Sirkka-Liisa Jämsä-Jounela

Future trends in process automation

The importance of automation in the process industries has increased dramatically in recent years. In the highly industrialized countries, process automation serves to enhance product quality, master the whole range of products, improve process safety and plant availability, efficiently utilize resources and lower emissions. In the rapidly developing countries, mass production is the main motivation for applying process automation. The greatest demand for process automation is in the chemical industry, power generating industry, and petrochemical industry; the fastest growing demand for hardware, standard software and services of process automation is in the pharmaceutical industry. The importance of automation technology continues to increase in the process industries. The traditional barriers between information, communication and automation technology are, in the operational context, gradually disappearing. The latest technologies, including wireless networks, fieldbus systems and asset management systems, boost the efficiency of process systems. New application fields like biotechnology and microtechnology pose challenges for future theoretical work in the modeling, analysis and design of control systems. In this paper the industry trends that are shaping current automation requirements, as well as the future trends in process automation, are presented and discussed. # 2007 Elsevier Ltd. All rights reserved. Authors accepted manuscript, published in Annual Reviews in Control 31 (2007) 211–220

A process monitoring system based on the Kohonen self-organizing maps

Process monitoring and fault diagnosis have been studied widely in recent years, and the number of industrial applications with encouraging results has grown rapidly. In the case of complex processes a computer-aided monitoring enhances operators possibilities to run the process economically. In this paper, a fault diagnosis system will be described and some application results from the Outokumpu Harjavalta smelter will be discussed. The system monitors process states using neural networks (Kohonen self-organizing maps, SOMs) in conjunction with heuristic rules, which are also used to detect equipment malfunctions.

Current status and future trends in the automation of mineral and metal processing

Abstract A review of the current status and future trends in the automation and control of mineral and metal processing is presented. An evaluation of publications on IFAC MMM events during the last 20 years shows some trends in the application of a number of process control methods in the MM industry. Classical control has seen an extraordinarily wide application, but its inability to solve all the application problems of interest has led to further developments in control system methodologies and theory. One aim of this paper is to review the success of the translation of theoretically based developments into practice. Finally, the importance of information technology for the MM industry and process automation is highlighted and discussed.

An online application of dynamic PLS to a dearomatization process

Abstract Early detection of process disturbances and prediction of malfunctions in process equipment improve the safety of the process, minimize the time and resources needed for maintenance, and increase the uniform quality of the products. The objective of online-monitoring is to trace the state of the process and the condition of process equipment in real-time, and to detect faults as early as possible. In this article the different properties of the online-monitoring methods applied in the process industries are first reviewed. A description of the systematic development of the online-monitoring system for an industrial dearomatization process, specifically for flash point and distillation curve analysers, is then presented. Finally, the results of offline and online tests of the monitoring system using real industrial data from the Fortum Naantali Refinery in Finland, are described and discussed. The developed online-monitoring application was successful in real-time process monitoring and it fulfilled the industrial requirements.PACS: 07.05.Mh; 07.05.Tp; 83.85.Ns

Intelligent control system of an industrial lime kiln process

Abstract In the face of strong competition, the pulp and paper industry is aiming at higher profitability through increased productivity and the reduction of costs. In addition, on the global scale the industry is facing increasing market demands for higher product quality, more specialty products and improved production flexibility and environmental protection. Consequently, extensive research is being conducted to improve existing processes. One alternative, which is gaining increasing attention within the industry, is the improved control of existing processes by means of intelligent systems. In this paper, an intelligent kiln control system is presented. In the system, neural network models are used in conjunction with advanced high-level controllers based on fuzzy logic principles and a novel linguistic equation approach. Finally, the testing results of the system are presented and discussed.

Ore Type based Expert Systems in Mineral Processing Plants

Artificial intelligence (AI) includes excellent tools for the control and supervision of industrial processes. Several thousand industrial applications have been reported worldwide. Recently, the designers of the AI systems have begun to hybridize the intelligent techniques, expert systems, fuzzy logic and neural networks, to enhance the capability of the AI systems. Expert systems have proved to be ideal candidates especially for the control of mineral processes. An expert system based on on-line classification of the ore type has been developed. Self-organizing maps (SOM) are used for pattern recognition of the type of feed. The system has been tested in two concentrators, the Outokumpu Finnmines Oy, Hitura Mine and Outokumpu Chrome Oy, Kemi Mine. The methodology for the development of the ore type based expert system is presented and the preliminary results obtained in the above plants are described.

State of the art in copper hydrometallurgic processes control

Abstract A review of the state of art and trends in automation and control of hydrometallurgic processes is presented. Besides the great expansion of hydrometallurgic processes world-wide, there are a number of unsolved problems related to lack of instrumentation, lack of process knowledge, odd operating practices, and in general, lack of use of data management and processing. In general, process control of local objectives are frequently achieved, however, application of mature and new techniques, successfully adopted in other mineral processing plants, are seldom reported. In the near future it is expected that intelligent techniques will be incorporated to solve a large variety of problems.

On-line determination of ore type using cluster analysis and neural networks

Abstract Expert systems have proved to be excellent tools for the control of mineral processes. An expert system based on on-line classification of the ore type has been designed and is described in this paper. The neural approach to computation has emerged in recent years for dealing with the sort of problems for which more conventional solutions have proven ineffective. In the study a comparison between the on-line cluster algorithm and the Kohonen feature map for ore type classification is presented. The study was carried out with measurement data from the Outokumpu Hitura mine.

Dynamic modelling of an industrial copper solvent extraction process

Abstract The dynamic behaviour of an industrial copper solvent extraction mixer–settler cascade is modelled to develop an advanced process control system. First, the process is introduced and the dynamical models are formulated. The testing environment is described and the successful results presented. Only industrially measured variables are required and plant-specific McCabe-Thiele diagrams are utilized to predict copper concentrations. The results with constant and adapted parameters are compared and the importance of parameter adaptation is discussed. Testing the simulator with adapted parameters over a period of 1 month of industrial operating data gave data that followed the real process measurements closely. In the future, the mechanistic models will be used for control system development and testing. The model can be used on all copper solvent extraction plants by modifying the flow configuration and adapting parameters.

Fault propagation analysis of oscillations in control loops using data-driven causality and plant connectivity

Abstract Oscillations in control loops are one of the most prevalent problems in industrial processes. Due to their adverse effect on the overall process performance, finding how oscillations propagate through the process units is of major importance. This paper presents a method for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.