zone-specific performance parameter calculated by the upstream object identifier can be used. Thus, the manufacturing process can be adapted in the downstream zone according to the process data of how the material was processed in the upstream zone. Thus, the granularity of control can be further improved and made more flexible. For example, any suboptimal processing upstream can be corrected by adapting the downstream zone-specific control settings.

[0105] Therefore, the method is, - Provide a downstream object identifier that includes at least a reference to an upstream object identifier via an interface, - Determining another subset of real-time process data based on downstream object identifiers and zone presence signals via the computing unit, - This may also include determining further zone-specific control settings for at least downstream equipment zones based on data from upstream object identifiers, another subset of real-time process data, and other historical data via a computing unit.

[0106] According to one embodiment, the historical data includes, for example, data from one or more historical downstream object identifiers related to previously processed input material in a downstream equipment zone. According to another embodiment, at least one of the historical downstream object identifiers is supplemented with at least a portion of process data indicating the process parameters and / or equipment operating conditions under which the previously processed input material was processed in the downstream equipment zone.

[0107] Therefore, in addition to determining further zone-specific control settings, downstream object identifiers may be supplemented with at least a portion of real-time process data from downstream equipment zones. Downstream object identifiers may be at least partially encapsulated or thereby enriched with data from upstream object identifiers, or more specifically, data from upstream object identifiers supplemented with at least a portion of a subset of real-time process data. Alternatively, downstream object identifiers may be linked to upstream object identifiers. In other words, downstream object identifiers are supplemented with upstream object identifiers. Downstream and upstream object identifiers may be located in the same place, or they may be located in different places. Therefore, downstream object identifiers are associated with upstream object identifiers by upstream object identifiers that are at least partially part of the downstream object identifier.

[0108] The calculation unit may also calculate at least one additional zone-specific performance parameter for a chemical product associated with a downstream identifier, based on another subset of real-time process data and other historical data. The downstream object identifier may also have at least one additional zone-specific performance parameter added to it.

[0109] Therefore, the method is, - Calculate at least one additional zone-specific performance parameter for a chemical product associated with a downstream object identifier based on another subset of real-time process data and other historical data via a computing unit, - This may also include adding at least one other zone-specific performance parameter to the downstream object identifier.

[0110] Furthermore, as explained earlier, the method is - This may also include adding at least a portion of another subset of real-time process data to downstream object identifiers.

[0111] Doing so can improve granular visibility into the quality of various components in the manufacturing chain. For example, performance parameters for each specific zone can be used to track and control the quality of materials in that particular zone.

[0112] Similar to the above description of the upstream equipment zone, models such as the ML model described herein, at least in part, can also be applied to the downstream equipment zone. Several examples of this are listed below.

[0113] For example, according to one embodiment, at least one downstream ML model, as described previously, may be trained on data from one or more historical downstream object identifiers. The data used to train the downstream 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, laboratory instruments, or other measurement techniques, may be used.

[0114] Therefore, at least one ML model trained on data from historical downstream object identifiers can be used to predict zone-specific control settings for that zone and other downstream zones. Additionally, one or more zone-specific performance parameters related to chemical products may be selectively predicted. Thus, at least some sampling and testing requirements can be eliminated, thus saving time and resources.

[0115] In the latter case, a downstream ML model, trained using historical downstream data to compute at least one zone-specific performance parameter, may accept at least a portion of a subset of downstream real-time process data as input. Thus, the downstream ML model can provide at least one zone-specific performance parameter as a computed value. Therefore, such a downstream ML model can also be used in conjunction with an ML model for upstream equipment zones to monitor and / or control the manufacturing process by adapting control settings, and thus to address any quality control issues more finely at an earlier stage.

[0116] Similarly, downstream ML models may also provide at least one downstream confidence value indicating the confidence level for at least one zone-specific performance parameter and / or further zone-specific control settings. The downstream confidence value may be added to the downstream object identifier, for example, as metadata. If the confidence level of the prediction or calculation of at least one zone-specific performance parameter and / or further zone-specific control settings falls below their corresponding precision thresholds, an alert may be triggered in the control system for manufacturing. The alert may be generated as a warning signal, for example, to initiate physical testing of a sample for laboratory analysis.

[0117] Those skilled in the art will acknowledge that the terms “add” or “add” may mean to include or attach, for example, “save,” different data elements, such as metadata, in the same database or in the same memory storage element, or in adjacent or different locations in the database or memory storage. The term may also mean a link of one or more data elements, packages, or streams in the same or different locations in a form that can be read and / or fetched and / or combined as needed. At least one of these locations may be part of a remote server or even part of a cloud-based service at least partially.

[0118] "Remote servers" refer to one or more computers or one or more computer servers located away from a plant. Therefore, remote servers may be located several kilometers or more from the plant. Furthermore, remote servers may be located in different countries. Remote servers may also be implemented, at least partially, as a cloud-based service or platform, for example, as a Platform-as-a-Service ("PaaS"). The term may also collectively refer to two or more computers or servers located in different locations. Remote servers may be data management systems.

[0119] It will be observed that input materials, after traversing the upstream equipment zone, may have substantially different properties than when they entered the upstream equipment zone. Therefore, as explained, upon entry of input materials into the downstream zone, they may have been transformed into derived materials or intermediate materials. However, for the sake of simplification, and without loss of generality of this teaching, the term "input material" is also used to refer to input materials that have been transformed into such intermediate or derived materials during the manufacturing process. For example, a batch of input material in the form of a mixture of chemical components may be traversed on a conveyor belt through the upstream zone, where the batch is heated to induce a chemical reaction. As a result, when the input material enters the downstream zone immediately after leaving the upstream zone or after traversing other zones, the material may have become a derived material with different properties from the original input material. However, as stated above, such derived materials can still be called input materials, because, at the very least, the relationship between such intermediate materials and input materials can be defined and determined through the manufacturing process. Furthermore, in other cases, the input material may still essentially retain similar properties even after crossing upstream zones or other zones, for example, if the upstream zone simply dries or filters the input material to remove traces of undesirable material. Thus, those skilled in the art will understand that the input material in the intermediate zone may or may not be converted into a derived material.

[0120] In some cases, there may be one or more intermediate zones between the upstream and downstream zones, but separate object identifiers are not provided for such zones. The applicant has found it more advantageous to generate downstream object identifiers when input materials or derived materials are combined with other materials, or when input materials or derived materials are divided or fragmented into multiple parts. Or, more generally, after providing an object identifier, the generation of downstream object identifiers or any further object identifiers may be performed only in zones where the material mass flow rate changes. A change in mass flow rate may be a change in mass resulting from the addition or mixing of new materials to the input materials or derived materials and / or the removal or division of materials from the input materials or intermediate materials. For example, a change in mass due to the removal of moisture, or in some cases the release of gases resulting from chemical reactions during manufacturing, may be excluded from occurrences that trigger a second or further object identifier. In particular, no further object identifiers may be provided in zones where there is no substantial change in the mass of the input materials. It is not required in this specification to explicitly state the limit for “substantial change” in mass. This is because those skilled in the art will recognize that it may depend, among other factors, particularly on the type of input materials and / or chemical product being manufactured. For example, in some cases a change in mass of 20% or more may be considered substantial, while in others it may be 5% or more, or in some cases 1% or more, or perhaps even lower. For example, in the case of a valuable product, a smaller change may be considered significant compared to another less valuable product.

[0121] As some examples, the determination of providing or generating object identifiers in an equipment zone following an upstream equipment zone may be based on any of the following: not providing a new object identifier if the degree of inverse mixing in the equipment zone is less than or approximately the size of the package in the zone preceding the equipment zone; providing a new object identifier if the degree of inverse mixing in the equipment zone is greater than the size of the package in the zone preceding the equipment zone; not providing a new object identifier in an equipment zone that is only a transport zone with one or more transport systems or elements; providing a new object identifier for one or more components if the equipment zone involves material separation in the zone and one or more components are separated components; providing at least one new object identifier in an equipment zone if it involves filling or packaging materials into at least one package and each package contains one or more chemical products.

[0122] As described, when samples of input materials, derived materials, or chemical products are collected for analysis, such samples may also be provided with a sample object identifier. The sample object identifier may be similar to the object identifiers described herein and the associated corresponding process data added as described herein. Thus, the sample may also be accompanied by an accurate snapshot of the manufacturing process related to the characteristics of the sample. Thus, analysis and quality control can be further improved. Furthermore, the manufacturing process can be synergistically improved, for example, based on improved training of one or more ML models.

[0123] In another embodiment, if the manufacturing process involves the physical transport or movement of input materials within or between zones using transport elements such as a conveyor system, the real-time process data may also include data indicating the speed of the transport elements and / or the speed at which the input materials are transported during the manufacturing process. The speed may be provided directly via one or more sensors and / or calculated via a computing unit, for example, based on the time of entry into a zone and the time of exit from a zone or the time of entry into another subsequent zone. Thus, object identifiers can be enriched by processing time-based aspects within a zone, particularly those that may affect one or more performance parameters of the chemical product. Furthermore, the requirement for speed-measuring sensors or devices for transport elements can be eliminated by using timestamps of entry and exit or subsequent zone entry.

[0124] In another embodiment, each object identifier includes a unique identifier, preferably a globally unique identifier ("GUID"). Tracking of chemical products can be enhanced by attaching a GUID to each virtual package of the chemical product. Data management of process data, such as time-series data, can also be reduced via GUIDs, and a direct correlation between virtual / physical packages, manufacturing history, and quality control history can be enabled.

[0125] As described with respect to ML models, in one embodiment, an upstream ML model, such as those described herein, may be trained on data from upstream object identifiers. The training data may include historical and / or current laboratory test data, or data from historical and / or recent samples of derived materials and / or chemical products. Furthermore, downstream ML models, such as regression models or deep learning models, may be trained on data from downstream object identifiers. The training data may include historical and / or current laboratory test data, or data from historical and / or recent samples of derived materials and / or chemical products.

[0126] In addition to the previously described advantages of ML models, having a zone-based trained model in the manufacturing line can enable more detailed tracking of materials and prediction of their respective performance parameters, and even chemical product performance parameters.

[0127] In some manufacturing scenarios, such as batch production, this type of model may be used on the fly to alert for quality control issues not only for the manufactured chemical product but also for any derived materials.

[0128] Therefore, any or each of the equipment zones may be monitored and / or controlled via individual ML models, which are trained based on data from their respective object identifiers within that zone.

[0129] In one embodiment, the provision of each object identifier for a zone may occur or be triggered in response to one or more values ​​representing the characteristics of the input material and / or one or more values ​​from the equipment operating conditions and / or one or more values ​​of process parameters that reach, meet, or exceed a predetermined threshold. Any such values ​​may be measured via one or more sensors and / or switches. For example, a predetermined threshold may be related to the weight value of the input material introduced into the equipment. Thus, a trigger signal may be generated when a quantity, such as the weight of the input material received by the equipment, reaches a predetermined quantity threshold, such as a weight threshold. Some examples of triggering events or occurrences to provide object identifiers have been described previously in this disclosure. Object identifiers may be provided in response to a trigger signal, or directly, in response to a quantity or weight that reaches a predetermined weight threshold. The trigger signal may be a separate signal or simply an event, such as a specific signal that meets a predetermined criterion, such as a threshold detected via a computing unit and / or equipment. Thus, it will also be recognized that object identifiers may be provided in response to a quantity of input material that reaches a predetermined quantity threshold. The quantity may be measured as weight, as described in the example above, and / or may be one or more other values ​​such as level, filling or degree of filling or volume, and / or by summing the mass flow of the input material or by applying an integral to the mass flow of the input material.

[0130] Therefore, for example, an upstream object identifier may be provided in response to a trigger event or signal, which is preferably provided via equipment or an upstream equipment zone. This may be done in response to the output of one or more sensors and / or switches operably coupled to the upstream equipment. The trigger event or signal may be related to the occurrence of a quantity value of input material, for example, a quantity value that reaches or exceeds a predetermined quantity threshold. Such occurrence may be detected via a computing unit and / or upstream equipment using, for example, one or more weight sensors, level sensors, filling sensors, or any suitable sensor capable of measuring or detecting the quantity of input material.

