The feed industry is characterized by high energy consumption during the grinding stage, where hammer mills can account for up to 50% of total electricity usage; furthermore, efficiency analyses are based only on the classical equations reported in the literature. In this context, the present theoretical-applied research aimed to improve the efficiency of a plant operating below its nominal capacity. To achieve this, a comprehensive mathematical model was developed, integrating power and grain disintegration equations while overcoming the limitations of classical comminution theories. The model incorporates key factors such as feed rate, moisture content, absorbed power and hammer wear. Additionally, specific correction factors for temperature ($K_t$) and mechanical degradation ($K_d$) were introduced to accurately represent real operating conditions. The study was based on extensive measurements of electrical current, power factor, energy consumption, particle size distribution and thermal variations under different load conditions. The statistical analysis, which included ANOVA, ANCOVA and multiple regressions, demonstrated a predictive accuracy of 98% (R²) and a pseudo-R² of 89%. This high correlation allowed for an 18% reduction in energy consumption equivalent to 4 kWh/t and up to a 30% improvement in particle size uniformity, surpassing typical factory performance. The findings highlight that integrating operational, thermodynamic and wear-related factors enhances the robustness of the model, promoting more reliable energy-management practices in hammer mills. Consequently, the results confirm that the developed model serves as a scientifically robust, efficient and applicable tool for improving energy efficiency and reducing environmental impacts in the agri-food industry.

The automation of biodigesters using programmable logic controllers (PLCs) is well established, yet mixing schedules are configured empirically, regardless of substrate characteristics. This study proposes a Substrate-Driven PLC Control (SD-PLC) methodology for fixed-dome biodigesters, in which every timing setpoint is derived from a measured substrate property: the homogenization pulse from the rheological mixing time (Metzner–Otto regime), the maintenance interval from a stratification and a substrate–biomass contact criterion, the duty-cycle ceiling from a net energy balance, and a safety gate from acid–base behavior (pH/EC). The methodology is instantiated on a 13.86 m³ fixed-dome biodigester in the Chillón Valley (Lima, Peru), co-digesting organic waste and guinea pig manure (30:70; theoretical methane potential 371 mL CH₄ g⁻¹ VS; field conversion ≈ 21%). The impeller operates in the transitional regime ($Re\approx 0.9$–$2.6\times {10}^3$), the homogenization pulse is ≈22 min, and the reconciled duty cycle ($\delta \approx 0.13$) remains a factor of four below the energy ceiling (${\delta }^{*}\approx 0.56$). An influence × feasibility matrix identifies a low-cost sensor set (pH, electrical conductivity, temperature, level) that gates mixing. A reduced COD-based dynamic model shows that, under acid shock, continuous mixing suppresses methanogenesis through shear while the absence of mixing fails through contact deficit, so that only the stability-first strategy preserves the process. The principal contribution is the methodology itself—a reproducible mapping from substrate characterization to PLC timing design—rather than the hardware.

The objective of this study is to show the analysis and behavior of symmetrical components and balanced faults in power transformers. Thus, the symmetric component methods solve directly the distribution of voltages and currents, allowing the correct verification of the procedure and the influence of earth circuit faults with the neutral point in distribution transformers. Therefore, the symmetric component analysis procedure has an innovative contribution to the determination of problem solving that solves practical cases and allows to determine the unbalanced failure analysis.