Method, system and non-transitory computer readable medium for manufacturing and tracking chemical product

Abstract

This invention relates to a method for digitally tracking a chemical product manufactured in an industrial plant, the industrial plant including at least one piece of equipment; and the product being manufactured by processing at least one input material through the equipment using a production process. The method includes: providing an object identifier containing input material information via an interface; wherein the input material information indicates one or more properties of the input material; receiving process data from the equipment via the interface; the process data indicating process parameters and / or equipment operating conditions when the input material is processed; and attaching at least a portion of the process data to the object identifier. This invention also relates to a system for digitally tracking a chemical product and a software product.

Description

[Technical Field]

[0001] This instruction is mainly about computer-aided chemical production. [Previous Technology]

[0002] In industrial plants, input materials are processed to manufacture one or more products. Therefore, the properties of the manufactured products are dependent on manufacturing parameters. It is usually necessary to correlate manufacturing parameters with at least some properties of the product to ensure product quality or production stability.

[0003] In process industries or industrial plants (such as chemical or biological production plants), one or more input materials are processed using production processes for producing one or more chemical or biological products. The production environment in process industries can be complex, and therefore, the properties of the product can vary depending on changes in production parameters that affect those properties. Typically, the dependence of properties on production parameters can be complex and entangled with additional dependencies on one or more combinations of specific parameters. Therefore, producing chemical or biological products with consistent and / or predictable quality can be challenging.

[0004] Compared to discrete processing, chemical or biological processes, such as continuous, motion, or batch processes, can provide a large amount of time-series data. However, machine learning via traditional time-series methods has proven less practical, as it may be difficult to integrate data according to the needs of horizontal integration across the value chain. Specifically, simple and meaningful data exchange or standardization poses a major problem.

[0005] Therefore, there is a need for methods that can improve the quality and production stability of the supply chain from the material container to the final product. [Summary of the Invention]

[0006] It will be shown that at least some of the problems inherent in the prior art are solved by the subject matter of the accompanying independent item. At least some of the other advantageous alternatives will be summarized in the appendix.

[0007] From a first point of view, a method for digitally tracking a chemical product manufactured at an industrial plant, the industrial plant including at least one piece of equipment; and the product being manufactured by processing at least one input material using a production process via the equipment, the method comprising: - providing an object identifier containing input material information via an interface; wherein the input material information indicates one or more properties of the input material; - receiving process data from the equipment via the interface; the process data indicating process parameters and / or equipment operating conditions when the input material is processed; and - attaching at least a portion of the process data to the object identifier.

[0008] The applicant has recognized that, thereby, process data on the processing of input materials to produce or process a product can be encapsulated within an object identifier. This improves the traceability of chemical products. Furthermore, relevant process data can be captured along with input material data, allowing any relationship between the properties of the chemical product and the input materials to be captured as well. This provides a more complete understanding of the relationships between various dependencies that can affect one or more properties of the chemical product. Another advantage is that combinations of various interdependencies that may exist between the properties of the input materials and / or process parameters are also captured within the object identifier. The attached object identifier thus possesses rich information that can be used not only to track chemical products and / or their specific components, or input materials, but also for specific process data responsible for producing the chemical product. Therefore, the attached object identifier can be more easily integrated for any machine learning (ML) and similar purposes.

[0009] "Industrial plant" or "plant" may refer to, but is not limited to, any technological infrastructure used for industrial purposes of manufacturing, producing, or processing one or more industrial products (i.e., manufacturing or production processes or processing carried out by an industrial plant). Industrial products may be, for example, any physical product, such as chemicals, biological products, pharmaceuticals, food, beverages, textiles, metals, plastics, semiconductors. Alternatively or alternatively, industrial products may even be service products, such as recycling or waste treatment, such as chemical processing, such as decomposition or dissolution into one or more chemical products. Thus, an industrial plant may be one or more of a chemical plant, a process plant, a pharmaceutical plant, a fossil fuel processing facility (such as oil and / or natural gas wells), a refinery, a petrochemical plant, a cracking plant, and the like. An industrial plant may even be any of a distillery, a processing plant, or a recycling plant. An industrial plant may even be a combination of any of the embodiments given above or the like.

[0010] The basic structure may include equipment or process units such as any one or more of the following: heat exchangers, towers such as fractionation towers, boilers, reaction chambers, cracking units, storage tanks, extruders, granulators, settlers, blenders, mixers, cutters, curing tubes, vaporizers, filters, screens, wires, chimneys, filters, valves, actuators, grinders, transformers, transmission systems, circuit breakers, machinery such as heavy rotating equipment, such as turbines, generators, grinders, compressors, industrial fans, pumps, conveying elements such as conveyor systems, electric motors, etc.

[0011] Furthermore, industrial plants typically include a plurality of sensors and at least one control system for controlling at least one parameter related to a process or process parameter in the plant. Such control functions are typically 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).

[0012] Therefore, at least some of the equipment or process units in an industrial plant can be monitored and / or controlled for the production of one or more industrial products. Monitoring and / or control can even be performed to optimize the production of one or more products. The equipment or process units can be monitored and / or controlled via a controller such as a DCS in response to one or more signals from one or more sensors. Additionally, the plant may even include at least one programmable logic controller (“PLC”) for controlling some of the processes. Industrial plants can typically include a plurality of sensors distributed throughout the industrial plant for monitoring and / or control purposes. Such sensors can generate large amounts of data. Sensors may or may not be considered part of the equipment. Thus, production such as chemical and / or service production can be a data-heavy environment. Therefore, each industrial plant can generate a large amount of process-related data.

[0013] Those skilled in the art will understand that industrial plants typically include instruments that may include various types of sensors. Sensors can be used to measure one or more process parameters and / or to measure equipment operating conditions or parameters related to equipment or process units. For example, sensors can be used to measure process parameters such as flow rate within a line, level within a tank, boiler temperature, chemical composition of gases, etc., and some sensors can be used to measure the vibration of a grinder, fan speed, valve opening, line corrosion, voltage across a transformer, etc. The differences between these sensors are not only based on the parameters they sense, but also on the sensing principles used by each sensor. Some embodiments of sensors based on their sensed parameters may include: temperature sensors, pressure sensors, radiation sensors such as 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 employ different sensing principles include, for example, piezoelectric sensors, piezoresistive sensors, thermocouples, and impedance sensors such as capacitive and resistive sensors.

[0014] An industrial plant may even be part of a plurality of industrial plants. As used herein, the term "plural industrial plant" is a broad term and is given its common and customary meaning to those skilled in the art, and is not limited to a particular or customary meaning. Specifically, the term may refer to, but is not limited to, a combination of at least two industrial plants having at least one common industrial purpose. Specifically, a plurality of industrial plants may comprise at least two, at least five, at least ten, or even more industrial plants that are physically and / or chemically coupled. A plurality of industrial plants may be coupled such that the industrial plants forming the plurality of industrial plants can share one or more of their supply chains, precipitates, and / or products. A plurality of industrial plants may also be referred to as a complex, a complex site, a verbund, or a verbund site. Furthermore, the supply chain production of a plurality of industrial plants from various intermediate products to final products may be dispersed in various locations (such as in various industrial plants) or integrated in a verbund site or chemical park. Such integrated sites or chemical parks may be or may contain one or more industrial plants, wherein products manufactured in at least one industrial plant may serve as raw materials for another industrial plant.

[0015] "Production process" means any industrial process that provides a chemical product when used in or applied to input materials. A production process can therefore be any manufacturing or processing process, or a combination of multiple processes used to obtain a chemical product. A production process may even include the packaging and / or stacking of chemical products.

[0016] The terms “in manufacture,” “in production,” or “in processing” will be used interchangeably in the context of a manufacturing process. These terms may cover any kind of application of input materials to an industrial process that produces one or more chemical products.

[0017] The term "chemical product" in this disclosure may refer to any industrial product, such as a chemical product, pharmaceutical product, nutritional product, cosmetic or biological product, or even any combination thereof. A chemical product may consist entirely of natural components, or may contain at least partially one or more synthetic components. Some non-limiting examples of chemical products are organic or inorganic compositions, monomers, polymers, foams, pesticides, herbicides, fertilizers, feed, nutritional products, precursors, pharmaceuticals or therapeutic products, or any one or more of their components or active ingredients. In some cases, a chemical product may even be a product available to an end user or consumer, such as a cosmetic or pharmaceutical composition. A chemical product may even be a product that can be used to manufacture one or more other products; for example, a chemical product may be a synthetic foam that can be used to manufacture shoe soles, or a coating that can be used on the exterior of an automobile. A chemical product may be in any form, such as a solid, semi-solid, paste, liquid, emulsion, solution, pellet, granule or powder.

[0018] Therefore, chemical products, especially during their production process, can be difficult to trace or track. During production, materials such as input materials may be mixed with other materials, and / or input materials may be divided into different parts downstream of the production chain, for example, for different processing methods. Sometimes, chemical products may be divided and packaged in different packages. Although in some cases it may be possible to mark packaged chemical products or portions thereof, it may be difficult to match the details of the production process responsible for producing a particular chemical product or portion thereof. In many cases, input materials and / or chemical products may be in a form that is difficult to physically mark. Therefore, this teaching provides one or more object identifiers that can be used to overcome such limitations.

[0019] Production processes can be continuous during operation, such as batch chemical production processes when based on catalysts that need to be recovered. A key difference between these production types lies in the frequency of data generated during production. For example, in a batch process, production data extends from the beginning of the production process to the last batch of different batches already produced in operation. In a continuous setup, data is more continuous due to potential deviations in production operations and / or due to maintenance-driven downtime.

[0020] "Process data" refers to data contained in the form of values ​​(e.g., numerical or binary signal values) measured by one or more sensors during a production process. Process data may be time-series data of one or more process parameters and / or equipment operating conditions. Preferably, the process data includes temporal information about the process parameters and / or equipment operating conditions; for example, the data contains timestamps for at least some of the data points related to the process parameters and / or equipment operating conditions. More preferably, the process data includes temporal-spatial data, i.e., temporal data and location or data related to one or more physically located equipment areas, such that temporal-spatial relationships can be derived from the data. Temporal-spatial relationships can be used, for example, to calculate the location of input materials at a given time.

[0021] "Process parameters" can refer to any of the variables related to the production process, such as temperature, pressure, time, content, etc.

[0022] "Input material" can refer to at least one raw material or untreated material used in the production of a chemical product. Input material can be any organic or inorganic substance or even a combination thereof. Therefore, input material can even be a mixture or may contain a plurality of organic and / or inorganic components in any form. In some cases, input material can even be intermediate processed material, for example, from an upstream processing facility or plant.

[0023] "Input material data" refers to data relating to one or more characteristics or properties of the input material. Therefore, input material data may include any one or more values ​​indicating properties such as quantity of the input material. Alternatively or additionally, the value indicating quantity may be the filling degree and / or mass flow rate of the input material. These values ​​are preferably measured via one or more sensors operatively coupled to or included in the device. Alternatively or additionally, input material data may include sample / test data relating to the input material. Alternatively or additionally, input material data may include values ​​indicating any physical and / or chemical properties of the input material, such as density, concentration, purity, pH, composition, viscosity, temperature, weight, volume, etc. Alternatively or additionally, input material data may include performance data relating to the input material.

[0024] It must be mentioned that input materials processed by the processing equipment in the underlying chemical production environment are classified as physical packages or real-world packages (or respectively referred to as "physical packages" or "product packages") in the category of "packaged objects". The package size of such packaged objects may be fixed, for example, fixed by material weight or material quantity, or may be determined based on weight or quantity, for which the processing equipment can provide significantly constant process parameters or equipment operating parameters. Such packaged objects may be generated from input liquid and / or solid raw materials by means of a dispensing unit.

[0025] The subsequent processing of such packaged objects is managed by means of corresponding data objects including so-called "object identifiers," which are assigned to each packaged object via a computing unit coupled to or even part of the device mentioned. The data object, including the corresponding "object identifier" of the underlying packaged object, is stored in the memory storage element of the computing unit.

[0026] Data objects can be generated in response to a trigger signal provided by the device, preferably in response to the output of a corresponding sensor configured at each device unit. As mentioned above, the underlying industrial plant may include different types of sensors, such as sensors for measuring one or more process parameters and / or sensors for measuring equipment operating conditions or parameters related to the equipment or process unit.

[0027] More specifically, the term "object identifier" refers to a digital identifier of input material. The object identifier is preferably generated by a computing unit. The provision or generation of the object identifier may be triggered by a device or in response to a triggering event or signal, such as from the device. The object identifier is stored in a memory storage element operatively coupled to the computing unit. The memory storage may contain at least one database or may be a part of at least one database. Therefore, the object identifier may even be a part of a database. It should be understood that the object identifier may be provided by any suitable means, such as being able to transmit, receive, or generate the object identifier.

