Genetic algorithm based PID optimization in batch process control

Tan, M. K. and Chin, Yit Kwong and Tham , Heng Jin and Teo, Kenneth Tze Kin (2011) Genetic algorithm based PID optimization in batch process control. In: 2011 IEEE Conference on Computer Applications and Industrial Electronics, ICCAIE 2011, 4-7 December 2011, Penang, Malaysia.

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Official URL: http://dx.doi.org/10.1109/ICCAIE.2011.6162124

Abstract

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimising the production of undesired by-product. However, due to the difficulties to perform online measurement, batch process supervision is based on the direct measurable quantities, such as temperature. During the process, a large amount of exothermic heat is released when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. Since the chemical industries are still applying PI and PID to control the batch process, researchers are keen to optimize PID parameters using artificial intelligence (AI) techniques. However, most of these PID optimization techniques need online process model to predetermine the optimizer parameters. However in practice, the dynamic model of the batch process is poorly known. As a result, majority of the studies focused on acceptable performance instead of optimum performance of the batch process control. This paper proposes a new genetic algorithm (GA) optimizer which consists of additional information of the online estimated model parameters in addition to the PID parameters as the string of the GA. The simulation results show that the proposed GA auto-tuning method is a better candidate than the regular GA where the estimated model parameters in fitness function is capable to control the process temperature while avoiding model mismatch and disturbance condition.

Item Type:Conference Paper (UNSPECIFIED)
Uncontrolled Keywords:Exothermic heat, Genetic algorithm, Process optimization, Temperature control
Subjects:?? QA75-76.95 ??
?? TP200-248 ??
Divisions:SCHOOL > School of Engineering and Information Technology
ID Code:4137
Deposited By:IR Admin
Deposited On:14 May 2012 15:53
Last Modified:08 Sep 2014 15:02

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