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.
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Publicly AvailableEvolutionary Algorithm-Based Multi-objective Control Scheme for Food Drying ProcessOctober 25, 2016
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Publicly AvailableStability and Bioaccessibility of Fucoxanthin in Nanoemulsions Prepared from Pinolenic Acid-contained Structured LipidJanuary 10, 2017
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Publicly AvailableExperimental Study on Heat and Mass Transfer of Millet in a Fixed FurnaceNovember 30, 2016
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Requires Authentication UnlicensedEffects of Processing Treatments on the Antioxidant Properties of Polysaccharide from Cordyceps militarisLicensedDecember 23, 2016
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Requires Authentication UnlicensedOptimization of the Spray-Drying Process for Developing Stingless Bee Honey PowderLicensedDecember 10, 2016