Method and device for the use of industrial plant simulators using cloud computing technologies
Patent Information
- Authority / Receiving Office
- DE · DE
- Patent Type
- Patents
- Current Assignee / Owner
- EMERSON PROCESS MANAGEMENT POWER & WATER SOLUTIONS INC
- Filing Date
- 2013-01-24
- Publication Date
- 2026-08-06
AI Technical Summary
Existing process control simulation systems in industrial plants are often inaccurate, costly, and difficult to integrate with the actual control network due to separation from the operational environment, leading to deviations and high setup and maintenance costs.
A cloud-based simulation system that synchronizes with the actual process control network, updating periodically to reflect changes and using the same user interface as the control system, allowing real-time simulation and prediction without significant configuration or setup.
Provides a cost-effective, accurate, and user-friendly simulation system that remains synchronized with the process plant, reducing setup and maintenance costs while maintaining high accuracy and ease of use.
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Abstract
Description
DESCRIPTION OF RELATED TECHNOLOGY
[0001] Distributed process control systems, as commonly used in power generation, chemical manufacturing, petroleum refining, or other process plants, typically include one or more process controllers that communicate with one or more field devices via analog, digital, or combined analog / digital buses. The field devices, which may include valves, actuators, switches, transmitters (e.g., temperature, pressure, level, and flow rate sensors), burners, etc., are located within the process environment and perform process functions, such as opening or closing valves, measuring process parameters, etc., in response to control signals generated and transmitted by the process controllers.Intelligent field devices, such as those compliant with any of the well-known fieldbus protocols, can also perform control calculations, alarm functions, and other functions that are often implemented within or by a process controller. The process controllers, which are also typically located within the plant environment, receive signals indicating process measurements taken by the field devices and / or information relating to the field devices. They execute a control application, for example, on various control modules that make process control decisions, generate process control signals based on the received information, and are synchronized with the control modules or blocks implemented in the field devices, such as HART and fieldbus field devices.The control modules within the controller send the process control signals to the field devices via the communication channels in order to control the process flow.
[0002] Information from field devices and the controller is typically provided via a data highway to one or more other computer devices, such as operator workstations, PCs, historical data loggers, report generators, centralized databases, etc., which are usually located in control rooms or other locations remote from the harsher plant environment. These computer devices can also run applications that allow an operator to perform process-related functions, such as changing process control routine settings, modifying the operation of control modules within the controller or field devices, viewing the current process state, viewing alarms generated by the field devices and controllers, maintaining and updating a configuration database, etc.
[0003] For example, the Ovation.RTM. control system, marketed by Emerson Process Management, includes several applications that reside and run on different devices at various locations within a process plant. A configuration application, located on one or more operator workstations, allows customers to create or modify process control modules and download these modules over a data highway to dedicated distributed controllers. These control modules typically consist of communicatively linked function blocks, which are objects in an object-oriented programming protocol. Based on inputs to the control scheme, these blocks execute functions within the control scheme and provide outputs to other function blocks within the same scheme.The configuration application can also allow a developer to create or modify operator interfaces used by a display application to show data to an operator and to allow the operator to change settings, such as setpoints within a process control routine.
[0004] Each of the dedicated controllers and, in some cases, field devices stores a control application that executes and runs the control modules assigned to it and downloaded to it, in order to implement actual process control functionality. The display applications, which can be run from one or more operator workstations, receive data from the control application via the data highway and display this data to process control system developers, operators, or customers through user interfaces, and can provide any number of different views, such as an operator view, an engineer view, a technician view, etc.A past data recorder application is typically stored and executed on a past data recorder device, which collects and stores some or all of the data provided over the data highway, while a configuration database application may be executed on another computer connected to the data highway to store the current process control routine configurations and related data. Alternatively, the configuration database may reside on the same workstation as the configuration application.
[0005] As noted above, operator display applications are typically implemented system-wide in one or more workstations and provide preconfigured displays regarding the operating status of the control system or equipment within the plant to the operator or maintenance personnel. These displays are usually alarm displays that receive alarms generated by the controllers or equipment within the process plant; control displays that indicate the operating status of the controllers and other equipment within the process plant; maintenance displays that indicate the operating status of equipment within the process plant; and so on. These displays are generally preconfigured to show information or data received from the process control modules or equipment within the process plant in a known manner.
[0006] In some well-known systems, displays are generated using objects, where a graphic is associated with a physical or logical element and communicatively linked to it to receive data about that element. The object can modify the graphic on the display screen based on the received data, for example, to show that a tank is half full, to display the flow rate measured by a flow sensor, and so on. Although the information required for the displays is sent from the devices or the configuration database within the process plant, this information is used solely to provide the customer with a display containing that information. Therefore, all functions necessary for generating alarms, detecting problems with the plant, etc., must be performed by the user.The information and programming used are generated and configured within the various devices linked to the plant during the configuration of the process plant control system, such as controllers and field devices. Only then is this information sent to the operator display for display during process operation.
[0007] Furthermore, it is often desirable to develop a simulation system within the plant to simulate the operation of the control network connected within the plant. Such a simulation system can be used to test the plant's operation in response to new or different control variables, such as setpoints, to verify new control routines, to perform optimization, to conduct training activities, and so on. Consequently, numerous simulation systems have been proposed and used in process plants. Nevertheless, due to the constantly changing conditions within the plant, including equipment wear and tear over time and the presence of unforeseen disturbances, typically only the most complex simulation systems can perform highly accurate simulations of the process plant.Furthermore, in many known control systems, it can be difficult to set up or generate a simulation of the process plant or a part of it, because simulation activities are performed separately from the display and control activities carried out in the operational environment of the process plant. For this reason, the simulation system is not closely coordinated with the actual operation of the control network within the process plant. In other words, once set up, simulation systems are typically run separately from the controls within the plant to simulate the operation of the process control network installed within the plant, and thus these simulation systems can quickly deviate from the actual control network within the plant. Moreover, the process model used in the simulation system can quickly diverge from the actual process operation.Therefore, integrating the simulation system with the operator displays or with the control modules implemented within the plant can be difficult.
[0008] Furthermore, simulation becomes even more complex in power plant control systems and other types of control systems where control functions are typically segmented into different control machines (or processors) based on criteria such as the physical location of the associated plant equipment, the dynamic properties of the process variables of interest, and considerations of fault tolerance and redundancy. The physical location of the equipment plays a significant role primarily due to mechanical considerations and limitations, for example, related to the length of the relevant cables. Here, process dynamics influence the control function segmentation by imposing requirements and constraints on the execution time of the control functions associated with the respective process variables, all of which must be simulated in the simulation system.In power plants, the consideration of fault tolerances serves to reduce the impact of processor and computer failures on power generation.
[0009] Furthermore, generating simulations at a plant site can be expensive. The processors and associated equipment required to perform a simulation are complex and costly. This equipment also requires space and a suitable operating environment, which can be difficult to create and maintain in a plant setting. The simulation application itself can also be expensive, complex, and operator-intensive. It is therefore specialized and often requires experienced operators to function effectively. In addition, there are ongoing costs, such as equipment and software maintenance, updates, and support. Cost estimates range from $500,000 for a small system to $2,000,000 for a larger one.
[0010] While most facilities and other installations use an offline simulator for operator training and engineering analysis, this conventional approach treats control and simulation functions as two completely separate and distinct instances, each of which must be created, run, and configured separately to function correctly. Therefore, the simulation systems used in these facilities can quickly deviate from the process, are consequently not very accurate, and are typically not very user-friendly. Additionally, the creation, operation, and maintenance of these local systems are costly. SUMMARY
[0011] Using data communicated within one or more plants via a stripped-down or simplified client, a process control simulation technique is generated in a network cloud. The simulation performs a real-time simulation or prediction of an actual process control network, synchronized with its operation, while that network is operating within a process plant. Specifically, this synchronized simulation system is automatically and periodically updated during the operation of the actual process control system to reflect changes made to the actual process control network and to represent changes occurring within the plant itself.Changes that require an update of a process model used in the simulation system are addressed because the relevant data is communicated to the network computers operating in the network cloud in a timely manner. The synchronized, cloud-based simulation system described herein provides a more cost-effective and usable simulation system because the plant models used in the simulation system are synchronized with and up-to-date in relation to the current process operating conditions, while the expertise for setting up, operating, and maintaining the simulations is provided by trained operators in the network cloud.
[0012] Additionally, the disclosed simulation system is highly accurate because it uses process models developed from the current state of the processes at the time a specific simulation is initiated by the system. Furthermore, this simulation system is easy to use because it can utilize the same or similar user interface applications as those used within the process control network to perform human-machine interface (HMI) activities, while minimizing the difficult steps of setup, operation, and maintenance, since these activities are performed in the network cloud.Similarly, this simulation system can be initialized and used at any time during the operation of the process plant without requiring significant configuration or setup activities, since the simulation system is always up-to-date with the actual control network used in the process plant when it is initially switched to a prediction mode. Therefore, the operator only needs to enter the respective changes to be used in the simulation into the simulation control system, and the simulation is ready to perform accurate simulations or predictions, as the simulation system remains synchronized with the process plant.
[0013] Furthermore, the cloud-based simulation system is more cost-effective than local simulations running on-site. Previous on-site simulations required significant investment in hardware, software, space, HVAC equipment, and operators to ensure proper system operation. In the case of the cloud-based simulation system described herein, the on-site software is a streamlined application that can run, for example, on a standard PC (though this example is not limited). This streamlined software can collect process-related data and communicate it to the network cloud, and calculate and display related indicators for the simulation system running in the network cloud, while the more sophisticated simulation software runs in the network cloud.
[0014] In the revealed network-cloud-based simulation, investments in computer systems and operators are significantly lower, as many customers can share the processors and operators available in the network cloud. Instead of just one remote workstation, the cloud also allows for multiple computing devices to be available to run numerous simulations or other applications simultaneously, resulting in improved responsiveness and availability of the simulation system. Furthermore, the centralized cloud-based architecture enables the centralized collection and storage of data, making centralized process control data analysis even simpler and more efficient.
[0015] In general terms, the simulation system described herein switches between running in one of two different modes: a tracking mode and a predictive mode. In tracking mode, the simulation system, operating in the network cloud, communicates with the process control network via a supervisor client on-site to obtain various types of state data from the process control network. This data is necessary to keep both the process control network and the simulation system's process model synchronized with the actual process control network and the process being controlled. This information includes, for example, state variables that define the operation of the process controls, measured process variables, and process control signals generated by the controllers within the process plant.This information can be received periodically during the operation of the process control network, in one embodiment with a sampling rate corresponding to the process controllers within the actual process control network (i.e., the rate at which the process controllers generate new control signals). During tracking mode, the simulation system uses state information collected in the cloud to develop an updated control state variable for use in configuring the simulated control network and updates a process model to recreate the process based on the latest collected information.
