An artificial intelligence software-based controller for temperature control of a partially simulated chemical reactor system

Wahab, Ahmad Khairi Abdul and Hussain, Mohd Azlan and Omar, R. (2008) An artificial intelligence software-based controller for temperature control of a partially simulated chemical reactor system. Chemical Product and Process Modeling, 3 (1). ISSN 19342659

Full text not available from this repository. (Request a copy)
Official URL:


The processes that take place in industrial reactors are mainly highly nonlinear and, due to this, their controls have become a major factor in determining the aspect of process safety, quality, and productivity of these systems. For this reason, we have designed an inexpensive, practical reactor on a pilot plant scale to conduct such online control studies where the heat of reaction in the reactor is simulated by injecting steam into the reactor system. This paper highlights the development of the pilot plant with its software development to implement advanced control strategies using artificial intelligence approaches such as fuzzy logic and genetic algorithm. The online implementation results conclude that the Genetic Algorithm Model Based Controller (GAMBC) gave similar performance with the Fuzzy Logic Controller (FLC) for the set point tracking studies but in the load disturbance rejection studies, it was found that the FLC performed better. The results obtained show the usefulness and importance of these experimental studies to further understand advanced control applications for a typical industrial chemical process such as a reactor.

Item Type: Article
Additional Information: Export Date: 5 March 2013 Source: Scopus Art. No.: 53 Language of Original Document: English Correspondence Address: Abdul Wahab, A.K.; University of MalayaMalaysia References: Abdul Wahab, A.K., Temperature control of a partially simulated exothermic chemical reactor using neural networks (2002) M. Eng. Sc., , Dissertation, University of Malaya, Malaysia; Bequette, B.W., (1999) Process Dynamics-Modeling Analysis and Simulation, p. 562. , Prentice Hall; Chipperfield, A., Fleming, P., Puhlheim, H., Fonseca, C., Genetic algorithm toolbox (1994) For Use with MATLAB® User's Guide, , Version 1.2, Department of Automatic Control and System Engineering, Univ. of Sheffield, UK; Garcia, C., Molina, F., Roca, E., Lema, J.M., Fuzzy-based control of an anaerobic reacor treating wastewaters containing ethanol and carbohydrates (2007) Industrial and Engineering Chemistry Research, 46 (21), pp. 6707-6715; Ge, S.S., Hang, C.C., Zhang, T., Nonlinear adaptive control using neural networks and its application to CSTR systems (1998) Journal of Process Control, 9 (14), pp. 313-323; Ghasem, N.M., Design of a fuzzy logic controller for regulating the temperature in industrial polyethylene fluidized bed reactor (2006) Chemical Engineering Research and Design, 84 (2 A), pp. 97-106; Ghasem, N.M., Sata, S.A., Hussain, M.A., Temperature control of a benchscale batch polymerization reactor for polystyrene production (2007) Chemical Engineering and Technology, 30 (9), pp. 1193-1202; Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , Addison-Wesley Publishing Company Inc; Hussain, M.A., Kershenbaum, L.S., Implementation of an inverse-modelbased control strategy using neural networks on a partially simulated exothermic reactor (2000) Trans IChemE, 78 (PART A), pp. 299-311; Fabro, J.A., Arruda, L.V.R., Neves Jr., F., Startup of a distillation column using intelligent control techniques (2005) Computers and Chemical Engineering, 30, pp. 309-320; Linko, S., Linko, P., Developments in monitoring and control of food processes (1998) Trans IChemE, 76 (PART C), pp. 127-137. , Sep; Lightbody, G., Irwin, G.W., Direct neural model based reference adaptive control (1995) IEEE Proc-Control Theory Appl., 142 (1), pp. 31-43; Morimoto, T., De. Baerdemaeker, J., Hashimoto, Y., An intelligent approach for optimal control of fruit-storage process using neural networks and genetic algorithms (1997) Computers and Electronics in Agriculture, 18, pp. 205-224; Mwembeshi, M.M., Kent, C.A., Salhi, S., A genetic algorithm based approach to intelligent modeling and control of pH in reactors (2004) Computers and Chemical Engineering, 28 (9), pp. 1743-1757; Roffel, B., Chin, P.A., Application of fuzzy logic in the control of polymerization reactors (1993) Control Engineering Practice, 1 (2), pp. 267-272; Rywotycki, R., Food frying process control system (2003) Journal of Food Engineering, 59 (4), pp. 339-342; Takashi, I., Yoshiaki, N., Yashusi, N., Application of fuzzy logic control system for reactor feed water control (1995) Fuzzy Sets and Systems, 74, pp. 61-72; Van Der Wal, A.J., Application of fuzzy logic in industry (1995) Fuzzy Sets and Systems, 74, pp. 33-41
Uncontrolled Keywords: chemical reactor; online control; partially simulated; software development; Advanced control; Advanced control strategy; Algorithm model; Experimental studies; Fuzzy logic controllers; Heat of reaction; Highly nonlinear; Industrial reactors; Load disturbance rejection; Major factors; On-line controls; Online implementation; Pilot plant scale; Process safety; Reactor systems; Set-point tracking; Software-based; Accident prevention; Artificial intelligence; Chemical reactors; Chemicals; Disturbance rejection; Fuzzy logic; Fuzzy systems; Genetic algorithms; Pilot plants; Safety factor; Software design; Controllers.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 03:52
Last Modified: 10 Feb 2021 03:46

Actions (login required)

View Item View Item