Computer-implemented method and system for generating a simulated model for digital twinning of a process of a

Abstract

According to the invention, a static flow-driven simulation model (41) of the process (3) is generated for a digital twinning of the process of the production plant, and in turn a static pressure-driven simulation model (51) of the process (3) is generated in turn. A dynamic pressure-driven simulation model (61) of the process is generated from the latter model, the dynamic pressure-driven simulation model comprising sensors and actuators of a control loop of the device. Each simulation model (41, 51, 61, 71) determines a measurable state variable (T) of the production plant (1), preferably also a quality indicator (Q) of the product (S), depending on the material flow of the raw material (E) and the operating medium (B) fed to the production plant (1). Model data (MD) is generated by each simulation model (41, 51, 61, 71) and stored in a data memory (125) such that the model data can be read by the simulation software (126) and thus the simulation models (41, 51, 61, 71) can be executed. Therefore, a static flow driven simulation model is always used for digital process twinning. The models are developed consistently and adapted to respective application cases while not losing earlier stage information or without re-manual input. Therefore, the development of the digital process twinning can be shared into a plurality of application cases.

Description

Technical Field

[0001] The invention relates to a computer-implemented method and system for generating a simulation model of a digital twin of a process of a production plant for a product according to claim 1 or 12. Background Art

[0002] Production plants based on process-based processes are used to produce and transform products (or substances) in an imprecisely defined form. Such plants are mainly used in process industries, such as papermaking, chemicals, pharmaceuticals, metals, oil and gas industries, etc. These usually involve very complex plants with multiple operating types, large spatial ranges and the interaction of multiple components. In order to operate and monitor the process-based processes and control / regulate them, these plants include at least one process control system. For example, the applicant's process control systems PCS 7 and PCS neo.

[0003] At the same time, so-called "digital twins" of process-based processes are increasingly being used. Such digital twins usually include design data from the design and engineering phase of the process as well as data from the operating phase, but also a description of the behavior in the form of simulation models. Such "digital process twins" can be used for a variety of application cases in the life cycle of a production plant.

[0004] The article "Evolution of Digital Twins in Ethylene Plants - Concept and Implementation" (The Evolution of Digital Twins in Ethylene Plants - Concept and Implementation) by Labisch D., Leingang C., Lorenz O., Oppelt M., Pfeiffer B.-M., Pohmer F. in atp magazin (06-07 / 2019, pp. 70-85) describes a concept for the development and consistent use of digital twins throughout the life cycle of process plants. The approach followed here is to consistently integrate individual models and software tools into a consistent semantically coupled system at all levels of the plant and at all stages of the plant's life cycle. Here, the use of simulations is divided into four groups in view of the stages of the plant life cycle: planning simulation, virtual commissioning simulation, training simulation, and operation-accompanying simulation (soft sensors, model predictive control). A picture of the future is depicted here, in which the developed models continue to be used and improved during the life cycle.

[0005] The concept is explained using the example of a steam cracker. The life cycle of a plant begins with the plant design. Here, the first "digital process twin" is created with simulation software based on the existing plant knowledge and the latest insights from publications. This digital twin is used for the design of the plant and its components (conceptual design). This includes, for example, determining the chemical reaction for the cracking furnace or determining the optimal reactor size and wall thickness. The dimensioning of pumps, heat exchangers and buffer tanks is also carried out with the help of static simulation models. In the further course of engineering, the digital process twin generated by the simulation software in the form of a process flow diagram (Process Flow Diagram) is transferred to the plant planning tool and then becomes the basis for the "digital plant twin". This will be gradually expanded to include other plant-related aspects, such as sensors, actuators and controller structures. The result is a piping and instrumentation diagram (R&I, English: piping and instrumentation diagram, P&ID). The analysis and verification of the controller concept is carried out simultaneously with the dynamic simulation model, that is, the two digital twins are always synchronized with each other. Changes in the digital plant twin directly affect the digital process twin. Faults in the plant design can be identified and eliminated early based on the simulation of the dynamic model.

[0006] The "digital instrument twin" is used to verify the created automation programs within the framework of virtual commissioning and to identify faulty behaviors before actual commissioning. For the training system of operators at the plant, the "digital process twin" is connected to the simulation model of the "digital instrument twin". Thus, the detailed process simulation that has already been created in the conceptual design in the process technology simulation software can be used throughout the entire life cycle.

[0007] Table 1 of the article shows the different components of the digital twin and their purpose of use. For example, the dynamic simplified linearized model of the unit (reactor, cracker) can be used for the following purposes during the life cycle: planning the automation concept (PID controller design), virtual commissioning, actual commissioning (using or updating the model for PID control), optimization in the operating phase (MPC) and control loop monitoring (CPM). For this purpose, the model is transferred to various software tools or updated there.

