Industrial Process Control Systems Research Articles

An interaction predictive approach to fault-tolerant control in network control systems

This paper illustrates some of the capabilities of previously proposed network control system (NCS) architectures to carry on functioning in the event of faults, without recourse to system reconfiguration. The principle of interaction prediction is used to set up a coordination strategy that encapsulates an ability to withstand or tolerate certain faults, thereby allowing the system to continue functioning. It is also shown that the coordination strategy can be made more effective if a learning agent is allowed to learn the coordination functions. This facilitates the use of different types of agent at the local level, together with recurrent networks and genetic algorithms (GAs) at the coordination level. The experimental test-bed system is a benchmark three-tank system that has some of the main features of an industrial process control system.

Automatic generation of PLC automation projects from component-based models

Industrial process measurement and control systems (IPMCS) are used in most of the industrial sectors to achieve production improvement, process optimization and time and cost reduction. Integration, reuse, flexibility and optimization are demanded to adapt to a rapidly changing and competitive market. In order to achieve these goals, the use of standards in the application field, methodologies for defining the application design and tools for supporting the development cycle are needed. This paper proposes a component-based model of the implementation of the control system under design (the hardware and software architectures). The software architecture is defined to follow the software model of the IEC 61131-3 standard for programming programmable logic controllers and the hardware architecture is composed by equipment characterised by vendor and range of product. The proposed modelling is implemented as a markup language that allows to describe models of industrial control systems (icsML). From the application model, described in the markup language, the automation project for every programmable logic controller (PLC) present in the application is automatically generated.

A CFD assisted control system design with applications to NO x control in a FGR furnace

In this paper, a novel technique to design control systems for industrial processes with non-linear distributed parameters is proposed. The technique utilizes computational fluid dynamics (CFD) simulation to extract the most essential characteristics from the non-linear industrial process, and then represent them as a set of linear dynamic models around a specific operating point. Based on the linear dynamic representation, a closed-loop feedback linear control system can be designed to maintain the desired performance for the system around the chosen operating point. To illustrate such a design process, an industrial reheating furnace with flue gas recirculation (FGR) is selected herein. The method involves the numerical solution of the partial differential equations describing the fluid flow, heat transfer and combustion process in the furnace. The resulting dynamic relations between the furnace inputs and outputs can then be represented in terms of a multi-input and multi-output transfer function matrix. The objective of the control system is then to maintain the optimally selected furnace operating conditions and compensate for any deviations caused by disturbances to minimize the nitric oxides (NO x ) emission through feedback mechanisms. The performance of the closed-loop controlled furnace is evaluated not only in the linear domain, but also with the detailed full-scale non-linear CFD model. The results have shown that the proposed method is viable and the designed control system can indeed minimize the deviation of the furnace from the desired operating conditions and hence to prevent any excessive NO x formation in the combustion process.

Performance Assessment of Plantwide Control Systems of Industrial Processes

Performance assessment of control systems has been receiving growing attention in the recent past to improve operability and profit margins of the process. However, such studies, and also the tools available to carry out such studies, from the plantwide perspective are rather limited. In this regard, a new dynamic performance index, called dynamic disturbance sensitivity (DDS) is proposed in this paper. It is then used to assess the performance of three plantwide control (PWC) structures (CS1, CS2, and CS3) for an industrially important petrochemical process, namely, the hydrodealkylation (HDA) of toluene to produce benzene. The three control structures are distinctly different from the throughput manipulator standpoint: CS1 uses an internal manipulator (e.g., reactor temperature), CS2 uses a fixed-feed control strategy, and CS3 uses an on-demand control strategy to control throughput; consequently, the rest of the control structure decisions are significantly different. By analyzing the results from rig...