[0028] The "computing unit" may include or may be a processing component or computer processor having one or more processing cores, such as a microprocessor, microcontroller, or the like. In some cases, the computing unit may be at least partially part of a device, for example, it may be a process controller, such as a programmable logic controller ("PLC") or a distributed control system ("DCS"), and / or it may be at least partially a remote server. Thus, the computing unit may receive one or more input signals from one or more sensors operatively connected to the device. If the computing unit is not part of the device, it may receive one or more input signals from the device. Alternatively or additionally, the computing unit may control one or more actuators or switches operatively coupled to the device. One or more actuators or switches may even be operatively part of the device.

[0029] "Memory storage" can refer to a device for storing information in data form in a suitable storage medium. More preferably, the memory storage is a digital storage device suitable for storing machine-readable information in digital form, such as digital data readable by a computer processor. The memory storage device can therefore be implemented as a digital memory storage device readable by a computer processor. Further preferably, the memory storage device in the digital memory storage device can also be controlled by a computer processor. For example, any part of the data recorded on the digital memory storage device can be partially or wholly written to and / or erased and / or overwritten with new data by a computer processor.

[0030] The "computing unit" may include or may be a processing component or computer processor having one or more processing cores, such as a microprocessor, microcontroller, or the like. In some cases, the computing unit may be at least partially part of a device, for example, it may be a process controller, such as a programmable logic controller ("PLC") or a distributed control system ("DCS"), and / or it may be at least partially a remote server. Thus, the computing unit may receive one or more input signals from one or more sensors operatively connected to the device. If the computing unit is not part of the device, it may receive one or more input signals from the device. Alternatively or additionally, the computing unit may control one or more actuators or switches operatively coupled to the device. One or more actuators or switches may even be operatively part of the device.

[0031] Therefore, the computing unit can control one or more parameters related to the production process by controlling any one or more of the actuator or switch and / or terminal effector units (e.g., by controlling one or more of the operating conditions of the control equipment). The control is preferably performed in response to one or more signals acquired from the equipment.

[0032] In this context, "terminal effector unit" or "terminal effector" refers to a device that is part of and / or operatively connected to the device, and is therefore controllable via the device and / or computing unit for the purpose of interacting with the environment surrounding the device. As several non-limiting embodiments, a terminal effector may be a cutter, gripper, sprayer, mixing unit, extruder tip or the like, or even individual parts thereof designed to interact with the environment (e.g., input materials and / or chemical products).

[0033] With regard to input materials, "property / properties" may refer to any one or more of the following: quantity of input materials, batch information, and one or more values ​​of specified quality (such as purity, concentration, or any characteristic of input materials).

[0034] An "interface" may be a hardware and / or software component that is at least part of a device or part of another computing unit that provides an object identifier. In some cases, an interface may also be connected to at least one network, for example, for interfacing hardware components and / or two chips of 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 communicatively coupled to a computing unit via a network. Therefore, an interface may even be a network interface, or may include a network interface. In some cases, an interface may even be a connectivity interface, or may include a connectivity interface.

[0035] "Network interface" means a device or a group of one or more hardware and / or software components that allow operational connection to a network.

[0036] "Connectivity interface" refers to a software and / or hardware interface used to establish communication (such as transmission or exchange of signals or data). Communication may be wired or wireless. The connectivity interface is preferably based on one or more communication protocols or supports one or more communication protocols. The communication protocol may be a wireless protocol, such as: short-range communication protocols, such as Bluetooth® or WiFi; or a long-range communication protocol, such as cellular or mobile networks, such as second-generation (2G), 3G, 4G, Long-Term Evolution (LTE), or 5G. Alternatively or additionally, the connectivity interface may even be based on proprietary short-range or long-range protocols. The connectivity interface may support any one or more standards and / or proprietary protocols. The connectivity interface and the network interface may be the same unit or may be different units.

[0037] The term "network" as used herein can refer to any suitable type of data transmission medium, wired, wireless, or a combination thereof. A specific type of network does not limit the scope or generality of this teaching. A network can therefore refer to any suitable, arbitrary 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. The interconnection of networks can be formed by means of physical hardwiring, optical, and / or radio frequency methods. Specifically, a network may be or may include physical networks that are wholly or partially made of hardwiring, such as fiber-optic networks or networks that are wholly or partially made of conductive cables, or combinations thereof. A network may at least partially include the Internet.

[0038] "Equipment" may refer to any one or more assets within an industrial plant. As a non-limiting embodiment, equipment may refer to any one or more of the following, or any combination thereof: computing units or controllers such as programmable logic controllers ("PLCs") or distributed control systems ("DCS"), sensors, actuators, terminal effector units, conveying elements such as conveyor systems, heat exchangers such as heaters, boilers, cooling units, reactors, mixers, mills, shredders, compressors, slicers, extruders, dryers, sprayers, pressure or vacuum chambers, pipes, bins, silos, and any other type of equipment directly or indirectly used in or during production within an industrial plant. Preferably, equipment systematically refers to assets, equipment, or components directly or indirectly involved in the production process. More preferably, assets, equipment, or components that can affect the performance of chemical products. Equipment may be buffered, or may not be buffered. Furthermore, the equipment may involve mixing or not mixing, separation or not separation. Some non-limiting embodiments of unbuffered equipment without mixing include conveyor systems or belts, extruders, granulators, and heat exchangers. Some non-limiting embodiments of buffered equipment without mixing include buffer silos, bins, etc. Some non-limiting embodiments of buffered equipment with mixing include silos with mixers, mixing containers, shear mills, double-cone mixers, solidification tubes, etc. Some non-limiting embodiments of unbuffered equipment with mixing include static or dynamic mixers, etc. Some non-limiting embodiments of buffered equipment with separation include towers, separators, extractors, thin-film vaporizers, filters, sieves, etc. The equipment may even be, or may include, storage or encapsulation elements, such as octabin filling, rollers, bags, tank trucks.

[0039] "Equipment operating conditions" means any characteristic or value that indicates the state of the equipment, such as set point, controller output, production sequence, calibration status, any equipment-related warnings, vibration measurement, speed, temperature, and fouling values ​​(such as filter differential pressure, maintenance date, etc.).

[0040] Therefore, it should be understood that at least a portion of the process data is attached to the object identifier. All process data related to the processing of input materials by the equipment is included in the object identifier, or a portion of such data is attached or stored. This makes a snapshot of the process data related to the processing of the input materials available or linked to the object identifier. Whether the process data is stored in its entirety or only a portion thereof can be based, for example, on a determination by the computing unit regarding which subset of the process data should be attached to the object identifier. This determination can be based, for example, on the most important process parameters and / or equipment operating conditions that affect the desired properties of the chemical product. This can be advantageous in certain situations, especially when the relevant process data is large, so that the computing unit can determine which subset of the process data should be attached, rather than attaching a large amount of data to the object identifier. Therefore, the portion of the process data attached to the object identifier can be determined by the computing unit. Furthermore, this determination can be based on one or more ML models. Such models will be discussed in more detail below this disclosure.

[0041] According to another embodiment, the object identifier is also accompanied by process-specific information. The process-specific information may be any one or more of the following: production process formulation, batch data, recipient information, and a digital model relating to the transformation of input materials into a chemical product. The digital model may be any one or more of the following: a computer-readable mathematical model representing one or more entities and / or chemical changes relating to the transformation of input materials into a chemical product. Recipient information may, for example, be information relating to one or more customer orders and / or instructions. Batch data may be related to batches produced and / or to previous products manufactured using the same equipment. Thus, the traceability of chemical products can be further improved by bundling associated process-specific information. More specifically, batch data can be used to more optimally sequence the production of chemical products that are at least partially produced using the same equipment, but which have one or more different properties or specifications. For example, the production of such chemical products can be subsequently adjusted and / or sequenced in a way that minimizes the impact attributable to previous batches. For instance, if two or more chemical products have different colors, their production sequence can be determined by a computing unit so that later-manufactured products are minimally affected by color traces from previous products attributable to previously manufactured chemical products.

[0042] According to a sample, the attached object identifier can be used to associate or map input material data and / or specific process parameters and / or equipment operating conditions to at least one performance parameter of the chemical product.

[0043] A "performance parameter" may be, or may indicate, any one or more properties of a chemical product. Therefore, a performance parameter is a parameter that should meet one or more predefined criteria indicating the suitability or fitness of a chemical product for a particular application or use. It should be understood that, in some cases, a performance parameter may indicate a lack of suitability, or unsuitability, for a particular application or use of a chemical product. As a non-limiting embodiment, a performance parameter may be, for example, any one or more of the following determined by testing using predefined criteria: strength such as tensile strength, color, concentration, composition, viscosity, hardness such as Young's modulus, purity or impurity such as parts per million (ppm), failure rate such as mean time to failure (MTTF), or any one or more values ​​or variations thereof. A performance parameter thus indicates the performance or quality of a chemical product. Predefined criteria may be, for example, one or more reference values ​​or ranges, against which the performance parameters of a chemical product are compared to determine the quality or performance of the chemical product. Predefined criteria may have already used one or more tests to determine suitability, thereby defining the requirements for the performance parameters of the chemical product to be suitable for one or more specific uses or applications.

[0044] Typically, performance parameters are determined from one or more samples of the chemical product collected during and / or after production. Samples can be brought into the laboratory and analyzed to determine performance parameters. It should be understood that the entire process of collecting, processing, or testing samples and then analyzing the test results can be time-consuming and resource-intensive. Therefore, there can be a significant delay between sample collection and 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 halt until the samples have been analyzed and any corrective actions have been taken by adjusting input materials and / or process parameters and / or equipment operating conditions.

[0045] As a solution to at least reduce the effects of hysteresis in sampling methods used to adjust input materials and / or process parameters and / or equipment operating conditions, the ML model is trained using the attached object identifier.

[0046] Therefore, based on a single sample, an ML model, such as a regression or deep learning model, can be trained using data from object identifiers. Data that can be used to train the ML model may also include historical and / or current laboratory test data, or data from past and / or recent samples. For example, quality data from one or more analyses, such as image analysis, laboratory equipment, or other measurement techniques can be used.

[0047] The ML model trained using the attached object identifier data can therefore be used to predict one or more performance parameters. This eliminates at least some of the sampling and testing requirements, saving time and resources. For example, the input to the ML model can be material data and / or process parameters and / or equipment operating conditions used in the production of chemical products, while the output of the ML model can be one or more performance parameters. Those skilled in the art will understand that this ML model can also be used to control one or more of the process parameters and / or equipment operating conditions to obtain chemical products with the desired or required one or more performance parameters. Therefore, the ML model can be used to monitor the production process.

[0048] Furthermore, ML models can be used, for example, to control production processes via computing units and / or equipment. For instance, an ML model can be used to adjust equipment operating conditions, preferably in a closed-loop manner, by measuring one or more values ​​of process parameters and / or equipment operating conditions, and then generating an output to adjust the equipment operating conditions so that the adjusted process parameters and / or equipment operating conditions produce controlled chemical products with one or more desired or predetermined properties or performance parameters. Therefore, waste of chemical products exceeding the required performance can be reduced at least. This allows for production control during operation while ensuring that equipment operating conditions adapt to undesirable variations in process parameters. Alternatively, the input to the trained ML model can also be input material data for the input materials at the start of production. Therefore, the ML model can adjust equipment operating conditions to also consider any changes in the properties of the input materials to produce chemical products with the desired properties or performance. This makes production or production processes more consistent and predictable. Furthermore, production can even be automatically customized according to the different degrees of chemical product properties required for different applications. This allows for more granular control of process parameters and / or equipment operating conditions, resulting in finer and more adjustable production processes. In some cases, the ML model can even be used by the computational unit to determine which process parameters and / or equipment operating conditions have the most significant impact on the chemical product. Therefore, the computational unit is activated to exclude data from process parameters and / or equipment operating conditions that have a negligible impact on the properties of the chemical product. The relevance of process data for a specific chemical product can thus be improved based on its individual object identifiers.

[0049] According to one embodiment, the equipment includes a plurality of zones, such that during a manufacturing or production process, input material progresses from a first equipment zone to a second equipment zone. According to another embodiment, an object identifier is provided in the first equipment zone, and a second object identifier is provided when the input material enters the second equipment zone after traversing the first equipment zone. The object identifier is accompanied by at least a portion of process data from the first equipment zone. The second object identifier is accompanied by at least a portion of process data from the second equipment zone. The second object identifier may at least partially encapsulate or enrich the object identifier, or more specifically, the data from the attached object identifier. Alternatively, the second object identifier may be linked to the object identifier. In other words, the second object identifier is accompanied by the object identifier or the attached object identifier. The attached object identifier and the second object identifier may be located at the same location, or they may be located at different locations. Therefore, the second object identifier is associated with the object identifier by means of an object identifier that is at least part of the second object identifier. Similarly, the second object identifier is associated with the attached object identifier by means of an attached object identifier that is at least part of the second object identifier.

