基于BO-TPE优化ERT模型的污泥焚烧SO<sub>2</sub>排放预测

罗松; 王丽花; 王飞

doi:10.19805/j.cnki.jcspe.2026.240709

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基于BO-TPE优化ERT模型的污泥焚烧SO₂排放预测

绿色低碳 | 更新时间：2026-10-10

- 基于BO-TPE优化ERT模型的污泥焚烧SO₂排放预测
- Prediction of SO₂ Emission Concentration from Sludge IncinerationBased on ERT Model with BO-TPE Optimization
- 动力工程学报 2026年46卷第2期页码：174-182
- 作者机构：
  
  1. 浙江大学能源高效利用全国重点实验室,浙江,杭州,310027
  2. 上海城投污水处理有限公司,上海,201203
- 作者简介：
  
  罗松(2001—),男,江西宜春人,博士研究生,研究方向为基于机器视觉的焚烧炉燃烧诊断研究
  王飞(通信作者),男,教授,博士,E-mail:wangfei@zju.edu.cn
- 基金信息：
  
  浙江省科技计划资助项目(2024C03129)
- DOI：10.19805/j.cnki.jcspe.2026.240709
  中图分类号：
- 网络首发：2026-02-10，
  
  纸质出版：2026-02-10
- 稿件说明：
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罗松,王丽花,王飞. 基于BO-TPE优化ERT模型的污泥焚烧SO₂排放预测动力工程学报, 2026, 46(2): 174-182 https://doi. DOI: 10.19805/j.cnki.jcspe.2026.240709.

org/10.19805/j.cnki.jcspe.2026.240709
罗松,王丽花,王飞. 基于BO-TPE优化ERT模型的污泥焚烧SO₂排放预测动力工程学报, 2026, 46(2): 174-182 https://doi. DOI: 10.19805/j.cnki.jcspe.2026.240709. DOI：

org/10.19805/j.cnki.jcspe.2026.240709 DOI：

摘要

为提高污泥焚烧过程中SO

排放的预测精度以优化焚烧与烟气处理工况

提出了一种高效稳定的SO

排放混合预测模型。首先

以鼓泡流化床污泥焚烧系统为研究对象

从火焰图像中提取静态与动态火焰特征

并结合分布式控制系统(DCS)参数构建输入特征

排放浓度设为模型输出。然后

利用互信息(MI)确定SO

与各输入特征的最优滞后时间并据此进行数据重组。最终构建基于树结构的贝叶斯优化(BO-TPE)的极端随机树(ERT)预测模型

并与多种主流预测模型进行性能对比。结果表明:基于BO-TPE优化的ERT模型相关系数

为0.93

平均绝对百分比误差(MAPE)小于3%

适用于污泥焚烧系统SO

排放的在线预测与过程优化控制。

Abstract

To enhance the prediction accuracy of SO

emissions during sludge incineration process and optimize incineration and flue gas treatment conditions

an efficient and stable hybrid prediction model for SO

emissions was prorosed. First

using a bubbling fluidized bed sludge incineration system as the research subject

static and dynamic flame features were extracted from flame images and the input features were set combined with distributed control system (DCS) parameters

while SO

emission concentration was set as the model output. Subsequently

mutual information (MI) was employed to determine the optimal lag time between SO

and each input feature

guiding data reorganization. Finally

the extremely randomized trees (ERT) model based on Bayesian optimization-TPE (BO-TPE) was constructed and compared with multiple mainstream prediction models. Results show that the BO-TPE-optimized ERT model achieves correlation coefficient

of 0.93 with mean absolute percentage error(MAPE) below 3%

making it suitable for online prediction and process optimization control of SO

emissions in sludge incineration systems.

关键词

Keywords

references

ZHANG Junhui, SUN Guang, LIU Jingyong, et al. Co-combustion of textile dyeing sludge with cattle manure:assessment of thermal behavior, gaseous products, and ash characteristics[J]. Journal of Cleaner Production, 2020, 253:119950.

张砺彦, 麦永强, 张月辉, 等. 基于神经网络的大型燃煤电厂SO 2 和NO x 的污染物预测模型[J ] . 热力发电, 2008, 37(10):9-13. ZHANG Liyan, MAI Yongqiang, ZHANG Yuehui, et al. Prediction model for pollutants SO 2 and NO x from large-scale coal-fired power plant based on neural network[J ] . Thermal Power Generation, 2008, 37(10):9-13.

KRZYWANSKI J, CZAKIERT T, BLASZCZUK A, et al. A generalized model of SO 2 emissions from large- and small-scale CFB boilers by artificial neural network approach:part 1. the mathematical model of SO 2 emissions in air-firing, oxygen-enriched and oxycombustion CFB conditions[J ] . Fuel Processing Technology, 2015, 137:66-74.