[0131] The advantage of using quantity as a trigger for providing upstream object identifiers is that any change in the quantity of material during the manufacturing process can be used as a trigger for providing one or more additional object identifiers, as described in this teaching. The applicant has shown that this can provide an optimal method for segmenting the generation of different object identifiers in an industrial environment for processing or manufacturing one or more chemical products, thereby enabling the tracking of input materials, any derived materials, and ultimately chemical products, while describing the quantity or mass flow rate, essentially throughout the entire manufacturing chain and, in some cases, beyond it. By providing object identifiers just at the point when new material is introduced or added, or when material is divided, the number of object identifiers can be minimized while maintaining traceability of the material not only at the end of manufacturing but also within it. In equipment or manufacturing zones where no new material is added or material is divided, knowledge of the process within such zones can be used to maintain observability within two adjacent object identifiers.

[0132] From another perspective, the use of zone-specific control settings generated in any of the method embodiments described herein for controlling the manufacturing processes of an industrial plant can also be provided.

[0133] By using zone-specific control settings, industrial plants can obtain the advantages disclosed herein.

[0134] From another perspective, a system for controlling a manufacturing process can also be provided, the system configured to do one of the methods disclosed herein. Or, a system for controlling a manufacturing process for producing a chemical product in an industrial plant, the industrial plant comprising a plurality of physically separated equipment zones, the product being produced by processing at least one input material using a manufacturing process through the plurality of equipment zones, and the system configured to do one of the methods disclosed herein.

[0135] For example, a system for controlling a manufacturing process for producing chemical products in an industrial plant, wherein the industrial plant includes a computing unit and a plurality of physically separated equipment zones, and the product is produced by processing at least one input material using the manufacturing process through the plurality of equipment zones, and the system - Provide an upstream object identifier via an interface that includes input material data and at least one desired performance parameter related to a chemical product, wherein the input material data represents one or more properties of the input material. - Determining a set of process and / or operational parameters based on an upstream object identifier and at least one desired performance parameter via a computing unit, - Determining zone-specific control settings for each equipment zone based on a determined set of process and / or operating parameters and historical data via a computing unit, - To provide zone-specific control settings for controlling the production of chemical products associated with upstream object identifiers via the output interface, We can provide a system that is configured to perform this task.

[0136] As explained, the output interface and the interface may be the same component or different components, and may be identical or non-identical to each other.

[0137] From another perspective, it is also possible to provide a computer program which, when the program is executed by a suitable computing unit, includes instructions causing the computing unit to perform any of the methods disclosed herein. It is also possible to provide a non-temporary computer-readable medium for storing a program that causes a suitable computing unit to perform any of the method steps disclosed herein.

[0138] For example, a computer program, or a non-temporary computer-readable medium for storing a program, which includes instructions, and when the program is executed by a suitable computing unit operably coupled to multiple equipment zones for manufacturing chemical products in an industrial plant by processing at least one input material using a manufacturing process, the computing unit, - The interface provides an upstream object identifier that includes input material data and at least one desired performance parameter related to the chemical product, and the input material data indicates one or more properties of the input material. - Determine a set of process and / or operational parameters based on an upstream object identifier and at least one desired performance parameter. - Determine zone-specific control settings for each equipment zone based on a determined set of process and / or operating parameters and historical data. - Provides zone-specific control settings for controlling the production of chemical products associated with upstream object identifiers via the output interface. It is possible to provide a computer program, or a non-temporary computer-readable medium for storing a program.

[0139] It may be observed that a computing unit may be operablely coupled to an interface and / or that the interface may be part of a computing unit.

[0140] Computer-readable data media or carriers include any suitable data storage device storing one or more sets of instructions (e.g., software) that embody any one or more of the methods or functions described herein. Instructions may, in whole or at least in part, constitute computer-readable storage media, and may also reside in main memory and / or within the processor during their execution by the compute unit, main memory, and processing unit. Instructions may also be transmitted or received over a network via network interface devices.

[0141] Computer programs for implementing one or more of the embodiments described herein may be stored and / or distributed on suitable media such as optical storage media or solid-state media supplied together with or as part of other hardware, or they may be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. However, computer programs may also be provided over networks such as the World Wide Web, and can be downloaded from such networks to the working memory of a data processor.

[0142] Furthermore, a data carrier or data storage medium may be provided to make the computer program product available for download, and this computer program product is arranged to perform any of the methods disclosed herein.

[0143] From another perspective, a computing unit including computer program code for performing the methods disclosed herein can also be provided. Furthermore, a computing unit operably coupled to memory storage including computer program code for performing the methods disclosed herein can also be provided.

[0144] It is obvious to those skilled in the art that two or more components are “operably” coupled or connected. In an unrestricted example, this means that there may be at least one communication connection between the coupled or connected components, for example, via an interface or any other suitable interface. The communication connection may be fixed thereto 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.

[0145] In this context, "parameter" refers to any relevant physical or chemical property and / or measure thereof, such as temperature, direction, position, quantity, density, weight, color, humidity, 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.

[0146] An "actuator" refers to any component that works, directly or indirectly, to move and control a mechanism associated with machinery or other equipment. Actuators may include valves, motors, and drive units. Actuators may be electrically, hydraulically, pneumatically, or a combination thereof.

[0147] "Computer processor" refers to any logic circuit configured to perform the basic operations of a computer or system, and / or, generally, a device configured to perform calculations or logical operations. In particular, a processing unit or computer processor may be configured to process basic instructions that drive a computer or system. As one example, a processing unit or computer processor may include at least one arithmetic logic unit ("ALU"), at least one floating-point unit ("FPU"), for example, a numerical coprocessor or numerical coprocessor, a number of registers, in particular registers configured to supply operands to the ALU and store the results of operations, and memory, for example, L1 and L2 cache memory. In particular, a processing unit or computer processor may be a multi-core processor. In particular, a processing unit or computer processor may be or include a central processing unit ("CPU"). The processing means or computer processor may be, or include, a multiple instruction set computing ("CISC") microprocessor, a reduced instruction set computing ("RISC") microprocessor, a very long instruction word ("VLIW") microprocessor, or a processor implementing a processor or combination of instruction sets. The processing means may also be one or more dedicated processing devices, such as application-specific integrated circuits ("ASIC"), field-programmable gate arrays ("FPGA"), coupled-programmable logic circuits ("CPLD"), digital signal processors ("DSP"), network processors, etc. The methods, systems and apparatus described herein may be implemented as software in a DSP, microcontroller or any other side processor, or as hardware circuitry in an ASIC, CPLD or FPGA.The terms "processing means" or "processor" may refer to one or more processing devices, for example, a distributed system of processing devices arranged across multiple computer systems (e.g., cloud computing), and should be understood not to be limited to a single device unless otherwise specified.

[0148] "Computer-readable data medium" or carrier includes any suitable data storage device or computer-readable memory storing one or more sets of instructions (e.g., software) that embody any one or more of the methods or functions described herein. Instructions may also reside in main memory and / or the processor during their execution by computing units, main memory and processing devices, which may, in whole or at least in part, constitute computer-readable data medium. Instructions may also be transmitted or received over a network via network interface devices.

[0149] Brief explanation of multiple drawings Herein, several aspects of this instruction will be described with reference to the following drawings illustrating those aspects as examples. Since the generality of this instruction does not depend on it, the drawings may not be to scale. Some of the illustrated features may be logical features shown together with physical features for the sake of understanding, without affecting the generality of this instruction. To facilitate the identification of any particular element or operation, the most prominent one or more digits in the reference number refer to the drawing number in which that element was first introduced. [Brief explanation of the drawing]

[0150] [Figure 1] Several aspects of the system described in this instruction are shown. [Figure 2] The method and manner of this instruction are shown. [Figure 3] The combined block / flowchart illustrates a first embodiment of the system and corresponding method described in this instruction. [Figure 4] The combined block / flowchart illustrates a second embodiment of the system and corresponding method described in this instruction. [Figure 5] A combined block / flowchart illustrates a third embodiment of the system and corresponding method described in this teaching. [Figure 6] A first embodiment of a graph-based database array representing the topological structure of an industrial plant or plant cluster is shown, which includes multiple equipment devices and, consequently, multiple equipment zones through which input materials advance during a production or manufacturing process. [Figure 7] Figure 6 shows a second embodiment of the graph-based database array. [Figure 8] The combined block / flowchart illustrates another embodiment of the system and corresponding method according to this instruction, using a cloud computing platform, where the machine learning (ML) process is implemented in the cloud. [Modes for carrying out the invention]

[0151] Detailed explanation Figure 1 shows an example of a system 168 for controlling a manufacturing process for producing a chemical product 170 in an industrial plant. At least some of the method embodiments can also be understood from the following description. The industrial plant includes at least one or more equipment zones for manufacturing or producing the chemical product 170 using the manufacturing process. The chemical product 170 may be in any form, for example, a pharmaceutical product, a foam, a nutritional product, an agricultural product, or a precursor. For example, the chemical product 170 may be a thermoplastic polyurethane in granular form. The chemical product 170 may also be in batches, for example, packages of 10 kg each. As described, due to the nature of such chemical products, they may be difficult to track in the manufacturing chain. However, it may be important to ensure that each component, for example, each unit or package, or even internal parts, have consistent desired properties or quality. This teaching can enable the achievement of a manufacturing process for the chemical product 170 that can produce desired performance parameters.

[0152] The equipment zone is shown in Figure 1 as equipment, for example, a hopper or mixing pot 104, which may be part of the upstream equipment zone. The mixing pot 104 receives input material, which may be a single material or a number of components, for example, methylenediphenyl diisocyanate ("MDI") and / or polytetrahydrofuran ("PTHF"). Here, the input material is received in two parts shown, supplied 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 the upstream equipment zone.

[0153] 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 depending on the specifications of a particular industrial plant and / or details of the chemical product 170 to be manufactured and / or date and time details and / or details of the specific input material to be used. The upstream object identifier 122 is indicated to be provided in memory storage 128. Memory storage 128 is operably coupled to a compute unit 124. Memory storage 128 may also be part of the compute unit 124. Memory storage 128 and / or compute unit 124 may be at least partially part of a cloud service.

[0154] The computing unit 124 is operably coupled to the upstream equipment zone or equipment belonging to the upstream equipment zone via a network 138, which may be any suitable type of data transmission medium. The computing unit 124 may also be part of the equipment in the plant, for example, at least partially part of the upstream equipment zone. The computing unit 124 may also be at least partially a plant control system such as a DCS and / or PLC. The computing unit 124 may receive one or more signals from one or more sensors operably coupled to the equipment in the upstream equipment zone. For example, the computing unit 124 may receive one or more signals from one or more sensors associated with the filling sensor 144 and / or transport elements 102a~b. These sensors are also part of the upstream equipment zone. The computing unit 124 may also control the upstream equipment zone or some parts thereof, at least partially. For example, the computing unit 124 may control valves 112a,b and / or heaters 118 and / or transport elements 102a~b via their respective actuators. In the example in Figure 1, the conveying elements 102a,b and others are shown as a conveyor system including one or more motors and a belt driven by the motors such that the belt moves so that the input material 114 is conveyed along the belt in the transverse direction 120 of the belt.

[0155] Without affecting the scope or generality of this instruction, other types of conveying elements may be used instead of or in combination with conveying systems. In some cases, any type of equipment involving a flow of material, e.g., one or more materials coming in and one or more materials going out, may be called a conveying element. Thus, in addition to conveying systems or belts, equipment such as extruders, pelletizers, heat exchangers, buffer silos, silos with mixers, mixers, mixing vessels, cutting mills, double-cone blenders, hardening tubes, columns, separators, extractors, thin-film evaporators, filters, and sieves may also be called conveying elements. Thus, it will be recognized that the presence of a conveying system as a conveying system may be selective, since in at least some cases material may move directly from one piece of equipment to another via mass flow, or as a normal flow through one piece of equipment to another. For example, material may move directly from a heat exchanger to a separator or even to a column, etc. Thus, in some cases, one or more conveying elements or systems may be specific to the equipment.

