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Multistage ramp-variable (MRV) retort temperature control for optimization thermal processing |
C. CHEN, Food Science, McGill University, Food Science Department, 21111 Lakeshore, Ste Anne de Bellevue, QC H9X 3V9, Canada and H. S. Ramaswamy, Food Science Department, McGill University, Ste Anne de Bellevue, QC H9X 3V9, Canada. The selection of optimal retort temperature profiles with a multistage ramp function involving multiple variables seeks optimization solutions which are complex and difficult to be handled by conventional optimization methods. Newer concepts such as artificial neural networks (ANN) and genetic algorithms (GA) have the potential to deal with such complex situations. The objective of this study was to develop ANN-GA based procedures for the selection of the optimal retort temperature profile under multistage ramp-variable (MRV) retort temperature control for optimizing thermal processing. ANN concepts were used for developing dynamic prediction models for process time, average quality retention and surface cook value, in which the input variables were ramp time (30 -70 min) and temperatures (five levels: 104-134C) in four consecutive stages of retort processing. GA-ANN based optimization procedure was then developed using a commercial GA and NN software, and used for searching the best combination of retort ramp/soak sequence in each of the four stages to give a continuous variable temperature profile that achieves the optimization objective under imposed constraints conditions. The statistical results of modeling performance for all NN models were: correlation coefficient R2>0.96 and relative error Er < 2%. The optimization results and processing efficiency using GA were affected by main GA configuration parameters including initial population number, mutation rate and crossover rate, and the optimal configuration parameters for GA was determined by trials. The optimal retort temperature profiles meeting different optimization objectives for different can sizes were obtained using GA-NN method. Compared to the constant retort temperature (CRT), the MRV process could improve both process time (up to 24%) and surface quality (up to 12%) significantly. The results suggest that the hybrid artificial intelligence techniques of neural networks and genetic algorithms can be efficiently used for modeling and optimization of the multi-stage ramp variable (MRV) retort temperature control for thermal processing.
Session 13, Food Engineering: Thermal processes
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