This document discusses using OPC technology to support the study of advanced process control techniques. It presents a co-simulation environment integrating MATLAB, LabVIEW, and an OPC server to simulate a nonlinear boiler model in real-time over a TCP/IP network. An MPC controller is designed using the OPC client to control the boiler's drum water level, steam pressure, and NOx emissions. The setup provides a cost-effective tool for academic research on advanced process control and networked control systems.
Improved control and monitor two different PLC using LabVIEW and NI-OPC server IJECEIAES
This paper proposes an improved control and monitors between two different PLCs, the Mitsubishi, and Omron. The main advantage is interoperability and communication between both PLC. The use of NI OPC server as the software interface reached interoperability and communication. There were developed two field applications to test interoperability. Laboratory virtual instrument engineering workbench (LabVIEW) uses as the software application for creating the user interface to control and monitor. This improvement show OPC server technology solves data compatibility issue between different driver controller’s and reducing development cost. Regardless of whether there are more than two different PLCs, it's enough to use the NI OPC server. So the benefit of the NI OPC server is not limited to two types of PLC used right now but can also use the other manufacturers. Besides, the improvement of the previous study is the use of the LabVIEW makes data from the OPC server displayed more realistic. The use of LabVIEW allows additional monitoring functions, one of which is LabVIEW vision. Data utilization becomes more flexible, and so it can use for more complex purposes. It is envisaged that this is very useful for Integrator engineer to implement this method in industrial automation.
The document summarizes research validating behavioral models of an Ericsson telecommunications system demonstrator against real measurements. Researchers modeled the extra-functional behavior of the demonstrator's components in REMES and translated it to priced timed automata to formally verify properties like response time and optimal resource usage using UPPAAL. The models were validated by using actual timing and resource values measured from the prototype implementation. The analysis derived an optimal processing trace that minimized total weighted CPU and memory costs for a given number of requests.
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The document provides details of the applicant's employment experience as a consultant test manager from 2011-2015 at the European Medicines Agency (EMA). Over this period, the applicant was responsible for testing a variety of systems used by EMA including systems for quality control, product information management, application forms, and good manufacturing practice inspections. Key responsibilities included test planning and management, requirements testing, risk-based testing, integration testing, and user acceptance testing. The applicant also trained new employees and enhanced EMA's quality standards and procedures.
Experimental evaluation of control performance of MPC as a regulatory controllerISA Interchange
Proportional integral derivative (PID) control is widely practiced as the base layer controller in the industry due to its robustness and design simplicity. However, a supervisory control layer over the base layer, namely a model predictive controller (MPC), is becoming increasingly popular with the advent of computer process control. The use of a supervisory layer has led to different control structures. In this study, we perform an objective investigation of several commonly used control structures such as “Cascaded PI controller,” “DMC cascaded to PI” and “Direct DMC.” Performance of these control structures are compared on a pilot-scale continuous stirred tank heater (CSTH) system. We used dynamic matrix control (DMC) algorithm as a representative of MPC. In the DMC cascaded to PI structure, the flow-loops are regulated by the PI controller. On top of that a DMC manipulates the set-points of the flow-loops to control the temperature and the level of water in the tank. The “Direct DMC” structure, as its name suggests, uses DMC to manipulate the valves directly. Performance of all control structures were evaluated based on the integrated squared error (ISE) values. In this empirical study, the “Direct DMC” structure showed a promise to act as regulatory controller. The selection of control frequency is critical for this structure. The effect of control frequency on controller performance of the “Direct DMC” structure was also studied.
This document outlines a test plan to evaluate initial data link terminal air traffic control (ATC) services through simulations. The plan involves training new air traffic controllers on the services over 4 days, then having them participate in full-scale simulations to validate the service designs and assess the impact of implementing the services. Data will be collected from the simulations and controller ratings/feedback to analyze impacts on communications, workload, and errors. The results will help guide development of operational data link systems.
Linked Data for Automation Systems EngineeringMarta Sabou
A talk about using Linked Data technologies to support the multi-disciplinary engineering processes typical for automation systems, cyber-physical systems and Industrie4.0 in general. Talk given at workshop for "Linked Data in Industry 4.0" at Semantics 2015 (http://www.semantics.cc/satellite-events/linked-data-industry-40).
Invited talk at SSSW'16 (http://sssw.org/2016/?page_id=232) introducing the Fourth Industrial Revolution and discussing how Semantic Web technologies can support this movement. Also a teaser for the upcoming Springer book "Semantic Web for Intelligent Engineering Applications" (http://www.springer.com/us/book/9783319414881).
Improved control and monitor two different PLC using LabVIEW and NI-OPC server IJECEIAES
This paper proposes an improved control and monitors between two different PLCs, the Mitsubishi, and Omron. The main advantage is interoperability and communication between both PLC. The use of NI OPC server as the software interface reached interoperability and communication. There were developed two field applications to test interoperability. Laboratory virtual instrument engineering workbench (LabVIEW) uses as the software application for creating the user interface to control and monitor. This improvement show OPC server technology solves data compatibility issue between different driver controller’s and reducing development cost. Regardless of whether there are more than two different PLCs, it's enough to use the NI OPC server. So the benefit of the NI OPC server is not limited to two types of PLC used right now but can also use the other manufacturers. Besides, the improvement of the previous study is the use of the LabVIEW makes data from the OPC server displayed more realistic. The use of LabVIEW allows additional monitoring functions, one of which is LabVIEW vision. Data utilization becomes more flexible, and so it can use for more complex purposes. It is envisaged that this is very useful for Integrator engineer to implement this method in industrial automation.
The document summarizes research validating behavioral models of an Ericsson telecommunications system demonstrator against real measurements. Researchers modeled the extra-functional behavior of the demonstrator's components in REMES and translated it to priced timed automata to formally verify properties like response time and optimal resource usage using UPPAAL. The models were validated by using actual timing and resource values measured from the prototype implementation. The analysis derived an optimal processing trace that minimized total weighted CPU and memory costs for a given number of requests.
Extending open up for autonomic computing english_finalversionupload 6vpssantos
This document proposes extending the OpenUP software process to better support the development of autonomous software systems with a focus on eliciting non-functional requirements (NFRs). It introduces two new artifacts: 1) an NFR Description to document identified NFRs and resolve any conflicts or ambiguities, and 2) Misuse Cases to help uncover additional hidden NFRs. A case study on a Brazilian Emergency System is presented to illustrate applying the extended OpenUP process with the new artifacts during requirements elicitation.
The document provides details of the applicant's employment experience as a consultant test manager from 2011-2015 at the European Medicines Agency (EMA). Over this period, the applicant was responsible for testing a variety of systems used by EMA including systems for quality control, product information management, application forms, and good manufacturing practice inspections. Key responsibilities included test planning and management, requirements testing, risk-based testing, integration testing, and user acceptance testing. The applicant also trained new employees and enhanced EMA's quality standards and procedures.
Experimental evaluation of control performance of MPC as a regulatory controllerISA Interchange
Proportional integral derivative (PID) control is widely practiced as the base layer controller in the industry due to its robustness and design simplicity. However, a supervisory control layer over the base layer, namely a model predictive controller (MPC), is becoming increasingly popular with the advent of computer process control. The use of a supervisory layer has led to different control structures. In this study, we perform an objective investigation of several commonly used control structures such as “Cascaded PI controller,” “DMC cascaded to PI” and “Direct DMC.” Performance of these control structures are compared on a pilot-scale continuous stirred tank heater (CSTH) system. We used dynamic matrix control (DMC) algorithm as a representative of MPC. In the DMC cascaded to PI structure, the flow-loops are regulated by the PI controller. On top of that a DMC manipulates the set-points of the flow-loops to control the temperature and the level of water in the tank. The “Direct DMC” structure, as its name suggests, uses DMC to manipulate the valves directly. Performance of all control structures were evaluated based on the integrated squared error (ISE) values. In this empirical study, the “Direct DMC” structure showed a promise to act as regulatory controller. The selection of control frequency is critical for this structure. The effect of control frequency on controller performance of the “Direct DMC” structure was also studied.
This document outlines a test plan to evaluate initial data link terminal air traffic control (ATC) services through simulations. The plan involves training new air traffic controllers on the services over 4 days, then having them participate in full-scale simulations to validate the service designs and assess the impact of implementing the services. Data will be collected from the simulations and controller ratings/feedback to analyze impacts on communications, workload, and errors. The results will help guide development of operational data link systems.
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A talk about using Linked Data technologies to support the multi-disciplinary engineering processes typical for automation systems, cyber-physical systems and Industrie4.0 in general. Talk given at workshop for "Linked Data in Industry 4.0" at Semantics 2015 (http://www.semantics.cc/satellite-events/linked-data-industry-40).
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Solving big data challenges for enterprise applicationTrieu Dao Minh
This document discusses the challenges of application performance monitoring (APM) systems that deal with "big data". APM systems instrument enterprise applications to monitor metrics like response times and failures across distributed systems. This generates enormous amounts of monitoring data. The document evaluates six open-source data stores (Cassandra, HBase, Voldemort, Redis, VoltDB, MySQL Cluster) for their ability to handle the throughput of APM workloads in memory-bound and disk-bound cluster setups. It aims to provide performance results, lessons learned on setup complexity, and insights for using these data stores in an industrial APM system context.
This paper describes an integrated performance monitoring environment for parallel systems. It consists of:
1) A distributed monitoring system that collects performance data from instrumented applications and sends it to analysis tools.
2) Graphical and command-line profiling and visualization tools that analyze the performance data to identify bottlenecks.
3) A common graphical interface that provides a consistent way to instrument applications, start tool runs, and view performance results across different tools.
The environment aims to handle large amounts of performance data from massively parallel applications and provide insights at both the application and system level. It is initially targeted for the Intel Paragon but is designed to support different programming models.
Evaluation of interoperability between automation systems using multi-criteri...MaiconSaturno1
The diagnosis of automation systems in an existing production system through the analysis of the interoperability level between its components represents the first step in its evaluation for improving it towards alignment with Industry 4.0. The analysis of the level of interoperability finds in the multi-criteria decision making methods an auxiliary tool to evaluate and classify the solution. This work uses the AHP method for this evaluation, drawing criteria from the literature and expert experience. The application of the method is illustrated, showing that it can direct investment decisions.
