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.

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions.

Grain storage in silos plays a fundamental role in preserving the quality and safety of agricultural products. This study presents a comparative evaluation of two-dimensional (2D) and three-dimensional (3D) mathematical models to predict the temperature and moisture distribution during unventilated corn storage in cylindrical silos with conical roofs. The models incorporate external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity via sorption isotherm (water activity), and grain respiration to simulate real-world storage conditions. The 2D model offers computational efficiency and is suitable for preliminary assessments but simplifies natural convection effects and underestimates axial temperature gradients. Conversely, the 3D model provides a detailed representation of heat and moisture transfer phenomena, capturing complex interactions such as buoyancy-driven flow and localized effects of solar radiation. The results reveal that temperature and moisture accumulation are more pronounced in the upper regions of the silo, driven by solar radiation and natural convection, with significant implications for large-scale silos where thermal inertia plays a key role. This dual modeling approach demonstrates that while the 2D model is valuable for quick evaluations, the 3D model is essential for comprehensive insights into thermal and moisture gradients. The findings support informed decision-making in silo design, optimization, and management, enhancing grain storage strategies globally.

The optimal noise amplitude for Stochastic Resonance (SR) is located employing an Artificial Neural Network (ANN) reference model with a nonlinear predictive capability. A modified Kalman Filter (KF) was coupled to this reference model in order to compensate for semi-quantitative forecast errors. Three manifestations of stochastic resonance, namely, Periodic Stochastic Resonance (PSR), Aperiodic Stochastic Resonance (ASR), and finally Coherence Resonance (CR) were considered. Using noise amplitude as the control parameter, for the case of PSR and ASR, the cross-correlation curve between the sub-threshold input signal and the system response is tracked. However, using the same parameter the Normalized Variance curve is tracked for the case of CR. The goal of the present work is to track these curves and converge to their respective extremal points. The ANN reference model strategy captures and subsequently predicts the nonlinear features of the model system while the KF compensates for the perturbations inherent to the superimposed noise. This technique, implemented in the FitzHugh-Nagumo model, enabled us to track the resonance curves and eventually locate their optimal (extremal) values. This would yield the optimal value of noise for the three manifestations of the SR phenomena.