The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious environmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions that promote the transition to renewable energy sources, such as biogas. Although progress has been made in optimizing biogas production, there is still no adaptable model for various environments that allows for the determination of optimal quantities of different organic wastes, simultaneously considering their composition, moisture content, and control of critical factors for biogas production, as well as the biodigester’s capacity and other relevant elements. In practice, the dosing of waste is conducted empirically, leading to inefficiencies that limit the potential for biogas production in real scenarios. The objective of this article is to propose a linear optimization model with nonlinear constraints that maximizes biogas production, considering fundamental parameters such as the moisture percentage, pH, carbon/nitrogen ratio (C/N), substrate volume, organic matter, volatile solids (VS), and biogas production potential from different wastes. The model estimates the optimal waste composition based on the biodigester capacity to ensure balanced substrates. The results for the proposed scenarios demonstrate its effectiveness: Scenario 1 achieved 3.42 m³ (3418.67 L) of biogas, while Scenario 2, with a greater diversity of waste, reached 8.06 m³ (8061.43 L). The model maintained pH (6.49–6.50), C/N ratio (20.00), and moisture (60.00%) within optimal ranges. Additionally, a Monte Carlo sensitivity analysis (1000 simulations) validated its robustness with a 95% confidence level. This model provides an efficient tool for optimizing biogas production and waste dosing in rural contexts, promoting clean and sustainable technologies for renewable energy generation.

The inefficient management of agro-industrial residues, particularly cocoa pod husk and mucilage, represents a critical environmental and economic challenge in cocoa-producing regions such as Santander and Norte de Santander, Colombia. These by-products, constituting approximately 70% of the fruit’s total weight, are currently underutilized, generating pollution and wasting resources with high valorization potential. This article proposes the design and rigorous experimental validation of an empirical model based on artificial intelligence capable of predicting quantities of valuable compounds, including bioethanol, essential oils, paraffins, antioxidants, and pectins, obtained from cocoa residues. The model integrates critical variables such as cocoa variety, extraction methods, and process conditions, incorporating advanced machine learning techniques trained on a 100% empirical database of eighty-four (84) laboratory trials, combined with a post-inference sensitivity analysis via the Monte Carlo method with 10,000 simulations. Preliminary results demonstrate significant varietal differences; for instance, the CCN-51 variety achieves a mean bioethanol yield of 79.30 ± 4.96 mL/kg with a 95% confidence interval of (69.44–88.93) mL/kg, while the Criollo variety reaches 43.55 ± 2.72 mL/kg (38.14–48.84 mL/kg), both exhibiting identical coefficients of variation (6.25%). Furthermore, the integration of an optimized extraction sequence combined with neural networks allows for maximizing by-product yields while reducing final residue generation by 40%. This tool not only contributes to the circular economy and alignment with the Sustainable Development Goals (SDGs 9 and 12) but also offers a tangible pathway to improve the competitiveness of the Colombian cocoa industry through data-driven decision-making and sustainable technology adoption.

The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious en-vironmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions that promote the transition to renewable energy sources, such as bi-ogas. Although progress has been made in optimizing biogas production, there is still no adaptable model for various environments that allows for the determination of optimal quantities of different organic wastes, simultaneously considering their composition, moisture content, control of critical factors for biogas production, as well as the biodigest-er's capacity and other relevant elements. In practice, the dosing of waste is done empiri-cally, leading to inefficiencies that limit the potential for biogas production in real scenari-os. The objective of this article is to propose a linear optimization model with nonlinear constraints that maximizes biogas production, taking into account fundamental parame-ters such as moisture percentage, pH, carbon-to-nitrogen ratio (C/N), substrate volume, or-ganic matter, volatile solids (VS), and the potential biogas production from different wastes. This model allows for the estimation of optimal quantities in kg of organic waste according to the capacity of the biodigester, aiming to achieve maximum biogas produc-tion. The results obtained for the proposed scenarios, complemented by a sensitivity anal-ysis using the Monte Carlo method, demonstrate that the model can be considered an effi-cient tool for optimizing biogas production and waste dosing in rural settings, with a con-fidence level of 95%. In addition to improving efficiency in biogas production, the model maximizes the utilization of organic waste, promoting clean and sustainable technologies. This model provides a distinctive value by offering a comprehensive methodology for bio-gas production, establishing a tool of great relevance for future research related to anaero-bic digestion processes.

The inefficient management of agro-industrial residues, particularly cocoa pod husk and mucilage, represents a critical environmental and economic challenge in Santander and Norte de Santander, Colombia, where over 55,000 tons of biomass waste are generated annually from a regional production exceeding 23,500 tons of cocoa beans. Furthermore, these residues, which constitute approximately 70% to 75% of the fruit’s total weight, are currently underutilized, generating severe localized pollution through uncontrolled decomposition and causing substantial economic losses by wasting compounds with high valorization potential. This article proposes the design and systematic experimental calibration and internal cross-validation of an empirical model based on artificial intelligence capable of predicting quantities of valuable compounds, including bioethanol, essential oils, paraffins, antioxidants, and pectins, obtained from cocoa residues. The model integrates critical variables such as cocoa variety, extraction methods, and process conditions, incorporating advanced machine learning techniques trained on a 100% empirical database of eighty-four (84) laboratory trials, combined with a post-inference sensitivity analysis via the Monte Carlo method with 10,000 simulations. Preliminary results demonstrate significant varietal differences; for instance, the CCN-51 variety achieves a mean bioethanol yield of 79.30 ± 4.96 mL/kg with a 95% confidence interval of (69.44–88.93) mL/kg, while the Criollo variety reaches 43.55 ± 2.72 mL/kg (38.14–48.84 mL/kg), exhibiting highly synchronized coefficients of variation of 6.255% and 6.246%, respectively. Furthermore, the integration of a cascading extraction sequence combined with neural networks may potentially contribute to maximizing by-product yields, with theoretical mass balance estimations suggesting a possible reduction of up to 40% in final residue generation compared to conventional isolated extraction pathways, as detailed in the proposed framework. This tool could potentially support the circular economy and alignment with Sustainable Development Goals 7, 9, and 12, while offering a possible pathway to contribute to the competitiveness of the Colombian cocoa industry through data-driven decision-making and sustainable technology adoption.