This document proposes extending algorithmic skeletons with event-driven programming to address the inversion of control problem in skeleton frameworks. It introduces event listeners that can be registered at event hooks within skeletons to access runtime information. This allows implementing non-functional concerns like logging and performance monitoring separately from the core parallel logic. The approach is implemented in the Skandium skeleton library, and examples are given of a logger and online performance monitor built using it. An analysis shows the overhead of processing events is negligible, at around 20 microseconds per event.
An Adjacent Analysis of the Parallel Programming Model Perspective: A SurveyIRJET Journal
This document provides an overview and analysis of parallel programming models. It begins with an abstract discussing the growing demand for parallel computing and challenges with existing parallel programming frameworks. It then reviews several relevant studies on parallel programming models and architectures. The document goes on to describe several key parallel programming models in more detail, including the Parallel Random Access Machine (PRAM) model, Unrestricted Message Passing (UMP) model, and Bulk Synchronous Parallel (BSP) model. It discusses aspects of each model like architecture, communication methods, and associated cost models. The overall goal is to compare benefits and limitations of different parallel programming models.
The document summarizes three journal articles about grid and cloud computing.
The first journal investigates the benefits of grid computing technologies for high-performance computing. It uses a case study approach and experimental methodology. Three scenarios are modeled to test average job response times.
The second journal aims to develop a prototype integrating grid technologies with NASA's web GIS software. It determines integration models and system architecture through data gathering and analysis. Components are developed and tested within a virtual organization environment.
The third journal comprehensively compares grid and cloud computing concepts from different perspectives. It collects data through observations and content analysis of definitions to ensure cloud computing is not just a renaming of grid computing.
Here is an paper published on software PLC Checker by Itris Automation Square, in the French journal "Mesures" : "La qualité des programmes vérifiée par leurs concepteurs".
Enjoy the reading!
Find us at http://www.itris-automation.com/
Contact us at commercial@itris-automation.com for more information.
This document provides details about a project to create an environment and power monitoring panel using an ARM microcontroller board. It includes an introduction describing the importance of automation and sensor monitoring in industrial systems. It then provides details on the hardware and software used, including a Texas Instruments LM3S9D92 microcontroller board, sensors, and a graphical user interface design. The project aims to remotely monitor and display parameters from an industrial cabinet to improve maintenance and optimization.
Integration of real time software modules for reconfigurable sensPham Ngoc Long
The document describes a framework for integrating reusable real-time software modules in a reconfigurable multi-sensor control system. The framework uses a global database of state variables through which modules can exchange information. Modules are modeled as port automata with input and output ports. A state variable table mechanism implemented in the Chimera II real-time operating system allows modules running on different processors to efficiently communicate and synchronize.
1. The document presents a case study applying an enterprise configuration management platform, ScriptRock, to a multi-agent robotic system to improve reconfiguration times and simplify troubleshooting.
2. The robotic system consists of unmanned ground vehicles and a ground control station running various software modules. ScriptRock allows validating configurations by encoding requirements as executable tests.
3. An experiment was conducted to gauge the benefits of using ScriptRock for configuration management over existing manual methods on the robotic system. Results showed improved reconfiguration times and simplified troubleshooting.
Quasi-Static Evaluation of a Modular and Reconfigurable Manufacturing CellHillary Green
This document presents a novel modular and reconfigurable manufacturing cell (MRMC) system that aims to provide flexible manufacturing capabilities. Some key points:
- The MRMC system consists of modular manipulation hardware and software that can be quickly configured and reconfigured for different assembly and packaging applications.
- It uses a unique interconnect design to allow mechanical and electrical connection between modules. Distributed intelligence and self-locating software enables automatic configuration.
- Analytical evaluation of precision shows the MRMC maintains necessary accuracy and repeatability for tasks like pick-and-place despite reconfiguration.
- The goal is to offer a low-cost, low-risk solution for prototyping and low-volume manufacturing through
An Overview of Workflow Management on Mobile Agent TechnologyIJERA Editor
This document discusses mobile agent technology for workflow management. It provides an overview of current research on using mobile agents to automate business processes across distributed systems. The document summarizes several related works on topics like inter-organizational workflows, mobile agent communication, coordination techniques, and workflow partitioning and scheduling algorithms. It aims to improve methods for designing and implementing prototype models for mobile agent-based workflow management systems.
System reliability is an important issue in designing modern multiprocessor systems. This paper proposes a fault-tolerant, scalable, multiprocessor system architecture that adopts a pipeline scheme. To verify the performance of the proposed system, the SimEvent/Stateflow tool of the MATLAB program was used to simulate the system. The proposed system uses twelve processors (P), connected in a linear array, to build a ten-stage system with two backup processors (BP). However, the system can be expanded by adding more processors to increase pipeline stages and performance, and more backup processors to increase system reliability. The system can automatically reorganize itself in the event of a failure of one or two processors and execution continues without interruption. Each processor communicates with its neighboring processors through input/output (I/O) ports which are used as bypass links between the processors. In the event of a processor failure, the function of the faulty processor is assigned to the next processor that is free from faults. The fast Fourier transform (FFT) algorithm is implemented on the simulated circuit to evaluate the performance of the proposed system. The results showed that the system can continue to execute even if one or two processors fail without a noticeable decrease in performance.
An Algorithm Based Simulation Modeling For Control of Production SystemsIJMER
This document describes an algorithm-based simulation approach for modeling and controlling flexible production systems. The approach models both the physical production system and the control system to evaluate their integrated performance. Key features include:
1) The approach integrates control system design into the physical simulation to evaluate their combined impact.
2) The algorithm-based design is extensible and allows modeling of different control programs and production system designs.
3) Finite automata formalism provides a mathematical foundation for logical and quantitative analysis of the system.
4) The framework facilitates robust controller models that can resolve issues like deadlocks and accommodate failures.
5) Analysts can evaluate how different control programs and production system designs impact
Tulasi has experience in technical program management, system architecture, software development, and test and measurement instruments. She has worked on projects involving mobile communication systems, wireless networks, transit automation, aerospace systems, medical devices, and oil exploration equipment. Tulasi led the development of several products and systems, including a medical centrifuge, pipeline communication system, and environmental controls for an F-22 aircraft. Her PhD research involved developing a secure cloud-based framework for point-of-care medical testing systems.
This document discusses integrating IDEF3 process modeling with queuing network analysis to provide quantitative performance measures without simulation. IDEF3 captures process knowledge visually but provides no metrics. Queuing network analysis can estimate metrics like utilization and wait times but requires a different modeling view. The authors develop a framework to convert IDEF3 models to queuing networks by extracting resource information from activities. A database stores all information to facilitate conversion and analysis. Results from the queuing network analyzer are compared to simulation, finding reasonable accuracy at low system utilization. This integration allows domain experts to obtain performance insights without complex simulation modeling.
Controller selection in software defined networks using best-worst multi-crit...journalBEEI
This document discusses selecting the best SDN controller using a multi-criteria decision making approach. It identifies 7 candidate SDN controllers (NOX, POX, Beacon, Floodlight, Ryu, ODL, ONOS) and defines both quantitative and qualitative criteria to evaluate them, such as throughput, latency, APIs, programming language, and legacy network support. It proposes using the best-worst multi-criteria decision making (BWM) method to determine the weights of each criterion and ultimately select the best controller based on user requirements and preferences. An optimization approach is applied to evaluate the controllers' performance on key criteria and determine which controllers, ONOS and ODL, are the most robust options overall.
Iaetsd pinpointing performance deviations of subsystems in distributedIaetsd Iaetsd
This document proposes the Cloud Debugger tool to help diagnose performance problems in cloud manufacturing systems. The Cloud Debugger allows developers to set watch points in code to get snapshots of variables when requests hit that line, without needing to change production code. It also utilizes cloud tracing to visualize time spent processing requests and cloud monitoring to identify and quickly repair performance issues from dashboards and alerts. The tool aims to significantly reduce the effort for cloud operators to diagnose problems compared to traditional approaches.
Svm Classifier Algorithm for Data Stream Mining Using Hive and RIRJET Journal
This document proposes using Hive and R to perform data stream mining on big data. Hive is used to query and analyze large datasets stored in Hadoop. Test and trained datasets are extracted from the data using Hive queries. The Support Vector Machine (SVM) classifier algorithm analyzes the data to produce a statistical report in R, comparing the accuracy of linear and nonlinear models. The proposed method aims to improve data processing speed and ability to analyze large volumes of data as compared to other tools.
An optimal general type-2 fuzzy controller for Urban Traffic NetworkISA Interchange
This document presents an optimal general type-2 fuzzy controller (OGT2FC) for controlling traffic signal scheduling and phase succession to minimize wait times and average queue length. The OGT2FC uses a combination of general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) to optimize the membership function parameters. Simulation results show the OGT2FC performs better than conventional type-1 fuzzy controllers in regulating urban traffic flow.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
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1) A distributed monitoring system that collects performance data from instrumented applications and sends it to analysis tools.
2) Graphical and command-line profiling and visualization tools that analyze the performance data to identify bottlenecks.
3) A common graphical interface that provides a consistent way to instrument applications, start tool runs, and view performance results across different tools.
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An Adjacent Analysis of the Parallel Programming Model Perspective: A SurveyIRJET Journal
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The document summarizes three journal articles about grid and cloud computing.
The first journal investigates the benefits of grid computing technologies for high-performance computing. It uses a case study approach and experimental methodology. Three scenarios are modeled to test average job response times.
The second journal aims to develop a prototype integrating grid technologies with NASA's web GIS software. It determines integration models and system architecture through data gathering and analysis. Components are developed and tested within a virtual organization environment.
The third journal comprehensively compares grid and cloud computing concepts from different perspectives. It collects data through observations and content analysis of definitions to ensure cloud computing is not just a renaming of grid computing.
Here is an paper published on software PLC Checker by Itris Automation Square, in the French journal "Mesures" : "La qualité des programmes vérifiée par leurs concepteurs".
Enjoy the reading!
Find us at http://www.itris-automation.com/
Contact us at commercial@itris-automation.com for more information.
