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Item type:Publication, Delphi/AHP-based method for biomass sustainable assessment in the sugar industry(Multidisciplinary Digital Publishing Institute (MDPI), 2024-09-01)Multi-criteria methods are highly attractive tools to address the inherent complexity of evaluating problems in various scientific areas. The combination of methods such as Delphi/AHP is emerging as a robust alternative to evaluate the sustainability of renewable energy sources. In this theoretical-descriptive research, the use of the Delphi method is proposed to select criteria and sub-criteria to obtain a high level of reliability, while the AHP method is used to establish an order of preference among the alternatives analyzed. This process requires the support of a committee of experts, whose role is to identify the various biomass alternatives that can be used in the sugar industry, considering aspects related to sustainability. The selected experts have identified energy, exergy, and emergetic indicators, in which economic, environmental, and social aspects are integrated. The multi-criteria analysis shows that the V1 variant was the most satisfactory in terms of biomass sustainability, representing 45% and 53% of the overall priorities in the evaluated case studies. In addition, the sensitivity analysis under an equal-weighted scenario for both study cases evidenced that variant V1 acquired the highest score (38.17%) among all alternatives. Variant V4 achieved the second highest score (31.79%), while alternative V2 achieved only 29.04%, respectively. The integration of Delphi/AHP methods emerges as a novel tool to assess sustainability in different industries of the energy sector. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transition to a Circular Bioeconomy in the Sugar Agro-Industry: Predictive Modeling to Estimate the Energy Potential of By-Products(Multidisciplinary Digital Publishing Institute (MDPI), 2025-06-01)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.4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Methodological Framework for the Integrated Technical, Economic, and Environmental Evaluation of Solar Photovoltaic Systems in Agroindustrial Environments(Multidisciplinary Digital Publishing Institute (MDPI), 2025-08-01)The transition to sustainable energy systems in the agroindustrial sector requires rigorous methodologies that enable a comprehensive and quantitative assessment of the technical and economic viability and environmental impact of photovoltaic integration. This study develops and validates a hybrid multi-criteria methodology structured in three phases: (i) analytical modeling of the load profile and preliminary sizing, (ii) advanced energy simulation using PVsyst for operational optimization and validation against empirical data, and (iii) environmental assessment using life cycle analysis (LCA) under ISO 14040/44 standards. The methodology is applied to a Cuban agroindustrial plant with an annual electricity demand of 290,870 kWh, resulting in the design of a 200 kWp photovoltaic system capable of supplying 291,513 kWh/year, thereby achieving total coverage of the electricity demand. The economic analysis yields an LCOE of 0.064 USD/kWh and an NPV of USD 139,408, while the environmental component allows for a mitigation of 113 t CO2-eq/year. The robustness of the model is validated by comparison with historical records, yielding an MBE of −0.65%, an RMSE of 2.87%, an MAPE of 2.62%, and an R2 of 0.98. This comprehensive approach demonstrates its superiority over previous methodologies by effectively integrating the three pillars of sustainability in an agroindustrial context, thus offering a scientifically sound, replicable, and adaptable tool for decision-making in advanced energy projects. The results position this methodology as a benchmark for future research and applications in emerging production scales.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Biopolygeneration Diagnostic Index (BDI): An Exergy-Based Framework for Quantifying Maximum Utilization and Thermodynamic Performance in Biomass-Based Bioenergy Plants(Multidisciplinary Digital Publishing Institute (MDPI), 2026-06-01)The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within [0,1] and integrates five sub-indices: energy efficiency (IE), thermal integration (IT), energy self-sufficiency (IA), exergetic quality of outputs (IQ), and co-product valorization (IV). Weights were determined using the Analytic Hierarchy Process (w1=0.40, w2=0.24, w3=w4=0.14, w5=0.08; CR=0.007). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP (C1→C2) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample (n=6) and the reconstruction of IA and IV from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting (wIA+wIV=0.22), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established.