[0016] During prediction mode, the operator can define new control variables, such as setpoints, to be used during the simulation. The cloud-based simulation system then works to simulate process control based on the latest process model. Depending on the operator's preferences, the cloud-based simulation system can operate in real-time, fast-forward, or slow-motion modes. In each case, the cloud-based simulation system can, for example, simulate the operation of the actual process control network in response to a changed control variable, a modified control routine, a process disturbance, and so on.Alternatively, if desired, the simulation system in the cloud can simulate the operation of the process plant in fast forward to determine an indication of the controlled state operation of the process at a control prediction time or to otherwise predict the operation of the plant or a variable thereof at a future time.
[0017] Because the simulation system in the network cloud communicates with the actual process control network, it synchronizes with the actual process control network and the process plant as it is currently operating when prediction mode is activated. In response to the control variables used in the simulation, the simulation system in the network cloud provides an accurate simulation or prediction of the process plant's operation. Furthermore, because the simulation system in the network cloud is synchronized with the process plant upon activation, the operator in the plant or in the cloud does not need to perform any significant configurations or updates to the simulation system before initiating it, thus simplifying operation.Since the simulation system in the network cloud is synchronized with the process control network, the simulation system can also use the local client to display the same user interface routines, making the local simulation system display look and function the same as the control system in the plant, which in turn makes the simulation system easier to use and understand.
[0018] Finally, a procedure for providing simulation services is described. Generally, the system and simulations are reviewed for their complexity to determine the expected workload for generating a simulation in the network cloud. Once the complexity has been determined, a minimum level of service can be established based on this level of complexity. This minimum level of service can then be used to determine a price proposal for the minimum services, which can be communicated to the customer. Additionally, by reviewing the system and simulation data, other services that might prove useful can be identified, and the price for these additional services can be determined and communicated to the customer. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] They show:
[0020] Fig. 1 a block diagram of a distributed process control network located within a process plant, including a computer device communicating with a network cloud of computer devices implementing a simulation system configured to synchronize with the operation of an actual process control network in order to simulate the operation of the process plant;
[0021] Fig. 2 a logical block diagram of a process plant control system and a simulation system for simulating the process plant control system;
[0022] Fig. 3. A general, logical block diagram of the control system, the simulator, and the supervisor;
[0023] Fig. 4. A simplified, logical block diagram of a control loop of the in Fig. 2 of the plant control systems shown;
[0024] Fig. 5 a simplified, logical block diagram of a through which in Fig. 2. Simulation system implemented, simulated control circuit;
[0025] Fig. 6. A logical block diagram showing the communication networks between the simulation system and the control system. Fig. 2 represents during a tracking mode of the operation;
[0026] Fig. 7 a block diagram of a simulation system implementing the functions described herein; and
[0027] Fig. 8 a block diagram of a process for selling network cloud-based simulation services. DESCRIPTION
[0028] With reference to Fig. 1 is an example of a control network, such as the one connected to a power plant, for the process plant 10 The process plant is described in detail. 10 out of Fig. 1 includes a distributed process control system that includes one or more controllers. 12 has, each of which has input / output (I / O) devices or cards 18 , which may be, for example, fieldbus interfaces, Profibus interfaces, HART interfaces, conventional 4–20 mA interfaces, etc., with one or more field device(s) 14 and 16 is connected. The controls 12 can also be accessed via a data highway 24 , which could be, for example, an Ethernet connection, with one or more host(s) or one or more workstation(s) 20 and 22 be connected. A database 28 can use the information superhighway 24 be connected and act as a historical data recorder to record parameters, state, and other information with the controllers. 12 and field devices 14 , 16 in the plant 10to collect and store associated data. Additionally or alternatively, the database can 28 They serve as a configuration database, containing the current configuration of the process control system within the plant. 10 , as they are in the controls 12 and field devices 14 and 16 Once downloaded and saved, it saves. While the controls 12 , the I / O cards 18 and the field equipment 14 and 16 Operator workstations are typically located within the often harsh plant environment and are usually distributed throughout it. 20 and 22 and the database 28 typically located in control rooms or other less harsh environments that are more easily accessible to an operator or maintenance personnel.
[0029] As is known, each of the controllers stores 12, which could be, for example, the Ovations controller distributed by Emerson Process Management Power and Water Solutions, Inc., a control application that uses any number of different, independently running control modules or blocks 29 It implements and executes a control strategy. Each of the control modules 29 It can consist of elements usually referred to as function blocks, each of which is part of a subroutine of a general control routine and works in conjunction with other function blocks (via communication links called links) to form process control loops within the process plant. 10to implement. As is known, function blocks, which can be objects in an object-oriented programming protocol but do not have to be, typically implement an input function, such as one connected to a transmitter, sensor, or other process parameter measuring device; a control function, such as one connected to a control routine that performs PID (proportional-integral-derivative), fuzzy logic, etc. control; or an output function that controls the operation of a device, such as a valve, to perform a physical function within the process plant. 10to carry out. Of course, there are hybrid forms and other types of complex function blocks, such as model predictive controllers (MPCs), optimizers, etc. Although the Fieldbus protocol and the Ovation.RTM system protocol use control modules and function blocks developed and implemented in an object-oriented programming protocol, the control modules can be developed in any desired control programming scheme, including, for example, sequential function chart, ladder logic, etc., and are not limited to being developed and implemented using the function block or any other programming technique.
[0030] In the Fig. 1 shown in the system 10 can they be used with the controls 12 connected field devices 14 and 16These can be conventional 4–20 mA devices, intelligent field devices such as HART, Profibus, or FOUNDATION.RTM fieldbus field devices containing a processor and memory, or any other desired type of field device. Some of these devices, such as the fieldbus field devices (in Fig. 1 with the reference number 16 (marked), modules or submodules, such as function blocks, which are associated with the controllers 12 The implemented control strategy is connected, stored, and executed. The function blocks 30 , which in Fig. 1 as in two different individual fieldbus field devices 16 As is well known, the arrangement shown can be used in conjunction with the execution of the control modules. 29 in the controls 12 They can be used to implement one or more process control loops. Naturally, the field devices can 14 and 16This could be any type of device, such as sensors, valves, transmitters, actuators, etc., and the I / O devices. 18 They can be any type of I / O device that conforms to any desired communication or control protocol, such as HART, Fieldbus, Profibus, etc.
[0031] Furthermore, one or more of the workstations can be used 20 and 22 In a known manner, user interface applications are included to provide a customer, such as an operator, a configuration engineer, a maintenance worker, a user, etc., with an interface to the process control network in the plant. 10 to enable this. In some implementations, the user interface application can be a "slimmed-down" client that resides in a network cloud. 48 It displays specific data. For example, the display data can be stored in the network cloud. 48generated and communicated as HTML data to a conversion module, such as a web browser, and then displayed on the screen 37 will be displayed. In other embodiments, the user interface application can be installed on a local workstation. 22 be executed. The workstation 22 is one or more user interface applications 35 , which run on a processor in the workstation 22 can be executed, containing the following information.
[0032] In almost all embodiments, the user interface application 35 with the database 28 , the control modules 29 or other routines in the controls 12 or I / O devices 18 , with the field devices 14 and 16 and the modules 30 These field devices, etc., communicate to receive information from the plant, e.g., from a supervisor. 32, to obtain and the information can relate to the ongoing state of the process control system. The user interface applications 35 can process this collected information and / or display it on a display device 37 display that is connected to one or more of the workstations 20 and 22 is connected. The information collected, processed and / or displayed can include, for example, process status information, alarms and warning messages generated in the system, maintenance data, etc.
[0033] Similarly, one or more applications may be used. 39 in the workstations 22 and 20 or in the network cloud 48 stored and executed to perform configuration activities, such as creating or configuring modules 29 and 30, which are to be carried out in the plant in order to perform operator control activities, such as changing setpoints or other control variables in the plant, etc. Naturally, the number and type of routines are 35 and 39 not limited by the description provided herein, and other numbers and types of process control-related routines can be implemented in the workstations if desired. 20 and 22 be implemented and stored.
[0034] The workstation 20 out of Fig. 1 is also available as a supervisor application 32 The supervisor application is shown below. 32 It can be a "simplified" or "stripped-down" application that provides limited functionality locally and is not processor-intensive. The supervisor application 32Supervisor data, which consists of control system and process system information, can be used for the simulation application. 40 Relevant data is collected and communicated. Communication can be provided using a known or conventional interface protocol, such as OPC, TCP / IP, etc.
[0035] The Supervisor application 32 It can be a complete application or have various modules, such as a data collection module, a data packaging module, a data communication module, a data reception module, a data recognition module. 82 etc. In some embodiments, the supervisor data can be "pulled" from the control system and process system, whereby the systems can be searched for the desired data. In other embodiments, the supervisor data can be obtained from the control system. 50 and process system 52 to the supervisor 32 to be "pushed" ( Fig. 2) Of course, a combination of "push" and "pull" to retrieve the supervisor data is possible and is being considered.
[0036] Fig. 3 is a superficial representation of the logical communication between the supervisor and the simulation application. 40 and the actual process control system 54 In one embodiment, the supervisor application is located 32 in a computer device within the system of the respective control system 54 and the simulator is located at a remote location in one or more computer devices. 49 in the network cloud 48 The supervisor application 32 communicates the required data from the control system 54 to the simulator 40 , which is in the network cloud 48 works.
[0037] In some embodiments, the supervisor application can 32temporarily store the supervisor data and send it to the simulation instruction. 40 Communication occurs when a threshold value is reached, a time period has elapsed, or a specific amount of data has been collected. For example, supervisor data can be communicated periodically. The communication interval can be related to the control system operating interval and the process system operating interval. If the control system 50 For example, if new data is captured every 0.5 seconds, communication can be managed by the supervisor application. 32 to the simulation application 40 every 0.5 seconds. In some situations, it is of course useful to use the supervisor application. 32 and the simulation application 40 They may communicate more frequently, and in other cases the communication distance may be greater. In other embodiments, the supervisor application may 32the supervisor data is transferred to the simulation application almost immediately. 40 transfer or send. While the supervisor data can be transferred, it can of course be converted into a format or scheme that facilitates communication with the simulation application. 40 makes it more suitable, reliable, more convertible, etc.
