热力发电2025，Vol.54Issue(7)：23-32,10.DOI:10.19666/j.rlfd.202410228
基于粒子群算法的燃煤CFB锅炉一氧化碳与多污染物在线减排优化
Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization
摘要
Abstract
Nowadays,circulating fluidized bed(CFB)coal-fired boilers face challenges in the process of deep peak regulation,such as high CO emission concentrations and the lack of theoretical guidance for collaborative emission reduction of multiple pollutants including NOx and SO2.Taking a 150 t/h CFB coal-fired boiler as the research object,a model for quickly predicting mass concentrations of CO,NOx and SO2 emitted from the furnace is established based on the long short-term memory(LSTM)neural network,the Attention mechanism and the XGBoost algorithm.Moreover,an online emission reduction strategy is proposed by coupling with the particle swarm optimization(PSO)algorithm.36 298 operational data points from the coal-fired boiler throughout 2023 are selected as training samples.A correlation analysis is conducted between the boiler inspection data and pollutant emission mass concentrations to determine the input parameters for the prediction model.The fitness function and boundary function are determined with the prediction model coupled with the PSO algorithm.Through the calculation of emission reduction optimization model,an online emission reduction optimization strategy for CO,NOx and SO2 mass concentrations of CFB boilers in different load ranges is proposed,and the feasibility of the algorithm in practical boiler tuning applications is evaluated.关键词
CFB锅炉/长短时记忆神经网络/粒子群算法/CO/协同减排Key words
CFB boiler/long short-term memory neural network/PSO algorithm/CO/multi-pollutant emission reduction引用本文复制引用
康子为,陈玲红,武燕燕,吴俊,徐碧涛,金杭良,曲培培..基于粒子群算法的燃煤CFB锅炉一氧化碳与多污染物在线减排优化[J].热力发电,2025,54(7):23-32,10.基金项目
浙江省"领雁"计划项目(2024C03113) Key Research and Development Program of Zhejiang Province(2024C03113) （2024C03113）
