Industrial process control with case-based reasoning approach

Jan Kusiak¹ , Gabriel Rojek¹, Łukasz Sztangret¹, Piotr Jarosz²

¹AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Department of Applied Computer Science and Modelling al. A. Mickiewicza 30, 30-059 Kraków, Poland.
²AGH University of Science and Technology, Faculty of Non-Ferrous Metals, Department of Physical Chemistry and Metallurgy of Non-Ferrous Metals al. A. Mickiewicza 30, 30-059 Kraków, Poland.

DOI:

https://doi.org/10.7494/cmms.2013.2.0447

Abstract:

The goal of presented work is an attempt to design an industrial control system that uses the production data registered in the past during the regular production cycle. The main idea of the system is processing of the production data in order to find some registered past cases of production that are similar to the present production period. If the found production past case fulfills the requirements of the given quality criterion, the registered control signals corresponding to that case are considered as the pattern for the actual control. Such approach is consistent with the core assumption of Case-Based Reasoning, namely that similar problems have similar solutions. The paper presents preliminary results of the implementation of the CBR system to industrial control of the oxidizing roasting process of sulphide zinc concentrates.

Cite as:

Kusiak, J., Rojek, G., Sztangret, Ł., & Jarosz, P. (2013). Industrial process control with case-based reasoning approach. Computer Methods in Materials Science, 13(2), 313 – 319. https://doi.org/10.7494/cmms.2013.2.0447

Article (PDF):

Keywords:

Industrial process control, Case-based reasoning, Oxidizing roasting process of sulphide zinc concentrates, Multi-agent system

References:

Aamodt, A., Plaza, E., 1994, Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches, AICom – Artificial Intelligence Communications, 7, 39-59.

Bergmann, R., Althoff, K. D., Minor, M., Reichle, M., Bach, K., 2009, Case-Based Reasoning – Introduction and Recent Developments, Kunstliche Intelligenz: Special Issue on Case-Based Reasoning, 23, 5-11.

Rojek, G., Kusiak, J., 2012a, Industrial control system based on data processing, Proc. Conf. ICAISC 2012, eds, Rutkowski, L., Zakopane, 502-510.

Rojek, G., Kusiak, J., 2012b, Case-Based Reasoning Approach to Control of Industrial Processes, submitted and accepted to Computer Methods in Material Science.

Rojek, G., Sztangret, Ł., Kusiak, J., 2011, Agent-based information processing in a domain of the industrial process optimization, Computer Methods in Materials Science, 11, 297-302.

Sun, J., 2008, CBR Application in Combustion Control of Blast Furnace Stoves, Proc. Conf. IMECS 2008, eds, Ao, S. I., Hong Kong, vol. I, 25-28.

Sztangret, Ł., Stanisławczyk, A., Kusiak, J., 2009, Bio-inspired optimization strategies in control of copper flash smelting process, Computer Methods in Materials Science, 9, 400–408.

Sztangret Ł., Szeliga D., Kusiak J., 2010, Analiza wrażliwości jako metoda wspomagająca optymalizację parametrów procesów metalurgicznych, Hutnik – Wiadomości Hutnicze, 12, 721-725 (in Polish).

Sztangret, Ł., Rauch, Ł., Kusiak, J., Jarosz, P., Małecki S., 2011, Modelling of the oxidizing roasting process of sulphide zinc concentrates using the artificial neural networks, Computer Methods in Materials Science, 11, 122-127.

Weiss, G., 1999, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press Cambridge, USA.

Wooldridge, M., 2001, Introduction to Multiagent Systems, John Wiley & Sons, Inc., New York, USA.