金秀章, 刘岳, 赵文杰, 等. 基于mRMR和MA-RELM的火电厂出口SO 2 质量浓度预测[J ] . 动力工程学报, 2022, 42(7):664-670, 676.JIN Xiuzhang, LIU Yue, ZHAO Wenjie, et al. Prediction of SO 2 mass concentration at outlet of the thermal power plant based on mRMR and MA-RELM[J ] . Journal of Chinese Society of Power Engineering, 2022, 42(7):664-670, 676.

倪绍佑, 姚晨蓬, 陈玲红 , 等. 基于后向传播神经网络的循环流化床锅炉SO 2 排放预测分析[J ] . 能源工程, 2021(6):31-37.NI Shaoyou, YAO Chenpeng, CHEN Linghong, et al. Prediction analysis of SO 2 emissions from circulating fluidized bed boiler based on BP neural network[J ] . Energy Engineering, 2021(6):31-37.

王琦, 柴宇唤, 王鹏程, 等. 基于改进INFO-Bi-LSTM模型的SO 2 排放质量浓度预测[J ] . 动力工程学报, 2024, 44(4):641-649.WANG Qi, CHAI Yuhuan, WANG Pengcheng, et al. Prediction of SO 2 emission mass concentration based on improved INFO-Bi-LSTM algorithm[J ] . Journal of Chinese Society of Power Engineering, 2024, 44(4):641-649.

BEJANI M M, GHATEE M. A systematic review on overfitting control in shallow and deep neural networks[J]. Artificial Intelligence Review, 2021, 54(8):6391-6438.

XIE Kai, CUI Yunjing, WANG Chunlin, et al. Study on threshold selection method of continuous flame images of spray combustion in the low-pressure chamber[J]. Case Studies in Thermal Engineering, 2021, 26:101195.

QIU Guoqing, LIU Shuai, CAO Dongmei, et al. Flame recognition based on video image[J]. Applied Mechanics and Materials, 2014, 687-691:3604-3607.

KWAK D K, RYU J K. A study on fire detection using deep learning and image filtering based on characteristics of flame and smoke[J]. Journal of Electrical Engineering & Technology, 2023, 18(5):3887-3895.

李畅. 城市污泥焚烧及污染物排放特性研究[D]. 保定:华北电力大学, 2015.

房文轩. 污泥干化与流化床焚烧试验研究[D]. 哈尔滨:哈尔滨工业大学, 2017.

LARKI I, ZAHEDI A, ASADI M, et al. Mitigation approaches and techniques for combustion power plants flue gas emissions:a comprehensive review[J]. Science of the Total Environment, 2023, 903:166108.

BOSTANI H, SHEIKHAN M. Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems[J]. Soft Computing, 2017, 21(9):2307-2324.

ESKAFI M, KOWSARI M, DASTGHEIB A, et al. Mutual information analysis of the factors influencing port throughput[J]. Maritime Business Review, 2021, 6(2):129-146.

KRASKOV A, STGBAUER H, GRASSBERGER P. Estimating mutual information[J]. Physical Review E, 2004, 69(6):066138.

ELEN A, AVULU E. Standardized variable distances:a distance-based machine learning method[J]. Applied Soft Computing, 2021, 98:106855.

SAGI O, ROKACH L. Ensemble learning:a survey[J]. WIREs Data Mining and Knowledge Discovery, 2018, 8(4):e1249.

DONG Xibin, YU Zhiwen, CAO Wenming, et al. A survey on ensemble learning[J]. Frontiers of Computer Science, 2020, 14(2):241-258.

RIGATTI S J. Random forest[J]. Journal of Insurance Medicine, 2017, 47(1):31-39.

ACOSTA M R C, AHMED S, GARCIA C E, et al. Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks[J]. IEEE Access, 2020, 8:19921-19933.

YANG Li, SHAMI A. On hyperparameter optimization of machine learning algorithms:theory and practice[J]. Neurocomputing, 2020, 415:295-316.

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基于BO-TPE优化ERT模型的污泥焚烧SO2排放预测

Prediction of SO2 Emission Concentration from Sludge IncinerationBased on ERT Model with BO-TPE Optimization

DOI：10.19805/j.cnki.jcspe.2026.240709

摘要

Abstract

关键词

Keywords

references

基于BO-TPE优化ERT模型的污泥焚烧SO₂排放预测

Prediction of SO₂ Emission Concentration from Sludge IncinerationBased on ERT Model with BO-TPE Optimization