[0156] An upstream object identifier 122 may be provided in response to a trigger signal or event, which may be a signal or event related to the quality of the input material. For example, a filling sensor 144 may be used to detect a value of at least one quantity, such as the degree of filling and / or weight of the input material. When the quantity reaches a predetermined threshold, the calculation unit 124 may automatically provide a first upstream object identifier 122 in the memory storage 128. The upstream object identifier 122 includes data related to the input material or input material data. The input material data indicates one or more characteristics of the input material.

[0157] The upstream object identifier 122 also provides at least one desired performance parameter associated with the chemical product 170 (selectively shown as a subset 126 of real-time process data, as will be described later). The desired performance parameter relates to the desired performance or quality of the chemical product 170.

[0158] Next, the calculation unit 124 is configured to determine a set of process and / or operating parameters based on the upstream object identifier 122 and at least one desired performance parameter. Thus, the calculation unit 124 can determine zone-specific control settings for each equipment zone based on the determined set of process and / or operating parameters as well as historical data. The historical data may include, for example, data from one or more historical upstream object identifiers related to previously processed input materials in the upstream equipment zone, each historical upstream object identifier being supplemented with at least a portion of process data indicating the process parameters and / or equipment operating conditions under which the previously processed input material was processed in the upstream equipment zone. The zone-specific control settings are then provided to control the manufacturing process of the chemical product 170. The zone-specific control settings may be provided via an output interface, which may be the same as the interface or a different set of components. Thus, the zone-specific control settings are used by the calculation unit 124 and / or the plant control system to manufacture the chemical product 170.

[0159] In some cases, the calculation unit 124 may receive process data from all equipment or equipment zones in an industrial plant. The calculation unit 124 may determine a subset of real-time process data based on upstream object identifiers and zone presence signals. For example, trigger signals or events may also be used to generate zone presence signals for upstream equipment zones. In addition, or alternatively, zone presence signals are provided by mapping real-time process data, which is time-dependent data in the manufacturing environment, to spatial data. Thus, zone presence signals can be used to determine not only process parameters and / or equipment operating conditions related to the processing of input material 114 in the upstream equipment zone, but also the time phases of said process parameters and / or equipment operating conditions included in the real-time process data.

[0160] The calculation unit 124 may also calculate at least one zone-specific performance parameter related to the chemical product 170, associated with the upstream object identifier 122. In this case, the calculation is based on a subset 126 of real-time process data selectively added and shown in the upstream object identifier 122. The calculation of the zone-specific performance parameter is also based on historical data, which includes data from one or more historical upstream object identifiers. Each historical upstream object identifier is associated with each input material that has been processed in the upstream equipment zone in the past. Each historical upstream object identifier is supplemented with at least a portion of process data that shows the process parameters and / or equipment operating conditions under which the previously processed input material was processed in the upstream equipment zone.

[0161] At least one zone-specific performance parameter may be added to the upstream object identifier 122 as metadata, for example. Thus, the upstream object identifier 122 is enriched with performance parameters related to the quality of the chemical product 170. Consequently, the quality control process can be simplified and improved, for example, by combining quality-related data with the resulting chemical product 170, while improving traceability. In addition, at least the calculated zone-specific performance parameters may be used downstream to adapt the downstream manufacturing process. Thus, the manufacturing process can be controlled with finer granularity and become more flexible while maintaining the performance of the chemical product 170.

[0162] A subset of real-time process data 126 from the upstream equipment zone may be data from a time window in which the input material 114 was in the upstream equipment zone, or the time window may be even shorter, just for the time the input material 114 was processed through the mixing pot 104. The real-time process data can be used to determine the time window. Thus, the upstream object identifier 122 can be enriched with highly relevant data by using the time dimension of the real-time process data. Thus, the object identifier can be used not only to track materials in the manufacturing process, but also to encapsulate high-quality data that can make edge computing and / or cloud computing more effective. Object identifier data can be well-suited for faster training and retraining of machine learning models. The data encapsulated in the object identifier can be more compact than traditional datasets, which can also simplify data integration.

[0163] At least a portion of the real-time process data 126 indicates process parameters and / or equipment operating conditions, i.e., the operating conditions of the mixing pot 104 and valves 112a-b, where the input material is processed in the upstream equipment zone, such as one or more of the following: incoming mass flow rate, outgoing mass flow rate, fill level, temperature, humidity, entry type stamp or time, exit time, etc. The equipment operating conditions may in this case be control signals and / or setpoints for valves 112a,b and / or mixing pot 104. Zone-specific control settings may be used to control these, for example. The subset of real-time process data 126 may be or may include time-series data, meaning it may include time-dependent signals that may be obtained via one or more sensors, such as the output of the fill sensor 144. The time-series data may include continuous signals, or one of them may be intermittent at regular or irregular intervals. A subset of the real-time process data 126 may further include one or more timestamps, for example, the time of entry into the mixing pot 104 and / or the time of exit from the mixing pot 104. Thus, a particular input material 114 may be associated with a subset of the real-time process data 126 related to that input material 114 via an upstream object identifier 122. The upstream object identifier 122 may be added to other object identifiers downstream of the manufacturing process so that specific process data and / or equipment operating conditions can be correlated to a particular chemical product. Other important advantages have already been described in other parts of the disclosure, for example, in the summary section.

[0164] The conveyor system, including the transport elements 102a,b and associated belts, may be considered an intermediate equipment zone located downstream of the upstream equipment zone. In this example, the intermediate equipment zone includes a heater 118 used to heat the input material traversing the belt. The conveyor system may further include one or more sensors, such as speed sensors, weight sensors, temperature sensors, or any other type of sensor for measuring or determining process parameters and / or characteristics of the input material 114 in the intermediate equipment zone. Any or all of the sensor outputs may be provided to a calculation unit 124.

[0165] As the input material 114 advances along the transverse direction 120, the input material 114 is heated via a heater 118. The heater 118 may be operably coupled to a 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 is controllable via the computing unit 124, for example, via one or more control signals and / or setpoints, which may be or may be obtained through zone-specific control settings. Thus, the manner in which the input material 114 is processed in the upstream equipment zone is determined via at least some of the zone-specific control settings. Some of the zone-specific control settings may be used to control downstream zones, as will be further described.

[0166] Similarly, a conveyor system including the transport elements 102a,b and associated belts may also be operably coupled to a 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 controllable via the computing unit 124, for example, via one or more control signals and / or setpoints provided through the computing unit 124, either as a zone-specific control setting or in response to a zone-specific control setting. Thus, the speed of the transport elements 102a,b may be observable and / or controllable by the computing unit 124.

[0167] Selectively, since the amount of input material 114 is constant or nearly constant in the intermediate equipment zone, no further object identifier may be provided for the intermediate equipment zone. Therefore, process data from the intermediate equipment zone, i.e., the heater 118 and / or conveying elements 102a,b, may be added to the object identifier of the previous or preceding zone, i.e., the upstream object identifier 122. Thus, the added subset of real-time process data 126 may be enriched to further indicate one or more of the process parameters and / or equipment operating conditions from the intermediate equipment zone in which the input material 114 is processed, i.e., the operating conditions of the heater 118 and / or conveying elements 102a,b, for example, the incoming mass flow rate, the outgoing mass flow rate, one or more temperature values ​​from the intermediate zone, the entry time, the exit time, the speed of the conveying elements 102a,b and / or the belt. In this case, the equipment operating conditions may be the control signals and / or setpoints of the conveying elements 102a,b and / or the heater 118, which can be derived from the zone-specific control settings.

[0168] It is clear that a subset of the real-time process data 126 is primarily related to the time period during which the input material 114 is present in each equipment zone. Therefore, a precise snapshot of the relevant process data for a particular input material 114 can be provided via the upstream object identifier 122. Further observability of the input material 114 may be extracted through knowledge of specific parts or portions of the manufacturing process within the intermediate equipment zone, e.g., through chemical reactions. Alternatively, or in addition, the rate at which the input material 114 traverses the intermediate equipment zone can be used to extract further observability via the calculation unit 124. More granular details of the conditions under which the input material 114 is processed in the intermediate equipment zone may be obtained from the upstream object identifier 122, relating to a subset of the real-time process data 126 with specific timestamps, or time-series data of the input material 114 in the intermediate equipment zone, as well as / or entry and / or exit times.

[0169] Data from the upstream object identifier 122 may be used to train one or more ML models for monitoring and / or controlling the entire manufacturing process and / or specific parts thereof, for example, parts of the manufacturing process within the upstream equipment zone and / or intermediate equipment zone. The ML models, preferably at least partially data-driven models, and / or the upstream object identifier 122 may also be used to correlate one or more performance parameters of a chemical product with details of the manufacturing process in one or more zones.

[0170] As the input material 114 advances along the transverse direction 120, it will be observed that the properties of the input material 114 may change, and that the input material 114 may be converted or transformed into a derivative material 116. For example, when the heater 118 heats the input material 114, the input material 114 may give rise to a derivative material 116. Those skilled in the art will recognize that, for simplicity and ease of understanding, the derivative material 116 may sometimes be referred to as the input material in this teaching. For example, in the context of the equipment zone or component being described, it will be clear which phase the input material is in within the manufacturing process described in this example.

[0171] Here, we describe an example of a zone in which the material is divided into multiple parts. Figure 1 shows such a zone as a downstream equipment zone, including a cutting mill 142 and second conveying elements 106a,b. The derived material 116, traversing along the transverse direction 154, is divided or fragmented using the cutting mill 142, thereby producing multiple parts, shown in this example as the first divided material 140a and the second divided material 140b.

[0172] Therefore, according to one aspect of this teaching, individual object identifiers may be provided for each part. However, in some cases, object identifiers may be provided for only one or some of the parts, rather than providing individual object identifiers for each part. This may be, for example, when it does not matter which part to track. For example, an object identifier may not be provided for the part of the derived material 116 that is to be discarded. Referring again to Figure 1, a first downstream object identifier 130a is provided for the first divided material 140a, and a second downstream object identifier 130b is provided for the second divided material 140b.

[0173] A first downstream object identifier 130a includes at least a portion of the upstream object identifier 122, and similarly, a second downstream object identifier 130b includes at least a portion of the upstream object identifier 122. The calculation unit 124 may then determine another subset of real-time process data (e.g., a first subset of downstream real-time process data 132a and / or a second subset of downstream real-time process data 132b) based on the downstream object identifiers and zone presence signals. The calculation unit 124 may then determine further zone-specific control settings for the downstream equipment zone, and selectively for other equipment zones downstream of the downstream equipment zone, based on data from the upstream object identifier 122, another subset of real-time process data, and historical data from one or more historical downstream object identifiers related to previously processed input materials in the downstream equipment zone.

[0174] The first downstream object identifier 130a is selectively given a first subset of downstream real-time process data 132a, and the second downstream object identifier 130b is selectively given a second subset of downstream real-time process data 132b. The first subset 132a of downstream real-time process data may be a copy of the second subset 132b of downstream real-time process data, or it 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., at essentially the same place and time, the process data added 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 undergo different processing within the downstream equipment zone, the first subset 132a of downstream real-time process data and the second subset 132b of downstream real-time process data may be different from each other.

[0175] However, those skilled in the art will recognize that in some cases, when a material processed through the cutting mill 142 is divided into multiple parts, only one object identifier may be provided at the cutting mill 142, and then multiple object identifiers may be provided to the cutting mill 142 thereafter. Thus, depending on the details of the particular manufacturing process, the cutting mill may or may not be a separation device. Similarly, in some cases, no new object identifier may be provided for the cutting mill so that process data from the zone is added to the preceding object identifier. Thus, new object identifiers may be provided at the zones where the material is divided and / or combined. For example, in some cases, downstream object identifiers 130a and a second downstream object identifier 130b may be provided after the cutting mill 142, for example, upon entry into different zones after the cutting mill 142.