[0038] In some additional embodiments, a change detector can be used. 82 part of the simulation system 52 In some embodiments, the change detector is located 82 with the supervisor 32 in communication. For example, the change detector 82 in Fig. 7 as part of the supervisor 32 depicted; however, as will be explained, the change detector can 82 in various ways with the simulation application 40 They must be in communication and he does not necessarily have to be physically present with the supervisor. 32be connected. The change detector 82 can historical values from the process control network 54 to track changes in the values of the process control network 54 communicate. In this way, the supervisor can 32 to the simulation device 52 The amount of data communicated can be reduced, thereby saving communication bandwidth, storage space and processor utilization.
[0039] The change detector 82 It may also be able to control the process control network 54 , the simulated control network 64 and the process model 66 to monitor and the received values from the process control network 54 , the simulated control network 64 and the process model 66 to compare with values that were previously received and stored. If the received values differ from the process control network54 , the simulated control network 64 and the process model 66 The new values can be sent to the update module to differ from the previously received values. 70 Only updated values are communicated in this way, saving communication bandwidth, storage capacity, and processor utilization.
[0040] With brief reference to Fig. Number 7 is the change detector 82 in another embodiment with the update module 70 in communication. The change detector 82 Can the incoming data be viewed by the supervisor? 32 check and forward only those process control network data that differ from the process control network data already in the storage module. 80 are stored or connected to the simulation control network 64 or the process model 66were communicated. Therefore, only new or updated data from the process control network can be transmitted. 54 in the memory module 80 stored or connected to the simulation control network 64 or the process model 66 Communication is possible, thereby reducing storage space and communication bandwidth to the simulation control network. 64 or process model 66 and processor load is saved. Furthermore, the change detector 82 the memory module 80 instead of simply functioning as a recorder of past data, it gives the storage module 80 Instead, intelligence is used to prevent all data from being stored or communicated, rather than just the data that has changed.
[0041] With renewed reference to Fig. 1. The simulation application can 40 at a different location than the process plant 10to be executed as in a remote network 48 In some implementations, the remote network 48 as a network or cloud of computer devices 49 can be viewed. The remote network cloud. 48 can be from one or more computer devices 49 Computers such as servers, workstations, PCs, etc., that run computer-executable applications, and can be reached through one or more types of electronic communication. The actual location of the computer equipment 49 It doesn't necessarily have to play a role. The computer devices 49 They can be located in the same place or distributed around the world, yet still communicate with each other. In some embodiments, the computer devices can 49 in the cloud 48 cooperate to share computing work, and in other embodiments, each of the computing devices can 49Run specific computing applications. As expected, the computing devices can 49 one or more processor(s) 46 and one or more storage devices 42 exhibit and the processors 46 and storage 42 They can be physically configured according to computer-executable instructions or applications.
[0042] The simulation application 40 can a process plant simulator 52 , a user interface application 74 and data structures for performing a synchronized simulation of the process plant 10 in the manner described herein. An authorized customer (such as a configuration engineer, operator, or other user) may access the simulation application. 40 access to run a simulation of the process plant control network 54 to carry out this through the control blocks 29 and 30as well as others in the controls 12 and possibly the field devices 14 , 16 The executed control routines are implemented. The simulation application 40 It can be protected by passwords, blind keys, or other appropriate security measures. The passwords can be specific to a user, workstation, system, or module.
[0043] The simulation application 40 It enables a customer to perform various simulation and prediction activities regarding the process plant. 10 to perform while the control system of the process plant 10 operational and online to maintain the system 10 to control. As in Fig. The simulation application shown in 1 is... 40 in a storage facility 42 one or more of the remote computer devices 49 in the network cloud 48saved and each of the components of the simulation application 40 can be adjusted to work on a computer connected to the remote device 49 connected processor 46 to be executed. During the entire simulation application 40 as in one of the remote computer devices 49 If stored and displayed, some components of the simulation application could 40 in other workstations or computer devices 49 , which is connected to the plant 10 or with the simulation application 40 or the remote workstation 49 are in communication, such as with other computer devices 49 in the network cloud 48 , can be saved and executed. Similarly, the simulation application can be used. 40 split up and distributed across two or more computers 49or machines that can be configured to work in conjunction with another, for example in a network cloud. 48 .
[0044] Furthermore, the simulation application can 40 Ad output to the display screen 37 , which is connected to the remote workstation 49 is connected, or any other desired display screen or display device 37 Provide, including handheld devices, laptops, tablet computers, mobile phones, other workstations, printers, etc. For example, the simulation application can 40 The input displays resemble the actual control displays of the process control system. In such a simplified implementation, the customer only needs to generate a display in the plant, not calculate and create the data contained in the display.
[0045] Generally speaking, the simulation application represents 40 the prerequisites for simulating the operation of the process plant 10 ready or enables this and in particular the simulation of the control routines 29 and 30 in the controls 12 and the field devices 14 and 16 implemented process plant control system 54 in conjunction with the actual controlled plant. While the plant being controlled is described herein as a power plant controlled using distributed control techniques, the synchronized simulation technique described herein can be used in other types of plants and control systems, including industrial manufacturing plants, water and wastewater treatment plants, and control systems that are centrally implemented or on a single computer and therefore not controlled by the plant. 10are distributed throughout.
[0046] Fig. Figure 2 generally shows a local (in the system) 10 (located) process control system 50 and a simulation system 52 , which is removed from the facility 10 out of Fig. 1 is implemented. In particular, the process control system includes 50 an actual process control network 54 , which is physically linked to a process 56 is coupled and is in communication with it. It goes without saying that the actual process control network 54 the control modules 29 and 30 out of Fig. 1 and any others in the various control devices (e.g. controllers) 12 ) and field devices (e.g. devices) 14 and 16 ) the facility 10 out of Fig. It includes 1 ordered and executed control routines. Likewise, the actual process includes 56the units, machines, devices, and associated hardware facilities used to implement the process to be controlled. In a power plant, the process can 56 for example, generators, fuel supply systems including heat exchangers, condensers, steam generators, valves, tanks, etc., as well as sensors and transmitters arranged in the plant for measuring various process parameters or variables.
[0047] As in Fig. Figure 2 shows the actual process control network. 54 the controllers that generate one or more control signals that are sent to the various control devices in the plant 56 must be transmitted and operate according to a specific control technology in order to control the system 56 to control. These control signals are in Fig. 2 represented by the vector U to show that the actual process control network 54a vector of control signals to the process 56 can provide the necessary information to control the plant's operation. In a similar way to how in Fig. 2 is shown in the process 56 A vector Y of process variables is measured (e.g., by sensors, etc.) and used as a feedback signal for use in generating the control signal U to the process control network. 54 sent. Of course, the actual control network 54 include any type of controller that implements any desired type of control routines or techniques, such as PID, fuzzy logic, neural network, model predictive control routines, etc.
[0048] As in Fig. As shown in 2, the simulation system includes 52 a simulated control network 64 and a process model 66 The simulated control model 64Generally speaking, it is a copy of the actual process control network. 54 including a duplicate of the control routines that are in the actual controllers and other devices of the process control network 54 are executed and / or connected to these. However, instead of being distributed across several different devices, the simulated control network can be 64 include one or more communicatively connected control module(s) that are implemented in one or more computer devices, such as a remote workstation 49 in the network cloud 48 out of Fig. 1. Such a simulation system 40, which stores and simulates various control routines designed to be implemented in different computers as part of a distributed control network, is described in detail in US patent application number 09 / 510,053, filed on February 22, 2000, entitled “Integrating Distributed Process Control System Functionality on a Single Computer”, the disclosure of which is hereby expressly included by reference.
[0049] In any case, the simulation system can 52 as part of the simulation application 40 out of Fig. 1. Furthermore, this is implemented in the simulation system. 52 used process model 66 developed and configured to streamline the process 56The process model can be replicated and implemented as any desired or suitable type of process model, such as an nth-degree transfer function model, a neural network model, etc. Naturally, the type of model to be used can be selected as the best model type for the particular type of plant or process to be replicated, and one that offers online updates, as described in more detail below. Furthermore, the process model can 66 , if desired, consist of a plurality of individual process models, each representing a different part of the plant 10 , like another control circuit in the plant 10 , replicates or is associated with it.
[0050] The overall concept of the simulation approach, as it is described in Fig. As described in section 2, a simulation system is required. 52 ready, that's a control network 64, which is a copy of the actual control network 54 was developed, and a process model 66 , which is the actual process 56 The system replicates the plant. In this configuration, the control network includes... 54 and therefore the simulated control network 66 all functions and components that make up the actual control network 54 form (e.g., the controllers, function blocks, human-machine interface applications (HMI), etc. of the actual control network). Of course, the simulated control network can 64 of the simulation system 52 by copying the actual control routines (e.g. the control routines) 29 and 30 out of Fig. 1) the user interface applications 74 , the configuration applications, etc., as they are found, for example, in the configuration database 28 out of Fig. 1, the controls 12 , the field devices 14 , 16 , the workstations 20 , 22 etc. are stored, in addition to storing data or other information regarding the identification of the associated inputs and outputs of the control routines within the process plant. The supervisor application is being developed. 32 can help to transfer the actual routines and related data to the simulation system 52 to communicate. The input / output signal identification data can prove helpful to the simulation system. 52 to enable, during the operation of the control system 50 via the Supervisor application 32 with the control system 50 to communicate, thereby enabling the operation of the simulation system 52 with the control system 50 It is synchronized while the process plant continues to operate.
[0051] As is understood, the actual control network works 54 during the operation of the plant in any conventional or known way to calculate the manipulated variables or control signals U that affect the process 56 be applied. The process 56 It then responds by creating actual process variables Y, which are measured by various sensors in the plant and sent as feedback to the control network. 54 The manipulated variables or process variables (U and Y, respectively) are represented as vector sets to indicate a plurality of values. Naturally, each of the associated elements of these vector sets can consist of discrete values with respect to time, where the size of each time step corresponds to the execution time of the associated control function, i.e., the sampling or operating rate of the controllers.
