A novel biomass-to-energy cogeneration system using zeotropic mixtures: Multi-objective optimization and environmental assessment

The heavy dependence on fossil fuels over the many past decades has resulted to critical environmental and health challenges that must be urgently addressed. Adopting renewable energy sources, e.g. biomass, and maximum utilization of sustainable energy resources, e.g. waste heat recovery, are proven to be a viable and indeed inevitable measures for this. The present study proposes a novel waste-to-energy combined heat and power (CHP) system driven by municipal solid waste (MSW), integrating a biomass gasifier with supercritical CO 2 (s-CO 2 ), Kalina, and zeotropic organic Rankine cycle (ORC) subsystems. The system is designed to maximize energy efficiency and sustainability by effectively utilizing waste heat streams at varying temperature levels and employing zeotropic mixtures such as R1233zd(E) in the ORC cycle, to enhance thermodynamic performance and reduce environmental impact. Detailed sensitivity analyses are conducted to evaluate the influence of key parameters on the system performance, along with a comprehensive energy, exergy, exergo-economic, and environmental analysis. To achieve a balance between energy efficiency, cost-effectiveness, and emissions reduction, a multi-objective optimization via the genetic algorithm approach combined with TOPSIS method is used. The results indicate that in the base design, the system achieves energy efficiency of 76.65%, exergy efficiency of 49.06%, net power output of 3621 kW, a total cost rate of 265.6 $/h, and CO 2 emissions of 489.8 kg/MWh. The optimization efforts enhance these key metrics by 13.93%, 27.13%, 28.8%, -5.42%, and -12.23%, respectively. Based on these findings, the system has potential to serve as an efficient and sustainable waste-to-energy system. • Proposing an efficient novel waste-to-energy system using municipal solid waste. • Implementation of zeotropic cycles and district heating for waste energy recovery. • Comprehensive energy, exergy, economic, and environmental performance evaluations. • Multi-objective optimization with genetic algorithm and TOPSIS for solution selection. • Considerable enhancements in achievable efficiency, cost-effectiveness, and emission index.