A novel biogas combustion-heat recovery for cooling/power co-production system considering a modified sCO₂ cycle and a generator-absorber-exchanger (GAX) cycle: Machine learning-driven optimization and economic study

Abstract

Biogas-driven combined cooling and power (CCP) systems face the challenge of simultaneously optimizing thermodynamic performance and financial viability under nonlinear design and operational constraints. This study proposes a novel biogas combustion–heat recovery configuration for CCP generation, evaluated through an integrated thermodynamic–financial framework and optimized using machine learning (ML)-driven softcomputing techniques. The system integrates a biogas combustion unit, a gas turbine, a modified supercritical CO₂ cycle, and a generator–absorber–exchanger (GAX) cycle. Thermodynamic analyses based on the first and second laws of thermodynamics are employed, while sustainability, financial, and environmental indicators are incorporated into the assessment. A hybrid optimization approach, combining ML with the genetic algorithm optimizer, is implemented to accelerate convergence and explore trade-offs among net present value (NPV), total unit product cost (TUPC), and sustainability index (SI). The optimized configuration achieves an NPV of 13.03 M $, an SI of 1.765, and a TUPC of 26.5 $/GJ. Besides, the system demonstrates an energy efficiency of 62.75%, an exergy efficiency of 43.32%, and a payback period of 3.79 years, confirming technical robustness and economic viability. Overall, ML-driven soft computing enables resilient, investment-ready CCP strategies, offering a scalable plan that aligns biogas utilization with sustainability, efficiency, and competitiveness.