电力科技与环保2024，Vol.40Issue(2)：97-107,11.DOI:10.19944/j.eptep.1674-8069.2024.02.001
基于数据驱动660MW循环流化床锅炉多目标燃烧优化
Multi-objective combustion optimization for 660 MW circulating fluidized bed boiler based on data-driven approach
摘要
Abstract
In order to reduce the pollutant emissions of a circulating fluidized bed boiler in a certain power plant and improve the economy of the boiler combustion operation,this article adopts the data-driven technology to achieve the multi-target combustion optimization for circulating fluidized bed boilers.Improved particle swarm optimization-based long short-term memory neural networks is used to establish the boiler's mathematic model with NOx emission,SO2 emission and exhaust gas temperature as outputs,respectively.The relative error is regarded as a predictive evaluation index to determine the optimal network parameters.Secondly,the NOx emission prediction model,the SO2 emission prediction model and exhaust gas temperature prediction model are constructed based on improved particle swarm optimization-based long short-term memory neural network,long short-term memory neural network(LSTM),generalized regression neural network(GRNN),and a backpropagation neural network(BPNN).By comparing the evaluation indicators,the effectiveness of the predictive models constructed was testified in this paper;Finally,based on the non-dominated sorting genetic algorithm(NSGA-II),the combustion optimization adjustment schemes for CFBB under different operating conditions are obtained so as to reduce NOx/SO2 emission and maintain the stability of exhaust gas temperature at the same time.The results showed that compared with before optimization,the average NOx emission was decreased by 10.583%,the average SO2 emission was reduced by 25.812%,and the maximum reduction of SO2 emission was 650 mg/m3.In addition,the average exhaust gas temperature was decreased by 0.143%.关键词
循环流化床锅炉/多目标燃烧优化/NOx/SO2排放/排烟温度/改进粒子群优化/长短期记忆神经网络Key words
circulating fluidized bed boiler/multi-objective combustion optimization/NOx/SO2 emissions/exhaust gas temperature/improved particle swarm optimization/long-short term memory分类
能源科技引用本文复制引用
张文祥,徐文韬,黄亚继,金保昇..基于数据驱动660MW循环流化床锅炉多目标燃烧优化[J].电力科技与环保,2024,40(2):97-107,11.基金项目
江苏省科技成果转化专项资金项目(BA2020001) （BA2020001）