[0050] Those skilled in the art will understand that the terms "attach" or "in addition" can mean including or attaching (e.g., storing) different data elements in the same database or memory storage at adjacent or different locations. The term can also mean linking one or more data elements, packages, or streams at the same or different locations in such a way that the data packages or streams can be read and / or retrieved and / or combined as needed. At least one of these locations may be part of a remote server or even at least partially part of a cloud-based service.

[0051] "Remote server" refers to one or more computers or computer servers located far from the factory. A remote server can therefore be located several kilometers or more away from the factory. A remote server can even be located in different countries. A remote server can even be implemented at least partially as a cloud-based service or platform. The term can also collectively refer to more than one computer or server located in different locations. A remote server can be a data management system.

[0052] It should be understood that after traversing the first zone, the input material may be substantially different in nature from when it entered the first zone. Therefore, when the input material enters the second zone, it may have been transformed into intermediate processing material. However, for simplicity and without losing the generality of this teaching, the term input material will also be used to refer to the situation where the input material has been transformed into this intermediate processing material during the production process. For example, a batch of input material in the form of a mixture of chemical components may have traversed the first zone on a conveyor belt in which that batch is heated to induce a chemical reaction. Therefore, when the input material enters the second zone directly after leaving the first zone or after traversing other zones, the material may have become intermediate processing material with properties different from the input material. However, as mentioned above, such intermediate processing material can still be called input material, at least because the relationship between this intermediate processing material and the input material can be defined and determined through the production process. Furthermore, in other cases, such as when the first zone is only used to dry the input material or filter it to remove traces of undesirable material, the input material can retain substantially similar properties even after traversing the first zone or other zones. Therefore, those skilled in the art should understand that the input material in the intermediate zones may or may not be converted into intermediate processing material.

[0053] Process data from the first equipment area or the first process data can be used to associate input material data and / or process parameters of the first area and / or equipment operating conditions of the first area with at least one performance parameter of the intermediate processed material.

[0054] Process data from the second equipment area or second process data can be used to associate input material data and / or intermediate processing material data and / or first area process parameters and / or first area equipment operating conditions and / or second area process parameters and / or second area equipment operating conditions with at least one performance parameter of the chemical product.

[0055] Intermediate processing materials may have intermediate object identifiers, or in some cases, intermediate processing materials may not have intermediate object identifiers. The applicant has found that generating a second object identifier is more advantageous when the input material or intermediate processing material is combined with other materials, or when the input material or intermediate processing material is divided or segmented into multiple parts. Or more generally, after providing an object identifier, the generation of a second object identifier or any other object identifier can only occur in areas where the material mass flow rate changes. A change in mass flow rate can be a change in mass, resulting from the addition or mixing of new material to the input material or intermediate processing material and / or the removal or segmentation of material from the input material or intermediate processing material. For example, in some cases, a change in mass caused by a chemical reaction during production, attributable to the removal of moisture or the release of gases, can be excluded by triggering the occurrence of a second or other object identifier. Especially in areas where there is no substantial change in the mass of the input material, an additional object identifier may not be provided. It is not necessary to specify a limit for "material change" here, as those skilled in the art will understand that it can depend on the type of input material and / or the chemical product being manufactured, as well as other factors. For example, in some cases, a change in mass of 20% or more may be considered material, while in others it may be 5% or more, or in some cases 1% or more, or even lower. For instance, in the case of valuable products, a smaller change may be considered significant compared to another less valuable product.

[0056] As some embodiments, the determination of providing or generating an object identifier in a device area following the first device area may be based on any of the following: if the degree of antimixing in the device area is less than or almost equal to the size of the package in the area preceding the device area, then no new object identifier is provided; if the degree of antimixing in the device area is greater than the size of the package in the area preceding the device area, then a new object identifier is provided; no new object identifier is provided in a device area that is only involved in a transport area of ​​one or more transport systems or components; if the device area involves the separation of materials in the area and one or more components are material separation components, then a new object identifier is provided for one or more components; if the device area involves filling or encapsulating materials into at least one package, each package containing one or more chemical products, then at least one new object identifier is provided in the device area.

[0057] When samples of input materials, intermediate processing materials, or chemical products are collected for analysis, such samples may also possess sample object identifiers. These sample object identifiers may be similar to those discussed in this disclosure and thus include relevant process information, as described. Therefore, the sample may also bear an accurate snapshot of the production process related to the properties of the sample. This allows for further improvements in analysis and quality control. Furthermore, the production process may be synergistically improved, for example, based on improved training of one or more ML models and / or more granular control of the production process provided by the object identifier.

[0058] Therefore, according to one embodiment, an object identifier or a first object identifier is provided at the first equipment area, and at least a portion of the process data from the second equipment area is attached to the same object identifier after entering the second equipment area. The second equipment area allows the amount of material entering the second equipment area to be the same or substantially the same as the amount of material in the area where the material was processed before entering the second equipment area. This area may be the first equipment area or may be an intermediate equipment area between the first and second equipment areas, wherein the input material or intermediate processing material passes through after leaving the first equipment area and before entering the second equipment area. Since new production material has not yet been added to the input material, and the amount is substantially the same as when it entered the first equipment area, the same object identifier can be used to attach relevant process data from such areas. It should be understood that if the amount changes between the first and second equipment areas (e.g., upon entering the intermediate equipment area), an intermediate object identifier can be provided at the intermediate equipment area for attaching data from the intermediate equipment area. In this case, if the amount of material entering the second equipment area is the same as or substantially the same as the amount of material in the intermediate equipment area, the intermediate object identifier can be used in the second equipment area.

[0059] Therefore, according to a sample, the occurrence or point of an event during the production process when two or more production materials converge or when one or more materials disperse (such as by dividing into equal or unequal portions) is a type of event, and according to a sample, these occurrences are used to trigger the generation of a second or additional object identifier. Or, in different terms, the portion size of a material triggers a new identifier due to combination with new materials or due to a change in division. By doing this, an excessive number of object identifiers can be prevented from being provided, while maintaining sufficient data granularity for tracing input materials through the production process. The applicant has recognized that knowledge of the production process, or more specifically, the process experienced within the zone, is sufficient to account for any quality changes therein, such as gaseous components attributable to evaporation or release from input materials due to chemical reactions occurring within the zone. Such changes are in many cases small and / or can be calculated based on process knowledge, such as mass balance calculations based on one or more chemical equations. For example, by knowing the mass of the input material upon entering the first zone and by knowing the chemical equations of the reactions the input material undergoes in the first zone, it is possible to calculate whether and by how much a mass change will occur when the input material or intermediate processing material leaves the first zone. As previously discussed, at least a portion of the process data is attached to an object identifier. Therefore, the attached object identifier can be used not only to track input or intermediate processing materials, but also to track data determining the properties of intermediate processing materials or the size of another portion of the material produced from the intermediate processing materials.

[0060] According to another embodiment, when the production process involves, for example, physically conveying or moving input materials within a zone using conveyor elements such as a conveyor system, the process data may also include data indicating the speed of the conveyor elements and / or the speed at which the input materials are conveyed during the production process. The speed may be provided directly by one or more sensors and / or may be calculated by a calculation unit, for example, based on the time of entry into the zone and the time of exit from the zone, or the time of entry into another zone after entering the zone. The object identifier can thus be further enriched with processing time profiles within the zone, particularly processing time profiles that may influence one or more performance parameters of the chemical product. Furthermore, by using time stamps for entry and exit or subsequent zone entry, the requirement for speed measurement sensors or conveyor element devices can be avoided.

[0061] Therefore, according to a more specific configuration, a second object identifier is provided or generated at this area where the mass of the input material is combined with intermediate materials and / or at this area where the mass of the input material is divided into different components. Thus, the second object identifier is provided when the input material enters the second equipment area after traversing the first equipment area, wherein the second equipment area involves a combination of the input material with another material and / or involves the input material being divided into multiple portions of equal or unequal sizes. The intermediate material or the other material may be the same as the input material, or it may be a different material from the input material. Therefore, such material flow center data processing allows for the collection of specific data that can be transferred to the next area, for example, the next intermediate production area in the supply chain or the final or end area.

[0062] As previously discussed, additional material may be added to the input material when the input material enters the second device area. The additional material may be of the same type as the input material or may be a different material from the input material. Therefore, since the amount of material entering the second area differs from the amount of material in the first device area due to the addition of additional material to the input material, a second object identifier is provided.

[0063] Similarly, in some cases, a portion of the input material may be removed before entering the second equipment area. For example, a portion of the input material may be provided at the third equipment area, for example, for further or different processing, or even for storage or disposal. In terms of quantity and / or type, the removed portion of the input material may be equal to or substantially equal to the portion of the input material entering the second equipment area, or alternatively, in terms of quantity and / or type, it may not be equal to the portion of the input material entering the second equipment area.

[0064] According to another embodiment, each object identifier includes a unique identifier, preferably a globally unique identifier (GUID). At least traceability of chemical products can be enhanced by attaching a GUID to each virtual package of the chemical product. The GUID also reduces the management of process data such as time-series data and enables a direct correlation between virtual / physical packages and production history.

[0065] According to another embodiment, a first ML model, such as a regression or deep learning model, can be trained based on data from object identifiers. The training data may also include past and / or current laboratory test data, or data from past and / or recent samples of intermediate-processed products and / or chemical products. Furthermore, a second ML model, such as a regression or deep learning model, can be trained based on data from a second object identifier. The training data may also include past and / or current laboratory test data, or data from past and / or recent samples of intermediate-processed products and / or chemical products.

[0066] In addition to the advantages of ML models discussed earlier, trained models based on zones within a production line allow for more detailed tracking of materials and prediction of their individual performance parameters, and even the performance parameters of chemical products. Control over each zone can also be more flexible and transparent; for example, it can allow for at least partial compensation for suboptimal processing in upstream zones by manipulating the processing in one or more downstream zones, enabling the production of chemical products with similar or identical desired performance parameters. Therefore, in this way, trained models can be used to customize specific product outputs to specific predefined performance parameters. In some production scenarios, such as batch production, such models can be used to control production accordingly during operation. Waste can thus be further reduced by maintaining optimal production, even if one or more zones are not in their optimal processing state. Furthermore, optimal production patterns can be dynamically explored based on the current state of the zones. Therefore, the granularity or controllability of production can be further improved.

[0067] Therefore, any or every one of the devices in the device area can be monitored and / or controlled by an individual ML model, which is trained based on data from individual object identifiers (which come from the device area).

[0068] According to one embodiment, the provision of an object identifier may occur or be triggered in response to any one or more values ​​indicating the properties of the input material and / or any one or more values ​​from the device operating conditions and / or any one or more values ​​of the process parameters reaching, meeting, or exceeding a predefined threshold value. Any such value may be measured via one or more sensors and / or switches. For example, the predefined threshold value may be related to the weight value of the input material introduced at the device. Therefore, a trigger signal may be generated when an amount, such as the weight of the input material received at the device, reaches a predefined quantity threshold value, such as a weight threshold value. Certain embodiments of triggering events or the occurrence of providing an object identifier are also discussed earlier in this disclosure. An object identifier may be provided in response to a trigger signal, or directly in response to the quantity or weight reaching a predefined weight threshold value. The trigger signal may be a single signal, or it may be simply an event, such as a specific signal that meets a predefined criterion, such as a threshold value detected by a computing unit and / or device. Therefore, it should also be understood that an object identifier can be provided in response to the amount of input material reaching a predefined quantity threshold. The quantity can be measured as weight as explained in the above embodiments, and / or it can be any one or more other values, such as content, fill or fill degree or volume and / or by summing the mass flow rate of the input material or by applying an integral to the mass flow rate of the input material.

[0069] Therefore, the object identifier may be provided in response to a triggering event or signal, preferably provided via the device. This may be in response to the output of any of one or more sensors and / or switches operatively coupled to the device. The triggering event or signal may be related to the quantity of the input material, for example, to the occurrence of a quantity reaching or conforming to a predetermined quantity threshold. This occurrence may be detected via a computing unit and / or device, for example, using one or more weight sensors, content sensors, fill sensors, or any suitable sensors capable of measuring or detecting the quantity of the input material.