[0176] In this example, the downstream equipment zone also includes an imaging sensor 146, which may be a camera or any other type of optical sensor. The imaging sensor 146 may also be operably 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 reject or divert material that does not meet a given quality criterion. Since the mass flow rate of the material is varied in the downstream equipment zone according to one aspect of this teaching, another object identifier (not shown in Figure 1) may be provided before the downstream object identifier 130a and the second downstream object identifier 130b.

[0177] The provision of downstream object identifiers 130a and a second downstream object identifier 130b may be triggered via the imaging sensor 146 in response to derived material 116 passing quality criteria. By correlating data from adjacent zones or object identifiers, e.g., mass flow rates from intermediate equipment zones and mass flow rates to downstream equipment zones, the calculation unit 124 may determine which particular input material 114 or derived material 116 is associated with material entering subsequent zones. Alternatively or in addition, two or more timestamps, e.g., a timestamp of exit from an intermediate equipment zone and a timestamp of detection via the imaging sensor 146 and / or entry into a downstream equipment zone, may be correlated between zones. The velocities of transport elements 102a,b, measured directly via sensor output or determined from two or more timestamps, can also be used to establish relationships between specific packets or batches of input material and their object identifiers. Thus, it may be determined where a particular chemical product 170 was in the manufacturing process at a given time, and thus a time-space relationship may be established. Some or all of these embodiments can be used not only to improve the traceability of chemical products 170 from input materials to finished products, but also to monitor and improve the manufacturing process and make it more adaptable and controllable.

[0178] As explained, the first downstream object identifier 130a and the second downstream object identifier 130b are appended from the downstream equipment zone with a first subset 132a and a second subset 132b of downstream real-time process data, respectively. The first subset 132a and the second subset 132b of downstream real-time process data are linked to or may be appended to the upstream object identifier 122. Similar to the upstream object identifier 122 described earlier, the first subset 132a and the second subset 132b of downstream real-time process data indicate one or more process parameters and / or equipment operating conditions, i.e., the output of the imaging sensor 146, the operating conditions of the cutting mill 142 and the second transport elements 106a,b on which the derived material 116 is processed in the downstream equipment zone, such as incoming mass flow rate, outgoing mass flow rate, fill level, temperature, optical properties, and timestamp. In this case, the equipment operating conditions may be the control signals and / or setpoints of the cutting mill 142 and / or the second transport elements 106a,b, which may be derived from further zone-specific control settings. Therefore, the further zone-specific control settings can be optimized based on data from the upstream object identifier 122, for example, at least one zone-specific performance parameter applied to the upstream object identifier 122.

[0179] The first subset 132a and the second subset 132b of the downstream real-time process data may include time-series data, which may include time-dependent signals that may be obtained via one or more sensors, such as the output of the imaging sensor 146 and / or the velocities of the second carrier elements 106a,b.

[0180] As the derived material 116 moves forward after encountering the imaging sensor 146, the derived material 116 is moved toward the cutting mill 142 in a transverse direction 154 driven by the second transport elements 106a,b. In this example, the second transport elements 106a,b are shown as part of a second conveyor belt system separate from the conveyor system containing the transport elements 102a,b. It will be observed that the second conveyor belt system may also be part of the same conveyor system containing the transport elements 102a,b. Thus, the downstream equipment zone may contain some of the same equipment used in another zone.

[0181] As shown in Figure 1, the first divided material 140a and the second divided material 140b follow different paths later in the manufacturing process, and their respective object identifiers, namely the downstream object identifier 130a and the second downstream object identifier 130b, allow them to be traced or tracked individually throughout the rest of the manufacturing process and, in some cases, beyond it.

[0182] After leaving the downstream equipment zone, the first divided material 140a is supplied to the extruder 150, while the second divided material 140b is transported for curing in a third equipment zone, which includes a curing device 162 and third transport elements 108a,b. Thus, the shown transport elements 108a,b are non-limiting examples, as previously described. It will be observed that the third equipment zone is located downstream of the upstream and downstream equipment zones.

[0183] When the second divided material 140b is moved transversely 156 via the belt, the second divided material 140b undergoes a curing process via the curing device 162, producing a cured second divided material 160. In one embodiment, since there may be no substantial mass change, a new object identifier may not be provided for the third equipment zone. Thus, as previously described, process data from the third equipment zone may also be added to the second downstream object identifier 130b. Similarly, the second subset 132b, supplemented with downstream real-time process data, may therefore be enriched to further show process parameters and / or equipment operating conditions from the third equipment zone, i.e., the operating conditions of the curing unit 162 and / or transport elements 108a,b in which the second divided material 140b is processed in the third equipment zone, such as the incoming mass flow rate, the outgoing mass flow rate, one or more temperature values ​​from the third zone, the inflow time, the outflow time, the speed of the transport elements 108a,b and / or the belt. In this case, the equipment operating conditions may be control signals and / or setpoints for the transport elements 102a,b and / or the curing unit 162, which may also be derived from further zone-specific control settings. Thus, further zone-specific control settings can be optimized based on data from the upstream object identifier 122, for example, at least one zone-specific performance parameter added to the upstream object identifier 122.

[0184] Similarly, the first divided material 140a advances to a fourth equipment zone, which includes an extruder 150, a temperature sensor 148, and a fourth transport element 110a,b. Here again, since there may be no substantial mass change, in one embodiment, a new object identifier may not be provided for the fourth equipment zone. Therefore, as previously described, process data from the fourth equipment zone may also be added to the downstream object identifier 130a. Similarly, the first subset 132a with added downstream real-time process data may therefore be enriched to further show process parameters and / or equipment operating conditions from the fourth equipment zone, i.e., the operating conditions of the extruder 150 and / or temperature sensor 148 and / or conveyor elements 108a,b in which the first divided material 140a is processed in the third equipment zone, such as one or more of the incoming mass flow rate, outgoing mass flow rate, one or more temperature values ​​from the third zone, inflow time, outflow time, and the speed of the conveyor elements 110a,b and / or belts. The equipment operating conditions in this case may also be control signals and / or setpoints for the conveyor elements 108a,b and / or extruder 150, which may be adapted based on calculated performance parameters and associated real-time process data, as previously described.

[0185] Furthermore, the characteristics and dependencies of the conversion of the first divided material 140a to the extruded material 152 may also be included in the downstream object identifier 130a. The fourth equipment zone will also be recognized as being downstream of the upstream and downstream equipment zones.

[0186] To enable this, the number of individual object identifiers can be reduced while improving material and product monitoring throughout the manufacturing process.

[0187] As the extruded material 152 moves in the generated transverse direction 158 via the transport elements 108a,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 manufacturing process. Additional material may be combined in the collection zone 166, and a cured second divided material 160 may be combined with the extruded material 152 as shown herein. Thus, a new object identifier may be provided, as previously described. Such an object identifier is shown as the final downstream object identifier 134. The final downstream object identifier 134 may include a subset 136 of final zone real-time process data, which may include all or part of the downstream object identifier 130a and the second downstream object identifier 130b. Thus, the final downstream object identifier 134 provides process parameters and / or equipment operating conditions from the collection zone 166, as described in detail herein. If any of the following occurs in the collection zone 166, depending on the function or further processing, data such as the incoming mass flow rate, the outgoing mass flow rate, one or more temperature values ​​from the collection zone 166, entry time, exit time, and speed may be included as the final zone real-time process data 136.

[0188] In some cases, individual lots from collection zone 166 may be sent for storage and / or sorting and / or packaging. Such individual lots are indicated as product collection bins 164a. Since the quantities are again divided, individual object identifiers may be provided for each silo, so that the individual object identifier for the chemical product 170 in that silo, i.e., for product collection bins 164a, can be associated with the process data or conditions to which the chemical product 170 is exposed.

[0189] As acknowledged, each object identifier may be a GUID. Each may contain, entirely or partially, data from a preceding object identifier, or they may be linked. Therefore, relevant quality data may be attached to a specific chemical product 170 as a snapshot or a traceable link.

[0190] As also described, one or more ML models, preferably at least partially data-driven models, may be used to calculate or predict one or more zone-specific performance parameters and / or zone-specific control settings. Each or some of the ML models may also be configured to provide confidence values ​​indicating a confidence level for at least one zone-specific performance parameter and / or zone-specific control setting. For example, a warning may be generated as a warning signal to initiate physical testing of the sample for laboratory analysis if the confidence level in predicting the performance parameter falls below a predetermined limit. A sampling object identifier may also be automatically provided via the interface in response to a prediction confidence level below an accuracy threshold. The sampling object identifier may be provided in a similar format, and the calculation unit 124 may add a subset of relevant process data to the sampling object identifier for the material to which the sampling object identifier pertains, shown here as sample material 172. The calculation unit 124 may also add at least one zone-specific performance parameter that had a low confidence level to the sampling object identifier. Thus, the sample material 172 can be collected, validated and / or analyzed using the object identifier to further improve quality control.

[0191] Figure 2 shows a flowchart 200 or routine illustrating a method embodiment of this teaching, particularly from the perspective of the first equipment zone. In block 202, an upstream object identifier 122 is provided via an interface, which includes input material data and at least one desired performance parameter related to the chemical product 170. The input material data represents one or more characteristics of the input material 114. In block 204, a set of process and / or operating parameters is determined via a calculation unit 124 based on the upstream object identifier 122 and at least one desired performance parameter. The desired performance parameter represents the desired performance or quality of the chemical product 170. In block 206, zone-specific control settings for each equipment zone are determined based on the determined set of process and / or operating parameters and historical data. The historical data may include, for example, data from one or more historical upstream object identifiers related to previously processed input materials in the upstream equipment zone. According to one embodiment, at least one, but most preferably each historical upstream object identifier, is supplemented with at least a portion of process data indicating the process parameters and / or equipment operating conditions under which the previously processed input material was processed in the upstream equipment zone. In block 208, a zone-specific control setting is provided via an output interface to control the production of a chemical product 170 associated with an upstream object identifier 122. In a selective block 210, this is performed on the production process using the zone-specific control setting.

[0192] Similarly, when input material advances to a subsequent zone, it may be determined whether a different object identifier is provided. If not, process data from the subsequent zone may also be added to the same object identifier. If it is determined that a different object identifier is provided, process data from the subsequent zone is added to that different object identifier. Details for each of these options, such as intermediate equipment zones and downstream equipment zones, are described in detail in this disclosure, for example, in the overview section and with reference to Figure 1.

[0193] The block diagram shown in Figure 3 represents a portion of the product manufacturing system of an industrial plant, in this embodiment, consisting of 10 product processing devices or units 300-318, each including technical equipment, arranged along the entire product processing line shown. In this embodiment, one of these processing units (processing unit 308) includes three corresponding equipment zones 320, 322, and 324 (see also embodiments shown in more detail in Figures 3 and 5).

[0194] In this example, chemical products are manufactured based on raw materials as input materials. The raw materials are supplied to the processing line via a liquid raw material reservoir 300, a solid raw material reservoir 302, and a recycling silo 304, which recycles any chemical products or intermediate products, for example, those with insufficient material / product characteristics or insufficient material / product quality. Each raw material fed into processing lines 306-318 is processed via its respective processing equipment, namely a dosing unit 306, a subsequent heating unit 308, a subsequent processing unit including a material buffer 310, and a subsequent sorting unit 312. Downstream of these processing equipment 306-312 is a transport unit 314, which transports materials that need to be recycled, for example, due to insufficient quality of the manufactured material, from the sorting unit to the recycling silo 304. Finally, the materials classified by the classification unit 312 are transferred to the first and second packing units 316 and 318, which pack the corresponding materials into material containers for transport, such as material bags in the case of bulk materials or bottles in the case of liquid materials.

