This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes.

A conventional approach to solving stochastic optimal control problems with time-dependent uncertainties involves the use of the stochastic maximum principle (SMP) technique. For large-scale problems, however, such an algorithm frequently leads to convergence complexities when solving the two-point boundary value problem resulting from the optimality conditions. An alternative approach consists of using continuous random variables to capture uncertainty through sampling-based methods embedded within an optimization strategy for the decision variables; such a technique may also fail due to the computational intensity involved in excessive model calculations for evaluating the objective function and its derivatives for each sample. This paper presents a new approach to solving stochastic optimal control problems with time-dependent uncertainties based on BONUS (Better Optimization algorithm for Nonlinear Uncertain Systems). The BONUS has been used successfully for non-linear programming problems with static uncertainties, but we show here that its scope can be extended to the case of optimal control problems with time-dependent uncertainties. A batch reactor for biodiesel production was used as a case study to illustrate the proposed approach. Results for a maximum profit problem indicate that the optimal objective function and the optimal profiles were better than those obtained by the maximum principle.

Ohmic heating, also known as electrical resistance heating, is a novel process of pasteurization that has several advantages including homogeneous heating, low heating time, high inactivation of microorganisms, and organoleptic and nutritional properties. Furthermore, mathematical tools allow for quick and low-cost analysis of behaviors with various operating parameters. This study aimed to improve the ohmic pasteurization process of the mango pulp using computational fluid dynamics (CFD) and response surface methodology (RSM), using the experimental properties of mango pulp. The generated mathematical model is an accurate tool for predicting ohmic pasteurization behavior in an industrial environment; it presents a 4.4% standard deviation error in the experimental data. Furthermore, the arrangement and intensity of the added voltage analysis suggested operating variables with better pasteurization performance. Another observation was that the production speed was related to the voltage intensities of the inlet speed in a non-proportional manner. Additionally, operation at a low inlet velocity may be more susceptible to thermal damage, whereas operation at a high inlet velocity requires a particular proportion of voltage insertion to ensure adequate heat distribution.