[0070] An advantage of using quantity values ​​as triggers for providing object identifiers is that any change in the quantity of material during the production process can act as a trigger to provide one or more additional object identifiers as explained in this teaching. The applicant has recognized that this provides an optimal way to segment the generation of different object identifiers in an industrial environment used to process or produce one or more chemical products, so that input materials, any intermediate processing materials, and the final chemical product can be traced both in terms of quantity and mass flow rate, substantially throughout the entire production chain and at least in some cases beyond. By providing object identifiers only at the point where new material is introduced or input, or where material is segmented, the number of object identifiers can be minimized, while maintaining material traceability not only at the endpoints of production but also within them. In production areas where no new material is added or where no material is segmented, knowledge of the process within such areas can be used to maintain observability within two adjacent object identifiers.

[0071] From another perspective, a system for digitally tracking chemical products manufactured at an industrial plant may also be provided, the system being configured to perform any of the methods disclosed herein. Alternatively, a system may be provided comprising at least one device for manufacturing chemical products at an industrial plant by processing at least one input material using a production process, the at least one device being operatively coupled to a computing unit, wherein the system is configured such that the computing unit is configured to perform the methods disclosed herein.

[0072] For example, a system may be provided comprising at least one device for manufacturing a chemical product by processing at least one input material using a production process at an industrial plant, the at least one device being operatively coupled to a computing unit, wherein the system is configured or adapted such that the computing unit is configured to: - provide an object identifier containing input material information via an interface; wherein the input material information indicates one or more properties of the input material; - receive process data from the device via the interface; the process data indicating process parameters and / or device operating conditions when the input material is processed; - attach at least a portion of the process data to the object identifier.

[0073] It should be understood that the computing unit may be operatively coupled to the interface and / or the interface may be part of the computing unit.

[0074] From another viewpoint, a computer program may also be provided, comprising instructions that, when executed by a suitable computing unit, cause the computing unit to perform the methods disclosed herein. A non-transitory computer-readable medium may also be provided storing a program that causes a suitable computing unit to perform any of the method steps disclosed herein.

[0075] For example, a computer program or a non-transitory computer-readable medium storing the program may be provided, comprising instructions that, when executed by a suitable computing unit, are operatively coupled to at least one device for manufacturing a chemical product at an industrial plant by processing at least one input material using a production process, such that the computing unit: - provides via an interface an object identifier containing input material information; wherein the input material information indicates one or more properties of the input material; - receives via the interface process data from the device; the process data indicating process parameters and / or device operating conditions when the input material is processed; - appends at least a portion of the process data to the object identifier.

[0076] It should be understood that the computing unit may be operatively coupled to the interface and / or the interface may be part of the computing unit.

[0077] A computer-readable data medium or carrier includes any suitable data storage device on which one or more instruction sets (e.g., software) embodying any one or more of the methods or functions described herein are stored. The instructions may also reside wholly or at least partially in main memory and / or processor during execution by a computing unit, main memory, and processing device, which may constitute a computer-readable storage medium. These instructions may further be transmitted or received over a network via a network interface device.

[0078] Computer programs used to implement one or more of the specific examples 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, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. However, computer programs may also be presented on a network such as the World Wide Web and downloaded from such a network to the working memory of a data processor.

[0079] In addition, a data carrier or data storage medium for making a computer program product available for download may also be provided, the computer program product being configured to perform a method according to any of the forms disclosed herein.

[0080] From another perspective, a computing unit may also be provided, which includes computer program code for performing the methods disclosed herein. Furthermore, a computing unit operatively coupled to a memory storage device may be provided, which includes computer program code for performing the methods disclosed herein.

[0081] It should be clear to those skilled in the art that two or more components are operatively coupled or connected. In a non-limiting manner, this means that at least a communication connection may exist between the coupled or connected components, for example, via an interface or any other suitable interface. The communication connection may be fixed or removable. Furthermore, the communication connection may be unidirectional or bidirectional. Additionally, the communication connection may be wired and / or wireless. In some cases, the communication connection may also be used to provide control signals.

[0082] In this context, "parameter" means any relevant physical or chemical property and / or its measure, such as temperature, orientation, location, quantity, density, weight, color, moisture, velocity, 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.

[0083] "Actuator" means any component responsible for the direct or indirect movement and control of mechanisms associated with equipment such as machinery. An actuator may be a valve, motor, drive element, or the like. An actuator may be operated electrically, hydraulically, pneumatically, or in combination thereof.

[0084] "Computer processor" refers to any logic circuit system configured to perform basic operations of a computer or system, and / or generally refers to a device configured to perform calculations or logical operations. Specifically, a processing element or computer processor may be configured to process basic instructions that drive a computer or system. As one embodiment, a processing element or computer processor may include: at least one arithmetic logic unit (ALU); at least one floating-point unit (FPU), such as a math coprocessor or a numeric coprocessor; a plurality of temporary registers, specifically configured to supply operands to the ALU and store operation results; and memory, such as L1 and L2 cache. Specifically, a processing element or computer processor may be a multi-core processor. Specifically, a processing element or computer processor may be or may include a central processing unit (CPU). The processing component or computer processor may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, or a processor implementing other instruction sets or combinations of instruction sets. The processing component may also be one or more special-purpose processing devices, such as Application-Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Complex Programmable Logic Devices (CPLDs), Digital Signal Processors (DSPs), network processors, or the like. The methods, systems, and apparatus described herein may be implemented as software in a DSP, a microcontroller, or any other side processor, or as hardware within an ASIC, CPLD, or FPGA. It should be understood that the term processing component or processor may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.

[0085] A "computer-readable data medium" or carrier includes any suitable data storage device or computer-readable memory that stores thereon one or more instruction sets (e.g., software) embodying any one or more of the methods or functions described herein. The instructions may also reside wholly or at least partially in the main memory and / or processor during execution by a computing unit, main memory, and processing device, which may constitute a computer-readable storage medium. The instructions may further be transmitted or received over a network via a network interface device.

Implementation Method

[0087] Figure 1 illustrates an embodiment of a system 168 for digitally tracking a chemical product 170 manufactured at an industrial plant. At least some of the method embodiments are also shown. The industrial plant includes at least one piece of equipment for manufacturing or producing the chemical product 170 using a production process. The chemical product 170 may be in any form, such as a pharmaceutical product, foam, nutritional product, agricultural product, or precursor.

[0088] The equipment is shown in FIG1, for example as a hopper or mixing furnace 104 and conveying elements 102a to 102b. Other equipment shown will be discussed individually in the following text. The mixing furnace 104 receives input material, which may be a single material or may contain multiple components. Here, the input material is received in two parts, shown as being supplied to the mixing furnace 104 via a first valve 112a and a second valve 112b respectively.

[0089] An object identifier is provided for the input material 114, or in this case, a first object identifier 122 is provided. The object identifier may be a unique identifier, preferably a globally unique identifier (“GUID”), which can distinguish it from other object identifiers. The GUID may be provided depending on the details of a particular industrial plant and / or the details of the chemical product 170 being manufactured and / or the details of the date and time and / or the details of the particular input material being used. The first object identifier 122 is shown to be provided at memory storage 128. Memory storage 128 is operatively coupled to computing unit 124. Memory storage 128 may even be part of computing unit 124. Memory storage 128 and / or computing unit 124 may be at least partially part of a cloud-based service. Computing unit 124 is operatively coupled to the device, for example, via network 138, which may be any suitable type of data transmission medium. The computing unit 124 may even be part of the equipment. The computing unit 124 may even be at least partially a factory control system such as a DCS and / or PLC. The computing unit 124 may receive one or more signals from one or more sensors operatively coupled to the equipment. For example, the computing unit 124 may receive one or more signals from the fill sensor 144 and / or one or more sensors associated with the conveying elements 102a to 102b. The computing unit 124 may even at least partially control the equipment or some of its components. For example, the computing unit 124 may control valves 112a, 112b, for example, via its respective actuators and / or heaters 118 and / or conveying elements 102a to 102b. The conveying elements 102a, 102b and others in the embodiment of FIG. 1 are shown as a conveyor system, which may include one or more motors and a belt driven by those motors, such that its movement causes input material 114 to be conveyed in the direction of the belt's transverse plane 120.

[0090] Other types of conveying elements may be used in place of or in combination with conveyor systems without affecting the scope or generality of this teaching. In some cases, any kind of equipment involving the flow of materials (e.g., the entry and exit of one or more materials) may be called a conveying element. Therefore, in addition to conveyor systems or belts, equipment such as extruders, granulators, heat exchangers, buffer silos, silos with mixers, mixers, mixing vessels, shear mills, double cone blenders, solidification tubes, towers, separators, extractors, thin-film vaporizers, filters, and sieves may also be called conveying elements. Therefore, it should be understood that the presence of a conveying system as a conveyor system may be optional, at least because in some cases, material may move directly from one device to another by mass flow rate, or by normal flow rate. For example, material may move directly from a heat exchanger to a separator or even further to a tower, etc. Therefore, in some cases, one or more conveying elements or systems may be inherent to the equipment.

[0091] The first object identifier 122 may be provided in response to a trigger signal or event, which may be a signal or event related to the amount of input material. For example, the fill sensor 144 may be used to detect at least one quantity value, such as the fill level and / or weight of the input material. When the quantity reaches a predetermined threshold, the computing unit 124 may provide the first object identifier 122 at the memory storage 128. The first object identifier 122 may contain data related to the input material, or input material data. The input material data indicates one or more properties of the input material.

[0092] In some cases, the mixing furnace 104 and associated instruments such as valves 112a, 112b and the fill sensor 144 can be considered as the first equipment area. Therefore, process data 126 from the first equipment area, such as data from the mixing furnace 104, can be attached to the first object identifier 122. The process data 126 indicates process parameters and / or equipment operating conditions, i.e., the operating conditions of the mixing furnace 104 and valves 112a to 112b when the input material is processed in the first equipment area, such as any one or more of the following: inlet mass flow rate, outlet mass flow rate, fill level, temperature, moisture content, timestamp or entry time, exit time, etc. In this case, the equipment operating conditions may be the control signals and / or setpoints of valves 112a, 112b and / or the mixing furnace 104. Process data 126 may be, or may include, time-series data, meaning it may include time-dependent signals that can be obtained via one or more sensors (e.g., the output of fill sensor 144). The time-series data may include continuous signals or signals that are interrupted at regular or irregular time intervals. Process data 126 may even include timestamps from one or more of the mixing furnace 104, such as entry and / or exit times. Therefore, a specific input material 114 can be associated with the process data 126 related to that input material 114 via an object identifier 122. The object identifier 122 can be attached to other object identifiers downstream in the production process, allowing specific process data and / or equipment operating conditions to be associated with a specific chemical product. Other important benefits have been discussed in other parts of this disclosure, such as in the overview section.

[0093] The conveyor system including conveyor elements 102a, 102b and associated belts can be considered as an intermediate equipment area. In this embodiment, the intermediate equipment area includes a heater 118 for applying heat to the input material on the belt. The conveyor system may even include one or more sensors, such as speed sensors, weight sensors, temperature sensors, or any other type of sensor for measuring or detecting process parameters and / or properties of the input material 114 at the intermediate equipment area. Any or all outputs of the sensors may be provided to the computing unit 124.

[0094] As the input material 114 advances along the transverse plane 120, it is heated by the heater 118. The heater 118 can be operatively coupled to the computing unit 124, that is, the computing unit 124 can receive signals or process data from the heater 118. Furthermore, the heater 118 can be controlled via the computing unit 124, for example, via one or more control signals and / or setpoints. Similarly, the conveyor system including the conveyor elements 102a, 102b and associated belts can also be operatively coupled to the computing unit 124, that is, the computing unit 124 can receive signals or process data from the conveyor elements 102a, 102b. Coupling can be, for example, via a network. Furthermore, the conveyor elements 102a, 102b can even be controlled via the computing unit 124 (for example, via one or more control signals and / or setpoints provided by the computing unit 124). For example, the speeds of the conveying elements 102a and 102b can be observed and / or controlled by the computing unit 124. Depending on the situation, since the amount of material 114 input in the intermediate equipment area is constant or nearly constant, it is not necessary to provide additional object identifiers for the intermediate equipment area. Therefore, process data from the intermediate equipment area, i.e., from the heater 118 and / or the conveying elements 102a and 102b, can also be attached to the object identifiers of the previous or earlier areas, i.e., the first object identifier 122. The attached process data 126 can therefore be enriched to further indicate the process parameters and / or equipment operating conditions of the input material 114 from the intermediate equipment area as it is processed in the intermediate equipment area, i.e., the operating conditions of the heater 118 and / or conveyor elements 102a, 102b, such as inflow mass flow rate, outflow mass flow rate, one or more temperature values ​​from the intermediate area, inflow time, outflow time, and any one or more of the speed of the conveyor elements 102a, 102b and / or the belt. In this case, the equipment operating conditions can be the control signals and / or setpoints of the conveyor elements 102a, 102b and / or the heater 118. It will be apparent that the process data 126 is significantly related to the duration of the input material 114's presence within each individual equipment area. Therefore, an accurate snapshot of the relevant process data for a specific input material 114 can be provided via the object identifier 122. Additional observability of input material 114 can be extracted via knowledge of specific parts or components of the production process within the intermediate equipment area (e.g., chemical reactions). Alternatively or additionally, the speed at which input material 114 traverses the intermediate equipment area can be used to extract additional observability via computing unit 124. By combining process data 126 with specific timestamps or time-series data and / or the entry and / or exit times of input material 114 in the intermediate equipment area, more granular details of the conditions under which input material 114 is processed in the intermediate equipment area can be obtained from object identifier 122.