[0195] In this embodiment, manufacturing systems 300-318 provide data interfaces for the computing units (neither of which are shown in this block diagram), through which data objects containing data about each input material and its changes due to processing are provided. The entire manufacturing process is controlled, at least in part, via the computing units.

[0196] The input material processed by processing units 306-312 is divided into physical or real-world so-called "package objects" (hereinafter also referred to as "physical packages" or "product packages"), and these package objects are handled or processed by each of the processing units 306-312. The package size of such package objects can be fixed, for example, by the weight of the material (e.g., 10 kg, 50 kg, etc.) or by the volume of material (e.g., 1 decimeter, 1 / 10 cubic meter, etc.), or it can be determined by weight or volume, and for this weight or volume, the processing equipment can provide fairly constant process parameters or equipment operating parameters.

[0197] The dosing unit 306 first generates such package objects from input liquid and / or solid raw materials and / or recycled materials provided by the recycling silo 304. Once the package objects are generated, the dosing unit transports these objects to the homogenization unit 308. The homogenization unit 308 homogenizes the materials of the package objects, i.e., homogenizes the processed liquid material and solid material, or two liquid or solid materials. After the heating process, the heating unit 308 transports the correspondingly heated package objects to the treatment unit 310, which converts the materials of the input package objects into different physical and / or chemical states, for example, by heating, drying, wetting, or by some chemical reaction. The correspondingly converted package objects are then transported to one or more of the three downstream packing units 316, 318, or the transport unit 314 mentioned above.

[0198] The subsequent processing of real-world package objects is managed by corresponding data objects 330, 332, 334 (or, respectively, the “object identifiers” described earlier) assigned to each package object via computing units operably coupled to or part of the equipment 306-312, and stored in the memory storage elements of the computing units. According to this embodiment, the three data objects 330-334 are generated via equipment 306-312, i.e., in response to trigger signals provided in response to the output of corresponding sensors located in each equipment unit 306-312, or the corresponding switches, respectively, and such sensors are operably coupled to equipment units 306-312. As previously mentioned, industrial plants may include different types of sensors, e.g., sensors for measuring one or more process parameters and / or equipment operating conditions or parameters related to equipment or process units. In this embodiment, sensors for measuring the flow rate and level of bulk and / or liquid materials processed within equipment units 306-312 are located in these units.

[0199] In this embodiment, the three exemplary data objects 330, 332, and 334 shown in Figure 3 relate to three different equipment zones 320, 322, and 324 of the entire product manufacturing process, respectively, based on processing units 306-312 and 314-318.

[0200] The first two data objects 330 and 332 include product package objects containing process data. The process data includes processing / handling information that the associated physical package undergoes during its stay / processing in multiple processing units. The process data can be aggregated data, such as the calculated average temperature during the stay time of the underlying physical package in the associated processing units, and / or time-series data of the underlying manufacturing process.

[0201] The first data object 330 is a first type of package (referred to as "A-package" in Figure 3) assigned to a physical package transported through two processing units, a dosing unit 306 and a heating unit 308, in this embodiment. The first data object 330 contains relevant data for both units during each stay at the current time in the processing time. The first data object includes a corresponding "product package ID".

[0202] The heating unit 308 includes multiple equipment zones, in this embodiment, three equipment zones 320, 322, and 324 ("Zone 1," "Zone 2," and "Zone 3"). These different equipment zones are used as classification groups for classifying or selecting relevant process data. Such classification may help to obtain only the data for package objects from the relevant equipment zone that are related to the processing of the underlying physical package within the corresponding time point while the relevant physical package is in that equipment zone. However, in this embodiment, the material composition of the physical package is not altered by both processing units 306 and 308.

[0203] When A-package 330 arrives at the next treatment unit 310 (in this embodiment, the "treatment unit with buffer"), the material composition of each physical package changes. This is because the processing unit 310 does not merely transport physical packages in plug-flow mode. Furthermore, the corresponding physical package contains a buffer volume larger than the original package size, thereby giving such a physical package a defined degree of reverse mixing. As a result, each physical package that leaves this treatment unit 310 is another type of physical package, referred to as a "B-package" in Figure 3.

[0204] The corresponding second data object 332 ("B-Package") also includes the corresponding "Product Package ID". Data object 332 further includes a specified number of previous data objects, in this example, a specified proportion of data object 330, designated as "A-Package", the so-called "collected data from related A-Packages". The corresponding aggregate scheme or algorithm depends, for example, on the underlying processing unit, the size of the underlying physical package, the material mixing capacity of the underlying physical package, and the dwell time of the underlying physical package within the underlying processing unit, or the corresponding equipment zone of the processing unit.

[0205] For example, when a processed physical (product) package is packed into a separate physical package by one of two packing units 316, 318, such as by packing the processed physical package into a container, drum, or octabine, in this embodiment, the corresponding packed physical package is handled or tracked via another data object 334 called “physical package”. This data object 334 includes the associated previous physical packages packed in it (e.g., “Package A” and “Package B” in this scenario). Specifying a corresponding “product package ID” is sufficient, for example, for tracking purposes, instead of using the entire data object, because such a product package ID can be easily linked during subsequent data processing, for example, data processing performed by an external “cloud computing” platform.

[0206] The first data object (or "object identifier") 330 contains, in particular, the following information: - "Product Package ID" for the underlying package; - General information about the underlying package, such as information or specifications about the processed materials that form the basis of the package; - The current position of the underlying package within the entire processing line 306-318; - Process data, i.e., process data as a set of temperature and / or weight values ​​of the processed material of the underlying package; - Time-series data of the underlying manufacturing process; and - This involves connecting the sample from the underlying package, where the product package passes through the sample station, and at a specified moment, the operator retrieves the sample from this product package and provides it to the laboratory. For this sample, a sample object (see Figure 6, reference numerals 634 and 638) is generated and linked to the associated product package (see Figure 6, reference numerals 626 and 630). This sample object includes, in particular, corresponding product quality control (QC) data from the laboratory and / or performance data from the corresponding test machine.

[0207] The second object identifier 332 is, in addition, - Aggregated data from the relevant A-package generated in the treatment unit equipped with buffer 310 Includes.

[0208] The third object identifier 334 is generated by two packing units 316,318 with the designation and timestamp "physical package 1976-02-06 19:12:21.123" and includes the following information: - Again, the corresponding package or object identifier ("Package ID") - Names of the products packed into two material containers for transport, as shown in Figure 3; - Order number for ordering the corresponding packaged product; and - The lot number of the correspondingly packaged product.

[0209] The package general information for the first and second object identifiers 330 and 332 includes material data of the input raw materials, which in this embodiment indicates the chemical and / or physical properties of the input materials or the respective processed materials, such as material temperature and / or weight, and in this embodiment also includes the aforementioned experimental samples or test data related to the input materials, such as historical test results.

[0210] As shown in Figure 3, the product manufacturing process is also represented by the interface mentioned, and process data is collected from the entire apparatus, which includes process parameters such as the temperature and / or weight of the processed material, as well as the operating conditions of the apparatus under which the input material is processed, such as the temperature of the heater mentioned and / or the dosing parameters applied in this embodiment. Only a portion of the collected process data, such as aggregated data from the relevant A-package in this embodiment, is added to the second object identifier 332 in this embodiment.

[0211] As previously described, the three object identifiers 330-334 are used in this embodiment to correlate or map the input material data and / or specific process parameters and / or equipment operating conditions mentioned to at least one performance parameter of a chemical product, where the performance parameter is, each, one or more properties of the underlying material, e.g., the corresponding chemical product, or a property thereof.

[0212] According to the embodiment shown in Figure 3, the collected process data (as aggregate values) contained in the two object identifiers 330 and 332 includes process parameters and, additionally, numerical values ​​indicating equipment operating conditions measured during the manufacturing process. In addition, object identifiers 330 and 332 include process data provided as one or more time-series data of process parameters and / or equipment operating conditions. Equipment operating conditions can be the status of the equipment, in this embodiment, the manufacturing machine setpoint, the controller output, and any characteristics or values ​​representing any equipment-related warnings, for example, based on vibration measurements. In addition, they may include fouling values ​​such as conveyor element speed, temperature, and filter differential pressure, and maintenance dates.

[0213] In the embodiment of the product manufacturing system shown in Figure 3, the entire product processing equipment 306-318 comprises a plurality of three equipment zones 320-324 as mentioned, thereby allowing input raw materials 300-304 to traverse the entire processing line 306-318 during the manufacturing process, advancing in this embodiment from the first equipment zone 320 to the second equipment zone 322 and from the second equipment zone 322 to the third equipment zone 324. In such a manufacturing scenario, a first object identifier 330 is provided in the first equipment zone 320 and, after being processed through the first equipment zone 320, a second object identifier 332 is provided upon entry of the input material into the second equipment zone 322. The second object identifier 332 includes or adds at least some of the data or information provided by the first object identifier 330, plus the last data / information "collected data from the relevant A-package".

[0214] It is worth noting that, in order to enable reliable and secure assignment of object identifiers to corresponding packages throughout the entire manufacturing process, any or each of the object identifiers 330–334 may include a unique identifier, preferably a globally unique identifier ("GUID").

[0215] In this product processing scenario, the process data mentioned in 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 further a portion of the process data collected from the second equipment zone 322, and the process data collected from the second equipment zone 322 indicates the process parameters and / or equipment operating conditions under which the input materials 300-304 were processed in the second equipment zone 322.

[0216] Table 1 below shows another example object identifier, again in tabular format. This object identifier contains significantly more information / data than the three object identifiers 330-334 described earlier.

[0217] This exemplary object identifier relates to a so-called "B-package" with an underlying date and timestamp "1976-02-06 18:31:53.401," such as the one shown in Figure 4 below, but containing more data than what is included in Figure 4.

[0218] The unique identifier ("unique ID") in this example includes the unique URL ("uniqueObjectURL"). The main details of the underlying package ("package details") in this example are the date and timestamp of the package's creation ("creation timestamp"), which has two values: "02.02.1976 18:31:53.401", and the package type ("package type"), which in this example has package type "B". The current location of the package along the underlying production line ("package location") is defined by the "package location link", which in this example is the transport link to "conveyor belt 1" on the production line.

[0219] On conveyor belt 1, a measuring instrument (see "Measurement Point" including exemplary processing data or values) and a corresponding description ("Description") of the underlying temperature zone are provided, in this example "Temperature Zone 1" for measuring the average temperature ("Average Value") currently representing a material temperature of 85°C. In addition, the measuring instrument may also include sensors for detecting the date / time of entry of a package onto conveyor belt 1 ("Entry Time"), in this example "02.02.1976 18:31:54.431") and the date / time of exiting conveyor belt 1 ("Leaving Time"), in this example "02.02.1976 18:31:57.234". Finally, the measuring instrument may include sensor equipment for detecting time-series values ​​("Time-Series Values") of underlying time-series information ("Time Series") relating to the manufacturing process.

[0220] In addition, the object identifier shown in this example further includes information about "conveyor belt 2", "mixer 1", and "silo 1", which are located downstream, for the intermediate storage of already processed materials.

[0221] [Table 1]

[0222] Figure 4 shows a second embodiment of the process part of a product manufacturing system that forms the basis of an industrial plant, in which the industrial plant includes six product processing devices 400, 402, 406, 410, 412, 416 or technical equipment, respectively.

[0223] An “upstream process” 400 for processing package objects is connected to a “classification unit” 402 for classifying the processed package objects. The upstream process 400 and the classification unit 402 are managed by a first data object 404. This data object 404 relates to a previously described “B-package” with an underlying date and timestamp “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 package object currently being processed. The data object 404 further contains n pre-described chemical and / or physical properties of the package object currently being processed, in this example “property 1” and “property n”.