[0008] An accurate rigorous dynamic model is implemented in the simulation tool gPROMS from Siemens Process Systems Engineering Ltd. and can be used in detailed engineering for planning operating modes (recipes), for planning automation concepts, for training purposes and for model-based soft sensors in the operational phase.

[0009] Taking a unit as an example, a strict static model is first developed within the framework of basic engineering. Once the dynamically relevant characteristic variables such as tank capacity, heat capacity, pump performance, etc. are known, a rough dynamic model can be derived from this, which can already simulate the water flow. This is improved to an accurate dynamic model in which, for example, chemical reactions and their reaction kinetics are also shown. A simplified dynamic model for control technology purposes can be extracted from the strict dynamic model.

[0010] For the prior art, reference is also made to the following article: Busse, C., Bozek, E., Pfeiffer, BM., Leingang, Ch., Roth, M., Krauβ, M., Schulz, Ch., Oppelt, M.: “Integration digitalerTechnologien für das Engineering, den Betrieb und die Instandhaltung einerverfahrenstechni-schen Anlage” (Integration of digital technologies for engineering, operation and maintenance of process technology equipment) (Chemie Ingenieur Technik 92(9): 1250-1250, September 2020).

[0011] In addition, refer to the article Pfeiffer, BM., Oppelt, M., Leingang, Ch., Pantelides, C., Pereira, F.: “Nonlinear Model Predictive Control based on Existing Mechanistic Models of Polymerisation Reactors” (2020 IFAC World Congress, Berlin (Virtual), July 2020). Summary of the invention

[0012] Based on this, the object of the present invention is to provide a computer-implemented method and system that can easily generate a simulation model of a digital twin of a process technology process of a production device during its life cycle.

[0013] This object is achieved by a computer-implemented method according to claim 1 and a system according to claim 12. The subject matter of claim 13 is a computer program comprising instructions which, when executed by a computer, cause the computer to perform the method according to the invention. The subject matter of claim 14 is a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform the method according to the invention. The respective subjects of the dependent claims are advantageous embodiments.

[0014] The computer-implemented method according to the invention for generating a simulation model of a digital twin of a process of a process technology production plant for a product comprises:

[0015] a) receiving process flow design data of the process of the production equipment (e.g. from a process flow diagram),

[0016] b) Generate a static flow-driven simulation model of the process from process flow design data,

[0017] c) generating a static pressure-driven simulation model of the process from a static flow-driven simulation model,

[0018] d) Receive the pipeline and instrument design data of the production equipment (e.g. from the R&I diagram, English P&I-Diagram),

[0019] e) generating a dynamic pressure driven simulation model of the process from the static pressure driven simulation model and pipeline and instrumentation design data, wherein the dynamic pressure driven simulation model includes sensors and actuators of the control loop of the equipment.

[0020] In this context, measurable state variables of the production plant and preferably also product quality indicators are determined for each simulation model as a function of the material flows of raw materials (eg hydrocarbons) and operating media (eg hot steam, cooling water) fed to the production plant.

[0021] Furthermore, model data (i.e. data describing or defining the simulation model) are generated for each simulation model (e.g. with regard to selection, content, structure) and the model data are stored in a data memory so that the model data can be read by the simulation software and the simulation model can be executed based on the read model data.

[0022] Therefore, according to the invention, a first simulation model of the process (here a static flow-driven simulation model of the process) is converted into a further simulation model of the process by a model transformation. The model transformation enables the consistent use of the first simulation model of the process in the digital twin of the process ("digital process twin") for different use cases during the life cycle of the plant. As has been demonstrated, the most important use cases of digital twins of production processes are met with the model transformation according to the invention (see steps b), c), e).

[0023] Here, according to the invention, each simulation model determines measurable state variables (such as temperature, pressure, liquid level, pH value, etc.) of the production plant based on the material flow of raw materials and operating media fed to the production plant, and preferably also determines product quality indicators. Here, the basic understanding is also that it is precisely these parameters that can meet the most important application cases for digital twins in the production process: the measurable state variables describe the state of the production plant or the production process, and the product quality is the decisive criterion for the success of the process. Since most processes in production plants are "non-linear" processes, the simulation model is preferably a nonlinear model. For example, the flow through a valve is usually nonlinearly related to the valve position. The pressure or head of a centrifugal pump is related to the square of the pump speed. The rate of a chemical reaction is always nonlinearly related to the temperature.

[0024] A “flow-driven” simulation is understood here to be a simulation in which the flow rate is specified starting from the feed of the raw material and the pressure distribution and the flow rate in the flow network are calculated independently of one another.

[0025] A "pressure driven" simulation is understood to be a simulation in which the raw material is taken from a source (eg a supply line) with a defined pressure and the flow in each component of the flow network is caused by the pressure difference and the flow resistance.