[0095] Data from object identifier 122 can be used to train one or more ML models for monitoring and / or controlling a production process and / or specific parts thereof, such as portions of the production process within the first equipment area and / or intermediate equipment area. The ML model and / or object identifier 122 can even be used to associate one or more performance parameters of a chemical product with details of the production process in one or more areas.

[0096] It should be understood that as the input material 114 moves along the transverse plane 120, its properties can change and it can be transformed into or converted into intermediate processing material 116. For example, when the heater 118 heats the input material 114, it can produce intermediate processing material 116. Those skilled in the art will understand that, for simplicity and ease of understanding, intermediate processing material 116 may sometimes also be referred to as input material in this teaching. For example, in the case of the equipment area or components discussed below, it will thus be apparent that the input material is involved in the stages of the production process described in the description of such embodiments.

[0097] An embodiment of a material divided into multiple parts is now discussed. Figure 1 shows this area as a second equipment area including a shear mill 142 and second conveying elements 106a, 106b. The intermediate processing material 116, which traverses along the direction of the transverse plane 154, is divided or segmented using the shear mill 142, thus producing multiple parts, shown in this embodiment as a first divided material 140a and a second divided material 140b.

[0098] Therefore, according to one aspect of this teaching, an individual object identifier can be provided for each part. However, in some cases, an object identifier may be provided only for one or some of the parts, rather than providing an individual object identifier for each part. For example, this may be the case if there is no interest in any of the tracking parts. For example, an object identifier may not be provided for the discarded part of the intermediate processing material 116. Referring now back to FIG1, a first divided object identifier 130a is provided for the first divided material 140a and a second divided object identifier 130b is provided for the second divided material 140b.

[0099] The first partitioned object identifier 130a is attached with first partitioned process data 132a, and the second partitioned object identifier 130b is attached with second partitioned process data 132b. The first partitioned process data 132a may be a copy of the second partitioned process data 132b, or may be partially the same data. For example, if the first partitioned material 140a and the second partitioned material 140b undergo the same process (i.e., in substantially the same location and time), the process data attached to the other first partitioned object identifier 130a and the second partitioned object identifier 130b may be the same or similar. However, if the first partitioned object identifier 130a and the second partitioned object identifier 130b are processed differently within the second equipment area, the first partitioned process data 132a and the second partitioned process data 132b may be different from each other.

[0100] However, those skilled in the art will understand that in some cases, only one object identifier may be provided at the shear mill 142, and subsequently, if the material processed by the shear mill 142 is divided into multiple parts, multiple object identifiers may be provided after the shear mill 142. Therefore, depending on the details of a particular production process, the shear mill may or may not be a separating device. Similarly, in some cases, no new object identifier may be provided for the shear mill so that process data from the zone is attached to the previous object identifier. New object identifiers may therefore be provided at the zone where the material is divided and / or combined. For example, in some cases, a first divided object identifier 130a and a second divided object identifier 130b may be provided after the shear mill 142, for example, at different zones after entering the shear mill 142.

[0101] In this embodiment, the second device area 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 operatively coupled to the computing unit 124. The imaging sensor 146 may be used to measure or detect one or more properties of the intermediate processing material 116 before it enters the second device area. 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 changes in the second device area, according to one aspect of this teaching, another object identifier (not shown in FIG. 1) may have been provided before the first divided object identifier 130a and the second divided object identifier 130b.

[0102] A first divided object identifier 130a and a second divided object identifier 130b are provided that can be triggered via an imaging sensor 146 in response to intermediate processing material 116 passing quality criteria. By correlating data from neighboring areas or from object identifiers (e.g., mass flow rate from the intermediate equipment area and mass flow rate to the second equipment area), the calculation unit 124 can determine which specific input material 114 or intermediate processing material 116 is associated with material entering the subsequent area. Alternatively or additionally, two or more timestamps can be correlated between these areas, for example, a departure timestamp from the intermediate equipment area and an entry timestamp detected by the imaging sensor 146 and / or at the second equipment area. The speed of the transport elements 102a, 102b, directly measured via sensor output or determined from two or more timestamps, can also be used to establish a relationship between a specific package or batch of input material and its object identifier. Therefore, it is even possible to determine the location of a specific chemical product 170 within the production process at a given time, thus establishing a time-space relationship. Some or all of these states can not only be used to improve the traceability of chemical product 170 from input materials to finished products, but also to monitor and improve the production process, making it more adjustable and controllable.

[0103] As discussed, the first partitioned object identifier 130a and the second partitioned object identifier 130b are respectively attached with first partitioned process data 132a and second partitioned process data 132b from the second equipment area. The first partitioned process data 132a and the second partitioned process data 132b may even be linked to or attached to the first object identifier 122. Similar to the object identifier 122 discussed above, the first partitioned process data 132a and the second partitioned process data 132b indicate the process parameters and / or equipment operating conditions when the intermediate processing material 116 is processed in the second equipment area, that is, the output of the imaging sensor 146, the operating conditions of the shear mill 142 and the second conveying elements 106a, 106b, such as any one or more of the following: incoming mass flow rate, outgoing mass flow rate, fill degree, temperature, optical properties, timestamp, etc. In this case, the equipment operating conditions may be the control signals and / or setpoints of the shear mill 142 and / or the second conveying elements 106a, 106b. The first divided process data 132a and the second divided process data 132b may include time-series data, meaning that they may include time-dependent signals, which may be obtained via one or more sensors (e.g., the output of the imaging sensor 146 and / or the speed of the second conveying elements 106a, 106b).

[0104] As the intermediate processing material 116 continues after encountering the imaging sensor 146, it moves toward the shear mill 142 in the direction of the transverse axial plane 154 driven by the second conveyor elements 106a, 106b. In this embodiment, the second conveyor elements 106a, 106b are shown as part of a second conveyor system separate from the conveyor system containing the conveyor elements 102a, 102b. It should be understood that the second conveyor system may even be part of the same conveyor system containing the conveyor elements 102a, 102b. Therefore, the second equipment area may include some of the same equipment used in another area.

[0105] As can be seen in Figure 1, although the first divided material 140a and the second divided material 140b are used in different ways in production later, their respective object identifiers (i.e., the first divided object identifier 130a and the second divided object identifier 130b) are allowed to follow or track the first divided identifier 130a and the second divided object identifier 130b individually through the remaining production process and in some cases beyond the remaining production process.

[0106] After leaving the second equipment area, the first divided material 140a is fed into the extruder 150, while the second divided material 140b is conveyed for curing in the third equipment area, which includes the curing device 162 and the third conveying elements 108a, 108b. The conveying elements 108a, 108b shown are therefore non-limiting embodiments, as previously discussed.

[0107] As the second diced material 140b moves along the belt in the direction of the transverse plane 156, it undergoes a curing process via the curing equipment 162 to produce a cured second diced material 160. Since no substantial quality change can occur, no new object identifier can be provided for use in the third equipment area, as is previously discussed. Therefore, process data from the third equipment area can also be attached to the second diced object identifier 130b. Similar to the above, the additional second segmented process data 132b can therefore be enriched to further indicate the process parameters and / or equipment operating conditions when the second segmented material 140b from the third equipment zone is processed in the third equipment zone, that is, the operating conditions of the curing equipment 162 and / or the conveying elements 108a, 108b, such as the inlet mass flow rate, the outlet mass flow rate, one or more temperature values ​​from the third zone, the entry time, the exit time, the speed of the conveying elements 108a, 108b and / or the belt, etc. In this case, the equipment operating conditions can be the control signals and / or setpoints of the conveying elements 102a, 102b and / or the curing equipment 162.

[0108] Similarly, the first divided material 140a advances to the fourth equipment area, which includes the extruder 150, temperature sensor 148, and fourth conveying elements 110a and 110b. Here, as no substantial quality change can occur, no new object identifier is provided for the fourth equipment area, as is generally true. Therefore, as previously discussed, process data from the fourth equipment area can also be attached to the first divided object identifier 130a. Similar to the above, the additional first categorized process data 132a can therefore be enriched to further indicate the process parameters and / or equipment operating conditions when the first categorized material 140a from the fourth equipment zone is processed in the third equipment zone, i.e., the operating conditions of the extruder 150 and / or temperature sensor 148 and / or conveyor elements 108a, 108b, such as incoming mass flow rate, outgoing mass flow rate, one or more temperature values ​​from the third zone, entry time, exit time, conveyor elements 110a, 110b and / or belt speed, etc. In this case, the equipment operating conditions can be the control signals and / or setpoints of the conveyor elements 108a, 108b and / or extruder 150. Therefore, the nature and dependence of the transformation from the first categorized material 140a to the extruded material 152 can also be included in the first categorized object identifier 130a.

[0109] As may be understood, the number of individual object identifiers can be reduced, while material and product monitoring can be improved throughout the production process.

[0110] As the extruded material 152 moves further in the direction of the transverse axial surface 158 generated by the conveying elements 108a, 108b, the extruded material can be collected in a collection area 166. The collection area 166 may be a storage unit, or it may be an additional processing unit for applying additional steps in the production process. In the collection area 166, additional materials can be combined, such as, as shown here, a cured second divided material 160 can be combined with the extruded material 152. Thus, a new object identifier can be provided. This object identifier is shown as a combined object identifier 134. The combined object identifier 134 may be accompanied by combined process data 136, which may include the whole or a portion of the first divided object identifier 130a and the second divided object identifier 130b. The combined object identifier 134 thus possesses process parameters and / or equipment operating conditions from the collection area 166, similar to those described in detail in this disclosure. Depending on the function or other processing (if any function or other processing is performed in the collection area 166), data such as any one or more of the following may be included as combined process data 136: incoming mass flow rate, outgoing mass flow rate, one or more temperature values ​​from the collection area 166, entry time, exit time, speed, etc.

[0111] In some cases, individual batches may be sent from collection area 166 for storage and / or sorting and / or packaging. These individual batches are shown as first silo 164a and second silo 164b. Since the quantity is further divided, individual item identifiers may be provided for each silo, so that the chemical product 170 in that silo (i.e., the individual item identifier for first silo 164a) can be associated with the process data or conditions at which the chemical product 170 was exposed.

[0112] As will be understood, each of the object identifiers may be a GUID. Each may include whole or part of information from previous object identifiers, or may be linked. The entire production process may thus be attached as a snapshot or traceable link to a specific chemical product 170.

[0113] Figure 2 illustrates a flowchart 200 or process route, particularly as viewed from the first equipment area, of the method described in this teaching. In block 202, an object identifier containing input material information is provided via an interface. The input material information indicates one or more properties of the input material 114. In block 204, process data is received from the equipment via an interface. The process data indicates the process parameters and / or equipment operating conditions when the input material is processed. In block 206, at least a portion of the process data is attached to the object identifier 122.

[0114] Similarly, if another equipment area exists, it can be determined whether another object identifier should be provided as the input material progresses to the subsequent area. If not, process data from the subsequent area can also be attached to the same object identifier. If it is determined that another object identifier should be provided, process data from the subsequent area is attached to the other object identifier. In this disclosure, each of these options, such as the details of the intermediate equipment area, is discussed in detail, for example in the overview section and with reference to FIG1.

[0115] The block diagram shown in Figure 3 represents a portion of the product manufacturing system of an industrial plant. In this specific example, these portions respectively include ten product processing devices or units 300 to 318 or technical equipment arranged along the entire product processing line shown. In this specific example, one of these processing units (processing unit 308) includes three corresponding equipment areas 320, 322, and 324 (see also more detailed examples in Figures 3 and 5).