[0052] As is understood, the values of the manipulated variable (control signals) U are calculated in each time step, and the values of the process variable Y result from querying the process variable in each time step. For the purposes of this description, the current time step is denoted as time k, and the values of the manipulated variable and the process variable at the current time step are therefore denoted as U.sub.k (U k ) or Y.sub.k (Y k ). According to this relationship, the time response of the control network is 54 accordingly determined by the vectors U, Y and a vector of internal state variables X, which is in the control network 54 defined the specifics of the control procedures (or control configurations) used, e.g., the control increments or other parameters that define the specifics of the controls in the control network. 54The implemented control techniques define the state vector X. In other words, the elements of the state vector X define the internal variables used by the control functions to compute the manipulated variables U. These state variables can be, for example, values that are a function of the setting parameters or accumulated time values used as timers by such functions, the integrator values used by PID controllers, the neural network weighting coefficients used by neural network controllers, the scaling factors used by fuzzy logic controllers, the model parameters or matrices used by model predictive controllers, and so on. These state values are also discrete with respect to time, and thus the state vector X at time step k is called X.sub.k.The entire set of state vectors U, Y, X can then be described as representing the entire state of the control system. These values are continuously calculated by the control system.
[0053] With reference to Fig. 4 is the control system 50 out of Fig. 2 is represented in block diagram form as a feedback control loop. In this case, the actual control network is 54 The process is represented by the block labeled C. 56 is represented by the block labeled P. Furthermore, the input to the control network is... 54 in this case represented as a vector of setpoints R, which are compared with the measured or determined process variables Y to generate an error vector E, which in turn is used by the control network 54This is used to generate the control signal vector or manipulated variable vector U. Naturally, the elements of the setpoint vector R represent the desired values for the process variables Y to be controlled, and these setpoint values are generally determined by an operator or an optimization routine (not shown). In the case of a power plant control system, these setpoint values might be the desired values of flow, pressure, temperature, megawatts, etc., for the associated process variables in the power plant equipment.
[0054] Similarly, the simulation system 52 in Fig. 5 is represented in block diagram form. The same vector R represents setpoint values from the actual control network. 54 is an input from the supervisor application 32 ( Fig. 3) into the simulation system 52 Here is the simulated control network. 64represented by block C^ and a replica of the control network 54 Regarding the control operation. Accordingly, all controllers, function blocks, and algorithms that comprise the actual control network are included. 54 form, in the simulated control network 64 The simulated, manipulated variables or control signals are represented as being controlled by the simulated control network. 64 generated or calculated, displayed, and are linked to the process model 66 provided.
[0055] In the remote, network cloud 48 working simulation system 52 However, the values of the process variables are determined using a mathematical model of the process. 56 calculated, which is used as a process model 66 is denoted and represented as P^. Of course, the exact structure of the process model can be... 66They may differ, and furthermore, several different model structures may be used for several different parts of the process. 56 This allows, for example, each process variable to use or be determined by a unique process model structure. Suitable model structures that can be used include fundamental principle (differential equation) models, transfer function (ARX) models, state-space models, neural network models, fuzzy logic models, etc.
[0056] As with the actual control system 50 The time response of the simulation system will be 52 The entire system is described by the vectors U^, Y^, and X^. Here, the elements of the simulator state vector X^ contain the same state variables X as the actual control system. 50 Nevertheless, the simulator state vector X^ also includes additional elements, which are the internal ones related to the process model. 66These are associated state variables, and these variables are, along with the manipulated variables, included in the process model. 66 used to calculate the simulated process variables Y^. Accordingly, the simulator state vector X^ is an extension of the control system state vector X, where X^ includes the control system state vector (here denoted as .theta. or Ѳ) and the vector of process model-internal state variables (denoted as .psi. or Ψ). Here, the values of .theta. are identical to X.
[0057] The simulator model architecture is preferably designed such that the value of each of the model's internal state variables (.psi..sub.k) can be calculated by the control system at the kth time step using U.sub.k-1 and Y.sub.k vectors. Naturally, the details of the specific calculations are specific to and pertinent to the respective model structure used, and these calculations are known to those skilled in the art. Furthermore, it is understood that the process state variables calculated by the simulator system can be a function of process variables and manipulated variables, and in some cases, of the process variables and / or the manipulated variables themselves, depending on the type of models used. In any case, this feature enables the synchronization of the actual control system. 50 and the simulation system 52during normal operation of the process plant. In particular, the absolute simulator state at the kth time step can be determined using U.sub.k-1, X.sub.k and Y.sub.k vectors, as defined by the Supervisor application. 32 The collected and communicated data are synchronized with the absolute control system. To update the absolute simulator state, the elements of .theta..sub.k are updated directly from the vector X.sub.k, and the elements of the process state vector .psi..sub.k are calculated (determined) using U.sub.k-1 and Y.sub.k. Again, it should be noted that the specific details of the calculations depend on the structure of the process model used.
[0058] According to this, the simulation system works 52 During operation, generally speaking, in parallel with, but in a synchronized manner, the operation of the process control system. 50 This applies in particular when the simulation system52 simply in parallel with the actual control system 50 However, if it were not executed synchronously, the simulated process variables would eventually deviate from the actual process variable Y output of the process due to unreplicated dynamics and deviations between the plant and the model. 56 differ.
[0059] To solve this problem, the remote simulation system remains. 52 in the network cloud 48 with the actual control system 50 synchronized by periodically operating in a tracking mode in which the simulation system 52 periodically, for example for each control time step, the U.sub.k-1, Y.sub.k and X.sub.k vectors from the actual control network 54 from the supervisor application 32 receives the simulation system. 52 then initializes the state of its simulated process control network64 with the status information from the actual control network 54 , as they were told by the supervisor 32 to be received. Furthermore, an update module of the simulation system calculates 52 In tracking mode, the internal state variables (.psi..sub.k) are recalculated using the U.sub.ki and Y.sub.k vectors to model the process. 66 to update in order to reflect the actual operation of the process during the last control time period, thereby revealing the actual characteristics of the process. 56 , as they are measured or evident from the last control scan time segment, are tracked or imitated. Accordingly, the simulation system 52 During operation in tracking mode, continuously monitors the current plant conditions as reported by the supervisor. 32 communicated, initialized, including the control conditions and plant characteristics.
[0060] Fig. Figure 6 shows the operation of the simulation system. 52 In more detail in tracking mode. In particular, the process control system 50 in Fig. 5 shown at the time interval k. In this case, the simulated process control network is 64 of the simulation system 52 However, it is configured to use the internal state vector X.sub.k of the controller. 54 , the control signal vector U.sub.k-1 and the process variable vector Y.sub.k from the supervisor 32 to receive and simulate control 64 to update with these vectors. Likewise, the process model receives 66 the control signal vector U.sub.k-1 and the process variable vector Y.sub.k from the supervisor 32 and determines the new process state vector .psi..sub.k from these values. In this way, the process model is 66updated periodically, for example after each scan of the process control system, to reflect the actual operation of the process plant.
[0061] It therefore goes without saying that the simulation system 52 in tracking mode continuously follows or monitors the process operation by receiving supervisor data from the supervisor 32 receives, and it updates its state parameters to reflect the current state, not just of the process control network. 54 but the properties of the process 56 even by recalculating or updating the state of the process model 66 This keeps the simulation system 52 at any time during the tracking mode with the operation of the process control system 50 and the process plant synchronized, thereby the simulation system 52is immediately available at any time to perform simulations with a high degree of reliability.
[0062] To perform a specific predictive simulation, the remote simulation system can be used. 52 can be put into a prediction mode at any time to perform an actual simulation of the process control system. 50 to be carried out beyond a specific time horizon. The actual simulation can take many forms or can simulate many different types of control / process activities. In all cases, the simulation system operates 52 however, in parallel with the actual control system 50 . In particular, during the forecast mode, the simulation system stops. 52 so that, the control network image 64 and the process model 66 with signals from the actual process plant, which are transmitted via the supervisor 32Instead of updating the received data, the system performs a prediction based on the most recent set of state variables {circumflex over (X)} developed during tracking mode. In other words, during prediction mode, the simulated process variables are updated based on the process model. 66 using the simulated process control network 64 and the one connected to the remote simulation system 52 The provided setpoint values R are calculated in a closed loop. In this case, the remote simulation system 52coupled with a user interface, this allows a customer, if desired, to modify one or more parameters of the simulated control system or process, thereby simulating the process's response to a control change or a change in process dynamics. Such a change could be, for example, a change to one or more setpoints R, a change to a measured process variable, a change to the control routine itself, a change to a disturbance variable in the process, etc.
[0063] If desired, the remote simulation system can 52While in prediction mode, it can execute one of three sub-modes, including real-time sub-mode, fast-forward sub-mode, and slow-motion sub-mode. In real-time sub-mode, the simulation of the process variables continues in real time (i.e., at the same speed or sampling rate as the actual control system). 50 In a power plant control system application, this mode can be used by plant personnel to test proposed actions or inputs to the control system. In this scenario, the proposed action is applied to the (simulated) plant, and the simulated response is monitored to ensure that the action has the desired effect and / or does not result in any abnormal conditions.
[0064] In fast forward mode, the simulated process variables are calculated faster than in real time (i.e., faster than the control sampling rate). This mode can be used to observe the predicted response to the process variables over a future time horizon, in order to test the plant's response to a new control setpoint, a specific orientation, different operator inputs, or any other change in the control routine, etc. For example, the predicted values and resulting trends of one or more process variables can be displayed for the next ten minutes or over another prediction horizon, such as one associated with the process returning to steady-state operation.
[0065] In slow-motion submode, the operator can view the operation of the simulated controls at a slower rate than the actual process operating time or sampling rate. This submode can be used, for example, in fast processes to give the operator more time to observe and analyze the process's operation in response to a planned change. Furthermore, this submode can be advantageous when the simulation system 52 It is used to conduct training sessions.
[0066] During operation, the integrated and synchronized remote simulation system uses both tracking and prediction modes alternately to perform simulations and predictions. In particular, the remote simulation system 52 during the periods in which the simulation system 52 operating in tracking mode, continuously through the supervisor application 32with the overall status information from the actual control system 50 Updated. This status data can be updated periodically by the control system, as described above, using the signal addresses stored as part of the configuration system. 50 about the supervisor 32 to the remote simulation system 52 be communicated.
[0067] In one mode, the remote simulation system receives 52 a new set of status data from the process control system via the supervisor 32 during each sampling of the controls in the process control system 50 or in response to it. In other words, the status data in the process control system 50 can be done in the supervisor after each control action or control scan. 32 collected and fed into the simulation system 52The supervisor data can be sent to the simulation system using appropriate communication procedures. 52 They can be addressed or sent individually, or sent as a large data set to reduce communication overhead in the process control system. Naturally, the remote simulation system can also be used. 52 Instead, the control state information is received at a different rate, for example periodically, such as after every second sample, every fifth sample, etc. This is how the actual control system operates. 50 and the remote simulation system 52 synchronously, while the remote simulation system 52 is in tracking mode, which results in the overall state of the remote simulation system being monitored. 52 at each time step belonging to the periodic rate by the supervisor 32is updated so that it matches the actual control system 50 exactly matches.
