Agribusiness ranks second as the sector with the highest greenhouse gas emissions linked to methane, constituting a crucial challenge for global sustainability. Although its impact on climate change is considerable, small rural farmers do not have effective technologies to manage the organic waste derived from their daily activities. In this context, anaerobic digestion is an innovative solution that converts waste into biogas and biofertilizers, promoting a sustainable and circular approach. However, its implementation faces significant barriers due to inadequate designs and poor operational practices, which makes its adoption difficult in rural areas. This applied theoretical research seeks to overcome these barriers by improving the design and operation of small-scale biogas plants. The system studied operates at 70% of its capacity, with a hydraulic retention time of 20 days and a feed of 4 kg organic matter. The substrates considered were 30% organic waste and 70% bovine manure, achieving an average production of 63.75 L CH₄/kg of organic matter, which exceeded the usual yields of small biodigesters. A mathematical model was created and applied to the case study with an R² correlation of 98% and a pseudo-R² of 89.5%, evidencing a remarkable predictive capacity. This biogas plant model is efficient and sustainable, and it is presented as a viable solution for small rural farmers.

The linear economy model in the sugar agroindustry has generated multiple impacts due to the underutilization of by-products and reliance on fossil fuels. Through predictive modeling and anaerobic digestion, the circular bioeconomy of sugarcane biomass enables the generation of biogas and electricity in an environmentally sustainable manner. This theoretical-applied research proposes a predictive model to estimate the energy potential of by-products such as bagasse, vinasse, molasses, and filter cake, based on historical production data and validated technical coefficients. The model uses milled sugarcane as a baseline and projects its energy conversion under three scenarios through 2030. In its most favorable configuration, the model estimates energy production of up to 15.5 billion Nm³ of biogas in Cuba and 9.9 billion in Peru. The model’s architecture includes four residual biomass flows and bioenergy conversion factors applicable to electricity generation. It is validated using national statistical series from 2000 to 2018 and presents relative errors below 5%. Cuba, with a peak of over 13,000 GWh of electricity from bagasse, and Peru, with a stable output between 6500 and 7500 GWh, reflect the highest and lowest projected energy utilization, respectively. Bagasse accounts for over 60% of the total estimated energy contribution. This modeling tool is fundamental for advancing a transition toward a circular economy, as it helps mitigate environmental impacts, improve agroindustrial waste management, and guide sustainable policies in sugarcane-based contexts.

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.

Decarbonizing transport in agro-exporting economies requires tools that couple biofuel production with by-product valorization without displacing food. This study extends a low-data predictive model of sugarcane by-product bioenergy with three ethanol pathways—direct juice fermentation (1G-A), flexible sugar–ethanol production (1G-B) and simultaneous saccharification and fermentation of bagasse (2G)—coupled to anaerobic digestion, and applies it to Peru, a structurally net sugar-importing sector. The research question is how much fuel ethanol such a sector can supply without reducing its commercial sugar output, and which route carries that supply with the least sensitivity to the sugar balance. The sugar-preserving allocation coefficient $f_{\mathrm{juice}}^{\max ,\mathrm{SP}}$ is derived in closed form and shown to depend on the industrial configuration mix only through the aggregate sucrose diverted. The sugar reference is the 2011–2018 mean of the official series, the same window as the cane baseline, and the recovery chain is calibrated on that pair (${\lambda }^{\ast }=0.9604$). The 1G-A cascade reproduces the nominal capacity ratio of the only Peruvian juice-to-ethanol plant within 1.24%, a design-level benchmark rather than an industrial validation; the 2G coefficient (254.9 L ${\mathrm{t}}^{-1}$ dry bagasse) agrees within 10% with an independent process simulation but remains unvalidated for Peruvian bagasse. Without productive growth the constraint is exactly binding: no juice can be diverted and the 2030 potential is 297 ML ${\mathrm{yr}}^{-1}$ from residual molasses and bagasse, rising to 498 and 731 ML ${\mathrm{yr}}^{-1}$ under 2.5% and 5% annual cane growth, with 790–1231 GWh ${\mathrm{yr}}^{-1}$ of cogenerated electricity as a gross upper bound. Single-year sugar references move the no-growth margin between 0 and 0.067; the 0.104 of an earlier formulation is an artefact of pairing an uncalibrated recovery chain with the lowest year of the series. Analytically, the constrained yield is a hinge function of cane sucrose content with an 8.3-fold slope change where domestic sugar demand is met, and the elasticity of the diversion margin to every multiplicative input of the sugar balance equals $(1-f)/f$. This structure explains why sucrose content governs 85–90% of the flexible-pathway variance, why the null-margin probability is 52% at the binding point, and why, within the model, the lignocellulosic route is least exposed to the sugar balance, its absolute level remaining conditional on the transferability of the 2G coefficient.