[0116] In this embodiment, the chemical product as input material is produced based on raw materials supplied to the processing line via a liquid raw material storage tank 300, a solid raw material storage tank 302, and a recycling silo 304. The recycling silo recycles any chemical product or intermediate product, such as those containing insufficient material / product properties or insufficient material / product quality. The individual raw materials input to processing lines 306 to 318 are processed via individual processing equipment, namely, a dispensing unit 306, a subsequent heating unit 308, a subsequent processing unit 310 including a material buffer, and a subsequent sorting unit 312. Downstream of these processing equipment 306 to 312, a conveying unit 314 is configured to convey materials that need to be recycled from the sorting unit to the recycling silo 304, for example, due to insufficient quality of the produced materials. Finally, the materials sorted by the sorting unit 312 are transferred to the first packaging unit 316 and the second packaging unit 318, which package the corresponding materials into material containers for transportation purposes, such as material packages in the case of bulk materials or bottles in the case of liquid materials.

[0117] In this specific example, production systems 300 to 318 provide a data interface for the computing unit (neither is depicted in this block diagram), through which data objects containing information about individual input materials and changes attributable to processing are provided. The entire production process is at least partially controlled by the computing unit.

[0118] The input material being processed by the processing equipment 306 to 312 is divided into physical or real-world so-called "packaged objects" (hereinafter also referred to as "physical packages" or "product packages"), wherein these packaged objects are disposed of or processed by each of the processing units 306 to 312. The package size of such packaged objects may be fixed, for example, by the weight of the material (e.g., 10 kg, 50 kg, etc.) or by the amount of material (e.g., 1 decimeter, 1 / 10 cubic meter, etc.), or even determined by weight or amount, for which the processing equipment can provide significantly constant process parameters or equipment operating parameters.

[0119] The dispensing unit 306 first produces such packaged items from input liquid and / or solid raw materials and / or recycled materials provided by the recycling silo 304. After producing the packaged items, the dispensing unit conveys these items to the homogenization unit 308. The homogenization unit 308 homogenizes the materials of the packaged items, that is, homogenizes, for example, liquid and solid materials or two liquid or solid materials. After a heating process, the heating unit 308 conveys the corresponding heated packaged items to the processing unit 310, which transforms the materials of the input packaged items into different physical and / or chemical states, for example, by heating, drying or humidifying or by a chemical reaction. The corresponding transformed packaged items are then conveyed to one or more of the three downstream packaging units 316, 318 or the aforementioned conveying unit 314.

[0120] Subsequent processing of real-world packaged objects is managed by means of corresponding data objects 330, 332, 334 (or the previously described "object identifiers"), which are assigned to each packaged object via computing units operatively coupled to or part of devices 306 to 312 and stored in memory storage elements of the computing units. According to this specific example, the three data objects 330 to 334 are generated in response to trigger signals provided via devices 306 to 312, i.e., in response to the output of corresponding sensors configured at each of the device units 306 to 312 or their respective switches, wherein such sensors are operatively coupled to device units 306 to 312. As previously mentioned, industrial plants may include different types of sensors, such as sensors for measuring one or more process parameters and / or for measuring equipment operating conditions or parameters related to equipment or process units. In this specific example, sensors for measuring the flow rate and content of bulk and / or liquid materials processed inside device units 306 to 312 are disposed in these units.

[0121] In this specific example, the three exemplary data objects 330, 332, and 334 depicted in Figure 3 are each associated with three equipment areas 320, 322, and 324 of the entire product manufacturing process based on processing units 306 to 312 and 314 to 318.

[0122] The first two data objects 330 and 332 include product package objects containing process data. The process data includes information on the processing / treatment that the relevant physical package has undergone during its residence / processing within several processing units. The process data may be aggregated data, such as the calculated average temperature during the residence time of the underlying physical package within the relevant processing unit, and / or it may be time-series data of the underlying manufacturing process.

[0123] The first data object 330 is a first type of package (referred to as "Package A" in FIG3), which in this specific example is assigned to a physical package that has been delivered by two processing units (dispensing unit 306 and heating unit 308). The first data object 330 includes relevant data of the two units at the current point in the processing time during each dwell period. The first data object includes a corresponding "Product Package ID".

[0124] The heating unit 308 contains several device areas, and in this specific example, it contains three device areas 320, 322, and 324 ("Area 1", "Area 2", and "Area 3"). These different device areas are used as classification groups for classifying or selecting relevant process data. This classification helps to obtain only the packaged object data from the relevant device area, which is related to the processing of the underlying physical package at the corresponding point during the relevant physical package within this device area. However, in this specific example, the material composition of the physical package is not changed by either the processing units 306 or 308.

[0125] Once package A 330 has reached the next processing unit 310 (in this specific example, a "processing unit with a buffer"), the material composition of each physical package changes because this processing unit 310 not only delivers the physical packages in a plug flow pattern, but also includes a buffer volume larger than the initial package size, resulting in a defined degree of demixing for such physical packages. Therefore, each physical package leaving this processing unit 310 is a different type of physical package, referred to as "package B" in Figure 3.

[0126] The second data object 332 ("B package") also includes a corresponding "product package ID". Data object 332 further includes data from a defined number of previous data objects. In this specific example, data object 330 is designated as "A package" by a defined percentage, referred to as "aggregated data from the relevant A package". The corresponding aggregation scheme or algorithm depends on, for example, the underlying processing unit, the size of the underlying physical package, the mixing capability of the underlying physical package materials, and the residence time of the underlying physical package within the underlying processing unit, or the corresponding device area of ​​the processing unit.

[0127] Once the processed entity (product) package is packaged into a discrete entity package by one of two packaging units 316, 318, such as by packaging the processed entity package into a container, roller, or octagonal container or the like, in this specific example, the corresponding packaged entity package is disposed of or tracked via another data object 334 referred to as the "entity package". This data object 334 includes the related previous entity packages already packaged therein (such as "package A" and "package B" in this context). The designation of a "product package ID" is sufficient, for example, for tracking purposes, rather than using the complete data object, because such product package IDs can be easily linked together during later data processing (e.g., data processing performed by means of an external "cloud computing" platform).

[0128] The first data object (or "object identifier") 330 specifically includes the following information: - "Product Package ID" for the underlying package; - General information about the underlying package, such as information or instructions about the underlying processed material of the package; - The current position of the underlying package within the entire processing line 306 to 318; - Process data, i.e., aggregate values ​​of temperature and / or weight of the processed material of the underlying package; - Time series data of the underlying production process; and a connection to a sample from the underlying package, wherein the product package passes through a sampling station, and at a defined time, the operator obtains a sample from this product package and provides it to the laboratory. For this sample, a sample object (see Figure 6, reference symbols 634 and 638) will be generated and linked to the relevant product package (see Figure 6, reference symbols 626 and 630). This sample object specifically contains corresponding product quality control (QC) data from the laboratory and / or performance data from the corresponding testing machine.

[0129] The second object identifier 332 further includes aggregated data from the associated A package, which is generated in the processing unit 310 with a buffer.

[0130] The third item identifier 334 is generated by the two packaging units 316 and 318 by the specified timestamp "physical package 1976-02-06 19:12:21.123" and includes the following information: - Similarly, the corresponding package or item identifier ("package ID"); - The name of the product packaged into the two material containers for the shipping purpose depicted in Figure 3; - The order number of the product ordered in the corresponding package; and the batch number of the product in the corresponding package.

[0131] The overall packaging information of the first object identifier 330 and the second object identifier 332 includes material information of the input raw material, which in this specific example indicates the chemical and / or physical properties (such as material temperature and / or weight) of the input material or the processed material, and in this specific example also includes the aforementioned laboratory samples or test data related to the input material, such as historical test results.

[0132] According to the product manufacturing process also illustrated in Figure 3, process data from the entire equipment is collected via the mentioned interface. This includes process parameters such as the temperature and / or weight of the processed material, and in this specific example, it also indicates the equipment operating conditions during material processing, such as the temperature of the heater and / or the applied dosing parameters. In this specific example, only a portion of the collected process data, such as the process data from the polymer data of the relevant A package, is attached to the second object identifier 332.

[0133] As previously described, in this specific example, three object identifiers 330 to 334 are used to associate or map the mentioned input material information and / or specific process parameters and / or equipment operating conditions to at least one performance parameter of the chemical product, which performance parameter is, respectively, or indicates any one or more properties of the underlying material (e.g., the corresponding chemical product).

[0134] According to the specific example shown in Figure 3, the collected process data (as aggregated values) included in the two object identifiers 330 and 332 contains values ​​indicating process parameters and additionally indicating equipment operating conditions measured during the production process. Furthermore, object identifiers 330 and 332 include process data provided as time-series data of one or more of the process parameters and / or equipment operating conditions. Equipment operating conditions can be, for example, any characteristic or value representing the state of the equipment based on vibration measurements; in this specific example, production machine setpoints, controller outputs, and any equipment-related warnings. Additionally, they may include conveyor speed, temperature, and fouling values, such as filter differential pressure and maintenance dates.

[0135] In a specific example of the product manufacturing system shown in Figure 3, the entire product processing equipment 306 to 318 includes the aforementioned plurality of three equipment areas 320 to 324, such that during the production process, input raw materials 300 to 304 traverse the entire processing line 306 to 318, and in this specific example, proceed from the first equipment area 320 to the second equipment area 322 and from the second equipment area 322 to the third equipment area 324. In this production scenario, a first object identifier 330 is provided at the first equipment area 320, wherein a second object identifier 332 is provided when the input material enters the second equipment area 322 after being processed by the first equipment area 320. The second object identifier 332 is accompanied by or includes at least a portion of the data or information provided by the first object identifier 330, and further includes the final data / information "aggregate data from the relevant A package".

[0136] It is worth noting that any or each of the object identifiers 330 to 334 may include a unique identifier, preferably a globally unique identifier (“GUID”), so as to allow the object identifier to be reliably and securely assigned to the corresponding package throughout the manufacturing process.

[0137] In this product processing scenario, the process data attached to the first object identifier 330 is at least a portion of the process data collected from the first equipment area 320. Therefore, the second object identifier 332 is attached with at least a portion of the process data collected from the second equipment area 322, wherein the process data collected from the second equipment area 322 indicates the process parameters and / or equipment operating conditions when the input raw materials 300 to 304 are processed in the second equipment area 322.

[0138] In Table 1 below, another exemplary object identifier is shown again in tabular format. This object identifier contains much more information / data than the three object identifiers 330 to 334 described previously.

[0139] This illustrative object identifier refers to a so-called "B package" with an underlying date and time stamp "1976-02-06 18:31:53.401", as shown in Figure 4 below, but includes more information than the B package included in Figure 4.

[0140] In this embodiment, the unique identifier ("unique ID") contains a unique URL ("uniqueObjectURL"). In this embodiment, the key details of the underlying encapsulation ("encapsulation details") are the date and timestamp ("generation timestamp") of the encapsulation with two values ​​"02.02.1976 18:31:53.401" and the encapsulation type ("encapsulation type"), which in this embodiment is encapsulation type "B". The current position of the encapsulation along the underlying production line ("encapsulation position") is defined by the "encapsulation position link", which in this embodiment is the transport link to "conveyor belt 1" of the production line.

[0141] At conveyor belt 1, a measuring device (see “Measuring Point” including exemplary processing data or values) is provided for measuring the average temperature (“Average”) of the corresponding description (“Description”) of the currently exposed material temperature of 85°C and the underlying temperature zone (“Temperature Zone 1” in this embodiment). Additionally, the measuring device may include sensors for detecting the entry date / time (“Entry Time”) of the package at conveyor belt 1, which in this embodiment is “02.02.1976 18:31:54.431”, and for detecting the departure date / time (“Departure Time”) of the package from conveyor belt 1, which in this embodiment is “02.02.1976 18:31:57.234”. Finally, the measuring device includes a sensing device for detecting time series values ​​(“Time Series Values”) of underlying time series information (“Time Series”) related to the production process.

[0142] In addition, in this embodiment, the object identifier shown further includes information about the downstream positioning "conveyor belt 2", the downstream positioning "mixer 1" and the downstream positioning "silo 1" for storing the processed material in the middle. - Package B, 1976-02-06 18:31:53.401 - Unique ID - Unique URL uniqueObjectUrl - Packaging details - Generate timestamp 02.02.1976 18:31:53.401 - Encapsulation type B - Package location - Encapsulated location link Conveyor Belt 1 - Conveyor Belt 1 - Measurement points - Average 85℃ - describe Temperature Zone 1 - Entry Time 02.02.1976 18:31:54.431 - Departure time 02.02.1976 18:31:57.234 - Time series Time series values - Conveyor Belt 2 - Mixer 1 - Silo 1 Table 1: Illustrative Table Object Identifiers

[0143] Figure 4 shows a second specific example of the process section of the underlying product manufacturing system of an industrial plant. In this second specific example, these process sections respectively include six product handling devices 400, 402, 406, 410, 412, 416 or technical equipment.