[0068] An operator or other customer can access the remote simulation system 52 However, it can be switched to prediction mode at any time. While operating in this mode, real-time mode can be selected as a sub-mode to, for example, implement an evaluation of the impact of a setpoint or a change in a setting parameter, to assess the effect of a control program change on the process, to evaluate a change in a process disturbance variable, etc. This function provides the operator with the ability to play through "what-if" scenarios. In the case of evaluating a setpoint change, the setpoint change can be displayed on the remote simulation system. 52 carried out or via a user interface identical to that of the control system 50The associated user interface system, which would enable or permit such a change, or is substantially similar to it, is provided to the remote simulation system. In this way, the operation of the remote simulation system appears 52 It looks as if the operator is controlling the actual control system 50 operate and also acts as such, thereby enabling the remote simulation system 52 It is easier to use and understand. Once the setpoint change is on the remote simulation system 52 Once a change has been implemented, the simulated process is monitored to ensure that the change has the desired or expected effect. This capability serves to eliminate human error in actual plant operation.
[0069] In the event of a change in the control program, the program change can be made again using a configuration application, which is the same configuration application used to make the programming change to the process control system. 50 to be carried out independently, or to be identical or similar, appears to be performed. Therefore, the remote simulation system can be used. 52 again include a whole set of supporting applications, such as user interface applications, configuration applications, trend applications, data processing or analysis applications, etc., which are necessary for the actual process control system. 50 are provided or connected to it. If the control routine change was made on the simulated control network, the simulated process will in any case be on the remote simulation system. 52It is monitored to ensure that the desired effect is achieved and no abnormal operating situations occur. Any human interaction with the remote simulation system is prohibited. 52 , the actions on the actual control system 50 to mimic, can be done with the remote simulation system 52 can be performed in real-time mode or in slow-motion sub-mode when the remote simulation system 52 for example, it is used to conduct training.
[0070] If desired, however, the effect of a longer time horizon can be observed by using the remote simulation system. 52 The simulation is set to fast-forward submode. Additionally, the operator can switch between different submodes during the simulation. For example, the operator can access the remote simulation system. 52The system switches to fast-forward mode as soon as the interaction (e.g., setpoint change or control program modification) is performed via the operator interface. In fast-forward mode, the simulation system's state evolves at a speed faster than the real-time sampling rate or the operating rate of the process control network. 56 Naturally, the fast forward and slow motion sub-modes can be changed by altering the sampling or operating period of the controllers and control programs within the simulated process control network. 64 can be implemented. If desired, the simulated process variables can also be collected, stored, and then applied to associated historical trends at the end of the fast-forward execution, instead of or in addition to displaying these variables to the operator, engineer, and maintenance personnel interfaces.
[0071] In some cases, the remote simulation system can 52 be operated in such a way that all `N` time steps of the control system are automatically 50 A rapid expedited execution cycle is executed at 50, where `N` can be defined by the operator if desired. In this case, the remote simulation system operates. 52 up to the 'Nth' time step in tracking mode, at which point the remote simulation system 52For a single execution of a fast-forward operation up to a selected time horizon, the system is automatically switched to a prediction mode. At the end of the fast-forward simulation, the simulator displays can be updated with the predicted process variables over the configured time horizon and / or other information, such as any simulated alarms or warnings generated during the fast-forward operation, etc. At the end of this fast-forward operation, the remote simulation system returns to its previous state. 52 automatically returns to tracking mode to view the process model 66 and the simulated control network 64 with new state variables from the supervisor 32, which monitors the actual process. This automatic operating state can be used to update trend indicators showing predicted trajectories of process variables of interest. This is particularly helpful, for example, in the real-time integration of control functions and simulation during the actual operation of a power plant, as well as in implementing an automated procedure that could potentially eliminate human-caused process disturbances and plant failures. Furthermore, in this mode, the impact of operator actions on plant emissions and thermodynamic / process efficiency can be observed.
[0072] If desired, some modules of the remote simulation system can also be used. 52 It may be distributed throughout the process plant in various devices. For example, the simulated process control network can be... 64in each control device in which the actual control module is located 29 and 30 It may contain a simulation control module (i.e., a copy of an actual control module). In this case, the process model can 66 This includes a submodel belonging to a specific section of the process plant (such as a particular process loop), which is located in the same process control device and communicates with the corresponding simulation control model. Here, the simulation control module and the process submodel work together to perform a simulation on a loop-by-loop basis across multiple different control devices. In this case, the simulation control modules can communicate using conventional interface routines, such as the supervisor. 32 , in communication, which is in the workstations 20 and 22They may be stored to display or represent the operation of the simulation control modules during prediction mode. The supervisor 32 The data can then be sent to the network cloud for further processing. 48 communicate. Similarly, the simulated control modules can 64 and the process models 66 in the various devices in the system directly from the associated control modules 29 and 30 process status information is received from the actual control network or from another update module located in the same or a different device.
[0073] It goes without saying that the remote simulation system 52, as described herein, when used in a power plant as well as in other types of installations, among other things (1) provides real-time integration of simulation and control functions during the actual operation of a power plant, (2) provides real-time forecasting of emissions from a power plant over a finite future time horizon, (3) provides a mechanism for future pricing by the market, (4) enhances the efficiency of plant operating personnel by providing a real-time forecasting function for each major process variable associated with the plant in response to the closed-loop action of the control system, (5) provides a real-time indication of the onset of an abnormal situation, (6) enables the restoration of initial simulator conditions to a specific point in time, so that operating dynamics of the power plant from that point in time,which corresponds to the original condition time step, can be “replayed” forward (which can be used to analyze previous plant operation), (7) enables operations and / or engineering personnel to evaluate the impact of a setpoint, setting parameter, configuration change or programming on the simulator before it is applied to the actual plant, and (8) reduces plant failures due to operator actions / omissions by providing a forecast of the main process variables for each time step over a finite future time horizon.
[0074] Furthermore, it is understood that the remote simulation system described herein 52This approach incorporates the novel concept of distributing simulation functions as an integral part of the overall control functions. In this approach, simulation is used as an extension of the control functions to provide predictive functions for the process variables. The requirements and constraints associated with distributing the simulation are identical to those of the corresponding control functions.
[0075] Fig. Figure 7 shows one implementation of the remote simulation system described herein. 52 In particular, the remote simulation system includes 52 out of Fig. 7 the simulated process control network 64 , which is related to the process model 66 is in communication link. As in Fig. However, what is shown in 7 is an update module. 70 Communicate with the supervisor application using any desired communication structure 32, the data from the actual process control network 54 receives, coupled to receive the process control network state variables, including the control state variable X and the associated process input and output state variables, such as the control signals U and the process variables Y.
[0076] If desired, the control state variables X can be received at any periodic rate, which can be the same rate as the periodic rate at which the state variables U and Y are received from the process, or a different rate. Furthermore, the control state variables X can be received at a periodic rate or updated only when one or more of these variables in the process control system is actually being updated. 50 a change was made, which was indicated by the change detector. 82is determined. In another embodiment, the control state variables X can be transmitted or communicated virtually immediately upon receipt. In yet another embodiment, the control state variables X can be collected until a threshold is exceeded and then communicated. This threshold can be a time, a quantity of data, or any other useful limit.
[0077] The update module 70 can be located in the network cloud 48 located in the same or a different device as / than the simulated process control network 64 (or part of it) and the process model 66 (or part of it). The update module 70It can operate during tracking mode to receive the state variables X, U, and Y, calculate the state vector .psi..sub.k, and assign the vectors .theta. and .psi..sub.k to the corresponding parts of the simulated control network. 64 and the process model 66 to provide.
[0078] The remote simulation system 52 It also includes a mode control module. 72 , which enables the operation of the remote simulation system 52 It controls one of two modes. In particular, the update module receives... 72 In a first mode, the first and second state variables are periodically updated, and the simulated process control network is updated. 64 and the process model 66 using the developed state variables .theta. and .psi..sub.k. In a second mode, the simulated process control network operates 64using the one or more simulated process variables to generate the one or more simulated control signal(s), and the process model 66 The mode control module uses one or more simulated control signals to generate one or more simulated process variables (U^ or Y^). 72 can the simulated process control network 64 operate it in the second mode to match the operating speed of the process control network. 54 associated real-time speed, or at a speed that is faster or slower than the operating or real-time speed of the process control network. 54 , to execute. Furthermore, the mode control module 72 in one embodiment the simulated process control network 64operate it in the second mode to run it at a speed faster than the operating speed of the process control network. 54 to execute in order to generate a predicted process variable over a time horizon.
[0079] Furthermore, a user interface application can 74 with the update module 70 , the mode control module 72 , the simulated control network 64 and the process model 66 They must be in communication to perform user interface and display actions. In this case, the user interface application can 74 receive the simulated process variables and / or the simulated control signals and / or provide them to a customer, enabling the customer to adjust parameters within the simulated process control network. 64to change, such as one or more setpoint(s), a control routine, etc., or one or more parameters within the process model 66 to perform any desired simulation activity. Furthermore, the user interface application can 74 in conjunction with the mode control module 72 work to periodically and automatically update the remote simulation system 52 to operate it in the second mode, in order to run it at a speed faster than the operating speed of the process control network. 54 to generate a predicted process variable at a given time horizon and to display the predicted process variable (or any other simulated variable or information) to a customer at that time horizon. Naturally, the user interface can also perform other desired actions.
[0080] In some implementations, the user interface application 74 working in the network cloud and the streamlined client on the workstation 20 , 22 in the plant 10 The user interface can be displayed to the customer. The user interface can be a web page created by the user interface application. 74 communication takes place in the network cloud to a web browser that is running on a local computer device 20 , 22 is executed. In such an arrangement, the computationally intensive application of the simulated control network can be carried out. 64 It can be run in the network cloud. Similarly, the user interface application can be... 74It can be run in the network cloud, and the user interface displayed on the client can be generated by a script- or HTML-based application that communicates back and forth with the network cloud. In other embodiments, the user interface application can be 74 It can be run on a stripped-down client in the system, and data can be accessed, for example, via the supervisor. 32 from the simulated control network 64 communicated to the user interface application.
