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 agro-industry is among the largest methane emitters, posing a critical challenge for sustainability. In rural areas, producers lack effective technologies to manage daily organic waste. Anaerobic digestion (AD) offers a circular pathway by converting waste into biogas and biofertilizers; however, its adoption is limited by inappropriate designs and insufficient operational control. Theoretical-applied research addresses these barriers by improving the design and operation of small-scale biodigesters, elevating pH and Electrical Conductivity (EC) from passive indicators to first-order control variables. Based on the design of a compact biodigester previously validated in the Chillón Valley and replicated in Huaycán under a utility model patent process (INDECOPI, Exp. 001087-2025/DIN), a stoichiometric NaHCO${}_3$ strategy with joint pH–EC monitoring was formalized, defining operational windows (pH 6.92–6.97; EC 6200–6300 μS/cm and dose–response curves (0.3–0.4 kg/day for 3–4 day) to buffer VFA shocks and preserve methanogenic ionic strength. The system achieved stable productions of 370–462 L/day, surpassing the theoretical potential of 352.88 L/day calculated by Buswell’s equation. A multivariable predictive model (linear, quadratic, interaction terms pH × EC, temperature, and loading rate) was developed and validated with field data: R² = 0.78; MAPE = 2.7%; MAE = 11.2 L/day; RMSE = 13.8 L/day; r = 0.89; residuals normally distributed (Shapiro–Wilk p = 0.79). The proposed approach enables daily decision-making in low-instrumentation environments and provides a replicable and scalable pathway for the safe valorization of organic waste in rural areas. The design consolidates the shift from reactive to proactive and co-optimized pH–EC control, laying the foundation not only for standardized protocols and training in rural systems but also for improved environmental sustainability.

There are numerous analytical and/or computational tools for evaluating the energetic sustainability of biomass in the sugar industry. However, the simultaneous integration of the energetic–exergetic and emergetic criteria for such evaluation is still insufficient. The objective of the present work is to propose a range of indicators to evaluate the sustainability of the use of biomass as fuel in the sugar industry. For this purpose, energy, exergy, and emergy evaluation tools were theoretically used as sustainability indicators. They were validated in five variants of different biomass and their mixtures in two studies of technologies used in Cuba for the sugar industry. As a result, the energy method showed, for all variants, an increase in efficiency of about 5% in the VU-40 technology compared to the Retal technology. There is an increase in energy efficiency when considering AHRs of 2.8% or Marabu (Dichrostachys cinerea) (5.3%) compared to the $V_1$ variant. Through the study of the exergetic efficiency, an increase of 2% was determined in both technologies for the case of the $V_1$ variant, and an increase in efficiency is observed in the $V_2$ variant of 5% and the $V_3$ variant (5.6%) over the $V_1$ variant. The emergetic method showed superior results for the VU-40 technology over the Retal technology due to higher fuel utilization. In the case of the $V_1$ variant, there was a 7% increase in the renewability ratio and an 11.07% increase in the sustainability index. This is because more energy is produced per unit of environmental load.

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 high dependence on fossil fuels for energy supply in hospitals compromises their operational sustainability, increases costs, and contributes significantly to polluting emissions. This study evaluates the technical, economic, and environmental feasibility of integrating photovoltaic and solar thermal systems in a hospital located in a tropical Caribbean environment, characterized by continuous operation and high energy demand. The methodology combines advanced simulation using PVsyst for the photovoltaic subsystem and the f-chart method for the solar thermal system, using real data on electricity and domestic hot water demand. The proposed system achieves an installed photovoltaic power of close to 390 kWp, with an annual production of around 0.7 GWh and an average performance ratio of 0.80, demonstrating high technical performance. The solar thermal subsystem covers approximately two-thirds of the annual domestic hot water demand, supported by thermal storage suitable for hospital operation. From an economic standpoint, the total estimated investment is recovered in less than 10 years, with a positive net present value, confirming the system’s profitability over its useful life. In environmental terms, hybrid integration avoids more than 400 t of CO₂ per year, contributing significantly to the decarbonization of the health sector and the strengthening of energy security. The results obtained demonstrate that photovoltaic–thermal integration in tropical hospitals is technically and economically viable and constitutes a replicable solution for regions with high solar radiation and energy vulnerability. This research provides a comprehensive and reproducible methodological framework that can support sustainable energy planning and the design of public policies aimed at low-emission healthcare infrastructure.