Multi-objective optimization of multistage jet impingement thawing processes

In food service environment the setup and the execution of a process are often based on professional expertise. When new emerging technologies are under investigation, the lack of knowledge about the behavior of new systems promotes the recourse of advanced design tools. The availability of computationally efficient models enables a structured analysis of complex parametric processes. We present a multi-objective optimization problem using an available black box model that describes a multistage thawing process executed with the jet impingement technology. Professional thawing machines operate by multistage heat transfer processes, whose efficiency is dependent on several parameters, like the air temperature, air velocity, geometric variables and stages duration. We solve the problem of finding the best combinations of such parameters using a genetic algorithm and we use a Taguchi design to tune the settings of the algorithm. The performances of the algorithm are evaluated by the hypervolume indicator on the resulting Pareto front, considering three objective functions. The results of the proposed methodology are used to speed up the experimental study of such machines by selecting the most promising cycles among the set of optimal solutions.