[0081] A memory module 80 can be part of the simulation system 52 It must be provided to store simulation data. The storage module 80 can be used with the update module 70 , the actual process 54 , the simulated control network 64 or the remote simulation system 52 and process model 66 are in communication. Fig. 6 is the memory module 80 as inside the update module 70 While depicted as being located there, this is not necessarily the case. In other embodiments, the memory module is 80 physically from the update module 70 It is separate, but maintains a communication link with it. In another embodiment, the memory module can 80 with the simulated control network 64 and the process model 66 , the status data from the update module 70 received, which are all in the memory module 80 can be stored, are in communication.
[0082] The advantages of saving simulation data are numerous and far-reaching. Saving simulation data allows actions of the simulation system to be reviewed, replayed, analyzed, and further investigated. Since the storage module 80Since it can reside in the network cloud, data for numerous simulation systems across numerous process plants can be stored and analyzed, creating a large dataset that can provide additional insights and be more useful to customers. Furthermore, the storage can accommodate significantly more additional data related to the actual control network. 52 , of the actual process 56 and include the prediction of process-related data.
[0083] The simulation data can be supervisor data and data from the simulation system. 52The generated data must be sufficient to allow the simulation to be replayed at a future time. For example, in some situations, the simulation algorithms may be known, and it may only be necessary to store weights of specific variables and some process control network variables to enable future re-runs. In other implementations, the simulation algorithms may have been modified to better mimic the process, and the modified algorithms can be stored as part of the simulation data, as they may be needed to recreate the simulation at a future time. Some example data might include application properties, configurations, user display data, input / output configuration data, and so on.
[0084] The data can be stored in a variety of ways. In some implementations, the data is stored in a database. The advantage of a database can be that the data can be retrieved more easily. For example, a current situation might be similar to a past one. The key variables can be retrieved, and similar situations in the past can be reviewed to provide a recommendation regarding a decision in light of the current situation. The data can also be stored in other formats. It can be stored as a simple file, an XML file, comma-separated values, files that can be read by conventional word processors, spreadsheets, or other databases.
[0085] One or more storage devices can communicate with the remote simulation application to store simulation data for future analysis. Generally speaking, the storage devices can be any type of storage device currently known or developed in the future, such as rotating magnetic disks, optical drives, semiconductor storage devices, or a combination of some or all of these. The storage devices can be configured in any way or format, such as RAID, or in a distributed manner, such as using Hulabaloo, etc.
[0086] Simulation data can be useful in many different ways. One aspect is that it can be used to provide guidelines for current or future operations or simulations. Given the substantial amount of data available from a wide variety of plant processes and simulations, many situations can arise that may occur, are expected to occur, or may have occurred in the past. These past situations serve to determine whether a previous scenario is similar enough to the current or proposed situation to provide guidelines on how the process can continue or how the process can be maintained and properly simulated.
[0087] The simulation data can also be used for training purposes, and training scenarios can be played sequentially for new users. A specific user's training responses can be recorded, their strengths and weaknesses can be identified, and the training can be adjusted to address weaknesses. Similarly, the training can be tailored to new equipment that has been added, new parts of the plant that have been added, and so on. Trainees can also be trained to operate additional equipment or additional aspects of the same plant. The training can be monitored by a higher authority, and in some situations, certifications can be earned upon successful completion of a training sequence.
[0088] The simulation data can also be subjected to data analysis. Data analysis can examine the simulation data and search for patterns or information that may be useful in verifying process control or simulation applications. For example, if a small number of valves in a single simulation have an unexpectedly high failure rate, the fault might go unnoticed. However, the availability of additional data can reveal the failure rate of that small number of valves.
[0089] The data analysis can also be used to improve the simulation application. If the data from the simulation system 52 and the data from the process control system 50 For example, if the values consistently deviate by a certain amount or percentage, it is likely that the simulation algorithm should be adjusted to better reflect the actual process. 56to better replicate. Since the network cloud can run simulation applications for numerous systems, even more data becomes available for verification, resulting in even more reliable data and better simulations for all network cloud customers. 48 leads.
[0090] One advantage of the fact that the remote simulation system 52 The challenge, especially when operated by a third party, is how easily another part of the plant process can be added to the simulation. Previously, the simulation ran on a local workstation, and the additional part of the plant would have had to be added there as well. Adding this extra part isn't as simple as ticking a box. The elements of the additional part must be added, connected, and replicated individually, which is a complex undertaking.
[0091] In the disclosed system, the task of setting up an additional part of the plant is assigned to operators in the network cloud. 48 transferred. Since the operators of the network cloud 48 Those with extensive experience in adding new elements and system components only need to provide a minimum amount of information to the network cloud operators. 48 Communication is possible. For example, in some cases only a drawing of the system elements is required for operators to access the network cloud. 48 They can add the additional part to the system. Naturally, this includes deploying operators in the network cloud. 48 This will incur costs. In some cases, the new element can be added automatically.
[0092] Additionally, the extra plant section can be added in real time or alongside the existing simulation. Previously, adding an extra plant section would have required shutting down the simulation on the local workstation. Furthermore, the extra section would have needed to be tested to ensure it was correctly configured within the existing plant. With the current system, the extra plant section can be configured and tested separately and then seamlessly added to the current plant simulation.
[0093] Furthermore, the network-cloud-based simulation system simplifies 52Other complex tasks significantly. As another example, in the past it was very difficult to add third-party technology. As previously explained, setup would have had to take place on the local workstation where the simulation was running. Furthermore, a customer or external consultant would have had to spend a considerable amount of time either virtualizing the third-party technology or integrating it into the simulation system already in place. 52 to incorporate existing technology. Regardless of how the task of virtualizing third-party software is approached, the likelihood that a customer has experience with third-party software virtualization is very low. Since the simulation system 52Working from a central cloud location, a team of technicians with extensive experience in virtualizing third-party software may be available. In some situations, the third-party software may already be virtualized, making the virtualization process much simpler and more efficient.
[0094] Another advantage of the fact that the simulation system 52 The advantage of a network cloud lies in the fact that the necessary infrastructure already exists. Instead of the customer having to purchase a suitable computer or computers and associated software, the computer and software can be provided by third parties. The cloud can be public, such as the internet, or a private network owned either by the simulation provider or a third party. The customer only needs a computer to access the supervisor. 32to operate a "slimmed-down" application that is not as processor-intensive as an on-site simulation application.
[0095] Another advantage of using the network cloud 48 for the simulation system 52 Performance is key. Even if the simulation were run on a remote workstation, that workstation and its associated processors would be specifically dedicated to the simulation. The processor can experience periods of high activity followed by periods of low activity. By using a network cloud 48 Numerous processes can use the same networked computing devices, and the overall utilization of processors and memory will likely be higher. Furthermore, more computing power may be available should more complex simulation needs arise.
[0096] Generally speaking, a cloud-based network of computing devices offers additional advantages. This is because the cloud consists of a multitude of computing devices. 49 existing, another computer device can be used. 49 continue running the applications if a computer device 49fails. Similarly, the costs for proper HVAC systems, emergency power supplies, computer equipment rooms, trained operators, etc., can be spread across numerous users, thus keeping the cost per user low. Cloud-based networks can be accessed from virtually anywhere, making them more accessible (assuming access is restricted by appropriate security measures to those with proper credentials). Cloud-based networks offer more storage at a reduced cost than local storage because bulk data storage is cheaper, and if more storage is needed, it is highly likely that only a higher fee will be required, rather than purchasing and installing more equipment. Furthermore, data can be backed up or copied at the time of creation, simplifying data backup.Updating software is also easy, as updates are installed in the background by experienced personnel, resulting in very little or no downtime for the user. Similarly, new modules can be added to or installed on the cloud-based computing system with little to no downtime for the user. Of course, these are just some of the obvious advantages, as there may be many more.
[0097] The operation of the remote simulation 52 This can open up new possibilities for processes, services, and equipment that can be offered to customers. In the past, simulation systems were run on local workstations and were tailored to the needs of the specific plant or process. By moving the simulation to a remote network, 48The local workstation may no longer be needed. A streamlined supervisor application. 32 presents the remote network 48 the relevant process and control data and the supervisor application 32 can be "slimmed down" enough to rely on existing equipment 20 , 22 to be executed. This allows the remote simulation application to be run. 52 and equipment open up new business opportunities.
[0098] For example, the remote application 52The service is offered to customers on a subscription basis. The subscription price can be based on several factors, such as the size of the system, the amount of data it contains, and the scope of analysis. Furthermore, setup may incur upfront costs, depending on the complexity of the process being simulated. Additional system components can be added for an extra charge. The network cloud could also host additional applications related to the system.
[0099] Additional services may be included as part of the subscription or available for an additional fee. These additional services may include predicting proposed changes to a process system or adding additional proposed components to a system. 10 to a remote simulation system 52This includes providing specialized data analysis, providing in-depth data analysis, providing data analysis for proposed additions or changes to a process, comparing a process with other processes, reviewing the process for potential performance improvements, etc.
[0100] Fig. 8 can be an exemplary procedure for providing remote simulation services 52 represent in a block 800 A plant description can be received. The plant description can be a simple printed diagram or consist of numerous diagrams depicting processes within a plant, field devices, connections, physical locations, connection blocks, etc. One of the many advantages of the simulation software is that 52 at a centralized, cloud-based location 48The reason is that the software required for reading and analyzing plant diagrams is used many times by many cloud customers. 48 can be used, thereby reducing costs for the numerous customers of the cloud-based simulation system. 52 The resolution can be reduced. Of course, in some embodiments the diagrams may be so coarse or so dense that human intervention is required to convert them into something that can be interpreted by the remote simulation system. 52 can be understood.
[0101] In block 810The complexity of the plant to be simulated can be determined. The complexity and the ability to simulate plants and processes vary from plant to plant. In some plants, including large ones, the complexity may be lower, making the simulation easier. On the other hand, some plants, including small plants with multiple controllers and several interconnected processes, can be extremely complex. Such plants can be more difficult to simulate.
[0102] In some embodiments, a formula can be used to assign a rating or value to plant complexity. The complexity rating can be an attempt to determine an objective value for plant complexity, allowing the complexity of one plant to be compared with that of another using a graded plant complexity rating. For example, if a plant has ten valves and one process, the complexity rating could be... 20 The values are as follows: each valve counts as 1 and each controller as 10. Another example is the scoring... 55The complexity level is calculated as follows if the system has five valves and five controls (5 × 1 = 5 for the valves and 5 × 10 for the controls). In other configurations, an experienced reviewer can simply check the system representations in the central simulation cloud and determine a complexity level based on their experience. The weighting of each element can vary based on the material, the age of the system, the distance the material has to travel, etc. Of course, other methods of calculating a complexity rating are also possible and are being considered.
