Abstract
This work proposes a two-stage optimization framework for the optimal utilization of biogas from pig manure, integrating process-level design with short-term operational planning under dynamic electricity tariff schemes in Mexico. In the first stage, a multi-objective optimization based on 3E (Exergy, Environment, and Energy) analysis was performed. The results demonstrate that increasing the biogas split fraction for upgrading significantly reduces the environmental and exergy indices, enhancing thermodynamic and environmental performance without compromising the energy index. High upgrading flows (split > 0.7) emerged as the most favorable compromise across the evaluated metrics. In the second stage, support vector regression (SVR) surrogate models were developed to approximate nonlinear relationships between the operational split and process outputs. These surrogates were embedded in a Mixed-Integer Linear Programming (MILP) formulation to optimize weekly scheduling under the Mexican Net Billing scheme, incorporating an economic objective (+1E index). The SVR-MILP framework successfully captured market-driven decisions, prioritizing biomethane upgrading during base and intermediate tariff periods and switching to electricity generation during peak hours to maximize economic profit. Biomethane upgrading accounts for approximately 90-100% of the total positive economic benefits, while electricity generation contributes the remaining 10%, depending on the exposure to peak electricity tariffs. Thus, the proposed methodology offers a robust, computationally efficient decision-support tool for flexible biogas systems, bridging the gap between steady-state design and dynamic market responsiveness based on a 4E approach.