Vaasa Energy Business Innovation Centre, University of Vaasa, Yliopistonranta 10, 65200 Vaasa, Finland.
Electrical Engineering, School of Technology and Innovations, University of Vaasa, Yliopistonranta 10, 65200 Vaasa, Finland.
Waste Manag. 2021 May 15;127:147-157. doi: 10.1016/j.wasman.2021.04.042. Epub 2021 Apr 29.
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 × 10 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.
本文提出了一种能源化废物（WtE）的多目标优化（MOO），以研究热、经济和环境目标的优化解决方案。这些目标由净效率、处理废物的总成本和环境影响来表示。通过使用终点单评分方法的生命周期评估（LCA）来整合环境目标，涵盖直接燃烧、试剂生产和基础设施、灰分管理和能量回收。工厂的初始净效率为 16.27%，而成本和环境影响分别为 75.63 欧元/吨废物和 -1.21×10 Pt/吨废物。应用非支配排序遗传算法（NSGA-II）来最大化效率、最小化成本和最小化环境影响。在单一目标方面，热效率、经济效率和环境效率的最高改进分别约为 13.4%、10.3%和 14.8%。这些改进不能同时实现，因为目标是相互冲突的。这些发现强调了决策者在为每个目标函数分配权重以获得最佳解决方案方面的重要作用。该研究还揭示了决策变量、废物输入和边际能源之间的不同影响。最后，本文强调了使用 MOO 来提高 WtE 在热、经济和环境方面的性能的多功能性，而无需额外投资。
