Control Of Industrial Plants Research Articles

Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neural Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations.

A sensitivity analysis and a comparison of two simulators performance for the process of natural gas sweetening

Chemical processes simulation is an important instrument for the design, optimization and control of industrial plants. Simulation cases can be accomplished with relatively low costs and the absence of risks for the operators. However, the precision of the results depends on the similarity between the simulation performed and the process considered. In this article, two simulators, Aspen Hysys V8.6 and Aspen Plus V8.6, are employed to simulate the process of natural gas sweetening using diethanolamine (DEA). Additionally, a parametric sensitivity analysis is performed to define the optimal operative range for the process. The required data and the conditions of the operating units are taken from a gas conditioning plant in northern Argentina. Finally, a comparison between the block of obtained results from both simulations is also detailed.

On the aspect of implementing solutions for information support of industrial plant control systems

The authors of this paper look at the aspect of engineering information systems for planning and scheduling operations of an industrial plant. The authors propose to introduce new steps to the engineering process which include functional modelling, elaboration of an initial request for proposal (or RFP) for a functional model, development of functional models and algorithms, and elaboration of a software specification. The engineering step-by-step process is presented as an entity set model. The authors introduce a system functionality design formula which includes business rules, business logic, project roles, and visualization. An example of a machining shop with repeated machining operations is used to demonstrate the effect of introduction of new design stages. The simulation software AnyLogic was chosen as a visualization tool to demonstrate all the functionalities of the new system. AnyLogic enabled to implement all the roles of the information system. Elaboration of the functional model took place along with development of an RFP for implementation of an information system as part of an automation package of an industrial plant. As a result of the functional model demonstration, certain drawbacks were identified in the project, and recommendations were provided on how to eliminate them.

An Industrial Perspective on Wireless Sensor Networks — A Survey of Requirements, Protocols, and Challenges

Wireless Sensor Networks (WSNs) are applicable in numerous domains, including industrial automation where WSNs may be used for monitoring and control of industrial plants and equipment. However, the requirements in the industrial systems differ from the general WSN requirements. In recent years, standards have been defined by several industrial alliances. These standards are specified as frameworks with modifiable parts that can be defined based on the particular application of WSN. However, limited work has been done on defining industry-specific protocols that could be used as a part of these standards. In this survey, we discuss representative protocols that meet some of the requirements of the industrial applications. Since the industrial applications domain in itself is a vast area, we divide them into classes with similar requirements. We discuss these industrial classes, set of common requirements and various state-of-the-art WSN standards proposed to satisfy these requirements. We then present a broader view towards the WSN solution by discussing important functions like medium access control, routing, and transport in detail to give some insight into specific requirements and the classification of protocols based on certain factors. We list and discuss representative protocols for each of these functions that address requirements defined in the industrial classes. Security function is discussed in brief, mainly in relation to industrial standards. Finally, we identify unsolved challenges that are encountered during design of protocols and standards. In addition some new challenges are introduced and discussed.

Web-Based Monitoring and Control of Industrial Processes Used for Control Education

Web-based technologies enable the implementation of remote monitoring and control of industrial plants. In this paper, some solutions and advantages of using distributed process control systems for control education are presented. In the area of process control teaching, practical experience plays an important role and web-based technologies enable the implementation of remote experiments onto real laboratory or industrial systems for effective engineering education. We present our experiences with two remote experiments based on distributed control systems realized on laboratory systems. The user interface of the first experiment is implemented by professional supervision tools and is using web-based SCADA technology. In the second experiment we discuss the process of developing fault detection and isolation applications for online and remote education by using industrial equipment from the field of process technology (batch, continuous) supported by web technologies. Through-out the course, the student can remotely develop and test model-based and data-driven FDI schemes in Matlab/Simulink by using OPC technology. Finally, the improved platform for web-based remote control system experiments suitable for control education is proposed.

The Expert-Analytical Forecasting Model in the Problem of Railway Track Facilities Control.

In the paper the application of intellectual data analysis methods for industrial plant control systems efficiency estimation and modeling is considered. To solve this problem the expert-classification and expert-statistical analysis is used. For the prediction model development, the structural forecasting method is used. Developed methods were applied for railway track condition analysis, modeling and forecasting in the framework of OJSC “Russian Railways” track facilities analysis problem.

