Energy analysis and optimization of a food defrosting system

This paper illustrates the benefits of two energy optimization strategies to improve the overall process efficiency of a food defrosting system. First, an off-line energy analysis, including both the effects of the refrigeration cycle and the fan used to control the cooling air temperature and speed, is carried-out. This first approach puts on display an optimal running point of the process for a specific cooling air temperature value, which leads to an optimization of the overall energy consumption. Second, an on-line energy optimization approach, based on a nonlinear model-based predictive control strategy, is developed. This second approach takes simultaneously into account the expected thawing time, the highest temperature accepted and above all an energetic cost. Simulation results show the benefits of this on-line energy optimization to significantly increase the overall process efficiency. Indeed, this strategy leads to an optimization of the overall energy consumption whatever the expected thawing time and the inlet air temperature.

► The energy consumption of a defrosting system used in the food industry is analyzed.
► The system energy optimization is undertaken considering two approaches.
► An off-line energetic optimization determines the optimal operating conditions.
► An innovative on-line optimization based on nonlinear predictive control is developed.
► In this approach the control actions determination is weighted by an energetic cost.