[0103] In block 820The simulation complexity can be determined. Simulation complexity can indicate the complexity of the process(s) or control routine(s) used in the plant that may need to be added to the simulation. For example, if the control routine requires numerous valves to be opened in a precise sequence based on field device measurements, the simulation can be considered complex. Conversely, if a control routine or process action is simple and involves few actions, the simulation can similarly be considered less complex.
[0104] The simulation can be performed by checking the plant representation from the steps. 800 and 810The process data can be determined from the plant representation, or separate process data describing the process can be transmitted. In another embodiment, the process can be determined using a combination of plant representation and process system-related intelligence. For example, if a valve is described as opening at 220 degrees Fahrenheit, the process logic might indicate that part of the process heats a substance to 220 degrees Fahrenheit. On the other hand, the valve might be described as an outlet valve opening at 220 degrees Fahrenheit, which could indicate that the process heats a substance to less than 220 degrees Fahrenheit. Similarly, a timer might indicate that part of the process occurs within a specific time period.
[0105] In some implementations, a simulation complexity score can be assigned to the simulation. The simulation complexity score can be an attempt to objectively compare the complexity of one simulation with another using a tiered rating system. The complexity score can be based on the process operations that occur in the process. For example, if four measurements need to be taken and five valve operations performed, the simulation complexity score could be the number of measurements multiplied by a measurement weighting and the number of valves multiplied by a valve weighting. The weightings can vary depending on the specific material, the risk of the process, the value of the material, and so on. Of course, other methods for calculating the simulation complexity score are possible and are under consideration.
[0106] In block 830can the minimum performance levels required by the cloud-based simulation service 52 The minimum performance levels that can be offered can be determined. These minimum levels can depend on a variety of factors and are negotiable. In some configurations, plant operators can specify a minimum level of performance required. This minimum level of performance may be the level of performance previously available to the customer, as determined by the simulation on an on-site or in-plant basis. 10 was operated at the workstation located there.
[0107] In other configurations, the minimum level of service can be set by a provider. This allows the cloud-based simulation service to... 52For example, if it makes economic sense, it might be expected that each customer uses and pays for a minimum level of shared services. Otherwise, the simulation operator would have no incentive to run the simulation. 52 to continue operating and improving it. Furthermore, the operator may know that a minimum level of performance is required for the simulation to function. 52 can operate efficiently. For example, customers may not be able to fully grasp the importance of some services or recognize that some services depend on others. For instance, simulations cannot be replayed if they are not saved. Therefore, sufficient storage capacity may be required to enable simulation replay.
[0108] In additional embodiments, the complexity of the plant and the complexity of the simulation can be re-evaluated to determine a minimum level of performance that should be provided. For example, a complex plant and a complex simulation might have a higher level of proposed performance, while a less complex plant and a less complex simulation might have a lower level of proposed performance. In some embodiments, the simulation complexity rating and the plant complexity rating can be used as part of a formula to determine the minimum level of performance. For example, a high simulation complexity rating and a high simulation complexity rating might result in a higher proposed minimum level of performance.
[0109] Furthermore, economic efficiency can play a role in determining a recommended level of performance. For example, if a process produces an extremely valuable substance, greater efforts may be made to provide a high level of performance to ensure that this extremely valuable substance is not destroyed by a faulty process, which could be identified through simulations. 52 This could have been predicted. If a hazardous substance is generated in a plant process, a higher level of performance can similarly be recommended by running more simulations. 52 than in other, less dangerous situations, dangerous situations can be avoided.
[0110] In some embodiments, the proposed minimum performance levels can be communicated to the customer. The customer may have the option to approve the level of performance, adjust the level of performance, approve the level of performance again, and so on. Furthermore, a description of how the minimum level of performance was determined and what other performance levels are available may be provided. In other embodiments, government safety regulations may play a role and mandate certain performance levels; these mandated performance levels may also be communicated to the customer.
[0111] In block 840A price can be calculated for specific minimum services. This price can be determined in a variety of ways. In some configurations, the price can have a minimum value regardless of the minimum level of service. In this way, it may be economically advantageous for the operator to bear the setup and maintenance costs incurred to ensure the service is available and functional. In other configurations, the price can depend on the minimum services to be provided. As mentioned previously, the minimum services can vary for each plant and process. Therefore, the minimum price can differ depending on the specific minimum services required.
[0112] In some embodiments, the simulation complexity rating and the plant complexity rating can be used as part of a formula to determine the minimum level of performance. For example, a high simulation complexity rating and a high simulation complexity rating might lead to a higher proposed minimum level of performance and a higher price. By using the ratings, the price can be automatically calculated using an algorithm with the ratings as inputs and the price as output. In another embodiment, each of the minimum performance levels can have a price, and the prices of each minimum performance level are added together to obtain a total price. Furthermore, a discount can be automatically offered depending on the size of the customer and previous business relationships.
[0113] Furthermore, price determination can be divided into levels or layers. For example, there could be three layers for price determination, and these layers could relate to the complexity of the system and the complexity of the simulation, although this example is not limited. As mentioned earlier, an algorithm or a lookup table can be used to determine a price proposal. The algorithm can similarly be used to classify a price into one of the layers. In this way, a simplified price determination process can be offered to the customer.
[0114] In some implementations, pricing can be based on past pricing for the customer or system, or for similar customers or systems. Analyzing past usage allows for the determination of a customer's / system's cost-benefit ratio. For example, some customers / systems may use more simulation services than expected during a given period, or the simulations may be more demanding than anticipated (this example is not limited). It may be beneficial to increase prices for these customers in the future. Similarly, some customers / systems may not use as much simulation time as expected, or the simulations may not be as computationally intensive as anticipated. In these cases, it may be beneficial to charge these customers / systems less in the future. Pricing can also be based on similar customers / systems.Experience with customers / systems that have similar systems or similar simulations can be helpful in determining the appropriate price for a customer / system.
[0115] In virtually all cases, using Cloud-48-based services is expected to be more cost-effective than running the simulation locally on a workstation. Numerous costs can be avoided by running the simulation in the cloud. For example, individual simulation systems often require a significant capital investment in equipment, including hardware, software, the cost of replicating a plant, the cost of replicating a process, and so on. Furthermore, all this equipment requires physical space. There are also ongoing costs, such as equipment and software maintenance, updates, and support. Personnel are also needed to manage all of these matters. Cost estimates range from $500,000 for a small system to $2,000,000 for a larger one.
[0116] Pricing can be licensed in a variety of ways for a fee. For example, the license can relate to specific modules and be charged per system, per unit, per user, or a combination of these pricing elements. The price can cover training on how to use the system, the services themselves, and simulator maintenance. 32 and the provision of personnel for updating and maintaining the simulator 32 This includes the following: Licenses may be transferable to a limited extent, for example, within the same institution or subsidiary, depending on the situation and the relationship. License details and expiration dates may be displayed on screens, and reminder messages may appear when the expiration date is approaching.
[0117] In block 850The price determined for the minimum level of services for a customer or facility can be communicated to the customer. This communication can take any suitable form sufficient to initiate the process of reaching a binding agreement. For example, the customer and supplier typically communicate by email, and thus an email containing the price and proposed services may be appropriate. In other situations, a formal letter with detailed attachments describing the fees and services may be appropriate. Naturally, several forms of communication can be used and be appropriate. At a certain point, an effort will be made to formulate the proposed services and fees as a legally binding agreement.
[0118] In some configurations, optional services that might be beneficial to the customer or plant can be determined by the provider. The minimum services can provide a helpful starting point for simulation services, but additional services can be even more helpful to a customer or plant. Some customers or plants may not be aware that such additional services exist. For example, some customers or plants may know that simulations of past events at the plant can be used for training purposes. However, an additional option might be to compare the simulations from the current plant and process simulations with other plant and process simulations operated by others. Other plant and process simulations might provide new ideas and approaches that could be useful to the customer.Furthermore, local simulations may have been slower and offered fewer options for recreating plants and simulations, and the cloud-based simulation system may have more computing capacity to handle more complex plants and simulations that the customer had not considered.
[0119] The process of determining whether optional services should be offered (and which ones) can be automated. Plant diagrams and process simulations can be automatically reviewed to determine if additional services would be beneficial. For example, older valves in the plant can be automatically identified from the plant diagram, and a recommendation can be made to replace them with newer, intelligent valves. A simulation can be offered to see the impact of the newer intelligent valves on the plant's performance, control, and output. As another example, the current plant can be compared with previously reviewed plants, and if increased performance was found in the previously reviewed plant, an efficiency study can be offered for the current plant.
[0120] Once the additional services have been identified, prices for the optional services can be calculated. Like the prices for the minimum level of services, the prices for additional services can be determined in a variety of ways. At a higher level, the price of the additional tasks can relate to the complexity of the task in light of the specific plant and simulation. In some embodiments, the prices can be set according to the complexity of the additional tasks, which relates to the complexity of the plant, the complexity of the simulation, and the complexity of the proposed task. Evaluation can be used to assist in pricing the additional tasks.For example, the complexity of the system to be analyzed can be multiplied by a factor related to the complexity of the proposed task, and the resulting score can be used to determine a price. Similarly, the score can be used to classify the proposed task into a level, and the price can be based on that level.
[0121] The proposed price may be based on past experience or a forecast of the amount of processor time, memory usage, setup time, operator time required in the cloud, etc., that may be necessary. In some additional implementations, the price may relate to the potential benefit to the customer. For example, if an additional analytics module saves a customer a significant amount of money by modifying a process or improving an output, the price may be based on a percentage of the potential savings. Power plant example
[0122] For example, a power plant may have a simulation system that runs on a local workstation. 20 , 22 based on the simulation system, which can be a workstation. 20 , 22 require a system that is considerably more complex than a conventional PC. The workstation 20 , 22It may also require a significant amount of storage capacity to transfer all the data from the system. 10 to capture and save the data used to create a simulation and replay it at a later time. Furthermore, there can be different operators who operate the system. 10 Replicating and maintaining the simulation system. The workstation, storage, and associated offices for the operators require office space and incur associated expenses.
[0123] Should the power plant decide to use a cloud-based network simulation 52Switching between the two would require several steps. A plant description must be provided to the network cloud operator. In some embodiments, if a plant simulation already exists on a local workstation, data representing the plant description can be transmitted electronically. In other embodiments, printouts and images of the plant can be transmitted to the network cloud operator. In some embodiments, the images can be scanned, and in other examples, the images can be handed to the cloud service operator manually.
