Abstract
This paper presents the corrective process control system for achieving a target quality level in glass melting processes. Since automated data collection devices would monitor and log process attributes that are assumed to correlate to a quality level in the glass melting process, appropriate process control logics utilizing the collected data are definitely needed. In this paper, an evolutionary algorithm based search logic is newly proposed. The objective of the proposed logic is to find the best process condition composed of the process attributes which can generate the target quality level. The proposed logic tries to find the best process condition that needs to satisfy the following two criteria: 1) a process condition should require minimal changes from the current setting of the process attributes; and 2) a process condition can generate the exact or closest value against the target quality level. A case study and a developed process control system are presented.
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References
Fink, P., Lusth, J.C.: Expert Systems and Diagnostic Expertise in the Mechanical and Electrical Domains. IEEE Transactions on Systems, Man, and Cybernetics 17(3), 340–349 (1987)
Li, C.C.: Path Analysis- A Primer. Boxwood Press, Pacific Grove (1975)
Peng, Y., Reggia, J.A.: A Connectionist Model for Diagnostic Problem Solving. IEEE Transactions on Systems, Man, and Cybernetics 19(2), 285–298 (1989)
Shortliffe, E.H.: Computer-Based Medical Consultations: MYCIN. American Elsevier, New York (1976)
Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Acosta, G., Todorovich, E.: Genetic Algorithm and Fuzzy Control: a Practical Synergism for Industrial Applications. Computers in Industry 52, 183–195 (2003)
Karr, C.L.: Control of a Phosphate Processing Plant via a Synergistic Architecture for Adaptive, Intelligent Control. Engineering Applications of Artificial Intelligence 16, 21–30 (2003)
Pham, Q.T.: Dynamic Optimization of Chemical Engineering Processes by an Evolutionary Method. Computers and Chemical Engineering 22, 1089–1097 (1998)
Sette, S., Boullart, L., Langenhove, L.V.: Using Genetic Algorithms to Design a Control Strategy of an Industrial Process. Control Engineering Practice 6, 523–527 (1998)
Chase, J.C.W.: The Role of the Demand Planner in Supply Chain Management. The Journal of Business Forecasting Fall, 23-25 (1998)
Jeong, B., Jung, H., Park, N.: A Computerized Causal Forecasting System using Genetic Algorithms in Supply Chain Management. The Journal of Systems and Software 60, 223–237 (2002)
Shi, G.: A Genetic Algorithm Applied to A Classic Job Shop Scheduling Problem. International Journal of Systems Science 28, 25–32 (1997)
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Jung, H., Chen, F.F. (2007). Evolutionary Algorithm Based Corrective Process Control System in Glass Melting Process. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_37
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DOI: https://doi.org/10.1007/978-3-540-70928-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70927-5
Online ISBN: 978-3-540-70928-2
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