Multi-objective optimization to improve energy, economic and, environmental life cycle assessment in waste-to-energy plant - Mayanti, Bening; Songok, Joel; Helo, Petri (Elsevier, 2021)
This paper presents a multi-objective optimization (MOO) of waste-to-energy (WtE) to investigate optimized solutions for thermal, economic, and environmental objectives. These objectives are represented by net efficiency, total cost in treating waste, and environmental impact. Integration of the environmental objective is conducted using life cycle assessment (LCA) with endpoint single score method covering direct combustion, reagent production and infrastructure, ash management, and energy recovery. Initial net efficiency of the plant was 16.27% whereas the cost and environmental impacts were 75.63 €/ton-waste and −1.21 × 108 Pt/ton-waste, respectively. A non-dominated sorting genetic algorithm (NSGA-II) is applied to maximize efficiency, minimize cost, and minimize environmental impact. Highest improvement for single objective is about 13.4%, 10.3%, and 14.8% for thermal, economic, and environmental, respectively. These improvements cannot be made at once since the objectives are conflicting. These findings highlight the significance role of decision makers in assigning weight to each objective function to obtain the optimal solution. The study also reveals different influence among decision variable, waste input, and marginal energy sources. Finally, this paper underlines the versatility of using MOO to improve WtE performance regarding the thermal, economic, and environmental aspects without requiring additional investment.
Keywords: Multi-objective optimization; Life cycle assessment; Life cycle costing; Energy efficiency; Waste-to-energy; Elitist non-dominated sorting genetic algorithm