Our paper, “Uncertainty-aware constrained optimization for air convective drying of thin apple slices using machine-learning-based response surface methodology,” has been published in the Journal of Food Engineering. This study introduces a Monte Carlo simulation approach to quantify inherent uncertainties in sample characteristics during food drying. By integrating machine-learning-based response surface modeling within a constrained optimization framework, this paper achieved an optimal balance among product quality, energy efficiency, and uncertainty management, demonstrating potential energy savings of up to 24.6%.
Read the full paper here.