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Evolutionary Algorithm-Based Multi-objective Control Scheme for Food Drying Process

Abstract Food drying is one of the important methods to prevent microbial growth during preservation. However, it is a complex non-linear process where the quality of the food depends on environmental conditions. Therefore, food drying must be carried out under controlled environment. In this paper, an internal model control (IMC) scheme is developed for pineapple drying using the evolutionary algorithms namely: genetic algorithm (GA) and particle swarm optimization (PSO) to achieve the desired quality (single objective). In order to reduce the control effort and hence the cost, without compromising the desired quality, a multi-objective control scheme is also formulated using weighted sum method. The closed loop performance of the control scheme for GA-based IMC-PI controller and PSO-based IMC-PI controller are analyzed for servo and regulatory operations. The results thus obtained are compared both qualitatively and quantitatively. From the simulation results it is observed that PSO-based IMC-PI controller gives better performance and better range of the temperature compared to the other control schemes.