[0026] According to an advantageous design of the method according to the invention, the dynamic pressure drive simulation model generated in step e) also already includes the actuators of the secondary control loop of the control loop, in particular the fluid dynamics model of the actuators of the secondary control loop, or in a further step f) the fluid dynamics model is expanded to include these actuators. This can further increase the number of application cases of digital process twins.

[0027] According to another advantageous design of the method of the present invention, the dynamic pressure-driven simulation model generated in step e) or step f) also already includes the PID controller of the control loop, or the dynamic pressure-driven simulation model is expanded to include these controllers in another step g). This can also further increase the number of application cases of digital process twins.

[0028] The simulation model generated in step b) and / or step c) can then advantageously be used for the process engineering design of the production plant, and the production plant can be physically realized in accordance with this design.

[0029] The simulation model generated in step e), step f) or step g) can be advantageously used for the development and validation of a control concept and subsequently the control of the production plant can be physically implemented according to the design.

[0030] The simulation model generated in step e), step f) or step g) is preferably used for virtual commissioning of the system and is connected to the control software of the system for testing the system for error-free operation. Information about errors or about error-free operation can then be output on an output unit (e.g. a display). In the event of an error, the control software can then be corrected.

[0031] The simulation model generated in step e), step f) or step g) can be used to train operators of the device and can be connected to the operating and monitoring software of the device for this purpose.

[0032] According to another advantageous design, the simulation model generated in step e), step f) or step g) is used for a model-based soft sensor of the device. The soft sensor receives the measured values ​​of the device from the input end and determines the unmeasurable (or difficult to measure) values ​​of the device from it, which are mainly product quality indicators in the production process of the product.

[0033] The simulation model generated in step e), step f) or step g) can also advantageously be used for model-based predictive control of the plant.

[0034] According to a further advantageous embodiment, the simulation model generated in step e), step f) or step g) is used for an assistance system for an operator of the system and is linked to control software of the system for this purpose.

[0035] The system according to the invention for generating a simulation model of a digital twin of a process of a process technology production plant for a product comprises:

[0036] - an interface designed for

[0037] a) receive process flow design data as well as pipeline and instrument design data for production equipment, and

[0038] b) outputting model data of each simulation model to store the model data in a data storage,

[0039] - a memory, said memory comprising instructions,

[0040] - A processor connected to a memory and designed to perform steps a) to e) of the above method when executing instructions, preferably also performing further steps f) and / or g), wherein each simulation model determines measurable state variables of the production plant based on the material flow of raw materials and operating media fed to the production plant, preferably also product quality indicators, and wherein model data are generated so that the simulation model can be executed by simulation software based on the model data.

[0041] The computer program according to the present invention comprises instructions which, when the program is executed by a computer, cause the computer to execute the above-mentioned method.

[0042] The computer-readable storage medium according to the present invention includes instructions, which, when executed by a computer, cause the computer to perform the above-mentioned method.

[0043] The advantages and advantageous embodiments mentioned for the method according to the invention also apply correspondingly to the system according to the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention and other advantageous designs of the present invention according to the features of the dependent claims are described in more detail below based on the embodiments in the accompanying drawings. Here, corresponding components all have the same reference numerals. The accompanying drawings show:

[0045] Figure 1 An exemplary basic structure of a process-based production facility showing a product,

[0046] Figure 2 A cracking furnace (steam cracker) is shown as an example of a process-based production facility.

[0047] Figure 3 The method flow according to the present invention is shown.

[0048] Figure 4 and Figure 5 Schematic diagram showing a digital process twin for process-based design of equipment,

[0049] Figure 6 Schematic diagram showing the digital process twin used for development and validation of control concepts,

[0050] Figure 7 Schematic diagram of a digital process twin showing virtual commissioning of a plant,

[0051] Figure 8 A schematic diagram showing a digital process twin for training operators of equipment,

[0052] Fig. 9 Schematic diagram showing a model-based soft sensor digital process twin for a plant.

[0053] Fig.11 Schematic diagram showing a digital process twin for model-based predictive control of a plant.

[0054] Fig.12 A system according to the invention is shown schematically. DETAILED DESCRIPTION

[0055] Figure 1 In a simplified and exemplary illustration, a process-based production plant 1 for a product is shown, which has an automation system 2 for controlling and / or regulating a production process 3. The term "production process" is understood here not only to mean a manufacturing process but also a processing, treatment or conversion process (e.g. an energy production process).

[0056] Such devices 1 are widely used in various industrial fields, such as process industries (such as papermaking, chemicals, pharmaceuticals, metals, oil and gas) and energy production. The automation system 2 includes, for example, a plurality of industrial controllers 4 , an automation server 5 and an engineering server 8 .

[0057] Each controller 4 controls the operation of a corresponding sub-area of ​​the process 3 according to its operating state. For this purpose, the process 3 includes actuators 6 that can be controlled by the controller 4. Here, these actuators can be individual actuators (e.g., motors, pumps, valves, switches), groups of such actuators or all parts of the system. In addition, the process also includes sensors 7, which provide the controller 4 with measured values ​​of process variables (e.g., temperature, pressure, liquid level, flow) for use.