This document provides details about a project to create an environment and power monitoring panel using an ARM microcontroller board. It includes an introduction describing the importance of automation and sensor monitoring in industrial systems. It then provides details on the hardware and software used, including a Texas Instruments LM3S9D92 microcontroller board, sensors, and a graphical user interface design. The project aims to remotely monitor and display parameters from an industrial cabinet to improve maintenance and optimization.
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1. The document presents a case study applying an enterprise configuration management platform, ScriptRock, to a multi-agent robotic system to improve reconfiguration times and simplify troubleshooting.
2. The robotic system consists of unmanned ground vehicles and a ground control station running various software modules. ScriptRock allows validating configurations by encoding requirements as executable tests.
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This document presents a novel modular and reconfigurable manufacturing cell (MRMC) system that aims to provide flexible manufacturing capabilities. Some key points:
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- It uses a unique interconnect design to allow mechanical and electrical connection between modules. Distributed intelligence and self-locating software enables automatic configuration.
- Analytical evaluation of precision shows the MRMC maintains necessary accuracy and repeatability for tasks like pick-and-place despite reconfiguration.
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System reliability is an important issue in designing modern multiprocessor systems. This paper proposes a fault-tolerant, scalable, multiprocessor system architecture that adopts a pipeline scheme. To verify the performance of the proposed system, the SimEvent/Stateflow tool of the MATLAB program was used to simulate the system. The proposed system uses twelve processors (P), connected in a linear array, to build a ten-stage system with two backup processors (BP). However, the system can be expanded by adding more processors to increase pipeline stages and performance, and more backup processors to increase system reliability. The system can automatically reorganize itself in the event of a failure of one or two processors and execution continues without interruption. Each processor communicates with its neighboring processors through input/output (I/O) ports which are used as bypass links between the processors. In the event of a processor failure, the function of the faulty processor is assigned to the next processor that is free from faults. The fast Fourier transform (FFT) algorithm is implemented on the simulated circuit to evaluate the performance of the proposed system. The results showed that the system can continue to execute even if one or two processors fail without a noticeable decrease in performance.
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1) The approach integrates control system design into the physical simulation to evaluate their combined impact.
2) The algorithm-based design is extensible and allows modeling of different control programs and production system designs.
3) Finite automata formalism provides a mathematical foundation for logical and quantitative analysis of the system.
4) The framework facilitates robust controller models that can resolve issues like deadlocks and accommodate failures.
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This document discusses integrating IDEF3 process modeling with queuing network analysis to provide quantitative performance measures without simulation. IDEF3 captures process knowledge visually but provides no metrics. Queuing network analysis can estimate metrics like utilization and wait times but requires a different modeling view. The authors develop a framework to convert IDEF3 models to queuing networks by extracting resource information from activities. A database stores all information to facilitate conversion and analysis. Results from the queuing network analyzer are compared to simulation, finding reasonable accuracy at low system utilization. This integration allows domain experts to obtain performance insights without complex simulation modeling.
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This document presents an optimal general type-2 fuzzy controller (OGT2FC) for controlling traffic signal scheduling and phase succession to minimize wait times and average queue length. The OGT2FC uses a combination of general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) to optimize the membership function parameters. Simulation results show the OGT2FC performs better than conventional type-1 fuzzy controllers in regulating urban traffic flow.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
State of charge estimation of lithium-ion batteries using fractional order sl...ISA Interchange
This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov’s stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estima- tion method is effective, and the designed observers have good performance.
Fractional order PID for tracking control of a parallel robotic manipulator t...ISA Interchange
This paper presents the tracking control for a robotic manipulator type delta employing fractional order PID controllers with computed torque control strategy. It is contrasted with an integer order PID controller with computed torque control strategy. The mechanical structure, kinematics and dynamic models of the delta robot are descripted. A SOLIDWORKS/MSC-ADAMS/MATLAB co-simulation model of the delta robot is built and employed for the stages of identification, design, and validation of control strategies. Identification of the dynamic model of the robot is performed using the least squares algorithm. A linearized model of the robotic system is obtained employing the computed torque control strategy resulting in a decoupled double integrating system. From the linearized model of the delta robot, fractional order PID and integer order PID controllers are designed, analyzing the dynamical behavior for many evaluation trajectories. Controllers robustness is evaluated against external disturbances employing performance indexes for the joint and spatial error, applied torque in the joints and trajectory tracking. Results show that fractional order PID with the computed torque control strategy has a robust performance and active disturbance rejection when it is applied to parallel robotic manipulators on tracking tasks.
Fuzzy logic for plant-wide control of biological wastewater treatment process...ISA Interchange
The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.
Design and implementation of a control structure for quality products in a cr...ISA Interchange
In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen HYSYS dynamic environment under real operating conditions. The simulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns.
Model based PI power system stabilizer design for damping low frequency oscil...ISA Interchange
This paper explores a two-level control strategy by blending a local controller with a centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favorably compared with some controllers prevalent in the literature.
A comparison of a novel robust decentralized control strategy and MPC for ind...ISA Interchange
This document summarizes a research article that compares a novel decentralized control strategy based on override control to a model predictive controller (MPC) for controlling an industrial high purity methanol distillation column. Both controllers were able to maintain tight product purity and high recovery specifications under disturbances. The MPC provided tighter control of product purity but used more energy, while the proposed override control provided tighter recovery control and had lower costs. An economic analysis showed the optimal choice depends on factors like energy costs.
Fault detection of feed water treatment process using PCA-WD with parameter o...ISA Interchange
This research article proposes a new fault detection algorithm called PCA-WD that combines wavelet denoising (WD) with principal component analysis (PCA) to improve fault detection performance for feed water treatment processes (FWTP). The algorithm is applied to operational data from a FWTP sustaining two 1000 MW coal-fired power plants. Parameter selection for the PCA-WD algorithm is formulated as an optimization problem solved using particle swarm optimization to determine optimal parameters automatically rather than relying on individual experience. Results show that WD effectively reduces noise in PCA statistics, improving fault detection. The optimized PCA-WD algorithm outperforms classical PCA and a related method in detecting various faults in the FWTP data.
Model-based adaptive sliding mode control of the subcritical boiler-turbine s...ISA Interchange
As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.
A Proportional Integral Estimator-Based Clock Synchronization Protocol for Wi...ISA Interchange
Clock synchronization is an issue of vital importance in applications of wireless sensor networks (WSNs). This paper proposes a proportional integral estimator-based protocol (EBP) to achieve clock synchronization for wireless sensor networks. As each local clock skew gradually drifts, synchronization accuracy will decline over time. Compared with existing consensus-based approaches, the proposed synchronization protocol improves synchronization accuracy under time-varying clock skews. Moreover, by restricting synchronization error of clock skew into a relative small quantity, it could reduce periodic re-synchronization frequencies. At last, a pseudo-synchronous implementation for skew compensation is introduced as synchronous protocol is unrealistic in practice. Numerical simulations are shown to illustrate the performance of the proposed protocol.
An artificial intelligence based improved classification of two-phase flow patte...ISA Interchange
Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are re- corded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows.
New Method for Tuning PID Controllers Using a Symmetric Send-On-Delta Samplin...ISA Interchange
In this paper we present a new method for tuning PI controllers with symmetric send-on-delta (SSOD) sampling strategy. First we analyze the conditions that produce oscillations in event based systems considering SSOD sampling strategy. The Describing Function is the tool used to address the problem. Once the conditions for oscillations are established, a new robustness to oscillation performance measure is introduced which entails with the concept of phase margin, one of the most traditional measures of relative stability in closed-loop control systems. Therefore, the application of the proposed robustness measure is easy and intuitive. The method is tested by both simulations and experiments. Additionally, a Java application has been developed to aid in the design according to the results presented in the paper.
Load estimator-based hybrid controller design for two-interleaved boost conve...ISA Interchange
This paper is devoted to the development of a hybrid controller for a two-interleaved boost converter dedicated to renewable energy and automotive applications. The control requirements, resumed in fast transient and low input current ripple, are formulated as a problem of fast stabilization of a predefined optimal limit cycle, and solved using hybrid automaton formalism. In addition, a real time estimation of the load is developed using an algebraic approach for online adjustment of the hybrid controller. Mathematical proofs are provided with simulations to illustrate the effectiveness and the robustness of the proposed controller despite different disturbances. Furthermore, a fuel cell system supplying a resistive load through a two-interleaved boost converter is also highlighted.
Effects of Wireless Packet Loss in Industrial Process Control SystemsISA Interchange
Timely and reliable sensing and actuation control are essential in networked control. This depends on not only the precision/quality of the sensors and actuators used but also on how well the communications links between the field instruments and the controller have been designed. Wireless networking offers simple deployment, reconfigurability, scalability, and reduced operational expenditure, and is easier to upgrade than wired solutions. However, the adoption of wireless networking has been slow in industrial process control due to the stochastic and less than 100% reliable nature of wireless communications and lack of a model to evaluate the effects of such communications imperfections on the overall control performance. In this paper, we study how control performance is affected by wireless link quality, which in turn is adversely affected by severe propagation loss in harsh industrial environments, co-channel interference, and unintended interference from other devices. We select the Tennessee Eastman Challenge Model (TE) for our study. A decentralized process control system, first proposed by N. Ricker, is adopted that employs 41 sensors and 12 actuators to manage the production process in the TE plant. We consider the scenario where wireless links are used to periodically transmit essential sensor measurement data, such as pressure, temperature and chemical composition to the controller as well as control commands to manipulate the actuators according to predetermined setpoints. We consider two models for packet loss in the wireless links, namely, an independent and identically distributed (IID) packet loss model and the two-state Gilbert-Elliot (GE) channel model. While the former is a random loss model, the latter can model bursty losses. With each channel model, the performance of the simulated decentralized controller using wireless links is compared with the one using wired links providing instant and 100% reliable communications. The sensitivity of the controller to the burstiness of packet loss is also characterized in different process stages. The performance results indicate that wireless links with redundant bandwidth reservation can meet the requirements of the TE process model under normal operational conditions. When disturbances are introduced in the TE plant model, wireless packet loss during transitions between process stages need further protection in severely impaired links. Techniques such as re-transmission scheduling, multi-path routing and enhanced physical layer design are discussed and the latest industrial wireless protocols are compared.
