Doctor Xiaotuo Qiao, a researcher of the base, has published a collaborative paper titled "A metaheuristic Multi-Objective optimization of energy and environmental performances of a Waste-to-Energy system based on waste gasification using particle swarm optimization" in Energy Conversion and Management.

ECM provides a forum for publishing original contributions and comprehensive technical review articles, covering interdisciplinary and original research on all important energy topics. The topics include energy generation, utilization, conversion, storage, transmission, conservation, management, and sustainability.

Abstract:

Studies focusing on the optimization of waste-to-energy systems using metaheuristic particle swarm optimization (PSO) are crucial in our pursuit of sustainable and renewable energy sources. The advancements made in this field have the potential to revolutionize waste management practices and reduce carbon footprint. This study investigates the application of metaheuristic PSO technique for enhancement of energetic and environmental performances of a waste-to-energy system including a waste gasification reactor and a solid oxide fuel cell conducting proton to produce heat and power. The methodology involves the creation of regression-based machine learning models, followed by the generation of various near-optimal solutions on the Pareto front through the use of the metaheuristic PSO. The effectiveness of these solutions is then thoroughly evaluated in terms of power output, heat production, and emissions. Subsequently, these near-optimal solutions are assessed and ordered through a multi-criteria decision-making process to identify the most favorable condition. The highest level of performance produced a heating rate of 1069.9 g/s, a power output of 365.5 kW, and an emission rate of 804.5 kg/MWs. This particular operating point had a steam to waste ratio of 1.315, an ideal temperature of 1200 K, a current density of 5000 A/m2, and a utilization factor of 0.76. The use of metaheuristic optimization techniques can help identify the optimal operating conditions in waste-to-energy systems with a bright outlook that maximize energy production while minimizing environmental impact.

Keywords：Waste-to-energy; Metaheuristic optimization; Particle swarm optimization; Gasification; Machine learning

Link：https://doi.org/10.1016/j.enconman.2024.118844

Teacher profile

Xiaotuo Qiao, Ph.D. in Finance, joint Ph.D. from the University of Texas at Dallas. During her Ph.D. study, she participated in a number of National Natural Science Foundation of China Youth Programs and National Key R&D Programs. After graduating with a doctorate in 2021, she entered Zhongnan University of Economics and Law as a lecturer in the School of Finance.