[0058] The communication network of the device 1 includes, at a higher level, a device network 11, via which the servers 5 and 8 communicate with a human-machine interface (HMI) 10, and a control network 12, via which the controllers 4 are communicatively connected to each other and to the servers 5 and 8. The connection of the controller 4 to the actuators 6 and sensors 7 can be realized via discrete signal lines 13 or via a fieldbus. The human-machine interface (HMI) 10 is usually designed as an operating and monitoring station and is arranged in the control center of the device 1.

[0059] The automation server 5 can be, for example, a so-called "operating system server" or "application server" in which one or more device-specific applications are stored and implemented during the operation of the device 1. For example, they are used to configure the controller 4 in the device 1, detect and execute operator activities at the human-machine interface (HMI) 10 (e.g. setting or changing setpoint values ​​of process variables), or generate messages for the device personnel and display them on the human-machine interface (HMI) 10.

[0060] An automation system 2 without field devices, ie without actuators 6 and sensors 7 , is often also referred to as a “process control system”.

[0061] The above components are widely used in large industrial production equipment. Sometimes, multiple production processes 3 can also be realized simultaneously. Therefore, the sensor 7 provides multiple measurement data of process parameters of the process 3. These measurement data are stored on the process data archive server 14 together with the messages of the automation server 5 and additional information (for example, batch data, status information of intelligent field devices).

[0062] As per Figure 2 As shown, process 3 can be, for example, steam cracking. Steam cracking is a petrochemical process in which long-chain hydrocarbons (naphtha, ethane, propane and butane) are converted into short-chain hydrocarbons, such as ethylene, propylene and butene, by thermal cracking in the presence of water vapor.

[0063] Steam cracking is carried out in a cracking furnace 20 (English: Steam cracker), schematically Figure 2 2 shows the cracking furnace with the most important material flows. The cracking furnace is used to produce intermediate products, which are mainly processed into plastics (e.g. polyethylene), coatings, solvents or crop protection agents. The cracking furnace 20 is a tubular reactor, in which hydrocarbons K are fed as raw materials and process steam P, air L and fuel gas G are fed as operating media. The hydrocarbons K and the mixture GM of hydrocarbons K and process steam P are heated by means of tube bundles 21, 22. In this process, the long-chain molecules are thermally split within a fraction of a second. Figure 2 Only a single tube string is shown in simplified form. COT (coil outlet temperature) and TMT (tube metal temperature) describe the temperature and thus the state variables of the cracking furnace 20. The starting product at the cracking furnace 20 is cracking gas S, the quality Q of which can be measured by means of a measuring device.

[0064] For example, it is possible to arrange a plurality of such cracking furnaces 20 at the beginning of the material flow of an ethylene plant. Then a plurality of large cracking furnaces 20 are operated in parallel. Subsequently, in a downstream multi-stage separation process using distillation columns, steam separators, coolers and similar equipment, various usable products are separated out.

[0065] Figure 1 The so-called “digital twin” of a process 3 based on a process plant 1, or e.g. Figure 2The so-called "digital twin" of the steam cracking process in the cracking furnace 20 can be used advantageously at different stages of the plant life cycle. The "digital twin" of the process is also called the "digital process twin" (English: Digital Process Twin). It usually includes design data from the design and engineering phase and equipment data from the operation phase, but also includes behavioral descriptions in the form of models, especially mathematical models. The digital twin develops along the plant life cycle and unifies existing data and knowledge bases at each step.

[0066] Here, the present invention opens up a way to consistently apply digital process twins to various use cases during the life cycle of the device through continuous model transformations. In addition to the process-based description of the behavior of the process itself, the basic automation of the device with the help of a large number of PID controllers is also taken into account in the different model transformations, and special attention is required to: the dynamic characteristics of the controller, the additional functions of the controller and the role of the controller in the context of the listed use cases. Below, a number of use cases ("Use Cases") will be examined one by one, and the required model transformations will be described. It is taken into account that different use cases also have different requirements for the digital process twin. Here, the cost of generating a digital process twin can be advantageously spread over multiple use cases.

[0067] according to Figure 3 According to the invention, a computer-implemented method 30 for generating a simulation model of a digital twin of a process of a process technology production plant for a product comprises the following steps:

[0068] In a first step 30a), process flow design data of a process of a production plant (for example, regarding a process flow diagram) are received,

[0069] - in a second step 30b): generating a static flow driven simulation model of the process from the process flow design data,

[0070] - in a third step 30c): generating a static pressure driven simulation model of the process from the static flow driven simulation model,

[0071] - In a fourth step 30d): receiving the piping and instrumentation design data of the production facility (e.g. from an R&I diagram, English P&I-Diagram),

[0072] - in a fifth step 30e): generating a dynamic pressure driven simulation model of the process from the static pressure driven simulation model and the pipeline and instrumentation design data, wherein the dynamic pressure driven simulation model includes sensors and actuators of the control loop of the production equipment,

[0073] In a sixth step 30f): the actuators of the secondary control loop of the production plant are extended for a dynamic pressure-driven simulation model of the process, in particular a fluid dynamics model of the actuators of the secondary control loop,

[0074] - In a seventh step 30g): the PID controller of the control loop of the production plant is extended for the dynamic pressure driven simulation model of step 30f).