[0224] The input material, i.e., the corresponding package object supplied to the upstream process 400 in this example, is provided by the “recycling silo” 406. The recycling silo 406, on the other hand, obtains the underlying recycled material from the “transport unit 1” 410, which transports package objects that must be recycled and are therefore classified into the recycling silo 406 by the classification unit 402. The underlying transport process step 410 is managed by a second data object 408 which includes the underlying date and timestamp “1976-02-06 18:51:43.431” with respect to the “B-package” mentioned above, the “package ID” of the package object currently being processed, and two chemical and / or physical properties “Property 1” and “Property n”. However, due to the mentioned requirement to recycle the underlying classified package object, the second data object 408 further includes another chemical and / or physical property of the underlying package object, in this example “Property 2”, which includes, in particular, a performance indicator for that package object, in this example “low or insufficient material or product performance”.

[0225] Package objects processed by the upstream process 400 but not classified by the classification unit 402 are provided by the classification unit 402 to the first "packing unit 1" 412 or the second "packing unit 2" 416, according to the performance value for the corresponding package object. The packing units 412 and 416 are used to pack the corresponding package objects into their respective containers 414 and 418. The packing process performed by the two packing units 412 and 416 is managed by the third data object 420 and the fourth data object 422.

[0226] Both data objects 420 and 422, relating to the "physical package," include the same date "1976-02-06" as the aforementioned "B-package," but also include a later timestamp "19:12:21.123" than the aforementioned "B-package." They also include the "package ID" of the underlying package object. However, data objects 420 and 422 further include performance indicators for the underlying final product, in this example, the "performance medium range" for the product stored in the first container (or filling sack) 414 and the "performance high range" for the product stored in the second container (or filling sack) 418. In addition, the two data objects 420 and 422 include the "order number" and "lot number" of the corresponding final product.

[0227] Figure 5 shows a third embodiment of a portion of an underlying chemical product manufacturing process or system implemented in an industrial plant, which in this second embodiment includes nine product processing devices 500-516 or technical equipment, respectively.

[0228] This product processing approach is based on two raw materials, namely "raw material liquid" 500 and "raw material solid" 502, to produce polymer materials in known forms. As in the manufacturing scenarios previously described in Figures 3 and 4, the technical equipment includes a "recycling silo" 504 for using recycled materials, as previously described.

[0229] The technical equipment further includes a “dosing unit 506” for generating package objects based on the input materials mentioned, the input materials being processed by a “reaction unit” 508 for transporting package objects along four indicated polymer reaction zones ("zones 1-4") 510, 512, 514, 516 for processing them, and a “curing unit” 518 for curing the polymer material (i.e., the corresponding package objects) produced in the reaction unit 508. In this embodiment, the curing unit 518 includes only a material buffer but does not include a backmixing device. The curing unit 518 also transports the correspondingly processed package objects.

[0230] The "transport unit 1" 520 transports package objects that are sorted for recycling by the recycling silo 504. The final processed, i.e., unsorted units are transported again to the first "packing unit 1" 522 and the second "packing unit 2" 524. The two packing units 522, 524 convert the corresponding package objects and transport them to their respective containers or filling sacks 526, 528.

[0231] The manufacturing process shown in Figure 5 is managed by the first data object 530 and the second data object 534.

[0232] The first data object 530 relates to an "A-package" with a production date of "1976-02-06" and a production time of "18:31:53.401". In this manufacturing scenario, data object 530 again includes a pre-described "package ID", process information about the dosing process performed by the dosing unit 506 ("dosing characteristics"), and further process information about the production of the polymer material by the reaction unit 508 ("reaction unit characteristics"). The dosing characteristics include information about the amount of raw materials for each package object, namely "percentage raw material 1 (liquid)", "percentage raw material 2 (solid)", and product temperature. The reaction unit characteristics include the temperatures of the four polymer reaction zones 510-516 ("temperature zone 1", "temperature zone 2", "temperature zone 3", and "temperature zone 4").

[0233] Based on this, the first data object 530 includes the current position of the underlying package object along the processing lines 506-524 ("current package position"). In this embodiment, the current position of the package object is managed by a "package position link" and a corresponding "zone position". Finally, it includes chemical and / or physical information about the underlying polymer reaction, namely the corresponding "reaction enthalpy / turnover degree". This allows the processing units 506-524, which transport a given package object, to calculate and permanently write / realize the reaction enthalpy value in the first data object 530. This is made possible by existing information about the package position and the corresponding residence time, as well as the corresponding process values, such as the package temperature. Via the 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 turnover degree contained in the first data object 530.

[0234] The second data object 534 contains the corresponding generation date / time information "1976-02-06 19:12:21.123" for a "physical package" processed by one of the packing units 522,524. It also contains the "package ID", "product" description / specification, "order number", "lot number", and the mentioned values ​​of the calculated enthalpy and / or turnover degree.

[0235] Figure 6 shows a first embodiment of a graph-based database array representing the hierarchical or topological structure of the underlying industrial plant 602, which is part of an industrial plant cluster 600 and includes multiple equipment devices and corresponding equipment zones that are part of the corresponding product processing line 604. The topological structure allows for the visualization of the functional relationships between the different underlying parts of the industrial plant 602 (or underlying plant cluster 600) to enable improved processing or planning of the underlying product package. The circular nodes shown in the graph-based database are linked via connecting lines, and different types of links are possible for this.

[0236] In this embodiment, the equipment includes material processing units 606, 614, which are connected via signal and / or data connections to sensor / actors 608, 616, which are part of the processing units 606, 614, and to a number of input / output (I / O) devices 610, 612, and 618, 620.

[0237] 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 further product packages (product packages 4-n) 628, 630, 632. For illustrative purposes only, “product package 3” 626 is connected to 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” 636, and “sample n” is further connected to “inspection lot n” 640. Finally, both inspection lots 636, 640 are connected to “inspection instruction 1” unit 642, which acts as a specification for how to generate the mentioned inspection lots and how to implement the analysis / quality control of the respective underlying samples 634, 638.

[0238] The topological structure shown in Figure 6 advantageously provides a data structure that enables an intuitive and easy understanding of the functionality and processing of the shown chemical plant, and consequently, the easy management of such complex manufacturing processes in a chemical plant or cluster of chemical plants by users, particularly machine / plant operators. This is because the shown objects (nodes) are modeled in remarkably similar terms to their corresponding real-world objects.

[0239] More specifically, this topological structure provides advanced contextual information, which allows users / operators to easily collect the technical and / or material properties of each object. This further enables users to perform fairly complex queries, such as those concerning related manufacturing connections or relationships between objects, particularly queries that span multiple nodes or even further topological / hierarchical levels. As a result, the objects (nodes) shown in Figure 6 can be easily expanded during runtime with additional properties and / or values.

[0240] Figure 7 shows a second embodiment of the graph-based database array shown in Figure 6, but only for manufacturing line 700 ("Line 1").

[0241] In this embodiment, the equipment includes material processing units 702 "Unit 1" and "Unit n" 708, which are connected via signal and / or data connections to sensor / actors "Sensor / Actor 1" 704 and "Sensor / Actor n" 710, which are connected to corresponding input / output (I / O) devices "I / O 1" 706 and "I / On" 712. These I / O devices include connections to a PLC (not shown) for controlling the operation of the manufacturing line 700.

[0242] In this embodiment, the first processing unit ("Unit 1") 702 is further connected to three exemplary product packages ("Product Parts" 1-3) 714, 716, and 718, and the second processing unit ("Unit n") 708 is further connected to two additional product packages ("Product Parts" 4 and n) 720 and 722. For illustrative purposes only, product package 3'' 718 is connected to product sample ("Sample 1") 724, and product package n 722 is connected to another product sample ("Sample n") 728.

[0243] In contrast to the embodiment shown in Figure 6, a first "sensor / actor 1" 704 is also connected to a first product sample ("sample 1") 724, and a second "sensor / actor n") 710 is also connected to a second product sample (sample n'') 728. These two additional connections have the advantage that samples can be taken independently at different sampling stations at independent times, or even simultaneously. For example, the sensor / actor 704 could be a push button located at the sampling station, which is pressed by a user or operator at the moment the sample is taken.

[0244] Alternatively, such data can be a signal that can be automatically generated by a sampling machine. Such an automatically generated signal can reach a sensor / actor object 704 via, for example, the indicated I / O object 706, which receives the mentioned push-button information from a PLC / DCS (not shown). At the moment of sampling, a sample object 724 (for example) is generated and linked to the product portion positioned at the sampling station location at that moment.

[0245] Based on the correspondingly generated samples 724,728, one or more test lots 726,730 can be generated for one (and identical) sample. However, one or more samples can be generated independently or even simultaneously within a single processing line.

[0246] Finally, as in the embodiment shown in Figure 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 and 730 are ultimately connected to an "Inspection Instruction 1" unit 732, which again functions as a specification for how to generate the mentioned inspection lots and how to implement the analysis / quality control of the underlying samples 724 and 728, as in the case of the "Inspection Instruction 1" unit 642 shown in Figure 6. The "Inspection Instruction 1" unit 732 can be generated independently, or it may be generated only once, using the inspection instruction 732 for more than one inspection lot, as shown in Figure 7 with "Inspection Lot 1" 726 and further "Inspection Lot n" 730.

[0247] Figure 8 shows the abstraction layer 800, which includes an object database 801 and acts as an abstraction layer for pre-described manufacturing equipment and corresponding raw materials, as well as for pre-described product data, including pre-described physical packaging or product packaging-related data, i.e., corresponding digital twins.

[0248] In this embodiment, the abstraction layer 800 provides a bidirectional communication line 802 with an external cloud computing platform 804. The abstraction layer 800 also communicates with a number of n manufacturing PLC / DCS and / or machine PLCs 806, 808 bidirectionally, as in the case of "PLC / DCS1" 806, or unidirectionally, as in the case of "PLC / DCSn" 808, 812. In this embodiment, the cloud computing platform 804 includes a bidirectional communication line 814 to a customer integration interface or platform 816, through which the customer of the manufacturing plant owner can communicate and / or deliver control signals to pre-described equipment units of the plant.

[0249] Object database 801 also includes other related objects, such as the aforementioned samples, inspection lots, sample instructions, sensors / actors, devices, device-related documentation, users (e.g., machine or plant operators), corresponding user groups and user rights, recipes, orders, setpoint-parameter sets, or inbox objects from cloud / edge devices.

[0250] In the cloud computing platform 804, an artificial intelligence (AI) or machine learning (ML) system is implemented to find or generate the optimal algorithm to be deployed via a dedicated deployment pipeline 818 to an Internet-of-Things (IoT) edge device or component 820, and to use the correspondingly generated or found algorithm to control the edge device 820. In this embodiment, the edge device 820 communicates bidirectionally with the abstraction layer 800 822.

[0251] The abstraction layer 800 and its included object database 801 generate the pre-described physical or product packages as described in this document. The abstraction layer 800 can also connect to a processing and / or AI (or ML) component within the cloud computing platform 804. For this connection, the known data streaming protocol "Kafka" can be used. This allows for the transmission of empty data packets as messages, first, in particular, independently of the underlying time-series data, at or near the time of the generation of the underlying product package. Then, when the final product package is processed, another message can be sent. These messages include the object identifier of the underlying package as a data packet ID, so that the related packets can later be relinked to each other on the cloud platform side. This avoids the need for large data packets for transmission to the cloud, thereby minimizing the required transmission bandwidth or capacity.

[0252] Within the cloud computing platform 804, streaming and received product data is used by the AI ​​or ML methods mentioned to find or generate algorithms for obtaining additional data related to the underlying product, such as predicted product quality control (QC) values. For this procedure to be performed within the cloud computing platform 804, additional data such as QC data or measured performance parameters of the relevant product (or physical) package is required. This can be received via the same method from an object database 801 in the form of sample objects and inspection lot objects (see also Figure 6), which contain such information about the relevant product package.

