Advanced Information System for Safety-Critical Processes

Authors

  • Štefan Kozák Institute of Automotive Mechatronics, Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 812 19 Bratislava
  • Slavomír Kajan Institute of Automotive Mechatronics, Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 812 19 Bratislava
  • Ján Cigánek Institute of Automotive Mechatronics, Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 812 19 Bratislava
  • Viktor Ferencey Institute of Automotive Mechatronics, Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 812 19 Bratislava
  • Igor Bélai Institute of Automotive Mechatronics, Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 812 19 Bratislava

Keywords:

Information system, soft computing methods, neural model, multilayer perceptron (MLP), training methods, critical processes, nuclear reactor

Abstract

The paper deals with the design and implementation of an intelligent modular information system (IMIS) for modeling and predictive decision making supervisory control of some important critical processes in a nuclear power plant (nuclear reactor) using selected soft computing methods. The developed IMIS enables monitoring critical states, safety impact analysis and prediction of dangerous situations. It also recommends the operator possibilities how to proceed to ensure safety of operations and humans and environment. The proposed complex IMIS has been tested on real data from a nuclear power plant process primarily used as supervisory information for decision making support and management of critical processes. The core of the proposed IMIS is a general nonlinear neural network mathematical model. For prediction of selected process variables an artificial neural network of multilayer perceptron type (MLP) has been used. The effective Levenberg-Marquardt method was used to train the MLP network. Testing and verification of the neural prediction model were carried out on real operating data measurements obtained from the NPP Jaslovske Bohunice.

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How to Cite

Kozák, Štefan, Kajan, S., Cigánek, J., Ferencey, V., & Bélai, I. (2015). Advanced Information System for Safety-Critical Processes. Computing and Informatics, 33(6), 1356–1376. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/2818

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Section

Special Section Articles