[0075] In this context, each simulation model determines product quality indicators and measurable state variables of the production plant as a function of the material flows of raw materials and operating media fed to the production plant.

[0076] Model data (ie data describing or defining the simulation model) are generated for each simulation model (eg with regard to selection, content, structure) and stored in a data memory so that the model data can be read by the simulation software and the simulation model can be executed based on the read model data.

[0077] As per Figure 3 and Figure 4 As shown, in step 30a), process flow design data of a process of a production plant (e.g. from a process flow diagram) are received as a starting point and in step 30b), a static flow-driven simulation model for the planned operating points of the process is generated therefrom. For example, starting from the PFD (process flow diagram), a flow network is constructed in the simulation tool by connecting existing component models (stirred tank reactors, distillation columns, heat exchangers, storage tanks, etc.) from a model library as much as possible with material flow connections (pipelines without flow resistance), branches and mixers. The flow network is first simulated as a static flow-driven simulation, that is, starting from the raw material input, the flow rate is specified and the pressure distribution and flow rate in the flow network are calculated independently of each other.

[0078] These simulations are used to design and select plant components, such as reactors, columns, pumps, heat exchangers, etc. For example, at this stage the inlet to the cracking furnace is shown in the simulation model simply as a material flow source with defined substance concentrations and nominal flow rates.

[0079] The digital process twin 40 includes a simulation model 41 and has values ​​for the material flows of the raw material E and the operating medium B fed to the process or production plant as input variables for the simulation model 41. The starting variables of the simulation model 41 or the digital process twin 40 are the values ​​of characteristic indices of the product quality Q (e.g. purity, homogeneity) and the values ​​of measurable state variables such as temperature T. Here, T represents other state variables of the production plant that are related to the input variables, such as flow, pressure, level, etc.

[0080] The simulation model 41 is usually based on a mathematical model of the process behavior, which is based on rigorous modeling starting from physical, chemical and thermodynamic laws. The simulation model 41 is preferably a nonlinear model.

[0081] In a third step 30c), according to Figure 5 , a static pressure-driven simulation model 51 of the process is generated from the static flow-driven simulation model 41, and a digital process twin 51 is generated thereby. That is, it is converted into a pressure-driven simulation that is more in line with the causal principle of physics. For example, a liquid or gaseous raw material is obtained from a supply pipeline using a defined pressure, and the flow in each component of the flow network is caused by the pressure difference and the flow resistance. The simulation model 51 is preferably also a nonlinear model.

[0082] The simulation models 41 , 51 or the digital process twins 40 , 50 generated in step 30 b ) and step 30 c ) are used for process-based design of the production plant and subsequently the production plant is physically realized in accordance with the design.

[0083] In step 30d), the pipeline and instrument design data of the production equipment (e.g. from the R&I diagram, English P&I-Diagram), in particular the types and positions of sensors and actuators, are received, and in step 30e), the static pressure driven simulation model 51 and the dynamic pressure driven simulation model 61 of the pipeline and instrument design data generation process are generated. The simulation model 61 is preferably also a nonlinear model.

[0084] In this case, it is advantageous to use simulation tools which allow switching between static and dynamic simulations, such as “gPROMS Process” from Siemens Process Engineering GmbH.

[0085] For example, once a dynamic simulation is selected, a dialog box opens for each model in order to specify additional parameters required for a dynamic simulation, such as the volumes of reactors and tanks, heat storage capacity, heat transfer, etc. In addition, a dynamic simulation requires the specification of initial conditions.

[0086] As per Figure 6 Schematically, such a dynamic pressure-driven simulation model 61 can be used particularly advantageously in a digital process twin 60 for the development and verification of control concepts, wherein secondary control loops are still not considered. Here, TIC stands for the temperature controller of the operating medium B, typically all controllers to be verified in the system.

[0087] However, in many cases, a controller (eg, a temperature controller of the operating medium) does not directly access the actuator, but rather accesses a subsequent controller (eg, a flow controller) which controls the flow of the operating medium B via a valve.

[0088] In order to take into account the secondary control loop, in step 30f), the actuators of the secondary control loop of the plant are extended for the dynamic pressure-driven simulation model 61 of the process and as Figure 7 The first extended dynamic pressure-driven simulation model 71 of the process is shown. This simulation model 71 is a component of the digital process twin 70 for the development and verification of the extension of the control concept. Here, the temperature controller TIC acts on the valve V via the flow controller FIC, via which the flow of the operating medium B can be controlled. For this purpose, the flow controller FIC generates a setpoint value VP for the position of the valve V. Advantageously, the simulation model 71 is also a nonlinear model.