Fault Detection in the Distillation Column ProcessISA Interchange
Chemical plants are complex large-scale systems which need designing robust fault detection schemes to ensure high product quality, reliability and safety under different operating conditions. The present paper is concerned with a feasibility study of the application of the black-box modeling method and Kullback Leibler divergence (KLD) to the fault detection in a distillation column process. A Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) polynomial model is firstly developed to estimate the nonlinear behavior of the plant. Furthermore, the KLD is applied to detect abnormal modes. The proposed FD method is implemented and validated experimentally using realistic faults of a distillation plant of laboratory scale. The experimental results clearly demonstrate the fact that proposed method is effective and gives early alarm to operators.
Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank SystemISA Interchange
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H1 framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
A KPI-based process monitoring and fault detection framework for large-scale ...ISA Interchange
Large-scale processes, consisting of multiple interconnected sub-processes, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel re- presentation of each sub-process, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods.
An adaptive PID like controller using mix locally recurrent neural network fo...ISA Interchange
Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional integral derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initi- alized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on- line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.
A method to remove chattering alarms using median filtersISA Interchange
Chattering alarms are the most found nuisance alarms that will probably reduce the usability and result in a confidence crisis of alarm systems for industrial plants. This paper addresses the chattering alarm reduction using median filters. Two rules are formulated to design the window size of median filters. If the alarm probability is estimated using process data, one rule is based on the probability of alarms to satisfy some requirements on the false alarm rate, or missed alarm rate. If there are only historical alarm data available, the other rule is based on percentage reduction of chattering alarms using alarm duration distribution. Experimental results for industrial cases testify that the proposed method is effective.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
AppSec PNW: Android and iOS Application Security with MobSF
Using OPC technology to support the study of advanced process control
1. Using OPC technology to support the study of advanced
process control$
Magdi S. Mahmoud n
, Muhammad Sabih, Moustafa Elshafei
Systems Engineering Department, King Fahd University of Petroleum and Minerals, P. O. Box 5067, Dhahran 31261, Saudi Arabia
a r t i c l e i n f o
Article history:
Received 4 December 2012
Received in revised form
13 July 2014
Accepted 18 July 2014
Available online 18 February 2015
This paper was recommended for publica-
tion by Prof. A.B. Rad
Keywords:
OPC (OLE for Process Control/Open Process
Control)
Distributed control systems
OPC client
OPC server
LabVIEW
MATLAB
a b s t r a c t
OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad commu-
nication opportunity between different kinds of control systems. This paper investigates the use of OPC
technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool
for the development and testing of advanced process control (APC) techniques in university research
centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here.
Several implementation issues and OPC based client/server control application have been addressed for
TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed
loop model identification, and to design a Model Predictive Controller. The MPC is able to control the
NOx emissions in addition to drum water level and steam pressure.
& 2014 ISA. Published by Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Motivation
In process automation, the control objectives have evolved
from physical parameter control at regulatory control level to
corporate levels, where the economic, financial, international or
national standards and environmental constraints play vital role.
Each of the mentioned objectives is owned by the concerned
department in the automation industry, and therefore, the control
objectives and functions are defined at several hierarchical levels.
These distributed objectives in a plant environment are illustrated
in the Fig. 1. This hierarchical and distributed nature of today's
plant automation objectives requires advanced techniques, com-
monly called by research community as advanced process control
(APC), to take care of the overall objectives. Distributed nature of
the automation systems, on the other hand, requires standard way
of communication and data exchange with several types of tags.
APC takes care of the overall control objectives and constraints
while, OPC can be used to exchange the various types of data in a
distributed control system (DCS) environment to implement and
maintain an efficient APC technique [8].
This work shows the integration of important tools like
MATLAB and LabVIEW along with Matrikon OPC server. The setup
is important for academic and research community interested in
advanced process control and networked control systems. It is
important to notice that the research on APC and NCS are not
worthy or convincing to practitioners and industry if only pre-
sented with simulation tool. In this work, well-known simulation
and research tools (i.e., MATLAB and LabVIEW) are integrated
along with real OPC server which is Matrikon's OPC server and
Ethernet. Research over APC techniques and Networked Control
System tested in the presented scenario will be more convincing
to the industry community and is near to the industry needs.
1.2. Contribution of the paper
This paper presents a way to develop a rich dynamic environ-
ment for academic research based on Matlab and LabVIEW
integrated with OPC standard. The major contribution of the paper
is the development of a cascaded MPC-PID controller for a non-
linear boiler system that can track the setpoints of a boiler model,
in addition of controlling the NOx output to the desired level. The
subsequent learning outcomes can be described as follows:
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/isatrans
ISA Transactions
http://dx.doi.org/10.1016/j.isatra.2014.07.013
0019-0578/& 2014 ISA. Published by Elsevier Ltd. All rights reserved.
$
This work is supported by the deanship for scientific research (DSR) at KFUPM
through group research project RG-1316-1.
n
Corresponding author.
E-mail addresses: msmahmoud@kfupm.edu.sa (M.S. Mahmoud),
msabih@kfupm.edu.sa (M. Sabih), elshafei@kfupm.edu.sa (M. Elshafei).
ISA Transactions 55 (2015) 155–167
2. development of co-simulation environment based on LabVIEW
and MATLAB [5] to run continuously a closed-loop plant model,
deployment of the plant model as OPC based periodic server that
can be interfaced with any OPC compliant client for conducting
remote identification, modeling, and designing of any advanced
process control technique, development of remote HMI to support
conducting identification tests, and to test developed controllers
over the closed-loop plant model.
1.3. Brief introduction to OPC technology
The OPC Task Force released the first specification for OPC in
1995 with the help of its industrial members and support and
consultation of Microsoft [1]. The initial members of OPC Task
Force were Fisher-Rosemount (now Emerson Process Manage-
ment), Intellution (now part of GE Fanuc), Intuitive Technology
(now part of Wizcon Systems), OPTO 22, and Rockwell Software.
With the collective opinion of industry, the OPC Task Force was
replaced with an independent, non-profit organization to be called
the OPC Foundation (www.opcfoundation.org). Since 1996, OPC
Foundation brought widespread collaborative work and demon-
strations and now OPC has confirmed it as the industry standard.
OPC is an open standard for distributed control systems. The
classical OPC is based on Microsoft's Distributed Components
Object Model (DCOM) service for communication and exchange
of data between distributed client/server models.
1.3.1. OPC technology for heterogeneous systems
The interconnection of heterogeneous control networks raises
technical difficulties and is a subject of concern in enterprise
information construction. In [2], OPC technology has been applied
in interconnection between two heterogeneous control networks
of Profibus-DP fieldbus system based on SIMATIC S7-400 PLC and
CENTUM CS3000 DCS from Yokogawa. Their application imple-
ments the data communication of production control information,
and virtual integration of sewage system. Such kind of technical
strategy can provide significant research opportunity to develop
similar subjects in process industry with higher extending value.
Using OPC as the main communication protocol seems to be a
big advantage for data transfer in a heterogeneous system. As
shown in Fig. 2, OPC acts as the common interface for mutual
communication among different devices used for data collection
from technological process (Measurex, SIEMENS, ABB, Valmet),
and liberates from dependence of specific monitoring software
(Genesis32, PI System database) on manufacturer of controller
systems (PLCs) [3]. Fig. 3 illustrates the use of OPC technology to
interconnect multi-tire systems.
The outline of the paper goes as follows. Literature review is
provided in Section 2. OPC specifications are discussed in Section 3.
Section 4 highlights the topic of APC in the context of this paper by
illustrating an example of distributed boiler system. The studied
problem is described from mathematical analysis and procedure in
Section 5. Section 6 describes the analytical framework used for
modeling, simulation and control design. Section 7 describes the
network-in-the-loop simulation setup over real network based on
OPC connectivity. This paper ends with concluding remarks
in Section 8.
2. Literature review
OPC provides a common standard for software interfacing enab-
ling horizontal integration of the automation solutions through
communication between the distributed components. Efficiency
and cost savings are achieved through the reuse of software
components and the flexible compilation of such components into
distributed automation solutions [7].
The investigation of OPC based APC in a Distributed Control
System environment shows potentially several research directions.
Our aim is to present the use of OPC connectivity as an
opportunity to study, design and test advanced process control
techniques using well established research tools. This pro
posed technique is based on the network-in-the-loop (NIL) sim
ulation with the OPC connectivity. The use of real network for
OPC connectivity between the design and simulation tools
(i.e., MATLAB and LabVIEW) enable to conduct data acquisition,
identification test, offline design of APC controllers and testing
over the real OPC connectivity in line of practical solutions for
process industry.
The need of advanced control techniques for economic benefits
has been felt since the distributed control systems were deployed.
Implementation of advanced control techniques along with the
standard regulatory controls in distributed environment requires a
DCS to have increased functional capabilities, better interfaces, and
ease of configuration [9]. OPC can be included in the overall
control strategy for building quality estimators to substitute for
analyzers. The use of OPC technology in a distributed environment
besides the regulatory control has demonstrated its potential to
increase company's profits and maintain its competitive edge.
Economy and environmental concerns are the main two driv-
ing forces in the development of advanced process control besides
the stable and quality controlled operation. In [10], authors have
conducted a survey on the economic assessment of process control
with the help of over 60 industrial APC experts.
Openness and smooth connectivity has been seen as the key
issue in implementing advanced monitoring and control strategies
in distributed heterogeneous system. The issue of openness has
been discussed critically in [12] by examining different vendors.
The DCS were not very open (see the study of [12]) as of middle of
90s, but the use of OPC technology bridge the gap and make the
systems talk each other in a distributed DCS environment. As of
today, every DCS system offers OPC based modules integrated for
better connectivity, which can be used without knowing the
specific programming details of the specific DCS system. Such
OPC based connectivity can bring cost effective desired function-
ality and availability in a system.