[0144] The "upstream process" 400 for processing packaged objects is connected to the "classification unit" 402 for classifying processed packaged objects. The upstream process 400 and the classification unit 402 are managed by means of a first data object 404. This data object 404 relates to the described "B package," which has an underlying date and timestamp depicting the date and time of its creation: "1976-02-06 18:51:43.431." The data object 404 includes a "package ID" (so-called "object identifier") for the currently processed packaged object. The data object 404 further includes n previously described chemical and / or physical properties of the currently processed packaged object, in this embodiment "property 1" and "property n."

[0145] In this embodiment, the input material (i.e., the corresponding packaged object fed into the upstream process 400) is provided by the "recycling silo" 406. On the other hand, the recycling silo 406 receives bottom-layer recycled material from the "transport unit 1" 410 that transports the packaged objects to the recycling silo 406. These packaged objects must be recycled and sorted accordingly by the sorting unit 402. The bottom-layer transport process step 410 is managed by means of a second data object 408, which relates to the "B package" described above and includes the mentioned bottom-layer date and time stamp "1976-02-06 18:51:43.431", the "package ID" of the currently processed packaged object, and two chemical and / or physical properties "property 1" and "property n". However, due to the aforementioned requirements of the recycled underlying packaged object, the second data object 408 further includes another chemical and / or physical property of the underlying packaged object, in this embodiment "property 2", which specifically includes individual performance indicators for the packaged object, in this embodiment "low or insufficient material or product performance".

[0146] Depending on the performance value of the corresponding packaged item, packaged items processed by the upstream process 400 but not classified by the sorting unit 402 are provided by the sorting unit 402 to the first "packaging unit 1" 412 or the second "packaging unit 2" 416. Packaging units 412 and 416 are used to package the corresponding packaged items into individual containers 414 and 418. The packaging process performed by the two packaging units 412 and 416 is managed by means of the third data item 420 and the fourth data item 422.

[0147] Both data objects 420 and 422 relate to "physical packaging" and include the same date "1976-02-06" as the "B package" described above, but include a later timestamp "19:12:21.123" compared to the "B package" described above. 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, which in this embodiment are the "intermediate performance range" for the product stored in the first container (or filling bag) 414 and the "high performance range" for the product stored in the second container (or filling bag) 418. In addition, the two data objects 420 and 422 include the "order number" and "batch number" corresponding to the final product.

[0148] Figure 5 shows a third specific example of a portion of an underlying chemical product manufacturing process or system implemented in an industrial plant. In this second specific example, these portions respectively include nine product handling devices 500 to 516 or technical equipment.

[0149] This product processing method is based on two raw materials, namely "raw material liquid" 500 and "raw material solid" 502, in order to produce polymeric materials in a known manner. As in the production scenario previously described according to Figures 3 and 4, the technical equipment includes a "recycling silo" 504 for using recycled materials, as previously described.

[0150] The technical apparatus further includes a "dosing unit 506" for producing encapsulated objects based on the input raw materials processed by the aforementioned reaction unit 508, which conveys the encapsulated objects along the four shown polymerization reaction zones ("Zones 1 to 4") 510, 512, 514, 516 for processing and by a "curing unit" 518 for curing the polymeric material (i.e., the corresponding encapsulated objects) produced in the reaction unit 508. In this specific embodiment, the curing unit 518 only includes a material buffer and does not include a remixing device. The curing unit 518 also conveys the corresponding processed encapsulated objects.

[0151] "Conveying Unit 1" 520 conveys packaged items sorted for recycling by means of recycling silo 504. The finally processed (i.e. unsorted) units are then conveyed again to the first "Packaging Unit 1" 522 and the second "Packaging Unit 2" 524. The two packaging units 522 and 524 convert the corresponding packaged items and convey them to individual containers or filler bags 526 and 528.

[0152] The production process depicted in Figure 5 is managed by the first data object 530 and the second data object 534.

[0153] The first data object 530 relates to "Package A" which has a production date of "1976-02-06" and a production time of "18:31:53.401". In this production context, data object 530 also includes the previously described "Package ID", process information regarding the dispensing process performed by dispensing unit 506 ("Dispensing Properties"), and additional process information regarding the production of polymer materials by means of reaction unit 508 ("Reaction Unit Properties"). Dispensing Properties include information regarding the amount of raw materials for each package, namely "Percentage Raw Material 1 (Liquid)", "Percentage Raw Material 2 (Solid)" and product temperature. Reaction Unit Properties include the temperatures of the four polymerization reaction zones 510 to 516 ("Temperature Zone 1", "Temperature Zone 2", "Temperature Zone 3" and "Temperature Zone 4").

[0154] Additionally, the first data object 530 includes the current position (current encapsulation position) of the underlying encapsulation object along processing lines 506 to 524. In this specific embodiment, the current position of the encapsulation object is managed by means of a "encapsulation position link" and a corresponding "zone position". Finally, it includes chemical and / or physical information about the underlying polymerization reaction, i.e., corresponding to "enthalpy / turnover". Thus, the processing units 506 to 524 that deliver a given encapsulation object calculate the enthalpy value and permanently write / implement it into the first data object 530. This is possible due to existing information about the encapsulation position and corresponding residence time, and about the corresponding process values ​​(e.g., encapsulation temperature). Based on the current value of the enthalpy and / or turnover included in the first data object 530, the curing time parameter is adjusted based on the calculated value of the enthalpy via the communication line 532 between the first data object 530 and the curing unit 518.

[0155] The second data item 534 relates to the "physical package" processed by one of the packaging units 522 and 524, and includes the corresponding date / time information "1976-02-06 19:12:21.123". It includes the "package ID", "product" description / instruction manual, "order number", "lot number" and the mentioned values ​​of calculated enthalpy and / or turnover.

[0156] Figure 6 illustrates a first concrete example of a graphical database configuration representing a hierarchical or topological structure of a bottom-level industrial plant 602, which is part of an industrial plant cluster 600 and includes a plurality of equipment units and corresponding equipment areas as part of corresponding product processing lines 604. This topology allows observation of the functional relationships between different parts of the bottom layer of the industrial plant 602 (or bottom-level plant cluster 600) to enable improved processing or planning of the bottom-level product packaging. The circular nodes shown in the graphical database are linked by connecting lines, for which different link types are possible.

[0157] In this specific example, the apparatus includes material processing units 606 and 614 connected to sensors / actuators 608 and 616 via signal and / or data connections. These sensors / actuators are part of the processing units 606 and 614 and are connected to several input / output (I / O) devices 610, 612 and 618, 620.

[0158] In this specific example, the first processing unit 606 is further connected to three exemplary product packages (product packages 1 to 3) 622, 624, and 626, while the second processing unit 614 is further connected to three other product packages (product packages 4 to n) 628, 630, and 632. Only the exemplary "product package 3" 626 is connected to the product sample (sample 1) 634, while "product package 5" 630 is connected to another product sample (sample n) 638. "Sample 1" 634 is further connected to "inspection lot 1" 636, while "sample n" is further connected to "inspection lot n" 640. Finally, both inspection lots 636 and 640 are connected to the "inspection instruction 1" unit 642, which serves as a specification on how the aforementioned inspection lots are generated and how the analysis / quality control of the individual underlying samples 634 and 638 is implemented.

[0159] The topology shown in Figure 6 provides a data structure that allows for an intuitive and easy understanding of the functionality and processes of the chemical plant, and thus allows users (especially machine / plant operators) to easily manage this complex production process of the chemical plant or cluster of chemical plants, as the objects (nodes) shown are modeled to be very similar to their corresponding real-world objects.

[0160] More specifically, this topology provides a high degree of contextual information, which users / operators can use to easily collect the technical and / or material properties of each object. This also allows users to perform fairly complex queries, such as those concerning production-related connections or relationships between objects, particularly across several nodes or even topological / hierarchical levels. Furthermore, the objects (nodes) shown in Figure 6 can be easily expanded during runtime using additional properties and / or values.

[0161] Figure 7 shows a second specific example of the graph-based database configuration shown in Figure 6, but only for production line 700 ("Line 1").

[0162] In this specific embodiment, the equipment includes material handling units 702 "Unit 1" and "Unit n" 708, which are connected to sensors / actuators "Sensors / actuators 1" 704 and "Sensors / actuators n" 710 via signal and / or data connections. These sensors / actuators are connected to corresponding input / output (I / O) devices "I / O 1" 706 and "I / O n" 712. These I / O devices include connections to a PLC (not shown) for controlling the operation of the production line 700.

[0163] In this specific example, the first processing unit ("Unit 1") 702 is further connected to three exemplary product packages ("Product Parts" 1 to 3) 714, 716, and 718, wherein the second processing unit ("Unit n") 708 is further connected to two other product packages ("Product Parts" 4 and n) 720 and 722. Exemplarily only, product package 3 718 is connected to a product sample ("Sample 1") 724, wherein product package n 722 is connected to another product sample ("Sample n") 728.

[0164] Compared to the specific example shown in Figure 6, the first "sensor / actuator 1" 704 is also connected to the first product sample ("sample 1") 724, and the second "sensor / actuator n" 710 is also connected to the second product sample ("sample n") 728. These two additional connections have the advantage that samples can be obtained independently at different sampling stations at independent times or even simultaneously. For example, the sensor / actuator 704 may be a button configured at the sampling station, which is pressed by a user or operator when a sample is obtained.

[0165] Alternatively, this sample may be a signal that can be automatically generated by the sampling machine. This automatically generated signal may, for example, reach the sensor / actuator object 704 via the shown I / O object 706, wherein the I / O object 706 receives the mentioned button information from a PLC / DCS (not shown). At the moment the sample is acquired, the sample object 724 will (for example) generate and link to the part of the product located at the sampling station position at that moment.

[0166] Based on the corresponding samples 724, 728, one or more inspection batches 726, 730 may be generated, or even for only one (and identical) sample. However, one or more samples may be generated independently or even simultaneously in a processing line.

[0167] Finally, in the specific example shown in FIG6, "Sample 1" 724 is further connected to the first "Inspection Unit 1" 726, wherein "Sample n" is further connected to the second "Inspection Unit n" 730. Both inspection units 726 and 730 are ultimately connected to the "Inspection Instruction 1" unit 732, which again serves as the specification, as in the case of the "Inspection Instruction 1" unit 642 depicted in FIG6, i.e., the specification regarding how to generate the mentioned inspection batch and how to implement the analysis / quality control of the underlying samples 724 and 728. The "Inspection Instruction 1" unit 732 can be generated independently and can be generated only once, while "Inspection Batch 1" 726 and another "Inspection Batch n" 730 use the Inspection Instruction 732 for more than one inspection batch, as illustrated in FIG7.

[0168] Figure 8 depicts an abstract layer 800, which includes an object database 801 and serves as an abstract layer for the previously described production equipment and corresponding raw materials. For the previously described product information, it may include the previously described physical packaging or product packaging related information, i.e., as a corresponding digital twin.

[0169] In this specific example, the abstraction layer 800 provides an external cloud computing platform 804 for the bidirectional communication line 802. Furthermore, the abstraction layer 800 also communicates with n production PLCs / DCSs and / or machine PLCs 806, 808, which are bidirectional (810) in the case of "PLC / DCS 1" 806, or unidirectional (812) in the case of "PLC / DCS n" 808. In this specific example, the cloud computing platform 804 includes a bidirectional communication line 814 to a customer integration interface or platform 816, through which customers of the production plant owner can transmit and / or deliver control signals to the previously described equipment units of the plant.

[0170] The object database 801 further includes other related objects, such as samples, inspection batches, sample instructions, sensors / actuators, devices, device-related documents, users (e.g., machine or plant operators), corresponding user groups and user rights, formulas, orders, setpoint parameter sets, or received objects from cloud / edge devices.

[0171] At the cloud computing platform 804, an Artificial Intelligence (AI) or Machine Learning (ML) system is implemented to find or generate the best algorithm deployed to the Internet-of-Things (IoT) edge device or component 820 via a dedicated deployment pipeline 818, so as to use the corresponding generated or discovered algorithm for controlling the edge device 820. In this specific example, the edge device 820 communicates bidirectionally with the abstraction layer 800 822.

[0172] The previously described entity or product encapsulations, as described herein, are generated using the abstraction layer 800 and the included object database 801. The abstraction layer 800 can also connect to specific processing and / or AI (or ML) components within the cloud computing platform 804. For this connection, the known data streaming protocol "Kafka" can be used. Thus, at or approximately at the time the underlying product encapsulation is generated, empty data packets can first be sent out as messages, particularly independently of the underlying time-series data. Subsequently, another message can be sent out when the final product encapsulation has been processed. These messages contain object identifiers of the underlying encapsulation as data packet IDs, enabling the related packets to be relinked to each other on the cloud platform side. This has the advantage of avoiding the transmission of large data packets to the cloud, thus minimizing the required transmission bandwidth or capacity.

