Genetic algorithm for control and optimisation of exothermic batch process - Tan, Min Keng (2013)
The aim of this research is to control and optimise the production of the desired product while minimising the waste production for an exothermic batch process. During the process, a large amount of heat is released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. Commonly, dual-mode controller (DMC) is employed in the industries to control the process. However, this approach is not robust in dealing with sudden disturbance. Hence, various advanced control strategies have been introduced. In general, most of the studies use predictive approach to estimate the process behaviour and a slave controller, usually proportional-integral-derivative (PID), is employed to control the process based on the estimated plant behaviour. Nevertheless, these methods require huge batch of data for tuning its parameters. Therefore, genetic algorithm (GA) is proposed in this work since precise mathematical model is not required. Although the proposed genetic algorithm controller (GAC) is able to regulate the process temperature to the desired path, it does not limit the waste production effectively. As such, another approach, GA is proposed to optimise the productivity without referring to a predetermined profile, namely genetic algorithm optimiser (GAO). The results show that GiAO is able to improve the yield ratio from 0.77 to 0.97 or approximate 25.0 % as comparecd to the GAC. Further evaluations have shown that GA with predetermined fitness function is unable to cope with the environmental changes. As a result, improved multivariable genetic algorithm (IMGA) with adaptable fitness function ability is introduced in this work. Results show that the IMGA is able to improve the yield ratio from 0.25 to 0.79 as compared to GAO in handling the p. rameter variant conditions.