[0253] Such information can also be received from any other system besides the object database. In this case, the other system sends QC and / or performance data along with the sample / inspection lot ID from the object database. Within the cloud computing platform 804, this data is combined and used, for example, to find ML-based algorithms / models. This allows for the effective use of computing power within the cloud platform 804.

[0254] In this embodiment, the correspondingly found algorithm or model is deployed to the edge device 820 via the deployment pipeline 818. The edge device 820 is located near the object database 801 of the abstraction layer 800, and therefore also near PLC / DCS1~PLC / DCSn806,808, i.e., in terms of network security level and location that enables low network latency and direct and reliable communication.

[0255] Since such computational power is not required for the use of the ML model, the edge device 820 uses the ML model to generate the latest information mentioned and provides it to the object database 801. Therefore, the edge device 820 requires the same information or a subset of the information, which is used in the cloud computing platform 804 to generate the ML-based algorithm or model, and the object database 801 can provide this data to the edge device 820 via an open network protocol for machine-to-machine communication, such as the well-known Message Queuing Telemetry Transport (MQTT) protocol.

[0256] This setup enables AI / ML-based, state-of-the-art process control, as well as autonomous manufacturing and corresponding autonomously operating machines.

[0257] As shown in the embodiment depicted in Figure 8, on the side of the cloud computing platform 804, an AI / ML system or corresponding AI / ML model is trained using such data as training data, based on the data from the previously described data objects 330-334 (Figure 3). Therefore, in this embodiment, the training data may include historical and current laboratory test data, in particular, historical data showing the performance parameters of chemical products.

[0258] The AI / ML model can be used to predict one or more of the previously described performance parameters, the prediction preferably performed via a computing unit. In addition, or alternatively, the AI / ML model can be used to control the manufacturing process at least partially, preferably by adjusting the equipment operating conditions, the control more preferably performed via the computing unit mentioned. In addition, or alternatively, the AI / ML model can be used, for example, by a computing unit to determine which of the process parameters and / or equipment operating conditions has the primary effect on the chemical product, thereby adding these dominant process parameters and / or equipment operating conditions to a data object or the object identifier mentioned, respectively.

[0259] Those skilled in the art will recognize that method steps, at least those performed via a computing unit, may be performed in a “real-time” or near-real-time manner. The term is 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, particularly 10 seconds or less, more specifically 5 seconds or less. Preferably, the delay is less than 1 second, more preferably less than 2 milliseconds. Thus, the computing unit may be configured to perform method steps in real-time. Furthermore, a software product may cause the computing unit to perform method steps in real-time.

[0260] The method steps may be performed in the order listed in the examples or embodiments, for example. However, it should be noted that under certain circumstances, a different order may also be possible. Furthermore, one or more method steps may be performed once or repeatedly. Steps may be repeated at regular or irregular intervals. Moreover, especially when some or more method steps are performed repeatedly, two or more method steps may be performed simultaneously or in a time-overlapping manner. The method may also include steps that are not listed.

[0261] The word “including” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude plural. A single processing means, processor or controller or other similar unit may satisfy the functions of multiple items described in a claim. The mere fact that a means is described in different dependent claims does not imply that a combination of these means cannot be used advantageously. Any reference numeral in a claim should not be construed as limiting scope.

[0262] Furthermore, in this disclosure, terms such as “at least one,” “one or more,” or similar expressions indicating that a feature or element may be present one or more times, are typically used only once when describing each feature or element. Therefore, in some cases, unless otherwise specified, the expressions “at least one” or “one or more” may 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.

[0263] Furthermore, the terms “preferably,” “more preferably,” “especially,” “more particularly,” “specifically,” “more specifically,” or similar terms are used in relation to selective features without limiting alternative possibilities. Thus, features introduced by these terms are selective features and are not intended to limit the scope of the claims in any way. This teaching may be carried out by using alternative features, as those skilled in the art will recognize. Similarly, features introduced by “according to one aspect” or similar expressions are intended to be selective features without any limitation on the choices of this teaching, without any limitation on the scope of this teaching, and without any limitation on the possibility of combining such introduced features with other selective or non-selective features of this teaching.

[0264] Various examples of methods for monitoring a manufacturing process, systems for performing the methods disclosed herein, systems for controlling a manufacturing process, uses, software programs, and computing units including computer program code for performing the methods disclosed herein are disclosed above. More specifically, this teaching relates to a method for controlling a manufacturing process, comprising providing an upstream object identifier including input material data and at least one desired performance parameter related to a chemical product; determining a set of process and / or operating parameters based on the upstream object identifier and at least one desired performance parameter; determining zone-specific control settings for each equipment zone based on the determined set of process and / or operating parameters and historical data; and providing zone-specific control settings for controlling the production of a chemical product related to the upstream object identifier. This teaching also relates to systems for controlling a manufacturing process, uses of control settings, and software products for performing the method steps disclosed herein. However, those skilled in the art will understand that changes and modifications may be made to these examples without departing from the idea and scope of the appended claims and their equivalents. Furthermore, it will be acknowledged that aspects from the method and product embodiments described herein may be freely combined.

[0265] In summary, without precluding further possible embodiments, the exemplary embodiments of this teaching can be summarized as follows: Item 1. A method for controlling a manufacturing process for producing a chemical product in an industrial plant, wherein the industrial plant comprises a plurality of physically separated equipment zones, the product is produced by processing at least one input material using a manufacturing process through the plurality of equipment zones, the method is performed at least partially via a computing unit, and the method is - Provide an upstream object identifier via an interface that includes input material data and at least one desired performance parameter related to a chemical product, wherein the input material data represents one or more properties of the input material. - Determining a set of process and / or operational parameters based on an upstream object identifier and at least one desired performance parameter via a computing unit, - Determining zone-specific control settings for each equipment zone based on a determined set of process and / or operating parameters and historical data via a computing unit, A method comprising providing a zone-specific control setting for controlling the production of a chemical product associated with an upstream object identifier via an output interface.

[0266] Item 2. The method according to Item 1, wherein the historical data includes data from one or more historical upstream object identifiers related to previously processed input material, wherein at least one of the historical upstream object identifiers is further augmented with at least a portion of process data indicating process parameters and / or equipment operating conditions in which the previously processed input material was processed, for example, within or in an upstream equipment zone.

[0267] Item 3. The method is also, - The method according to any one of items 1 to 2, comprising performing a manufacturing process using zone-specific control settings, preferably by automatically providing the zone-specific control settings to the plant control system.

[0268] Item 4. The method described in any one of items 1 to 3, wherein the output interface has the same components as the interface.

[0269] Item 5. The method described in any one of Items 1 to 4, wherein the output interface and the interface are different components.

[0270] Item 6. The method further - includes receiving real-time process data from one or more of the equipment zones in the calculation unit, the real-time process data including real-time process parameters and / or equipment operating conditions, the method according to any one of Items 1 to 5.

[0271] Item 7. The method - includes determining a subset of real-time process data based on the upstream object identifier and the zone presence signal via the calculation unit, the zone presence signal indicating the presence of the input material in a specific equipment zone during the manufacturing process, the method according to Item 6.

[0272] Item 8. The method also - includes adding the subset of real-time process data and / or data from an enterprise resource planning ("ERP") system to the upstream object identifier, the method according to Item 7.

[0273] Item 9. The method - includes calculating at least one zone-specific performance parameter of the chemical product related to the upstream object identifier based on the subset of real-time process data and the historical data via the calculation unit, preferably, at least one zone-specific performance parameter is added to the upstream object identifier, the method according to Item 7 or 8.

[0274] Item 10. The zone presence signal is generated by performing a zone-time conversion via the calculation unit, the conversion mapping at least one characteristic related to the input material to a specific equipment zone via one or more time-dependent signals from the real-time process data, the method according to any one of Items 7 to 9.

[0275] Item 11. The method - The method of any one of items 9-10, comprising controlling the manufacturing process via a computing unit such that the difference between at least one of the zone-specific performance parameters and the respective associated values ​​of the desired performance parameters is minimized.

[0276] Item 12. The method described in any one of items 9 through 11, wherein the calculation of at least one zone-specific performance parameter is performed at least in part using at least one machine learning ("ML") model trained on historical data.

[0277] Item 13. The method according to Item 12, wherein the ML model is configured to provide at least one confidence value indicating a confidence level for calculating at least one zone-specific performance parameter.

[0278] Item 14. The method of Item 13, wherein a warning signal is generated, preferably in a control system for a manufacturing process, in response to the confidence level of the calculation or prediction of at least one zone-specific performance parameter falling below an accuracy threshold.

[0279] Item 15. The method of Item 13 or Item 14, wherein a sampling object identifier is automatically generated in response to the confidence level of the calculation or prediction of at least one zone-specific performance parameter falling below a precision threshold, or in response to a warning signal, and the sampling object identifier is associated with the material in its respective zone at or near the point in time when the confidence level precision exceeds the precision value.

[0280] Item 16. The method of Item 14 or Item 15, wherein at least one laboratory analysis is performed in response to a warning signal, preferably the analysis is performed on the material in the respective zone associated with the warning.

[0281] Item 17. The method of Item 16, wherein the date and / or results of the analysis are added to the sampling object identifier, and preferably, the data from the sampling object identifier is included in the historical data for future calculations by the calculation unit.

[0282] Item 18. Multiple physically separated equipment zones, including downstream equipment zones, are used to allow input materials to traverse from upstream to downstream equipment zones during the manufacturing process, and the method is also... - Provide a downstream object identifier that includes at least a portion of the upstream object identifier via an interface, - Determining another subset of real-time process data based on downstream object identifiers and zone presence signals via the computing unit, - This includes determining further zone-specific control settings for at least downstream equipment zones based on data from upstream object identifiers, another subset of real-time process data, and other historical data via a computing unit. Preferably, the method according to any one of items 7 to 17, wherein the additional historical data includes data from one or more historical downstream object identifiers related to previously processed input material in a downstream equipment zone, and more preferably, at least one historical downstream object identifier is further augmented with at least a portion of process data indicating, for example, process parameters and / or equipment operating conditions processed in a downstream equipment zone by the previously processed input material.

[0283] Item 19. The method, also, - Calculate at least one additional zone-specific performance parameter for a chemical product associated with a downstream object identifier, based on another subset of real-time process data and other historical data via a computing unit. - The method of item 18, which includes adding at least one other zone-specific performance parameter to the downstream object identifier.

[0284] Item 20. The method, also, - The method described in item 18 or 19, which includes adding at least a portion of another subset of real-time process data to the downstream object identifier.

[0285] Item 21. The method described in any one of items 1 to 20, wherein one of the object identifiers is provided in memory storage operably coupled to a compute unit.

[0286] Item 22. The method described in Item 21, wherein the computing units and / or memory storage are implemented at least partially via a cloud-based service.

[0287] Item 23. The method described in any one of items 1 through 22, wherein the chemical product is one or a combination of a chemical product, a pharmaceutical product, a nutritional product, a cosmetic product, or a biological product.

[0288] Item 24. The method according to any one of items 1 through 23, wherein the chemical product is in a solid, semi-solid, paste, liquid, emulsion, solution, pellet, granule, or powder state.

[0289] Item 25. The method described in any one of items 1 through 22, wherein the chemical product is thermoplastic polyurethane ("TPU"), or more specifically, expanded TPU.

[0290] Item 26. The method according to Item 1 or Item 25, wherein the input material is methylenediphenyl diisocyanate ("MDI") and / or polytetrahydrofuran ("PTHF").

[0291] Item 27. The method according to any one of Items 1 to 26, wherein any one of the machine zones includes any other type of device directly or indirectly used for or during the manufacturing process in a conveying element such as a conveyor system, a heat exchanger such as a heater, a furnace, a cooling unit, a reactor, a mixer, a milling machine, a chopper, a compressor, a slicer, an extruder, a distillation unit, an extractor, a dryer, a sprayer, a pressure or vacuum chamber, a tube, a bin, a silo, an octabin, or an industrial plant, more preferably such a device and / or component that affects the performance of chemical products, including the component of any one of the preceding paragraphs.