[0173] Within the cloud computing platform 804, the streamed and received product data is used by the mentioned AI or ML methods to find or generate algorithms for obtaining additional data related to the underlying product, such as predicted product quality control (QC) values. For this process performed within the cloud computing platform 804, additional data is required, such as QC data or quantified performance parameters of the relevant product (or physical) package. This can be received via a database 801 containing sample objects and inspection batch objects (see also Figure 6) that present this information about the relevant product package in the same manner.

[0174] This information can also be received from any other system besides the object database. In this case, the other system sends the QC and / or performance data along with the product / inspection batch ID from the object database. Within the cloud computing platform 804, this data will be combined and used to find, for example, ML-based algorithms / models. Thus, the computing power within the cloud platform 804 can be effectively utilized.

[0175] In this specific example, the correspondingly discovered algorithm or model is deployed to the edge device 820 via deployment pipeline 818. The edge device 820 may be a component located near the object database 801 of the abstraction layer 800 and therefore also near PLC / DCS 1 806 to PLC / DCS n 808, i.e., in terms of network security level and location allowing low network latency and direct and secure communication.

[0176] Since this computational capability is not required for the use of the ML model, the edge device 820 uses the ML model to generate the aforementioned advanced information and provides it to the object database 801. Therefore, the edge device 820 needs the same information or a subset of information used at the cloud computing platform 804 to generate ML-based algorithms or models. The object database 801 can provide this information to the edge device 820, for example, via an open network protocol for machine-to-machine communication, such as the known Message Queuing Telemetry Transport (MQTT) protocol.

[0177] This setting enables advanced process control based on AI / ML and the autonomous manufacturing and corresponding autonomous operation of machines.

[0178] As illustrated in the specific example shown in Figure 8, based on data from the previously described data objects 330 to 334 (Figure 3), this data is used as training data to train the AI / ML system or corresponding AI / ML model on the cloud computing platform 804. In this specific example, the training data may therefore include historical and current laboratory test data indicating the performance parameters of chemical products, especially data from the past.

[0179] The AI / ML model can be used to predict one or more of the previously described performance parameters, preferably via a computing unit. Alternatively or additionally, the AI / ML model can be used to at least partially control the production process by adjusting equipment operating conditions, and more preferably, this control is performed via the aforementioned computing unit. Alternatively or additionally, the AI / ML model can also be used, for example, by the computing unit to determine which of the process parameters and / or equipment operating conditions has a major influence on the chemical product, such that the major process parameters and / or equipment operating conditions are respectively attached to the data object or the mentioned object identifier.

[0180] Those skilled in the art will understand that method steps performed, at least by a computing unit, can be performed "instantaneously" or nearly instantaneously. These terms are understood within the field of computer technology. As a specific example, the time delay between any two steps performed by the computing unit is no more than 15 seconds, more specifically no more than 10 seconds, and more specifically no more than 5 seconds. Preferably, the delay is less than one second, and more preferably less than a few milliseconds. Therefore, the computing unit can be configured to perform method steps in an instantaneous manner. Furthermore, software products can enable the computing unit to perform method steps in an instantaneous manner.

[0181] For example, the method steps may be performed in the order shown in the embodiments or examples. However, it should be noted that different orders may be possible in certain situations. Furthermore, it is possible to perform one or more of the method steps once or repeatedly. These steps may be repeated at regular or irregular times. Additionally, it is possible to perform two or more of the method steps simultaneously or in a timely overlapping manner, specifically, when some or more of the method steps are repeated. The method may include additional steps not listed.

[0182] Various embodiments have been disclosed above with respect to: a method for digitally tracking chemical products; a system for performing the methods disclosed herein; a system for digitally tracking chemical products; a software program; and a computing unit comprising computer program code for performing the methods disclosed herein. However, those skilled in the art will understand that changes and modifications can be made to these embodiments without departing from the spirit and scope of the appended claims and their equivalents. It should be further understood that various combinations of specific examples of the methods and products discussed herein are permissible. [Simplified Explanation of the Diagram]

[0086] Certain aspects of this teaching will now be discussed with reference to the following diagrams, which are explained by means of embodiments. Since the generality of this teaching does not depend on them, the diagrams may not be to scale. Some features shown in the diagrams may be logical features presented together with physical features for the sake of understanding, without affecting the generality of this teaching. For easy identification of any particular element or action, one or more of the most significant digits of the element symbol refer to the figure number that first introduces the element. [Figure 1] Illustrates certain aspects of a system according to this teaching. [Figure 2] Illustrates a method according to this teaching. [Figure 3] Shows a first concrete example of a system and corresponding method according to this teaching by means of a combined block / flowchart. [Figure 4] Shows a second concrete example of a system and corresponding method according to this teaching by means of a combined block / flowchart. [Figure 5] Shows a third concrete example of a system and corresponding method according to this teaching by means of a combined block / flowchart. [Figure 6] illustrates a first concrete example of a graph-based database configuration representing the topology of an industrial plant or cluster of plants comprising a plurality of equipment units and corresponding plurality of equipment areas, wherein input materials move between these plurality of equipment areas during the manufacturing or production process. [Figure 7] illustrates a second concrete example of the graph-based database configuration shown in Figure 6. [Figure 8] illustrates another concrete example of a system and corresponding method according to this teaching using a cloud computing platform, with the aid of a combined block / flowchart, wherein the machine learning (ML) process is implemented in the cloud.

Claims

1. A method for digitally tracking a chemical product manufactured at an industrial plant, the industrial plant comprising at least one device; and the product being manufactured by processing at least one input material via the device using a production process, the method comprising: providing an object identifier containing input material information via an interface; wherein the input material information indicates one or more properties of the input material; receiving process data from the device via the interface; the process data indicating process parameters and / or device operating conditions when the input material is processed; and attaching at least a portion of the process data to the object identifier.

2. The method of claim 1, wherein the input material processed by the device is divided into at least two packages, wherein the size of one package is fixed or determined based on the weight or quantity of the input material, and the device provides significantly constant process parameters or device operating parameters for the weight or quantity of the input material.

3. The method of request 1 or request 2, wherein the processing of the at least two encapsulations is managed by means of corresponding data objects, each of the data objects including at least one object identifier.

4. The method of claim 1 or claim 2, wherein a data object is generated in response to a trigger signal provided by the device.

5. The method of claim 4, wherein the trigger signal is provided in response to the output of a corresponding sensor configured at each device unit in the device.

6. The method of claim 1 or claim 2, wherein the process data includes at least one numerical value and / or binary value indicating the process parameter and / or the equipment operating conditions measured during the production process.

7. The method of claim 1 or claim 2, wherein the device operating condition is any characteristic or value representing the state of the device, such as set point, controller output, production sequence, calibration status, any device-related warnings, vibration measurement, speed such as conveyor speed, temperature, and any one or more of the following, such as filter differential pressure and maintenance date.

8. The method of claim 1 or claim 2, wherein the process data comprises time-series data of one or more of the process parameters and / or the equipment operating conditions.

9. The method of Request 1 or Request 2, wherein the input material data includes laboratory samples or test data related to the input material, such as historical test results.

10. The method of claim 1 or claim 2, wherein the object identifier is provided via a computing unit operatively coupled to one of the devices, preferably, the computing unit is part of the devices.

11. The method of claim 1 or claim 2, wherein the object identifier is provided or stored in a memory storage element.

12. The method of claim 1 or claim 2, wherein the object identifier is provided or generated in response to a triggering event or signal, the event or signal being preferably provided via the device, and more preferably in response to the output of any one or more sensors and / or switches operatively coupled to the device.

13. The method of claim 6, wherein the production process may be controlled at least partially via the computing unit or at least partially via the computing unit.

14. The method of claim 1 or claim 2, wherein the appended object identifier is used to associate or map the input material data and / or specific process parameters and / or equipment operating conditions to at least one performance parameter of the chemical product, the performance parameter being or indicating any one or more properties of the chemical product.

15. The method of claim 1 or claim 2, wherein a machine learning ("ML") model is trained using training data including data from the appended object identifier, the training preferably being performed via the computing unit.

16. The method of claim 15, wherein the industrial plant includes an Internet of Things (IoT) edge device or component, and wherein an underlying ML system is implemented to find or generate an algorithm, which is deployed to the IoT edge device or component to control the IoT edge device using the correspondingly generated or discovered algorithm.

17. The method of claim 15, wherein an abstraction layer is provided, the abstraction layer including an object database and the abstraction layer serving as one of the abstraction layers for the production equipment, for the corresponding input materials, and for encapsulating related information.

18. The method of claim 17, wherein the abstraction layer is connected to a specific processing and / or ML component within a cloud computing platform, wherein a data streaming protocol is used for this connection, and wherein the streamed and received product data is used by the ML system to find or generate algorithms for obtaining additional data related to an underlying chemical product.

19. The method of claim 18, wherein the additional information is predictable product quality control (QC) information relating to the underlying chemical product.

20. The method of claim 15, wherein the training data used to train the ML model also includes historical and / or current laboratory test data or data from past and / or recent samples, the historical and / or current laboratory test data indicating the performance parameters of the chemical product.

21. The method of claim 15, wherein the ML model is used to predict one or more of the performance parameters, the prediction preferably being performed via the computing unit.

22. The method of claim 15, wherein the ML model is used to at least partially control the production process, preferably by adjusting the operating conditions of the equipment, and more preferably, the control is performed via the computing unit.

23. The method of claim 15, wherein the ML model is used, for example, by the computing unit to determine which components of the process parameters and / or the equipment operating conditions have a major impact on the chemical product, so as to attach the process parameters and / or the equipment operating conditions that have a major impact on the chemical product to the object identifier.

24. The method of claim 1 or claim 2, wherein the device comprises a plurality of device areas such that during the production process, the input material proceeds from a first device area to at least a second device area.

25. The method of claim 24, wherein the object identifier is provided at the first device area, and at least one second object identifier is provided when the input material enters the at least one second device area after traversing the first device area.

26. The method of claim 25, wherein the process data attached to the object identifier is at least a portion of the process data from the first equipment area.

27. The method of claim 25, wherein the at least one second object identifier is appended with at least a portion of process data from the at least one second equipment area, the process data from the at least one second equipment area indicating the process parameters and / or the equipment operating conditions when the input material is processed in the at least one second equipment area.

28. The method of claim 25, wherein the at least one second object identifier is supplemented with at least a portion of the data from the first object identifier.

29. The method of claim 25, wherein the input material traverses an intermediate device area before entering the at least one second device area, the intermediate device area being an area between the first device area and the at least one second device area and the input material.

30. The method of claim 25, wherein an additional material is added to the input material entering the at least one second device area, the additional material being a material of the same type as the input material or a material different from the input material.

31. The method of claim 25, wherein a portion of the input material is removed before entering the at least one second device area.

32. The method of claim 31, wherein said portion of said input material is provided in a third device area.

33. The method of claim 24, wherein the object identifier is provided at the first device area, and at least a portion of process data from the at least one second device area is appended to the object identifier after entering the at least one second device area, wherein the at least one second device area is such that the amount of material entering at the at least one second device area is the same or substantially the same as the amount of material in a previous area that was processed before entering the at least one second device area, wherein the previous area is the first device area or an intermediate device area between the first device area and the at least one second device area.

34. The method of Request 1 or Request 2, wherein any or each of the object identifiers includes a unique identifier, preferably a globally unique identifier (“GUID”).

35. The method of claim 24, wherein any or each of the device areas is monitored and / or controlled by an individual ML model, the individual ML model being trained based on data from the respective object identifiers, the respective object identifiers being from the device area.

36. A system comprising at least one apparatus for manufacturing a chemical product, the at least one apparatus manufacturing the chemical product at an industrial plant by processing at least one input material using a production process, the at least one apparatus being operatively coupled to a computing unit, wherein the system is configured or adapted such that the computing unit is configured to: provide an object identifier containing input material information via an interface; wherein the input material information indicates one or more properties of the input material; receive process data from the apparatus via the interface; the process data indicating process parameters and / or apparatus operating conditions when the input material is processed; and attach at least a portion of the process data to the object identifier.

37. A non-transitory computer-readable medium for tracking a chemical product manufactured at an industrial plant, comprising instructions that, when executed by a suitable computing unit, are operatively coupled to at least one device for manufacturing the chemical product at the industrial plant by processing at least one input material using a production process, such that the computing unit: provides an object identifier containing input material information via an interface; wherein the input material information indicates one or more properties of the input material; receives process data from the device via the interface; the process data indicating process parameters and / or device operating conditions when the input material is processed; and appends at least a portion of the process data to the object identifier.

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