[0089] Since only small delays usually occur in the flow control loop (valve operation time, mass inertia of the medium, low-pass filters in the sensor), it is usually possible to work with simplifications and standard assumptions here. For example, it is assumed that the temperature controller TIC can control the mass flow B of the material flow source for the operating medium with a standard delay. That is, at this modeling depth, the secondary control loop with valves and flow controllers in the simulation model 71 still does not require an explicit fluid dynamics model, but only a simplified model 72 for the valve V is integrated into the simulation model 71.

[0090] However, Figure 8 As shown in FIG. 1 , the dynamic pressure-driven simulation model of the actuator extended with the secondary control loop can also include an explicit fluid dynamics model of the actuator. Figure 8 The simulation model 81 is denoted in FIG. 8 and is a component of a digital process twin 80 for virtual commissioning of a system. The simulation model 81 is preferably also a nonlinear model.

[0091] For the actuators of the secondary control loop, a distinction is made here between temperature controller TIC characteristics and control technology characteristics, here using the example of a valve V with Va (fluid mechanics) and Vb (control technology). The fluid mechanics characteristics Va are described by the model Ma, while the control technology characteristics are described by the model Mb.

[0092] Model Ma is a component of the simulation model 81 and model Mb is a component of the digital twin 86 of the instrument which will be described in detail below.

[0093] For virtual commissioning of the plant, a process simulation tool running the simulation model 81 is connected to the real (original) control software of the process control system 85. In this case, the real (original) control software runs on real or virtual automation hardware.

[0094] Here, the dynamic pressure-driven simulation model can also be extended with a PID controller for the control loop (see Figure 3Step 30g) in the method flow of ). However, for this application case, a PID controller is usually not required and is preferably left in the model and placed in tracking mode. The control values ​​for the tracking mode come from the corresponding controller in the process control system, which is therefore responsible for the control with respect to the process simulation.

[0095] For virtual commissioning, a more precise mapping of the field level is required. The simulator should also generate feedback from the actuators (e.g. position feedback of valves, status of motors) and, if necessary, status signals for sensors. This functionality can be highly standardized with the help of corresponding actuator or sensor models for the control system, but usually goes beyond the functional scope of typical process-based simulation tools. It is therefore advantageous to introduce an intermediate layer between the process simulator (i.e. the simulation model 81) and the process control system 85, which represents the field level and can be regarded as a digital instrument twin ("Digital Instrumentation Twin"), which is in Figure 8 86. Ideally, this intermediate layer is derived semi-automatically from the configuration of the process control system 85. In this case, VP.SP is the setpoint value of the valve position and VP.Rbk is the measured actual value of the valve position.

[0096] By means of virtual commissioning, the control software of the control system can be tested for error-free operation and corrections can be made in the event of an error. Information about the error or error-free operation can then be output on an output unit (eg a display).

[0097] Like from Fig. 9 It can be seen that Figure 8 The overall architecture can also be used for operator training systems (OPS) after virtual commissioning. Ideally, the control system hardware is simulated on a PC ("virtueller Controller") and the original graphical user interface of the control system's operating and monitoring software is used. Fig. 9 This combination of a virtual controller and the original operating and monitoring software of the process control system is indicated at 95. Fig. 9 The digital process twin 90 or simulation model 91 in Figure 8 The digital process twin 80 or simulation model 81 in FIG. 8 is thus connected to the operating and monitoring software of the control system. Advantageously, the simulation model 91 is also a nonlinear model.

[0098] This type of operator training system enables the training participants to experience the real “see and feel” of the control center in real time. From the perspective of the equipment operator, there is virtually no difference between whether he is operating the real process or the digital process twin of the real process. As a result, all tasks that the equipment operator has to complete in the control center can be practiced close to reality, especially scenarios that hardly occur in real equipment or are associated with high risks.

[0099] according to Fig.10 The simulation models 61, 71, 81 generated in step 30e), step 30f) or step 30g) can be used for parallel real-time simulation of the model-based soft sensor 105 of the device. Here, the simulation model is denoted by 101 and is a component of the digital process twin 100.

[0100] The model 101 is supplied with all input variables with the same values ​​as those currently present in the real process or in the real process control system 102, for example the feeds of all raw materials and operating media in the plant. All measured values ​​of the real process (represented here by the measured values ​​TIC.PV of the temperature) are compared with the corresponding state variables calculated by the model 101 (represented here by the temperature T). For example, the detected deviations are fed into an EKF algorithm (EKF=Extended Kalman Filter) 106 in order to adapt the selected uncertain model state changes or time-varying model parameters of the model 101 to the current state of the real process. This adaptation is represented by arrows 107. The simulation model 101 (in contrast to the real process) is completely transparent, so that every variable that may be useful for process control can be read online from the model 101 even if it cannot be directly measured in the real process. In this case, the setpoint value for the temperature TIC is represented by TIC.SP and the actual value for the feed raw material flow is represented by FIC_E.PV.