In [13], OPC based controller performance monitoring technol-
ogy is introduced for industrial applications. The main components
Plant wide Optimization
& International and Local
Environmental Laws
Production Unit
Optimization
Advanced Control
Regulatory Control
Sensors / Actuators
Controller Setpoints
Meeting Operation Objectives
Optimum operation, &
Environmental requirements
Dataandmodelbaseddecisions
Fig. 1. Overview of plant control and optimization at different layers.
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167156
3. covered by [13] include data collection, system identification and
control valve stiction monitoring, and performance monitoring
of MPC.
In [14], process-model is implemented in Matlab/Simulink and
controller is implemented in ABB's commercial OPC-MMS (Manu-
facturing Message Specifications) server. The connection between
these two is through a gateway. ABB has its own OPC-MMS server
that makes it possible to write and read global MMS variables from
the controller. The OPC-MMS server acts as a MMS client to the
controller that it is connected to. Then the OPC-MMS server
converts the global MMS-variables from the controller to OPC items
that can be read/write from OPC-clients.
In [15], a tutorial is provided for the OPC based applications in
Labview environment. It should be noted that LabVIEW supports
only the OPC Data Access specification [15]. The tutorial discussed
the use of new Compact Field Points from the National Instru-
ments. The importance of using standard components in automa-
tion industry has been discussed in [6] with detailed OPC
Specification. The focus in [6] was on the automation software
integration based on OPC standards in view of the multi-tire
architecture of industrial automation system.
Experimental validation of PID based cascade control system
through different architectures including SCADA, PLC, OPC and
Internet architectures has been addressed recently in [16]. The
performance and effectiveness of individual architecture is eval-
uated on the basis of data rate, rise time, peak time and settling
time. In this setup, a PID controller is implemented on Micrologix-
1200 PLC and RSView-32 SCADA has been used with RSLinx
communication software. The control loop for SCADA-PLC is
implemented with the functionalities such as real time data
analysis, set point modifications, automatic report generation
and integration of data with MS-Excel and MS-Access.
The Internet has become an indispensable technology in
today's globalized world. Besides the many conveniences to every-
day life, Internet has also become indispensable for process control
systems [17]. Integration of OPC technology and Internet is quite
straightforward, and thus brings many facets of improved archi-
tecture for monitoring and control applications in economical way.
A web-based distributed OPC system has been developed for
remote control and monitoring in [17]. The described system
consists of N different local control units over the Internet
realizing a distributed OPC (DOPC). Every local control unit can
control and monitor every other control point in the DOPC
architecture. The architecture permits different OPC-based process
control architectures to realize a distributed and heterogeneous
system. Dynamic web page which is constructed using Active
Server Pages (ASP) are used for remote control and monitoring.
OPC based remote real-time communication between MATLAB
and PLC (Siemens S7-300) has been reported in [18] on the
Ethernet. This setup indicates that the function of exchanging
Fig. 2. An example of using OPC technology for the interconnection of heterogeneous systems [3].
Fig. 3. Using OPC technology to interconnect multi-tire systems [4].
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167 157
4. remote real-time data can be attained between MATLAB and
S7-300 PLC through OPC server, and shows that it is an effective
and feasible method to realize the real-time remote communica-
tion between MATLAB and PLC. Such a method can be used to
realize data process and advanced control in industry to improve
the quality of control, safety, optimization and to comply with
standards. MATLAB OPC Toolbox simplifies the process of devel-
opment and provides an effective method to realize the remote
real-time communication between MATLAB and process devices
having OPC connectivity. With the set up discussed in [18], one can
realize advanced control of complicated industrial process based
on network environment to devise improved control, economic
operation and to comply with environmental standards.
Remote control over Ethernet is demonstrated for an induction
drive system using OPC technology in [19]. This set up uses
OMRON PLC, LabVIEW and NI OPC server which has OMRON
Ethernet driver that allow the communication between OMRON
PLC with LabVIEW.
A cost effective OPC based application is reported in [20] for a
boiler system. This setup uses OPC technology, MATLAB interface
(working as OPC client), and Siemens configuration software
WinCC (working as OPC server). Control algorithm (at OPC client)
developed in Simulink communicate with OPC DA server of
industrial control software WinCC. Such setups offer a new way
of thinking for application of intelligent control algorithm in
industrial process control [20].
The importance of simulators to facilitate the execution of
engineering activities of control systems is discussed in [21].
Simulation tools offered by the main DCS suppliers (ABB's Simpow
and Simcomx ITS, Delta V/Emerson's Delta V Simulate, Foxboro/
Invensys's FSIM, Honeywell's Shadow plant, Siemens's SIMIT and
SIVAT, and Triconex's TRISIM) include the OPC connection that
enable one to include real time system into the simulators [21].
Industrial systems are designed for robustness, and typically
have limited possibilities for research, optimization, testing and
simulation [22]. Co-simulation environment based on OPC tech-
nology and numerical simulation package like MATLAB can pro-
vide good learning and research opportunity. Educational
examples of OPC-driven data exchange between MATLAB and
PLC-controlled Systems is discussed in [22]. A solution is discussed
in [22] on how to connect an existing system to the Matlab
environment by using the OPC. Through an OPC connection, the
process data can be used for the analyses and optimization of
procedures, early fault detection and diagnosis, further data
processing or data documentation. Persin et al. [22] presented an
example where synchronous reading is used for the ‘worst case’
scenario. In this scenario each piece of data changes every time
and the same computer run a server, two clients, as well as the
SCADA and Matlab software. The OPC based real time simulators
are beneficial for the training of engineers and operators in
industry [22]. In [23], the practical design and implementation of
a network-based cascade control system on a real lab pilot plant
using alternative FB and NCS control approaches.
A methodology for the development of distributed process simula-
tion based on OPC for the complex continuous processes is presented
in [24]. The distributed components are supposed to operate as OPC
servers enclosing continuous simulations. The simulation method is
applied to a large process simulator of a beet sugar factory used for
control room operator training. The simulator includes a process
simulation operating in a network of six computers, a SCADA system
for operation on the process, an instructor console and the corre-
sponding software for real time communication and synchronisation
[24]. Several advantages of such simulation approach include:
1. independent development of the simulation from the commu-
nication mechanisms,
2. wide range of applications can access the simulations, due to
the standard use of OPC,
3. low cost and
4. large scale simulations support.
OPC due to its several advantages has been adopted by most of
the companies in the process control sector as a standard for
communications among control and instrumentation equipment
[24]. The simulator by [24] operates in the CTA, a joint research
center between the University of Valladolid and AEA, a sugar
company.
Another application of OPC supported simulation environment
is reported in [25]. The work in [25] is focused on developing a
hardware-in-the-loop (HIL) system for a boiler-turbine process,
employing decoupled adaptive control based on gain scheduling
technique. Simulation environment for the boiler-turbine process
is carried out in Labview running on a PC under a general purpose
operating system. Communication is provided by an NI OPC server
at the software level, while Industrial Ethernet is used at the
physical level.
3. OPC—specifications and tools
OLE for Process Control (OPC), also known as Open Process
Control, and as Openness, Productivity, and Connectivity, is a series
of specifications defined by the OPC Foundation for supporting
open connectivity in industrial automation [1]. OPC has been
designed to serve the need for reliable communication of informa-
tion in process and manufacturing industry, such as a petrochemical
refinery, an automobile assembly line, or a paper mill [7].
The OPC Foundation portfolio of standards includes three core
parts: OPC Classic (OPC DA, OPC HDA, OPC A&E), OPC Xi, and OPC
Unified Architecture (OPC UA).
3.1. OPC Classic
Classical OPC was based on client/server model and uses
Microsoft ActiveX and DCOM technology to provide a commu-
nication link between OPC servers and OPC clients. The OPC
Foundation developed the first specification, called Data Access
Specification 1.0a, in early 1996. Using this specification, vendors
were able to quickly develop client/server software.
OPC Data Access, or OPC DA, provides access to real time
process data. Using OPC DA, a client requests the OPC server for
the most recent values of flows, pressures, levels, temperatures,
densities, and more [7]. OPC Historical Data Access, or OPC HDA, is
used to retrieve and analyze historical process data, which is
typically stored in a Process Data Archiver, database, or RTU. OPC
Alarms and Events, or OPC A&E, is used to exchange process
alarms and events. OPC Xi is a new addition to the OPC Foundation
portfolio.
3.2. OPC Xi
OPC Xi was produced as the result of collaboration of several
OPC Foundation vendor companies to develop an easily integrated
and secure OPC solution that provides a .Net (dot NET) migration
path from OPC Classic. This is called OPC Express Interface (Xi).
OPC Xi is based on Microsoft .NET technology [7].
3.3. OPC UA
The next generation of OPC technology is OPC Unified Architecture
(OPC UA). OPC UA is based on the functionality of all the OPC Classic
Specifications (OPC DA, OPC A&E, Commands, and Complex Data). OPC
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167158
5. Unified Architecture is designed to be platform independent, scalable,
secure, and provide high performance [7]. Core technologies for the
new OPC developments include XML, SOAP and WebService.
4. Advanced process control
The modern computer era also brings us other advanced non-
linear and adaptive controllers, automation of supervisory real-time
economic optimization of controller set points, computer perception
and monitoring of status and health to trigger corrective actions,
control of inferential variables, computer-based planning and sche-
duling, supervisory process health analysis, fault detection and
abnormal situation support, device function and reliability.
There are advanced control versions for regulatory control, some-
times also termed as ARC (Advanced Regulatory Control). ARC meth-
ods include gain scheduling, ratio, cascade, feedforward, decouplers,
override, and related and ancillary techniques such as anti-windup,
bumpless transfer, PID modifications, and adaptive tuning techniques.
APC (Advanced Process Control) has many meanings to the
industrial and academic community. Within the model predictive
control (MPC) community, APC means MPC [26].
Generally, any advanced control method, that is implemented
over the traditional regulatory control for optimum performance,
can be considered as an advanced process control.
This paper is primarily focused on OPC based implementation
to provide a platform for future studies in advanced process
control, therefore APC is not discussed in detail. A good discussion
of APC methods and their economic benefits can be found in
[10,26] and references therein. Readers are referred to [10,26] for
details discussion and economic benefits of APC methods.