EVOLUTION CONTROL ENVIRONMENT FOR DISTRIBUTED AUTOMATION COMPONENTS

Future manufacturing is envisioned to be highly flexible and adaptable. New technologies for efficient engineering of reconfigurable systems and their adaptations are preconditions for this vision. Without such solutions, engineering adaptations of Industrial Process Measurement and Control Systems (IPMCS) will exceed the costs of engineered systems by far and the reuse of equipment will become inefficient. Especially the reconfiguration of control applications is not sufficiently solved by State-of-the-Art technology. This paper gives an overview of the use of evolution applications for downtimeless system evolution of control applications on basis of the standard IEC 61499. A new approach for the reconfiguration of IEC 61499 based control applications and the corresponding modeling is discussed. This new method significantly increases engineering efficiency and reuse in component-based IPMCS.

Attributes of industrial machine and process control systems

The design of advanced Machine and Process Control Systems (MPCSs) is highly complex and interdisciplinary in nature involving state-of-the-art technology. Machine and process control design is built on experience of major failures, requirements, optimisation and methodological change. Although, the underlying concepts of run-time architecture have already been firmly established, an exploration is necessary to radically improve the effectiveness as far as integrability is concerned. This paper reviews some of the important attributes of industrial MPCSs. Having a view of flexibility and agility, it is perceived that the MPCSs have a typical set of essential requirements. Especially, attributes such as Resource Conscious Software Development (RCSD), components-based electronic control, Diagnostics and Prognostics (DAP) and Integration of Data and Control Network (IDCN) are mainly discussed.

FORMAL MODELLING OF INDUSTRIAL DISTRIBUTED CONTROL SYSTEMS

Industrial Process Measurement and Control Systems (IPMCS) are used in most of the industrial sectors to achieve production improvement, process optimisation and time and cost reduction. Integration, reuse, flexibility and optimisation are demanded to adapt to a rapidly changing and competitive market. There is also a growing requirement that all software tools that support the different phases of the development process (design, configuration, management) can be integrated as well. Thus, a consolidation of modelling methodologies for achieving this goal is needed. This paper presents a formal modelling for IPMCS that captures all aspects of the system to design in terms of functionality and implementation (hardware and software). The modelling language (eXtensible Markup Language, XML) allow to implement model validation as well as to easily transform information coming from / going to different software tools, achieving tool integration.

The Effect of Practice on Visual Change Detection in Computer Displays

People are sometimes unable to detect changes in their field of view, especially when the change coincides with a distraction. This failure to detect visual changes is known as change blindness. Our research on change blindness is motivated by the increasing use of complex digital displays in both military and industrial process control systems. We are interested in the extent to which important changes in displayed information might be missed, and methods to decrease vulnerability to change blindness. The present study examined whether practice detecting specific changes would lead to better change detection performance in general. Participants practiced detecting specific visual changes, such as an icon changing from blue to red, using the flicker paradigm with feedback given after each trial. Practice did lead to improved detection performance for the practiced changes (i.e. the blue to red changes). As performance improved, reaction time declined as well. To assess the generality of this effect, following practice, participants were given a transfer test in which changes that had not occurred during practice were scheduled (e.g., green to yellow changes or icon shape changes). Performance on these novel changes was significantly worse than for the practiced changes and there was no significant difference between the within-category changes (such as green to yellow instead of blue to red), and the between-category novel changes (the shape changes). These results therefore suggest that the improvements observed were specific to the changes practiced. On the other hand, the possibility exists that the procedure used actually taught participants to ignore the features that changed in transfer. During practice, these features were presented, but never changed. If participants learned to ignore these features during practice, that would work to oppose any generalization of heightened visual attention. This and other factors that may have affected performance are discussed.

Methods for performance monitoring and diagnosis of multivariable model-based control systems

Methods for performance monitoring and diagnosis of multivariable closed loop systems have been proposed aiming at application to model predictive control systems for industrial processes. For performance monitoring, the well-established traditional statistical process control method is empolyed. To meet the underlying premise that the observed variable is univariate and statistically independent, a temporal and spatial decorrelation procedure for process variables has been suggested. For diagnosis of control performance deterioration, a method to estimate the model-error and disturbance signal has been devised. This method enables us to identify the cause of performance deterioration among the controller, process, and disturbance. The proposed methods were evaluated through numerical examples.