[0101] Typical use cases are characteristic variables of product quality or substance concentrations, which cannot be measured online but can only be determined occasionally with the aid of laboratory samples. The characteristic variables calculated by the model 101 are available online and can be used in the control loop instead of the real sensor measurement value. If the result QL of the characteristic variable of the laboratory sample from the laboratory analysis 108 is confirmed, this result is additionally used for the state comparison in the soft sensor 105, wherein the time delay is taken into account in the laboratory analysis by the time element DT.

[0102] The dynamic process model 101 of such a soft sensor 105 must realistically represent the behavior of the automation system. Therefore, the PID controller is preferably part of the process model, that is, a dynamic pressure-driven simulation model is preferably used, which includes the PID controller of the control loop (see Figure 3 Step 30g)).

[0103] It is important that the closed control loop behaves correctly in time in the interaction between the controlled system and the controller. This requires careful parameterization of both the process-based model components and the controllers in the model, which is performed without an EKF solver by means of extensive offline simulations of the process model. The model is fed with the time course of the input variables from a real historical data set and the course of the output variables is compared with the historical data. If necessary, the static behavior is adjusted first and the dynamic behavior is adjusted later.

[0104] Special attention must be paid to the switching of the controller operating mode. If the controller operating mode is important in operation, not only the setpoint value, but also the operating mode and, if necessary, the manual value from the control system must be read into the corresponding controller of the model. At least for this application case, the simulation model must also have a representation of the actuator (e.g. valve) in order to be able to process the manual control value by the model at all costs.

[0105] The dynamic model 101 of the automated process with a PID controller and parameters matching the real process state, that is, the model 101 developed for the soft sensor 105, is exactly the right model form for nonlinear model-based predictive control (MPC). Fig.11 The simulation model 101 is used in a real-time simulation in parallel with a nonlinear model-based predictive controller 109. The controller 109 receives the value Q for the quality calculated by the soft sensor 105 or the model 101 on the input side and calculates the setpoint value TIC.SP for the temperature therefrom, which is transmitted to the process control system 102.

[0106] In this case, certain PID controllers of the basic automation of the process control system 102 are controlled as slave controllers in a cascade circuit, while the controller 109 is the master controller. In contrast to commercially available linear predictive controllers with black box I / O models, time-consuming jump tests in the plant are no longer necessary in order to identify the linear dynamic black box model from the measured data. The (nonlinear) dynamic model 101 can describe the behavior of the plant not only in the surroundings of the operating point, but also during load changes, product type changes, startup or shutdown processes. If there is no such requirement in production operation at all, the strict dynamic model 101 can be numerically linearized and used for a more economical linear MPC (if necessary also directly integrated in the process control system 102).

[0107] Fig.12 A schematic diagram shows a system 120 for generating a simulation model of a digital twin of a process of a process technology production facility for a product according to the present invention. The system 120 comprises an interface 121 (eg, an Internet interface, a USB interface) designed for

[0108] a) receiving the process flow design data VP and pipeline and instrument design data RI of the production equipment, and

[0109] b) Outputting the model data of each simulation model to store the model data in the data storage 125 .

[0110] To this end, the system 120 comprises a first memory 122 containing instructions 123 and a processor 124. The processor 124 is coupled to the first memory 122 and is designed to execute the instructions according to Figure 3 The received process flow design data VP and the pipeline and instrument design data RI as well as the generated model data MD can be temporarily stored in the memory 122 or the second memory 127.

[0111] Each simulation model determines product quality indicators and measurable state variables of the production plant based on the material flows of raw materials and operating media fed to the production plant. Model data are generated (e.g. with regard to selection, content and structure) so that the simulation model can be executed by simulation software based on the model data.

[0112] Data storage 125 and simulation software 126 can be an integrated component of system 120, or can also be separated therefrom. For example, system 120 can be located on a digital platform based on the Internet, and the generation of the simulation model can be provided as a service by a service provider. In this case, components 121, 122, 124 and 127 are under the monitoring of the service provider and are located on the platform. On the contrary, components 125 and 126 are monitored by the customer and are located locally on site or are also located on the platform or other platforms for the customer. Then, the customer downloads the generated model data from system 120 and stores the model data in his own data storage 125. Then the model data can be read from there by the simulation tool 126 and the simulation model is realized.

[0113] Alternatively, the system 120 , the data storage device 125 and the simulation tool 126 can also be integrated, for example a PC-based overall system.

[0114] The preferred system 120 allows switching between static and dynamic simulations and between flow driven and pressure driven simulations.

