Global and local optimization of food quality in batch thermal processes
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Conventionally, food is significantly over-processed to ensure safety. Dynamic optimization can be used to compute optimal thermal operation condition to ensure maximum product quality while assuring food safety. Local optimization (LO) algorithms have been used to compute optimal profiles. However, LO is not guaranteed to find the best solution. We show that the problem can be formulated as a convex problem with a reverse convex constraint and we implement Tuy's algorithm to optimize globally. The method is deterministic and guaranteed to find the global optimum and therefore it is suitable to evaluate the effectiveness local optimization to compute global optima. We compared the results of LO and global optimization (GO) to find that GO gives significantly better results for 2 and 3 heating time periods. However, for 4 periods the local optimizer catches up. This suggests that LO is good enough for this problem if we consider strategies with more than 4 periods implementable. However for many commercial processes less than 4 heating-cooling stages are used.