4.1. OPC: cost effective way to APC
Millions of dollars are invested in a typical process plant in terms of
DCS and historians. Now, OPC standard is an integrated part of
available DCS systems and also a part of academic and research tools
(e.g., LabVIEW and MATLAB). Wide horizon of OPC tools covering the
DCS systems and academic environment encourages to use OPC for
return on the investment. This is economical in the sense that all the
departments at the plant floor including production, engineering,
maintenance, and management can have access to the historian via
OPC standard even in case of different software systems. According to
[27], OPC is the best economical and easiest way to study the plant
behavior and to find the points where the operation can be exploited
for better operation.
5. Mathematical analysis
Consider a nonlinear dynamic system,
_xðtÞ ¼ f ðxðtÞ; uðtÞÞ; xðt0Þ ¼ x0 ð1Þ
where xARn
and uARm
are the state and control vectors respec-
tively. Suppose that the control input is constraint to a compact
and convex set U , i.e., uðtÞAU. Generally, the MPC problem can be
stated as (see [28]): for any state x at time t, find a continuous
function uðτ; xðtÞÞ : ½t; tþTŠ-U, in a moving horizon time frame, T,
such that the performance index
J ¼ gðxðtþTÞÞþ
Z T
0
xðtþτÞT
QxðtþτÞ
þuðtþτ; xðtÞÞT
Ruðtþτ; xðtÞÞ dτ ð2Þ
is minimized where Q Z0; R40. Then the MPC law is determined
by uðtÞ ¼ uðt : xðtÞÞ. To establish the stability notion, we followed
from [28] that the system in (1) obeys the following assumptions:
5.1. Assumptions
1. Let the function f : Rn
 Rm
-Rn
be twice continuously differ-
entiable and f ð0; 0Þ ¼ 0 is an equilibrium point with u¼0.
2. g(x) in (2) is continuously differentiable function of x, and
gð0Þ ¼ 0; gðxÞ40 for all xARn
; xa0.
3. The system (1) has a unique solution for initial condition x0 ARn
and any piece-wise continuous and right-continuous.
4. The nonzero state of the system is detectable in the cost.
5. All the states are available for the control.
Theorem 1. Suppose that Assumptions 1–5 are satisfied and the
MPC algorithm is feasible at time t ¼ t0. Then the MPC algorithm for
system (1) is asymptotically stable if there exists control u(t) such
that the following condition is satisfied:
∂gðxÞ
∂x
f ðx; uÞþxT
QxþuT
Rur0 ð3Þ
for any state x belonging to the terminal region.
Proof. See [28] for proof. □
It has also been shown in [31] that the terminal constraints in
the MPC formulation ensure stability.
Remark. It has been mentioned in the literature on MPC that
often there is no theoretical proof of stability for industrial MPCs.
Simulations are used to check stability and performance [29]. This
is inline of what we have accomplished.
5.2. Procedure
The overall procedure can be listed as below:
Step 1 – Modeling, Control and Simulation: Assume that a plant
_x ¼ f ðx; uÞ is given, with the control u ¼ gðx; uÞ ensuring closed
loop stable system. The system is stabilized with the local PID
controllers for stable set point tracking. This closed-loop plant
is simulated to run continuously using LabVIEW's periodic
server capability. Deploy the closed-loop plant simulation as
OPC server which makes the selected process variables avail-
able to OPC client applications over the network.
Step 2 – Identification: The input–output data of the continuously
running simulated plant is accessed via OPC connection to identify
an ARX model of the plant. Small step testing is conducted around
the operating point which is close to the real practice in industry.
The identified MIMO ARX model is represented by (4)–(6).
Step 3 – MPC Design: The identified model in Step 2 is used to
design an MPC controller. The MPC takes into account the
disturbance variable (steam demand in the example studied)
and the NOx which are not cared by the low-level PID controllers.
Step 4 – Testing of MPC: The designed MPC is tested over the
existing PID controlled plant over the OPC connectivity. This
cascaded MPC-PID control will result in improved disturbance
rejection and control over the emission of NOx while the
set-point tracking for drum pressure and drum level are
satisfactory.
6. Analytical framework
This section discusses the analytical framework considered in this
paper. This framework can be defined in the following major
categories: (1) continuous plant simulation, (2) OPC tools for data
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167 159
6. connectivity, (3) identification of closed loop system, and (4) adv-
anced process control design.
Practically, process plant are in continuous operation when
disturbances (both measured and unmeasured) affect the output
of the process. The effect of drifts, ageing and noise in sensing and
actuation devices also affect the plant operation. These facts drive
the operation people to find some optimum way to bring the
process plant to optimum performance without stopping the plant
or affecting the operation too much. It is also tedious to re-tune
the individual PID loop parameters throughout the plant. Due to
the coupling, tuning of one PID may affect and de-tune another
coupled loop. Model predictive control technique is one of the best
advanced process control techniques to optimize the closed loop
operation without changing PID parameters. MPC can be used
with local PID loops in a cascade scheme to improve the overall
performance and to meet the control objectives (see Fig. 4).
To achieve the objectives of the paper, we have made a
continuous plant simulation. LabVIEW is used to deploy the plant
as periodic server with process variables defined as shared vari-
ables. So, the important process variables are published for OPC
clients. Matlab's simulink is used on another computer equipped
with the OPC toolbox. The plant simulation is accessed from
Matlab via OPC toolbox to make closed-loop identification of the
continuous process. The identified model is then used to develop a
Model Predictive Controller. This MPC controller can be tested
back in Matlab's Simulink via OPC functions of the OPC toolbox.
6.1. Plant modelling, control and simulation
Plant model used in this paper is a nonlinear boiler model
originally proposed in [11]. The nonlinear model equations are
discussed in Appendix A. The model has four inputs (qs, qf, Q,
Excess Air), two outputs (steam pressure and drum water level)
and four states (steam pressure, total volume of water (Vwt),
steam-mass fraction in risers (αr), steam volume in drum, (Vsd)).
Further research and studies around this model enabled
researcher to propose estimator for the undesired outcome of
boiler combustion which is Nitrogen Oxides, normally known as
NOx. In this paper, the neural network based NOx estimator
proposed by [32] is also used to estimate the NOx. With the
addition of NOx estimator in the above boiler model, the output
becomes (steam pressure, drum water level, and NOx).
6.2. Closed-loop identification
The closed loop identification is based on the Matlab's Identi-
fication Toolbox. First the data is collected via OPC connectivity.
Then Matlab's ident is used to identify the MIMO model to be used
in MPC formulation. In order to make similar to real scenario, a
graphical user interface is designed on LabVIEW to conduct step
testing, and data collection. The interface is designed rich for
testing, monitoring and control. Fig. 5 shows the front panel of the
overall configuration. The figure shows the closed loop boiler
simulation which is running in LabVIEW (as OPC server), while
MPC interface is in Matlab (as OPC client). Steps are applied on
pressure setpoint, level setpoint, excess air and steam demand,
and data is saved for identification. After several iterations, best
available model (a MIMO ARX model) is selected for the design of
MPC controller. Fig. 6 shows the validation of the identified model
over a set of plant data. The plant model is identified as a
multivariable ARX model with the following structure:
A0nyðtÞþA1nyðt ÀTÞþ⋯þAnnyðtÀnTÞ
¼ B0nuðtÞþB1nuðtÀTÞþ⋯þBmnuðt ÀmTÞþeðtÞ ð4Þ
The discrete transfer function model between an input and an
output is represented by
yðkÞnAðzÞ ¼ uðkÀdÞnBðzÞþe ð5Þ
yðkÞ ¼ a0yðkÀ1Þþa1nyðkÀ2Þ⋯þb0uðkÀdÀ1Þ
þb1uðkÀdÀ2Þ⋯þeðkÞ ð6Þ
Matlab's ARX routine is used to solve A and B parameters to
minimize the error between real output data and projected output
data while assuming that the error signal is white noise.
The considered framework is a cascaded configuration of MPC
over PID control loops as shown in Fig. 4. For a clearer discussion,
the process variables are given appropriately different symbols.
In Fig. 4, SPMPC represent the setpoints at MPC controller, MVMPC
represent the manipulated variables of MPC, CVMPC are the con-
trolled variables from MPC point of view, while DVMPC represent
the measured disturbance in MPC context. From PID aspect, SPPID
represent the setpoints at the local PID controllers, UPID represent
the control signals after PID controllers, YPID represent the output
of the plant from PID point of view. Note that in the defined
scenario, MVMPC ¼ SPPID and CVMPC ¼ YPID.
6.3. Model Predictive Control
Before implementing MPC strategy, the terminologies of the
process variables from MPC point of view should be reviewed. The
commonly used description of the process variables in an MPC
project include defining variables as MVs, DVs, SPs, and PVs. These
terms are explained below:
Plant
PID
ControllersMPC
MVMPC SP PID UPID CVMPC
SP MPC
Setpoint at MPC
Manipulated
variable of MPC
PID
Setpoints
Manipulated variables
of PIDs
Controlled
variables
of the plant
Disturbance Variable DVMPC
OPC based communication (e.g., via plant historian)
YPID
MPC defined variables: SPMPC, MVMPC, CVMPC, DVMPC
PID defined variables: SP PID, UPID YPID.
where, MVMPC =SP PID, and CVMPC = YPID
Legends:
Fig. 4. Block diagram of the advanced controller (MPC) over low level PID control loops.
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167160
7. Manipulated variables (MV): These are the variables that the
controller can move (manipulate), for example set point of a
low level flow PID controller, or a valve position.
Controlled variables (CV): These are the variables that can be
measured (Process Variables, PV) and have a target. The
controller minimizes the difference (error) between the PV
and the target value. Sometimes it is hard to have a direct
measurement of process variable (e.g., NOx emission in case of
boilers). Such variables can be treated as controlled variables
while their measurement is indirect (e.g., using a softsensor).
Feedforward variables or disturbance variables (DV): These
are the variables available to the controller but the controller
cannot manipulate or control them. But, taking them into
prediction helps improve controller steps and good quality of
control.
Constraint variables or limit variables: These include measured
variables which are controlled within limits. The good con-
troller minimizes or eliminates the limit violations, thus
extending the operation range of the plant.
Predictive horizon: This is the number of prediction steps of the
process output. Usually this should be large enough to capture
the plant dynamics.
