Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization

康子为; 陈玲红; 武燕燕; 吴俊; 徐碧涛; 金杭良; 曲培培

doi:10.19666/j.rlfd.202410228

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Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization

Thermal Power Generation Vol. 54, Issue 7, Pages: 23-32(2025)

更新时间：2026-08-11

- Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization
- 作者机构：
  
  1. 浙江大学能源工程学院
  2. 杭州杭联热电有限公司
  3. 杭州和达能源有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202410228
  CLC： X773;TP18;TM621.2
- Published：2025
- 稿件说明：
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[1]康子为,陈玲红,武燕燕,等.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],2025,54(07):23-32.
[1]康子为,陈玲红,武燕燕,等.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],2025,54(07):23-32. DOI： 10.19666/j.rlfd.202410228.

DOI：10.19666/j.rlfd.202410228.

摘要

深度调峰过程中燃煤循环流化床(CFB)锅炉面临CO排放质量浓度高及其与NOx、SO2等多种污染物协同减排缺乏理论指导等问题。以某150 t/h燃煤CFB锅炉为研究对象，构建基于长短时记忆(LSTM)神经网络、注意力机制(Attention)与极端梯度提升(XGBoost)算法的锅炉炉膛出口CO、NOx、SO2等多种污染物排放质量浓度快速预测模型，并耦合粒子群优化(PSO)算法建立CO在线减排优化策略模型。采用2023年36 298条实际运行数据作为训练样本进行炉膛出口污染物排放相关性分析，筛选出污染物质量浓度预测模型的输入参数，并设定适应度函数和边界函数，通过减排优化模型计算，提出了不同负荷范围CFB锅炉CO、NOx、SO2质量浓度在线减排寻优策略，并评估其在实际锅炉在线优化运行应用的可行性。

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

Keywords

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