Nitrogen removal in a SBR using the OGAR process control system

OGAR is an industrial process control system that utilises on-line redox measurements to control the aeration sequence in an activated sludge process. Compared to conventional process control systems that use dissolved oxygen, OGAR makes use of redox as a control parameter during both aerobic and anoxic conditions. This paper reports on its first application in a sequencing batch reactor. The principal aim of this project is to demonstrate the capability of OGAR to achieve concentrations of total nitrogen in the treated effluent of less than 10 mg/L, which are typically stipulated by Queensland's Environmental Protection Agency (EPA). In this study the use of the control system resulted in the following SBR performance: consistent effluent total nitrogen less than 5.0 mg/L; ammonia concentration reduced from 31.6 mg/L to 0.32 mg/L, effluent nitrate 2.8 mg/L; DO setpoint 1.5 mg/L had 10% higher effluent total nitrogen compared to DO setpoint 4.0 mg/L and redox end-points for nitrification 400 mV, denitrification 150 mV.

A case Study on the Designing of Human Machine Interface for Industrial Process Control System

A case Study on the Designing of Human Machine Interface for Industrial Process Control System

Artificial intelligence-based sampling planning system for dynamic manufacturing process

Abstract The dynamic sampling instead of static sampling can more effectively utilize the inspection capacity for quicker excursion detection and increase the throughput of inspection machines without affecting the quality of inspection, so that achieve cycle time reduction. Accordingly, many researchers and semiconductor fabs start investigating the impacts of using dynamic sampling and there is currently a growing need for the dynamic sampling strategies in today's highly competitive semiconductor industry. Meanwhile, the use of artificial intelligence (AI) for knowledge discovery has become more common in industrial and manufacturing process control systems and recent advances in technology, particularly in networking, and information processing, have made the implementation of dynamic process sampling feasible. In this paper the optimal dynamic sampling method and the associated decision process based on AI technique are proposed and the effectiveness of them is validated through actual data sets collected from a semiconductor fabrication line. Finally, we present an AI-based dynamic sampling planning system incorporated the proposed methodology, which possesses four sub-components: wafer bin map (WBM) data mart, optimal sampling method generator (OSMG), sampling knowledge, and sampling adaptation monitor. Our research results provide an effective solution to implement a successful dynamic process sampling.

Model‐based control of batch chromatography

AbstractIn the chemical industry, life science products are considered the most promising markets for the near future. Since pharmaceutical products, food, and fine chemicals are subject to increasingly more complex standards and stricter legal restrictions, efficient methods for the separation of thermally unstable products are needed. Chromatographic separations, for example, usually create the main costs of the overall production process. To realize the full potential of the chromatographic separation, the desired product quality has to be maintained, while operating the process at the economic optimum by reliable and efficient process monitoring and control. For control of batch chromatography, model parameters were estimated and operating parameters were optimized online. The concept has been tested on a laboratory‐scale column with an industrial process control system. Experimental results for a sugar separation are given.

Industrial application of neural networks — an investigation

Abstract This paper summarises a 2-year industrial investigation into the application of artificial neural networks in the area of process monitoring and control. The investigation was a collaborative programme between the University of Newcastle-upon-Tyne, EDS and 24 UK based international companies. Descriptions of the major activities undertaken in this programme, which included the application of neural networks for fault detection in a vitrification process and the model based predictive control of a gasoline engine are provided. The paper also describes some of the practical difficulties that were experienced while applying neural networks and lists the important lessons that were learned through the completion of this project. The main conclusion from the work was that neural networks are capable of improving industrial process monitoring and control systems. However, the level of improvement must be analysed on a problem specific basis and in many applications the use of neural networks may not be justified.

A neuro-fuzzy supervisory control system for industrial batch processes

The automation of complex industrial batch processes is a difficult problem due to the extremely nonlinear and variable system behavior or the conflicting goals within the different process phases. The introduction of a single multiple-input multiple-output controller is not useful because of the rather high design effort and the low transparency of its complex structure. A more suitable hierarchical fuzzy-logic (FL) based supervisory control concept is proposed. It permits the decomposition of the complex control problem into a series of smaller and simpler ones. In the upper level of the hierarchy the FL-based supervisory controller classifies the actual process phase in terms of the available process sensor signals and activates dynamically the appropriate situation specific low-level controllers. The paper presents the generic concept of the FL supervisory controller that comprises both a FL process diagnosis and a control mode selection as well as experiences with the industrial application.