Pole Placement-Based NMPC of Hammerstein Systems and Its Application to Grade Transition Control of Polypropylene

This paper presents a new nonlinear model predictive control (MPC) algorithm for Hammerstein systems subject to constraints on the state, input, and intermediate variable. Taking no account of constraints, a desired linear controller of the intermediate variable is obtained by applying pole placement to the linear subsystem. Then, actual control actions are determined in consideration of constraints by online solving a finite horizon optimal control problem, where only the first control is calculated and others are approximated to reduce the computational demand. Moreover, the asymptotic stability can be guaranteed in certain condition. Finally, the simulation example of the grade transition control of industrial polypropylene plants is used to demonstrate the effectiveness of the results proposed here.

Wireless Networking for Monitoring and Control System of a Steel Plant

The wired networks have been traditionally used in monitoring and control of industrial plants. The trend is changing with the fast development of wireless technologies, which are challenging the wired technologies. This paper concerns with the issues related to the design of wireless network for monitoring and control of an industrial plant. In particular, measurements were conducted inside a steel plant to characterize the radio propagation environment and make a choice on a suitable technology. The channel impulse response (CIR) is measured at 1.8 GHz and from the collected data a number of link parameters are extracted. The measured CIR is also used to simulate performance of a number of data transmission formats. Finally, a number of wireless technologies are evaluated to determine their suitability to the characterized environment.

Networked control of industrial automation systems—a new predictive method

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.

Especificación de Perfil Zigbee para Monitoreo y Control de Plantas Industriales

ZigBee es una tecnología inalámbrica de corto alcance, bajo consumo de potencia, baja transmisión de datos y alta seguridad de uso creciente en diversas aplicaciones. Los perfiles ZigBee permiten utilizar esta tecnología para sistemas específicos, de tal forma que se desarrollen aplicaciones distribuidas que se puedan desplegar fácilmente en cualquier dominio de interés. Este artículo, presenta la especificación formal de un perfil ZigBee para monitoreo y control de plantas industriales, el cual se implementa utilizando herramientas de última generación para microcontroladores de 8 bits. Se despliega una red en un ambiente controlado que utiliza el perfil especificado, lográndose facilidad en el desarrollo de aplicaciones para redes inalámbricas de área personal, así como alta confiablidad en la transmisión de datos.

Development of instrumentation systems as a base for control of digestion process stability in full-scale agricultural and industrial biogas plants

This article deals with the analysis of instrumentation from three modern German full-scale biogas plants with different inputs and typical process engineering concepts for German conditions. The measured results from each plant and the suitability of the instrumentation used are evaluated and assessed. Conclusions are also made about improving the use and architecture of the instrumentation. The analysis results show which benefits and optimum combination of on-line and off-line instrumentation could result for the control and automation of industrial and agricultural biogas plants.

Robust performance design of single-input fuzzy control system for plant with time delay

Fuzzy logic has recently gained popularity in the development of robust non-linear control convenient for real-time applications, satisfying the high-performance requirements of the control systems by dealing with data imprecision and noise. There are, however, problems in designing a unique controller that will provide system stability and desirable performance in the presence of plant model uncertainties and time delays. The aim of this investigation is to develop a simple approach for the design of an incremental PI-like single-input fuzzy controller (SI FC) that ensures a robust performance in the closed-loop system for the control of industrial plants with time delays and model uncertainties. The main results are: 1) derivation of a robust performance criterion of the SI FC system in the frequency domain, employing the stabilization of the system linear part, the linearization of the fuzzy unit and the Popov stability approach; 2) development of an SI FC tuning algorithm; 3) application of the design approach for the control of air temperature in a furnace using MATLAB TM, and assessment of the advantages over an ordinary PI controller. The designed SI FC preserves system stability and performance for a high range of plant model uncertainties.

