Intelligent Operational Optimization of Municipal Solid Waste Incineration Process Based on Multi-objective Particle Swarm Algorithm
Municipal solid waste incineration(MSWI)technology has become the main way to address solid waste pollution due to its characteristics of reduction,harmlessness,and resource utilization.However,it is difficult for ac-tual industries to operate efficiently due to the complex composition of municipal solid waste,dynamic fluctuations in moisture content and calorific value,coupling conflicts in solid waste combustion,waste heat utilization and flue gas purification.To enhance combustion efficiency and flue gas purification efficiency,this paper proposes an intelli-gent operational optimization method of MSWI process based on multi-objective particle swarm algorithm.First,operational index models are established by designing self-organizing radial basis function(SORBF)neural net-works to achieve online evaluation of operational performance in MSWI process.Second,an improved multi-object-ive particle swarm optimization algorithm is presented by incorporating regional congestion degree index to obtain the Pareto solutions of operating variables.Then,the entropy weight method is employed to determine the optimal set value of operating variables,achieving efficient operation of MSWI process.Finally,the proposed method is veri-fied through actual operational data from a MSWI plant in Beijing,and the experimental results demonstrate that the intelligent operational optimization method based on multi-objective particle swarm algorithm can improve combustion efficiency and reduce nitrogen oxide emissions.
Municipal solid waste incineration(MSWI)processcombustion efficiencyNOx emissionsoperational index modelmulti-objective particle swarm algorithm
万方数据
维普