An architecture for metamorphic control of holonic manufacturing systems

Conventional centralised scan-based control systems are not suited to the evolutionary nature of holonic manufacturing systems, i.e. the form and substance of holonic systems undergo constant transformation (metamorphosis) throughout their lifetime. As a result, real-time control of such holonic systems requires a radically different approach from that of traditional unit level regulatory control systems necessitating a new system-level distributed control approach. Control systems based on this approach can termed as metamorphic control systems. The engineering of such software-centric metamorphic control systems for dynamically reconfigurable distributed multi-sensor-based holonic systems is addressed in this paper. An integrated and uniform event-driven control architecture is specified for various functional levels of the metamorphic control system. This architecture utilises the emerging International Electrotechnical Commission function block standard (IEC 61499) for industrial process measurement and control systems to specify the requisite behaviour of distributed control software components (agents).

A tuning of the nonlinear PI controller and its experimental application

Many industrial chemical process control systems consist of conventional PID and nonlinear controllers, even though many advanced control strategies have been proposed. In addition, nonlinear control methods are widely used even for linear processes to achieve better control performance compared with linear PID controllers. However, there are few tuning methods for these nonlinear controllers. In this work, we suggest new controller tuning methods for the error square type of nonlinear PI controller. These control methods can be applied to a large number of linear and nonlinear processes without changing control structures. We also propose new tuning rules for integrating processes. In addition, we suggest application guidelines for performing the proposed tuning rules at the pilot scale multistage level control system. Finally, in this work we confirmed good control performances of the proposed tuning methods through both simulation studies and experimental studies.

Technical Application of Hybrid Modeling Methods to specify Function Block Systems

In spite of large improvements of formal modeling methods, their technical application in industrial process control implementations is still rare. Two reasons can be pointed out. First, there is still a gap between the formal modeling languages and the control languages used in industrial process control systems respectively specified in normative standards. Second, the focus of the modeling aspects is mostly on the control algorithms itself. Looking at industrial applications, these pure control algorithms yield often just 10 % or less of the behaviour and the signal flows of a control unit. All other aspects like synchronisiation or occupying mechanisms are usually unconsidered. As a consequence of these arguments, first a new framework was developed for specifying modular function block applications in a comprehensive and formal way. Second, a generic template for batch-oriented process control blocks was designed, which takes all aspects of operational handlings into account. Both models – the framework and the template – are described using graphgrammars and hybrid modeling methods. The whole approach appears usefull for normative commitees and for large vendors of process control systems who have to develop function block applications or maintain and merge big function block libraries.

Fuzzy modeling and expert optimization control for industrial processes

This paper presents the development of a real-time fuzzy expert optimization control system for industrial processes. The focus of this study is the application of fuzzy optimization control to a fluidized catalytic cracking unit (FCCU) in an oil refinery to optimize the cracking product distribution under a variable production environment. The system has been successfully implemented in a large production scale FCCU and the application results show significant benefits through fuzzy optimization control.

Statistical process control of an industrial process in real time

Most of the real-time industrial control methods suffer from the fact that the corrective action is often taken after the instability has been detected, In other words the control strategy has always to catch-up with the problems by bringing the process back to optimum condition. Most of the time correct assignable cause of variation is not known. The conventional methods of quality control are always to catch-up with the product quality. In addition to this, conventional methods may cause financial losses due to machinery downtime and in many cases may not be able to predict the problem to avoid machinery downtime. The new computer-aided statistical process control (SPC) provides several windows in to the industrial process. These windows help collect statistical information which in turn can be used to find assignable causes of variation in product quality, quantitative measure of process capability, presence of extraneous influences and deterioration of certain equipment. We have introduced various types of problems into the industrial grade process control system and by using the SPC software tool we have collected data samples of the variables over time. These data sample were used to generate control charts of various process variables during runtime. Later, by analyzing the trend of the control charts we have been able to relate back to the problems which were originally introduced in to the process.