Control horizon: This is usually a subset of the projected horizon
when the controller adjusts the manipulated variables.
Fig. 5. Front panel of the overall configuration. Closed loop boiler simulation in LabVIEW (as OPC server), while MPC in Matlab (as OPC client).
2000 4000 6000 8000 10000 12000 14000
44
46
48
y1. (sim)
y1
arx551; measured
data1; fit: 95.75%
2000 4000 6000 8000 10000 12000 14000
0.6
0.7
0.8
y2. (sim)
y2
arx551; measured
data1; fit: 70.14%
2000 4000 6000 8000 10000 12000 14000
80
100
120
y3. (sim)
y3
arx551; measured
data1; fit: 62.19%
Fig. 6. Validation of the identified model on a data set from boiler model.
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167 161
8. In the considered example of boiler, MVs are the setpoints of
pressure, level, and excess air, PVs (CVs) are steam pressure, water
level in the boiler, and Nitrogen Oxides as emission from boiler, DV
is the steam demand.
6.4. MPC design for tracking
In this work, the MPC formulation for setpoint tracking is based
on [33,34]. In tracking, the control u is adjusted such that the
output y tracks a time-varying set-point. The tracking accuracy
depends on the plant characteristics, constraints on the system
variables, and the accuracy of the input u to output y model. For
MIMO plants, the challenge is to tune the controller to achieve
multiple objectives. In case of several outputs to be controlled, the
important outputs are prioritized for accurate setpoint tracking
then the less important outputs when encounter the constraints.
In MPC, optimization problem is solved much like the LQG optimal
control except that the MPC optimization includes explicit con-
straints on u and y and optimizes over a finite horizon.
In setpoint tracking, the primary control objective is to drive
the plant output to track their corresponding setpoints. Specifi-
cally, the controller predicts how much each output will deviate
from its setpoint within the prediction horizon. In optimization,
controller multiplies each deviation by the output weight, and
computes the weighted sum of the squared deviations. The
optimization objective used in the MPC design for the setpoint
tracking is given below:
J ¼ ∑
P
i ¼ 1
∑
ny
j ¼ 1
wy
j ½srjðkþiÞÀyjðkþiÞŠ2
þ ∑
M
i ¼ 1
∑
nmv
j ¼ 1
wΔu
j ΔujðkþiÀ1Þ2
ð7Þ
where k is the current sampling interval, kþi is a future sampling
interval, P is the prediction horizon, ny is the number of the plant
outputs, wj
y
is the weight for output j, ½rjðkþiÞÀyjðkþiÞŠ is the
predicted deviation at future instant kþi, M is the control horizon,
nmv is the number of manipulated variables, ΔujðkþiÀ1Þ is the
prediction adjustment (i.e., move) in the manipulated variable j at
future (or current) sampling interval kþiÀ1, and wΔu
j is a weight,
which must be zero or positive. If wy
j owy
i aj, the controller does its
best to track rj, sacrificing ri tracking if necessary. If wy
j ¼ 0, the
controller completely ignores deviations rj Àyj. The weights are
critical to tune the controller for desired behavior.
The second term of the optimization cost is used by MPC
controller to monitor the weighted sum of the controller adjust-
ments. Increasing wΔu
j forces the controller to make smaller, more
cautions Δuj moves. The small control moves may result in
degraded setpoint tracking in some cases.
All the process control systems have some constraints to be
satisfied for stable and safe operation. These constraints may be
physical (e.g., actuator limits) or to ensure safety (e.g., low–low or
high–high pressure and temperature limits). In addition to these
constraints, there might be some soft-constraints related to the
performance and optimum operation like overshoot. These con-
straints can be incorporated into an MPC formulation according to
the following categories:
constraints on outputs: ymin ryrymax,
constraints on the rate of control signal: Δumin rΔyrΔumax,
constraints on the control signal: umin rurumax.
The constraints of the considered boiler system are given below:
Input constraints are:
30ru1ðPressure SetpointÞr80,
0:35ru2ðLevel SetpointÞr1,
3ru3ðExcess AirÞr25.
Output constraints are:
35ry1ðPressureÞr70,
0:3ry2ðLevelÞr1,
0ry3ðNOxÞr100.
6.5. Stability tests
Process plants are operated within safe limits to ensure stable and
safe operation. The PID loops of the presented example are tuned for
stable operation at the nominal operating points. The PIDs are also
Fig. 7. Simulink block diagram showing OPC based MPC interface with closed-loop boiler model.
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167162
9. tested for tracking setpoints of pressure and level with satisfactory
follow up. In the following lines, we discuss the closed-loop stability
of the overall system when MPC is designed and implemented on the
existing PID loops. One way to ensure stability of MPC controller is by
adding terminal constraints and keeping the manipulated variables
well into the prescribed limits [31]. MPC in the presented example is
designed using Matlab's mpctool. This is a graphical user interface
which allows rich environment to design a full MPC controller along
with testing scenarios. In addition to this, there is a capability to
review the MPC controller. After designing the MPC controller,
“review” command is used to check the designed controller [34].
The designed controller passed all tests including the Controller
Internal Stability Test, Closed-Loop Nominal Stability Test, and Hard
MV Constraints Test. Online testing of the MPC controller showed
that all process variables satisfy the constraints.
7. Network-in-the-loop (NIL) simulation setup
This section describes the network-in-the-loop simulation
setup of the studied example. This setup involves Boiler simula-
tions and APC design over Ethernet network using two different
computer systems. One computer system is running OPC server
with a nonlinear Boiler simulations. OPC client is configured on
another computer system over the Ethernet. The OPC client and
server are built using LabVIEW's Shared Variable Engine. Second
setup shows the integration of MATLAB, and LabVIEW on the same
local host. This setup shows the complete picture of the distrib-
uted control using OPC technology.
7.1. Boiler simulation setup over Ethernet
This section describes the setting up of an OPC-based scenario
for potential research in distributed control systems using com-
munication networks. Softwares used are Mathwork's MATLAB
and National Instruments LabVIEW and the communication net-
work used is a shared TCP/IP Ethernet Network.
This setup is developed with the help of MATLAB–LabVIEW
based co-simulation environment to run a nonlinear plant simula-
tion. An OPC server from National Instruments which is Shared
Variable Engine is configured on the Simulation Computer. The OPC
server is connected to the process variables of the continuously
running nonlinear plant simulation via shared variables. The shared
variables are published on the communication network for other
OPC clients. On client side, LabVIEW is used to setup an OPC client.
During the setup, DCOM settings for OPC client and OPC server
are configured appropriately. Security settings for the user has to be
carefully configured before having a successful communication
between the OPC server and client. It is recommended that the
interactive user (i.e., the user who is currently logged in) should be
selected to ensure that the OPC client can communicate with the
OPC server. Note that the DCOM level settings are accessible from
the Component Services. The Component services can be accessed
by entering “dcomcnfg” in RUN option in windows. It is important
to note that if the interactive user is disabled or not available in the
corresponding “opcEnum.exe”, one can use same login name and
password in the “This User” option which is available through
properties of opcEnum. opcEnum (abbreviation of OPC Enumera-
tor), is an inter-computer communications driver which is essentialFig. 8. OPC based distributed control system.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
40
45
50
Pressure
with MPC
without MPC
Set point
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0.6
0.8
1
Level
with MPC
without MPC
Set point
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
100
200
NO
with MPC
without MPC
Set Point
Fig. 9. Comparison of steam pressure, drum level, and NOx with and without MPC.
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167 163
10. for OPC data transfer in a remote machine on a network or domain
(Figs. 7 and 8).
7.2. Simulation results
Simulation results are shown in Figs. 9–12. The emission of
NOx is not controlled by the PID control loops. With the addition
of MPC, the pressure and level setpoints are tracked satisfactorily
and also the NOx is regulated to much lower values are compared
to the case of PID control only. The setpoint for NOx is 40 in the
current simulation. Operation point of the boiler model is pressure
setpoint (44.9 bar), level (0.64 cm), and excess air (16%). MPC
manipulates excess air Q, qf and _mf to keep the NOx low and
ensuring stable set point tracking of pressure and level. Fig. 9
shows matching performance of controller configurations with
and without MPC for pressure and level setpoint tracking, while
MPC outperforms the local PID controllers in controlling NOx to
the desired setpoint.
8. Remarks and issues about OPC tools
Setting up OPC for control applications may present different
levels of difficulty and complexity depending upon intended
applications and objectives. Setting up OPC client/server has
different issues to tackle from different aspects. Due to the
diversity of issues, we discuss them in the following sections as
different entities based on [30]:
Although using OPC in control applications increases the
connectivity options but at the cost of uncertain processing
time of the OPC application because OPC is based on Micro-
soft's Operating System services. When OPC application is
deployed on shared network like Ethernet, the network com-
munication delay adds the uncertainty of the overall
communication delay.
Since, OPC uses operating system services, it depends on the
version of operating system used for server and client. This is
because the operating systems like Windows XP (and its
variants and service packs) pose different security options than
Windows 7 for example. This actually needs a very careful
DCOM settings on server and client sides.
Version of Windows to be used for the deployment of OPC
client and OPC server can raise very important issue due to
different types of security embedded in that version.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
30
40
50
60
70
80
90
100
110
120
Q
with MPC
without MPC
Fig. 12. Comparison of heat flow rate to the risers (Q) with and without MPC.
Fig. 13. Schematic picture of the boiler.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
8
10
12
14
16
18
20
22
24
26
28
30
Excess Air
with MPC
without MPC
Fig. 11. Comparison of heat flow rate to the risers (Q) with and without MPC.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
40
42
44
46
48
50
52
Steam Demand
Fig. 10. Steam demand as measured disturbance.
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167164
11. Interactive User setting in the Windows Component Services is
also important to configure appropriately. In case interactive
user is not used, same user and password should be configured
in the component service for the opcEnum and the correspond-
ing OPC server (for example National Instruments Variable
Engine).