Robust stability of single input fuzzy system for control of industrial plants with time delay

Most industrial plants are complex, nonlinear, time-varying with time delay, difficult to control. The single input fuzzy controllers (SI FCs) are promising in satisfying the high performance demands to the control systems of such plants and ensuring the fuzzy system stability, coping with system nonlinearity, plant model uncertainty and SI FC design. The aim of the present investigation is to derive robust stability requirements for an incremental PI-like SI FC closed loop system that serve the development of a SI FC design method for the control of industrial plants with time delay and model uncertainty. The main contributions are: 1) modification of the Popov stability approach to study the SI FC system stability and to derive design rules; 2) derivation of robust stability criterion for a SI FC system and on its basis development of a SI FC tuning algorithm. The results are applied for the stabilisation of the air temperature in a furnace using MATLAB™. The advantages of the designed SI FC system are assessed in comparison with ordinary PI control system via simulations. The SI FC system preserves stability for a great range of plant uncertainties. The tradeoff is longer settling time of the nominal system transient responses.

Design of Intelligent Information Support Systems for Human-Operators of Complex Plants

Complex equipment of an industrial plant subject to control, large body of information flows, complexity of control problems, lack of time available to make a decision, etc. are the reasons which lead to a number of cases to contradiction of human-operator control capabilities and actual control problem requirements. Within such a problem, a natural way out is to design an intelligent information support system (IISS) which would be able to assist the human-operator to implement the complex industrial plant control functions. These functions are to involve operative mode control of automatic systems, monitoring the systems performance, and prediction of the plant status, both within normal and abnormal situations. Involving such an IISS into control process enables one to decrease the human-operator load, to increase the human-operator performance, and to increase the plant performance reliability.

Data mining and knowledge discovery for process monitoring and control, by X.Z. Wang, Advances in Industrial Control, Springer, London, 1999, pp. 1–251, ISBN 1‐85233‐137‐2

Data mining and knowledge discovery for process monitoring and control, by X.Z. Wang, Advances in Industrial Control, Springer, London, 1999, pp. 1–251, ISBN 1‐85233‐137‐2

ENERGY SAVING ALGORITHMS AND CONTROL SYSTEM

The following paper aims at development of a method for analysis and quantitative evaluation of energy losses in control systems, which should implement a model of the losses in the control valves. The presented method is applicable when evaluating and comparing the losses in designed during operation standard robust systems for control of static and astatic industrial plants. The results allow determining the inner energy losses of the examined systems and of the energy-saving control algorithms.

Interactive control system design

This contribution describes a LabVIEW-based interactive computer-aided control system design tool. Although it does not provide the flexibility of common low-level design tools, it offers a set of comfortable, ready-to-use solutions for plant identification, loop-shaping or LQR/LQG-controller design and controller implementation. The tool integrates into industrial plant control and enables engineers to efficiently do the control system design from plant identification to controller implementation on the same platform. The user interface with convenient system editors and analysis tools facilitates the user to interactively attain the design goals. If desired, a configurable wizard leads the user through the design process.

VIRTUAL PLANT LABORATORY SYSTEM OF PROCESS INDUSTRIES FOR EDUCATION

A kind of virtual plant laboratory system for process systems engineering education and research is developed, which is deemed an effective and applicable tool. The proposed system consists of pilot tanks, package simulation software of unit operation and a real enterprise information system including DCS, management database and MES (manufacturing execution system). Particularly, positive effects of customizing the virtual education systems to satisfy the needs of specific engineering education and research domain such as modeling, optimization and control of industrial plant, and development and implementation of enterprise information systems are highlighted.

A23: Logix-based system for distributed process control of semi industrial pilot plants

Complex system of programmable controllers and SCADA applications in the field of distributed process control was presented. Five especially chosen pilot plants, having permanent importance in process biotechnology and heat transfer have been developed. Logix-based distributed control system has been designed and developed. Architecture of OPC and TCP/IP data transmission between local and remote SCADA systems is proposed and developed.

A New Method for Adaptive Model-Based Control of Dynamic Industrial Plants using Neural Networks, Fuzzy Logic and Fractal Theory

We describe in this paper a new method for adaptive model-based control of non-linear dynamic plants using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy-fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic Plant Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic plants. We illustrate in this paper our new methodology with the case of controlling biochemical reactors in the food industry. For this case, we use mathematical models for the simulation of bacteria growth for several types of food. The goal of constructing these models is to capture the dynamics of bacteria population in food, so as to have a way of controlling this dynamics for industrial purposes.