Temperature Control of a Continuous Stirred Tank Reactor by Means of two Different Intelligent Strategies

• Published in: International journal on smart sensing and intelligent systems Vol. 4; no. 2; pp. 149 - 153
• Main Authors: Rahmat, Mohd Fua’ad, Yazdani, Amir Mehdi, Movahed, Mohammad Ahmadi, Mahmoudzadeh, Somaiyeh
• Format: Journal Article
• Language: English
• Published: Sydney Sciendo 01.01.2011; De Gruyter Poland
• Subjects: Continuously stirred tank reactors; Controllers; CSTR; Evolutionary algorithms; fuzzy controller; Fuzzy logic; Gain scheduling; ICA; Least squares; modeling; Optimization; PI controller; Reference signals; Temperature control
• ISSN: 1178-5608; 1178-5608
• DOI: 10.21307/ijssis-2017-438

Continues Stirred Tank Reactor (CSTR) is an important subject in chemical process and offering a diverse range of researches in the area of the chemical and control engineering. Various control approaches have been applied on CSTR to control its parameters. This paper presents two different control strategies based on the combination of a novel socio-political optimization algorithm, called Imperialist Competitive Algorithm (ICA), and concept of the gain scheduling performed by means of the least square and fuzzy logic approaches. The goal is to control the temperature of the CSTR in presence of the set point changes. The works followed with designing those controllers and simulating in MATLAB software. The performance of the proposed controllers have been consider based on the Sum of the Square Error (SSE) and Integral Absolute Error(IAE) Criteria. The results clearly indicate that both suggested control strategies offer an acceptable performance with respect to the functional changes of the process. In other word, robustness of the proposed methods in dealing uncertainties throughout the tracking of the reference signal take the highlighted point into account. Furthermore, fuzzy based structure strategy gives the more flexibility and precise behavior in control action in comparison to the least square based approach.