| Process conditions of a moist air impingement cooking system were optimized to achieve maximum yield and to satisfy safety and quality constraints, simultaneously. To accomplish this goal, various strategies were tested by combining different modeling approaches, global optimization algorithms, and parameterization of control profiles.; In this study, a finite element model (FEM) predicting yield, Salmonella inactivation, internal color change, and surface color change was considered as an actual experiment with which all the results were compared. Static neural network models (SNNM) and a dynamic neural network model (DNNM) were utilized as potential, faster alternatives to the finite element model. For the global optimization algorithms, genetic algorithms (GA), simulated annealing (SA), and integrated controlled random search in dynamic system (ICRS/DS) algorithms were tested along with the finite element model and alternative models. In addition, piecewise linear interpolation (PLI) and Fourier series (FS) were used for the control profile parameterization.; This study was conducted in two different ways. In the first part, overall aspects of this optimization problem and the effectiveness of the various strategies were investigated to identify the best strategy for ideal dynamic control profiles. Secondly, based on prior knowledge, the optimization strategies were applied to several industrially-relevant case studies.; The performance of the alternative models (DNNM and SNNM) was fast, general, and robust, with a few exceptions. Even though the accuracy and the power of classification were not as high as the finite element model results, the neural network models showed potential as reliable alternative models. The highest goal (yield) was 73%, which was obtained by using the ICRS algorithm, FEM, and PLI. However, the optimization strategies with alternative models could not find such a high yield; rather, they committed critical classification errors at the later stages of the optimization process. Generally, all the global optimization algorithms showed convergence to an optimal solution, albeit with different convergence speed and goal achievement. Although comprehensive evaluation was impossible, ICRS was observed as the most recommendable algorithm.; Single-stage, double-stage, and multi-zone processes were studied by using three different models (FEM, DNNM, and SNNM) and the ICRS algorithm. The maximum yield (67%) was achieved in the double-stage process. The case studies showed that a simple and minor design change of the single-stage oven might improve the performance. In addition, the objective function (yield) for the single-stage oven was replaced with a cost function, and the operating conditions for maximum profit were determined, which were different from the results when the objective function was yield. Finally, Monte Carlo simulation showed that all the optimal profiles were highly sensitive to small perturbations, which implied difficulties in the actual application of the optimal solutions, due to unavoidable control errors of a cooking system. |
