Carbon-Negative Dual-Source polygeneration integrating biogas combustion and geothermal energy for Water–Energy–Carbon Nexus; 5E + LCA assessment and Ridge Regression–ALA optimization

Abstract

The pressing necessity to reduce carbon emissions and shift to sustainable energy systems stimulates the creation of innovative polygeneration technologies, maximizing resource productivity and reducing environmental impact. This paper suggests and critically analyzes an innovative dual-source multi-generation system as a synergistic integration of biogas-combustion flue gas, geothermal energy, and LNG regasification with cold-energy recovery to co-produce electricity, heating, cooling, liquid CO 2 , hydrogen, oxygen, and desalinated water. This system has net carbon-negative performance, and the global warming potential of the life cycle is −0.09 kgCO 2 -eq/kWh, which highlights its environmental excellence and compliance with the objectives of cleaner production. Based on a comprehensive 5E (Energy, Exergy, Economic, Environmental, and Exergo-environmental) analysis and the stepwise Life Cycle Assessment (LCA), the system has proven a baseline polygeneration energy efficiency of 50.30% and a levelized cost of energy (LCOE) of 0.23 $/kWh. Moreover, an advanced machine learning and multi-objective optimization (ML-MOO) framework, based on the Ridge Regression surrogate modeling and Artificial Lemming Algorithm (ALA), leads to a significant improvement in the system performance, namely, an increase in polygeneration exergy efficiency to 28.69% while reducing LCOE to 0.210 $/kWh. The work offers a validated, scalable template of sustainable energy integration within the water-energy-carbon nexus, and a technically viable, economically competitive, and environmentally transformative route to decarbonized multi-generation.