[0292] Item 28. The method according to any one of Items 1 to 27, wherein the manufacturing process is at least partially a batch manufacturing process.

[0293] <J Item 29. The method according to any one of Items 1 to 28, wherein the manufacturing process is at least partially a campaign manufacturing process.

[0294] Item 30. The method according to any one of Items 1 to 29, wherein the manufacturing process is at least partially a continuous manufacturing process.

[0295] Item 31. The method according to any one of Items 1 to 30, wherein the equipment operating conditions are any one of any characteristic or value representing the state of the equipment, for example, a set point, a controller output, a manufacturing sequence, a calibration status, any equipment-related warning, a vibration measurement, a speed such as a conveyor element speed, a temperature, a fouling value such as a filter differential pressure, a maintenance date.

[0296] Item 32. The method according to any one of Items 1 to 31, wherein the process data includes at least one numerical value indicating a process parameter and / or an equipment operating condition measured during the manufacturing process.

[0297] Item 33. The method according to any one of items 1 to 32, wherein the process data includes at least one binary value indicating process parameters and / or equipment operating conditions measured or detected during the manufacturing process.

[0298] Item 34. The method described in any one of Items 1 to 33, wherein the process data includes time-series data of one or more process parameters and / or equipment operating conditions.

[0299] Item 35. The method described in any one of Items 1 through 34, wherein the process data includes temporal information or time-series data of process parameters and / or equipment operating conditions.

[0300] Item 36. The method of Item 35, wherein the temporal information is in the form of data indicating timestamps for at least some of the data points relating to process parameters and / or equipment operating conditions, or time-series data.

[0301] Item 37. The method according to any one of items 1 through 36, wherein the input material is at least one raw material or untreated material used to manufacture a chemical product.

[0302] Item 38. The method according to any one of items 1 through 37, wherein the input material is any organic or inorganic substance, or a combination thereof including multiple organic and / or inorganic components in any form.

[0303] Item 39. The method described in any one of Items 1 through 38, wherein the input material data includes data relating to or indicating one or more characteristics or properties of the input material.

[0304] Item 40. The method described in any one of items 1 through 39, wherein the input material data includes laboratory samples or test data related to the input material, such as historical test results.

[0305] Item 41. The method according to any one of Items 1 to 40, wherein the input material data includes one of the following values ​​that indicate the physical and / or chemical properties of the input material, e.g., density, concentration, purity, pH, composition, viscosity, temperature, weight, volume, and / or performance data related to the input material.

[0306] Item 42. The method according to any one of items 32 to 41, wherein at least some of the following are obtained or measured at least partially via signals from one or more sensors and / or switches operably coupled to the instrument: at least one numerical value, or at least one binary value, or time-series data, or values ​​indicating the physical and / or chemical properties of the input material.

[0307] Item 43. The method according to any one of items 1 to 42, wherein an object identifier is provided via a computing unit operably coupled to an equipment zone, preferably the computing unit being part of the equipment.

[0308] Item 44. The method described in Item 43, wherein the computing unit is a controller or control system, such as a distributed control system ("DCS") and / or a programmable logic controller ("PLC").

[0309] Item 45. The method according to any one of items 1 to 44, wherein an object identifier is provided or generated in response to a trigger event or signal, the event or signal is provided preferably via a device, more preferably in response to the output of one or more sensors and / or switches operably coupled to the device.

[0310] Item 46. The method of Item 45, wherein a trigger event or signal is related to the occurrence of a quantity of input material, more specifically, a quantity that reaches or satisfies a predetermined quantity threshold, and such occurrence is detected via a computing unit and / or instrument.

[0311] Item 47. The method according to Item 46, wherein the quantity value is the weight value and / or the filling factor and / or the level value and / or the volume value.

[0312] Item 48. The method according to any one of items 43 to 47, wherein the device is operably coupled to one or more actuators and / or end effector units, preferably the actuators and / or end effector units being part of the device.

[0313] Item 49. The method described in any one of items 1 to 48, wherein any or each of the object identifiers includes a unique identifier, preferably a globally unique identifier ("GUID").

[0314] Item 50. The method described in any one of items 18 through 49, wherein any or each of the equipment zones is monitored and / or controlled via individual ML models, and the individual ML models are trained on data from their respective object identifiers from that zone.

[0315] Item 51. A system for a monitoring system for controlling a manufacturing process for producing chemical products in an industrial plant, wherein the industrial plant comprises a plurality of physically separated equipment zones, the product is produced by processing at least one input material using a manufacturing process through the plurality of equipment zones, and the system is configured to perform steps according to any of the above Method items.

[0316] Item 52. A computer program or a non-temporary computer-readable medium storing a program, which includes instructions that cause a computing unit to perform one of the method steps of the above method items when the program is executed by a suitable computing unit.

[0317] Item 53. A system for controlling a manufacturing process for producing chemical products in an industrial plant, wherein the industrial plant includes a computing unit and a plurality of physically separated equipment zones, and the product is produced by processing at least one input material using a manufacturing process through the plurality of equipment zones, and the system - Provide an upstream object identifier via an interface that includes input material data and at least one desired performance parameter related to a chemical product, wherein the input material data represents one or more properties of the input material. - Determining a set of process and / or operational parameters based on an upstream object identifier and at least one desired performance parameter via a computing unit, - Determining zone-specific control settings for each equipment zone based on a determined set of process and / or operating parameters and historical data via a computing unit, - To provide zone-specific control settings for controlling the production of chemical products associated with upstream object identifiers via the output interface. A system that is configured to perform a certain action.

[0318] Item 54. A computer program, or a non-temporary computer-readable medium for storing a program, which includes instructions, and when the instructions are executed by a suitable computing unit operably coupled to multiple equipment zones for manufacturing chemical products in an industrial plant by processing at least one input material using a manufacturing process, the computing unit, - The interface provides an upstream object identifier that includes input material data and at least one desired performance parameter related to the chemical product, and the input material data indicates one or more properties of the input material. - Determine a set of process and / or operational parameters based on an upstream object identifier and at least one desired performance parameter. - Determine zone-specific control settings for each equipment zone based on a determined set of process and / or operating parameters and historical data. - Provides zone-specific control settings for controlling the production of chemical products associated with upstream object identifiers via the output interface. A computer program, or a non-temporary computer-readable medium that stores a program.

[0319] Item 55. Use of zone-specific control settings generated in any one of items 1 through 50 to control the manufacturing process of an industrial plant.

Claims

1. A method for controlling a manufacturing process for producing a chemical product in an industrial plant, wherein the industrial plant comprises a plurality of physically separated equipment zones, the product is produced by processing at least one input material using the manufacturing process through the plurality of equipment zones, the method is performed at least partially via a computing unit, and the method is - Provide an upstream object identifier via an interface, which includes input material data and at least one desired performance parameter related to the chemical product, wherein the input material data represents one or more characteristics of the input material. - Determining a set of process and / or operational parameters based on the upstream object identifier and the at least one desired performance parameter via the calculation unit, - Determining zone-specific control settings for each equipment zone based on the determined set of process and / or operating parameters and historical data via the calculation unit, - Includes providing the zone-specific control setting for controlling the production of the chemical product associated with the upstream object identifier via the output interface, A method wherein the historical data includes data from one or more historical upstream object identifiers associated with previously processed input material, wherein at least one of the historical upstream object identifiers is accompanied by at least a portion of its process data indicating process parameters and / or equipment operating conditions under which the previously processed input material was processed.

2. - The method according to claim 1, further comprising performing the manufacturing process using the zone-specific control setting.

3. - The method according to any one of claims 1 or 2, comprising receiving real-time process data from one or more of the equipment zones in a computing unit, wherein the real-time process data further includes real-time process parameters and / or equipment operating conditions.

4. The method according to claim 3, wherein at least one zone-specific performance parameter is added to the upstream object identifier.

5. The method according to claim 4, comprising determining a subset of the real-time process data based on the upstream object identifier and zone presence signals via the calculation unit, wherein the zone presence signals indicate the presence of the input material in a particular equipment zone during the manufacturing process.

6. - The method according to claim 5, further comprising adding a subset of the real-time process data to the upstream object identifier.

7. The method according to claim 5 or 6, comprising calculating at least one zone-specific performance parameter of the chemical product related to the upstream object identifier based on a subset of the real-time process data and the historical data via the calculation unit.

8. The method according to any one of claims 5 to 7, wherein the zone presence signal is generated via the computing unit by performing a zone-time conversion, and the conversion maps at least one characteristic related to the input material to the specific equipment zone via one or more time-dependent signals from the real-time process data.

9. The method according to claim 7 or 8, comprising controlling the manufacturing process via the calculation unit such that the difference between at least one of the zone-specific performance parameters and the associated values ​​of each of the desired performance parameters is minimized.

10. The method according to any one of claims 7 to 9, wherein the calculation of the at least one zone-specific performance parameter is performed at least in part using at least one machine learning ("ML") model trained on the historical data.

11. The plurality of physically separated equipment zones also include downstream equipment zones, and during the manufacturing process, the input material traverses from upstream equipment zones upstream of the downstream equipment zones to the downstream equipment, and the method also, - To provide a downstream object identifier that includes at least a portion of the upstream object identifier via the interface, - Determining another subset of the real-time process data based on the downstream object identifier and the zone presence signal via the calculation unit, The method according to any one of claims 5 to 10, further comprising determining, via the computing unit, further zone-specific control settings for at least the downstream equipment zone based on data from the upstream object identifier, another subset of the real-time process data and another historical data.

12. - Calculating, via the calculation unit, at least one additional zone-specific performance parameter of the chemical product associated with the downstream object identifier, based on another subset of the real-time process data and the other historical data, The method according to claim 11, further comprising adding the other at least one zone-specific performance parameter to the downstream object identifier.

13. The method according to claim 11 or 12, further comprising adding at least a portion of the other subset of the real-time process data to the downstream object identifier.

14. A system for controlling a manufacturing process for producing chemical products in an industrial plant, wherein the industrial plant comprises a plurality of physically separated equipment zones, the product is produced by processing at least one input material using the manufacturing process through the plurality of equipment zones, and the system - Provide an upstream object identifier via an interface, which includes input material data and at least one desired performance parameter related to the chemical product, wherein the input material data represents one or more characteristics of the input material. - Determining a set of process and / or operational parameters based on the upstream object identifier and the at least one desired performance parameter via a computing unit, - Determining zone-specific control settings for each equipment zone based on the determined set of process and / or operating parameters and historical data via the calculation unit, - Configured to provide, via an output interface, the zone-specific control setting for controlling the production of the chemical product associated with the upstream object identifier, A system in which the historical data includes data from one or more historical upstream object identifiers related to previously processed input material, wherein at least one of the historical upstream object identifiers is accompanied by at least a portion of its process data indicating the process parameters and / or equipment operating conditions under which the previously processed input material was processed.

15. A computer program comprising computer instructions, wherein when the computer instructions are executed, the computer program is executed by a suitable computing unit operably coupled to a plurality of equipment zones for producing chemical products in an industrial plant by processing at least one input material using a manufacturing process, the computing unit, - Provides an upstream object identifier via an interface, which includes input material data and at least one desired performance parameter related to the chemical product, wherein the input material data indicates one or more properties of the input material. - Determine a set of process and / or operation parameters based on the upstream object identifier and the at least one desired performance parameter. - Determine the zone-specific control settings for each equipment zone based on the determined set of the process and / or operating parameters and historical data. - Provide the zone-specific control setting for controlling the production of the chemical product associated with the upstream object identifier via the output interface. A computer program wherein the historical data includes data from one or more historical upstream object identifiers associated with previously processed input material, wherein at least one of the historical upstream object identifiers is accompanied by at least a portion of its process data indicating process parameters and / or equipment operating conditions under which the previously processed input material was processed.

16. Use of zone-specific control settings generated in any one of claims 1 to 13 for controlling a manufacturing process in an industrial plant.

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