9. Conclusions
In our vision, OPC technology is the economical solution in
introducing new APC technologies to a real process. Control
algorithms for local regulatory control loops are quite standard
and off-the-shelf available. The research potential is to develop
and test control algorithm for distributed and interconnected
systems. OPC provides a good interpretability option between
different systems. This work describes the integration of important
research tools like MATLAB and LabVIEW to build periodic OPC
servers (to run closed-loop process plant continuously) and OPC
clients (for interactive identification and advanced controller
design). The presented OPC based setup is important for academic
and research community interested in advanced process control,
networked control systems, and distributed control. In the pre-
sented example, OPC server is simulating a nonlinear boiler model,
while OPC client is developed for identification and control. An
MPC controller is designed over two local PID control loops of a
nonlinear boiler. This MPC added NOx control in the overall
control objectives of the simulated example. The presented exam-
ple motivates how existing MIMO control systems can be extended
with advanced control techniques to include uncontrolled vari-
ables like NOx emission in boilers. The MPC is interfaced with the
local PID controller via OPC toolbox from MATLAB's SIMULINK
environment.
Acknowledgments
The authors would like to thank the deanship for scientific
research (DSR) at KFUPM for financial support through research
group project RG-1316-1 and partial support from NSTP.
Appendix A
The model has been adapted from the work of Astrom and Bell.
The considered boiler is 160 MW oil fired boiler unit in Sweden. In
this model, much of the system behavior is captured by consider-
ing the mass and energy balance for total system so that a fourth
order non-linear state space model can be obtained. The model
describes the complicated dynamics of the drum, downcomer, and
riser components. It is derived from the first principles and is
characterized by a few physical parameters and can be easily
scaled to represent any drum power station. The basic schematic
of a boiler is given in the following figure as shown by Astrom
and Bell.
In Fig. 13, Q is the heat applied on the riser tubes. This applied
heat causes the water in the drum to boil. The applied heat also
causes saturated steam to rise in riser–drum–downcomer loop.
Feedwater, qf, is the flow rate of water being supplied to the boiler.
Saturated steam, qs, is the flow rate of the steam which is fed to the
superheaters and the turbine.
A.1. Model derivation
A simple model of the drum boiler that captures the pressure
dynamics very well is a second order model based on the global
mass and energy balances.
Three inputs to the model are qs; qf ; Q and two measurable
outputs are drum pressure, p and the drum water level, l.
Standard notations used to write the balance equations are V
denotes volume, ρ denotes specific density, u specific internal
energy, h specific enthalpy, t temperature and q mass flow rate.
Also the subscripts s; w; f and m refer to steam, water, feedwater
and metal, respectively. The double subscripts, t; d and r denoting
the total system, drum and the riser, are used for clarification of
the system components. The total mass of the metal tubes and the
drum is mt and the specific heat of the metal is Cp.
The global mass balance is
d
dt
½ρsVst þρwVwtŠ ¼ qf Àqs ð8Þ
The global energy balance is
d
dt
½ρsusVst þρwuwVwt þmtCptmŠ ¼ Q þqf hf Àqshs ð9Þ
Since the internal energy is u ¼ hÀp=ρ, the global energy balance
can be written as
d
dt
½ρsusVst þρwhwVwt ÀpVmt þmtCptmŠ ¼ Q þqf hf Àqshs ð10Þ
The total volume of the drum, downcomer, and risers is
Vt ¼ Vst þVwt ð11Þ
These equations along with saturated steam tables capture the
gross behaviour of a simple boiler and describe the drum pressure
responses due to input qf and qs fluctuations. The second order
model which follows describes the total water in the system but
does not capture the drum water level dynamics because the
distribution of steam and water are not included. The state
variables for the state model are p and Vwt.
e11
dVwt
dt
þe12
dp
dt
¼ qf Àqs
e21
dVwt
dt
þe22
dp
dt
¼ Q þqf hf Àqshs ð12Þ
where
e11 ¼ ρw Àρs
e12 ¼ Vst
∂ρs
∂ρ
þVwt
∂ρw
∂ρ
e21 ¼ ρwhw Àρshs
e22 ¼ Vst hs
∂ρs
∂ρ
þρs
∂hs
∂ρ
þVwt hw
∂ρw
∂ρ
þρw
∂hw
∂ρ
þmtCp
∂t
∂s
ð13Þ
But the serious deficiency in this simple model lies in its failure to
model drum water level. Although it does determine the total
amount of water in the system it does not take into account the
steam in the risers and below the water surface level in the drum.
To do this separate mass and energy balances must be written for
the risers and the drum.
Riser dynamics: The global mass balance for the riser is
d
dt
ðρsανVr þρwð1ÀανÞVrÞ ¼ qdc Àqr ð14Þ
where αν is the average volume fraction in the risers, qr is the total
mass flow rate out of the risers and qdc is the total mass flow rate
into the risers.
The global energy balance of the riser section is
d
dt
ðρshsανVr þρwhwð1ÀανÞVr ÀpVr þmrCptsÞ
¼ Q þqdchw Àðαrhc þhwÞqr ð15Þ
M.S. Mahmoud et al. / ISA Transactions 55 (2015) 155–167 165
12. Eliminating the flow rate out of the risers, qr, multiplying Eq. (14)
by Àðhw þαrhcÞ and adding to Eq. (15) give
d
dt
ðρshsανVrÞÀðhw þαrhcÞ
d
dt
ðρsανVrÞ
þ
d
dt
ðρwhwð1ÀανÞVrÞ
Àðhw þαrhcÞ
d
dt
ðρwð1ÀανÞVrÞ
ÀVr
dp
dt
þmrCp
dts
dt
Þ ¼ Q Àαrhcqdc
This can be simplified to
hcð1ÀαrÞ
d
dt
ðρsανVrÞþρwð1ÀανÞVr
dhw
dt
ðÀαrhcÞ
d
dt
ðρwð1ÀανÞVrÞþρsανVr
dhs
dt
ÀVr
dp
dt
þmrCp
dts
dt
Þ ¼ Q Àαrhcqdc ð16Þ
Drum dynamics:
The dynamics for the steam in the drum is
ρs
dVsd
dt
þVsd
dρs
dt
þ
1
hc
ρsVsd
dhs
dt
þρwVwd
dhw
dt
ÀðVsd þVwdÞ
dp
dt
þmdCp
dts
dt
þαrð1þβÞVr
d
dt
ð1ÀανÞρw þανρsÞ
¼
ρs
Td
ðV0
sd ÀVsdÞþ
hf Àhw
hc
qf ð17Þ
Astrom and Bell conveniently chose four state variables with good
physical interpretation that describe the storage of mass, energy and
momentum. These state variables capture the pressure, water, riser,
and drum dynamics. The state variable for the drum pressure p
represents the total energy. The state variable for the total water
volume Vwt represents the accumulation of water. The state variable
for the steam mass fraction or quality in the riser outlet αr represents
the distribution of steam and water. Finally, the state variable for the
steam volume under the liquid level inside the drum is represented by
Vsd. The time derivatives of these state equations can be rewritten as
e11
dVwt
dt
þe12
dp
dt
¼ qf Àqs
e21
dVwt
dt
þe22
dp
dt
¼ Q þqf hf Àqshs
e32
dp
dt
þe33
dαr
dt
¼ Q Àαrhc Àqdc
e42
dp
dt
þe43
dαr
dt
þe44
dVsd
dt
¼
ρs
Td
ðV0
sd ÀVsdÞþ
hf Àhw
hc
qf ð18Þ
where
e11 ¼ ρw Àρs
e12 ¼ Vst
∂ρs
∂ρ
þVwt
∂ρw
∂ρ
e21 ¼ ρwhw Àρshs
e22 ¼ Vst hs
∂ρs
∂ρ
þρs
∂hs
∂ρ
þVwt hw
∂ρw
∂ρ
þρw
∂hw
∂ρ
ÀVt þmtCp
∂t
∂s
e32 ¼ ρw
∂hw
∂ρ
Àαrhc
∂ρw
∂ρ
ð1ÀανÞVr
e33 ¼ ðð1ÀαrÞρs þαrρwÞhcVr
∂αr
∂αr
e42 ¼ Vsd
∂ρs
∂p
þ
1
hc
ρsVsd
∂hs
∂ρ
þρwVwd
∂hw
∂ρ
ÀVsd
ÀVwd þmdCp
∂ts
∂ρ
þαrð1þβÞVr αν
∂ρs
∂ρ
þð1ÀανÞ
∂hw
∂ρ
þðρs ÀρwÞ
∂αr
∂ρ
;
The outputs are chosen as the drum-level l and the drum pressure p:
l ¼
Vsd þVwd
Ad
where
Vwd ¼ Vwt ÀVdc Àð1ÀανÞVr
Steam tables are required to calculate hs; hw; ρs; ρw; ts; ∂hs=∂p; ∂hw=∂p;
∂ps=∂p; ∂pw=∂p and ∂ts=∂p at the pressure, p
Steam table was interpolated with a function using MATLAB,
the drum boiler dynamic model of Astrom and Bell is based on
physical parameters.
The set of nonlinear differential equations (18) representing the
time dependence of the state variables can be presented in a
matrix form as follows:
e11 e12 0 0
e21 e21 0 0
0 e32 e33 0
0 e42 e43 e44
2
6
6
6
4
3
7
7
7
5
dVwt=dt
dp=dt
dx=dt
dVsd=dt
2
6
6
6
6
4
3
7
7
7
7
5
¼
qf Àqs
Q þqf hf Àqshs
Q Àαrhcqdc
ρs
Td
ðV0
sd ÀVsdÞþ
hf Àhw
hc
qf
2
6
6
6
6
4
3
7
7
7
7
5
A.2. Augmentation of NOx emission model
In [32], an artificial neural network based softsensor for the NOx
emission is proposed. This NOx estimator requires few parameters
from the boiler system including fuel flow rate, low heating value,
flame maximum temperature, average combustion chamber tem-
perature, and excess air to estimate the NOx emission.
EstimatedÀNOx ¼ f NOx
ð _mf ; LHV; Tmax; Tav; ExairÞ
where _m is the fuel flow rate, LHV is the low heating value, Tmax is
the flame maximum temperature, Tav is the average combustion
chamber temperature, Exair is the excess air, and f NOx
is the artificial
neural network based soft-sensor for estimating NOx. This NOx
estimator uses values from the nonlinear boiler model.
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