[0115] In summary, the invention enables the consistent use of a process master model (here a static flow-driven simulation model) for a digital process twin. The model is continuously improved and adapted to the respective use case without losing information from earlier stages or having to be manually input again. In this way, the development investment for the digital process twin can be spread over multiple use cases. Model-based solutions can therefore also be attractive in those application areas where the expenditure required for model development could not be justified by a single use case so far. Overall, the relationship between the benefits and costs of model-based solutions is improved as a result.

Claims

1. A computer-implemented method (30) for generating a simulation model (41, 51, 61, 71) for a digital twin (40, 50, 60, 70) of a process (3) of a process-based production facility (1) of a product (S), the method comprising: a) receiving process flow design data (VP) of the process (3) of the production equipment (1), b) generating a static flow driven simulation model (41) of said process (3) from said process flow design data (VP), c) generating a static pressure driven simulation model (51) of the process (3) from the static flow driven simulation model (41), d) receiving pipeline and instrument design data (RI), e) generating a dynamic pressure driven simulation model (61) of the process from the static pressure driven simulation model (51) and the pipeline and instrumentation design data (RI), wherein the dynamic pressure driven simulation model includes sensors and actuators of the control loop of the production equipment (1), wherein each of the simulation models (41, 51, 61, 71) determines a measurable state variable (T) of the production plant (1) based on the material flows of the raw material (E) and the operating medium (B) fed to the production plant (1), and Wherein, model data (MD) is generated by each of the simulation models (41, 51, 61, 71) and the model data is stored in a data memory (125), so that the model data (MD) can be read from the data memory by simulation software (126) and the simulation model (41, 51, 61, 71) can be executed based on the read model data (MD).

2. The method according to claim 1, wherein: Each simulation model (41, 51, 61, 71) determines a quality indicator (Q) of the product (S) based on the material flows of raw materials (E) and operating medium (B) fed to the production plant (1).

3. The method according to claim 1 or 2, wherein: The dynamic pressure driven simulation model generated in step e) includes the actuator (VB) of the secondary control loop of the control loop, in particular the fluid dynamics model (V) of the actuator of the secondary control loop, or in another step f) the dynamic pressure driven simulation model is expanded to include the actuator.

4. The method according to any one of claims 1 to 3, wherein: The dynamic pressure driven simulation model generated in step e) or step f) includes the PID controller of the control loop, or in a further step g) the dynamic pressure driven simulation model is expanded to include the controller.

5. The method according to claim 1 or 2, wherein: The simulation model generated in step b) and / or step c) is used for the process engineering design of the production plant, and the production plant is physically implemented based on this design.

6. A method according to any one of the preceding claims, wherein: The simulation model generated in step e), step f) or step g) is used to develop and verify the control concept and to physically implement the control of the production plant based on the design.

7. A method according to any one of the preceding claims, wherein: The simulation model generated in step e), step f) or step g) is used for virtual commissioning of the system, for which purpose the simulation model is linked to the control software of the system in order to test the system for error-free operation.

8. A method according to any one of the preceding claims, wherein: The simulation model generated in step e), step f) or step g) is used for training the plant operators, for which purpose the simulation model is linked to the operator control and monitoring software of the plant.

9. A method according to any one of the preceding claims, wherein: The simulation model generated in step e), step f) or step g) is used for a model-based soft sensor of the device.

10. A method according to any one of the preceding claims, wherein: The simulation model generated in step e), step f) or step g) is used for model-based predictive control of the plant.

11. A method according to any one of the preceding claims, wherein: The simulation model generated in step e), step f) or step g) is used in an assistance system for the system operator, for which purpose the simulation model is linked to the control software of the system.

12. A system for generating a simulation model (41, 51, 61, 71) for a digital twin (40, 50, 60, 70) of a process (3) in a process-based production facility (1) of a product (S), the system comprising: - an interface (121) designed to: a) receiving process flow design data (VP) and pipeline and instrument design data (RI) of the production equipment (1), and b) outputting model data (MD) of each of the simulation models (41, 51, 61, 71) to store the model data in a data memory (125), - a memory (122), said memory comprising instructions (123), - A processor (124), which is connected to the memory (122) and is designed to, when the processor executes the instructions (123), implement steps a) to e) of the method according to any one of claims 1 to 4, and preferably also implement further steps f) and / or g), wherein each of the simulation models (41, 51, 61, 71) determines a measurable state variable (T) of the production plant (1) based on the material flow of raw materials (E) and operating medium (B) fed to the production plant (1), and preferably also determines a quality indicator (Q) of the product (S), and wherein the model data (MD) are generated so that the simulation model (41, 51, 61, 71) can be executed by simulation software (126) based on the model data (MD).

13. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 4.

14. A computer-readable storage medium comprising instructions, which, when executed by a computer, cause the computer to implement the method according to any one of claims 1 to 4.

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