[0124] The plant description can be analyzed to determine the plant's complexity level. At a high level, the complexity level can refer to how difficult it is to electronically replicate and manipulate the plant description, which is highly likely related to the physical complexity of the plant itself. A more complex physical plant may be more expensive, and a less complex physical plant may be cheaper. The analysis can be performed in a variety of ways. In some implementations, the analysis can be automated, for example, when electronic data representing the plant description is transmitted, such as from a standalone plant simulation.
[0125] In other embodiments, the plant layout can be sent as an electronic image (or converted from an image to one), and the electronic image can be analyzed to determine the various physical elements in the plant. For example, a smart valve might have a standard representation, and the plant image can be analyzed to determine whether smart valves are present in the plant image. Other plant elements might also have standard representations, and the images can be checked against these standard representations.
[0126] Furthermore, intelligence can be used to interpret diagrams that are not immediately recognizable. For example, if a controller has an analog-to-digital converter, it is likely that the input to the converter is an analog signal, and that elements that generate analog signals can be searched for to see if there is a match with the element in the plant diagram. Similar uses of intelligence and prediction for identifying elements in the plant diagram are possible and are being considered.
[0127] The complexity of the plant simulation can also be determined from the plant description. Again, the plant simulation can be determined by receiving an electronic version of an existing representation of the plant simulation, such as a plant simulation running on a local workstation. In another case, the plant simulation can be determined by analyzing representations of the plant operation to be simulated. In some cases, a trained operator may be required to review and further refine the proposed simulation.The complexity of the simulation can reflect the difficulty of the simulated process, as some processes are relatively simple (a gas-fired power plant where a valve opens when a single field device measures a temperature above a threshold of 212 degrees Fahrenheit when steam is formed), while others can be complex (a nuclear power plant with many valves, many temperatures, many pressure measurements, highly hazardous products, etc.). As mentioned earlier, simulation complexity can be represented by a calculated value.
[0128] By checking the plant complexity and the simulation complexity, a minimum level of simulation performance can be determined for the power plant. 10 The performance level can be determined based on the plant complexity and the simulation complexity of the power plant. 10The assessment is based on several factors. A small power plant, for example, which is rarely indispensable and has a conventional and proven design, may have a low simulation power level. Conversely, a nuclear power plant on which a significant number of people depend and which has a new and untested design poses a greater risk if the plant is shut down. Therefore, the incentive to ensure the safe operation of the nuclear power plant may be extremely high, and the recommended power level may also be high.
[0129] The price of the proposed simulation service can be determined. At a higher level, the more complex the system, the higher the price. 10The more complex the simulation, the higher the price for the cloud-based simulation. Other simulations can be used for comparison to determine the price, or the price can be set based on a formula that incorporates the complexity of the physical plant and the complexity of the simulation. Naturally, a combination of factors can be used to determine the price, and the price is negotiable. In the power plant example, a gas-fired plant is likely to be less complex than a nuclear power plant, and therefore, simulating the gas-fired plant will likely be cheaper than simulating the nuclear power plant. Furthermore, the cost of both simulations will be significantly lower than the cost of a single simulation run on-site at the plant.
[0130] The proposed price can then be submitted to the power plant or power plant operator. Communication can take various forms; however, it should be noted that at a certain point, a service contract should be drawn up based on the price and the proposal. If contact with the power plant is via email, an email can be sent. If contact was made in person, a printed copy of the proposal, along with a personal presentation and demonstration of the proposed services, can be delivered. Of course, other methods of submitting the price are also possible and will be considered.
[0131] In addition to the price and minimum services, the proposal may include suggested additional services and associated costs. The respective appendix 10Simulations can be analyzed to determine whether additional performance improvements are beneficial for a given plant. This determination process can be automated. For example, it might be determined that a gas-fired power plant is similar to other gas-fired power plants, and these existing gas-fired plants may have already been reviewed (this example is not exhaustive). The review of these plants may have revealed additional performance enhancements, such as faster responses to common problems, eliminating the need for costly plant shutdowns to resolve them. Logically, it would be beneficial to offer a review of the power plant's operations to determine whether these additional performance improvements would be feasible for the plant in question.Furthermore, costs for these additional services can be determined, which may depend on the time cloud technicians need to prepare the investigation, make any necessary changes to the system, perform simulations, etc. These prices and proposed services can also be communicated to the customer in any logical way.
[0132] It should be noted that, once implemented, any simulation software described herein can be stored in any computer-readable memory, such as on a magnetic disk, laserdisc, or other storage medium, in the RAM or ROM of a computer or processor, etc. Likewise, this software can be delivered to a user, process plant, or operator workstation using any known or desired delivery method, for example, on a computer-readable disk or other portable computer storage mechanism, or via a communication channel such as a telephone line, the internet, the World Wide Web, any other local or wide area network, etc. (where delivery is considered identical or interchangeable with making such software available via a portable storage medium).Furthermore, this software can be provided directly without modulation or encryption, or it can be modulated and / or encrypted using any suitable carrier wave modulation and / or encryption technique before being transmitted over a communication channel.
[0133] Although the present invention has been described with reference to specific examples which are intended to illustrate, not limit, the invention, it is apparent to those skilled in the art that modifications, additions, or omissions to the disclosed embodiments are possible without departing from the spirit and scope of the invention. Describing all possible embodiments would be impossible and, above all, impractical.
Claims
[1] Network cloud-based simulation system for simulating the operation of a process control network as connected within a process plant, wherein the simulation system comprises: a local supervisor module in the process plant, wherein the supervisor collects the following: a first state variable that specifies a current configuration of the process control network during operation of the process control network, and a second state variable that indicates whether a process is operating during operation of the process control network from the process plant; a remote simulation module that communicates with the supervisor module, wherein the remote simulation module comprises the following: a simulated process control network that uses one or more simulated process variable signal(s) to generate one or more simulated control signal(s) to a simulation of the operation of the process control network connected within the process plant; a process model that is in communication with the simulated process control network, which uses the simulated control signals to generate one or more simulated process variable signals; and an update module that communicates with the process control network to: periodically receive the first state variable, which indicates a current configuration of the process control network during the operation of the process control network, from the supervisor module, and in order to periodically receive the second state variable, which indicates whether the process is operating during the operation of the process control network, from the supervisor module. the update module periodically configures the simulated process control network with the first state variable and wherein the update module periodically uses the second state variable to update the process model; a storage module that communicates with the remote simulation module and stores the following: the first state variable at a given time; the second state variable at that time; and Simulation data, which represent the simulation of the operation of the process control network, enable the simulation to be replayed and the simulation data to be subjected to further analysis. [2] System according to claim 1, wherein the simulation data includes: a) Data representing the process model and the simulated process control network; and / or b) a prediction of the first state variable and a prediction of the second state variable. [3] System according to claim 1 or 2, wherein the remote simulation module comprises; a) a plurality of simulated process control networks; and / or b) a plurality of process model versions. [4] System according to any of the preceding claims, wherein additional plant processes can be added while the system is in operation. [5] System according to any of the preceding claims, wherein plant processes at different locations are replicated in the remote simulation module; and / or wherein technologies from third parties are replicated in the remote simulation module. [6] System according to one of the preceding claims, wherein the storage module enables the simulation data to be subjected to data analysis, the data analysis comprising checking the simulation data for a plurality of plants and generating improved simulations based on the simulation data, the first state variable and the second state variable for the plurality of plants. [7] Method for providing cloud-based simulation services for a process control plant in a network cloud, in particular in a simulation system according to one of the preceding claims, comprising: Receiving a plant description that describes the process control system and processes within the process control system; Determining the complexity of the process control system to be simulated by reviewing the system description; Determining the complexity of the simulation by checking the processes in the process control system; Determining minimum performance levels offered by the cloud-based simulation service by verifying the complexity of the process control system and the complexity of the simulation; Calculating a price for the specified minimum services; and Notification of the price determined for the minimum services. [8] Method according to claim 7, wherein determining the complexity of the process control system comprises: a) generating a physical plant assessment that evaluates the complexity of the process control plant on a physical process control plant complexity scale; and / or b) generating a process evaluation that assesses the complexity of the processes on a process complexity scale. [9] Method according to claim 7 or 8, wherein determining minimum performance levels offered by the cloud-based simulation service comprises: a) analyzing the physical asset valuation and the process evaluation to determine a minimum level of performance; b) the existence of a higher level of minimum performance for processes that are particularly dangerous or particularly costly. [10] Method according to any one of claims 7–9, wherein calculating a price for the specified minimum services includes comparing the price and complexity for other projects. [11] Method according to any one of claims 7–10, in particular according to claim 10, further comprising: Identifying additional services that may be useful based on plant and process complexity; Determining a price for the additional services; and Offering additional services for purchase. [12] Method for providing network cloud simulation services to a process plant for a fee, comprising: a local supervisor module in the process plant that collects the following: a first state variable that specifies a current configuration of a process control network during the operation of the process control network, and a second state variable that indicates the operation of a process during the operation of the process control network from the process plant; in a remote simulation module connected to the supervisor module, Executing a simulated process control network that uses one or more simulated process variable signal(s) to generate one or more simulated control signal(s) to the simulation of the operation of the process control network connected within the process plant; Executing a process model that is in communication with the simulated process control network, which uses the simulated control signals to generate one or more simulated process variable signal(s); Executing an update module that communicates with the process control network to: periodically receive the first state variable, which indicates a current configuration of the process control network during the operation of the process control network, from the supervisor module, and in order to periodically receive the second state variable, which indicates whether the process is operating during the operation of the process control network, from the supervisor module. the update module periodically configures the simulated process control network with the first state variable and where the update module periodically uses the second state variable to update the process model; Executing a memory module that is in communication link with the remote simulation module to save the following: the first state variable at a given time; the second state variable at that time; and Simulation data, which represent the simulation of the operation of the process control network, enabling the simulation to be replayed and the simulation data to be subjected to further analysis. [13] Method according to claim 12, wherein the simulation data comprise: a) Data representing the process model, the simulated process control network, a prediction of the first state variable, and a prediction of the second state variable; and / or b) a plurality of simulated process control networks and a plurality of process model versions. [14] Method according to claim 12 or 13, wherein the storage module enables the simulation data to be subjected to data analysis, the data analysis comprising checking the simulation data for a plurality of plants and generating improved simulations based on the simulation data, the first state variable and the second state variable for the plurality of plants. [15] Computer-readable medium containing instructions that implement the method according to any one of claims 7–14 when executed.
Citation Information
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synchronized real-time control and simulation within a process plant
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