发电技术2025，Vol.46Issue(3)：627-636,10.DOI:10.12096/j.2096-4528.pgt.23135
基于数据驱动的循环流化床机组深度调峰NOx预测
NOx Prediction for Deep Peaking Regulation of Circulating Fluidized Bed Units Based on Data-Driven
摘要
Abstract
[Objectives]Under the deep peaking regulation operation of circulating fluidized bed(CFB)unit,NOx emission will fluctuate significantly,which will have a serious impact on the stability and economy of the denitrification control system.Therefore,a data-driven NOx prediction model based on the deep peaking regulation of CFB unit is proposed.[Methods]The generation mechanism and influencing factors of NOx are analyzed in depth.Combined with the field operation data of a 300 MW CFB unit,the whale optimization algorithm(WOA)is used to optimize the parameters of the long short-term memory(LSTM)neural network,and the WOA-LSTM neural network model is established.[Results]The model realizes the online prediction of NOx emission mass concentration of CFB unit under deep peaking regulation.Compared with other neural network models,the proposed model has better prediction results.The mean absolute error of the model is 2.49 mg/m3,the mean absolute percentage error reaches 6.8％,and the model correlation coefficient reaches 0.99.[Conclusions]The model can accurately reflect the NOx emission trend under the wide range of variable load operation of the unit.The results of the study are a good guide for the operation of the automatic control system of denitrification in the field.关键词
火电机组/循环流化床(CFB)/NOx排放/深度调峰/人工智能(AI)/鲸鱼优化算法(WOA)/长短期记忆(LSTM)神经网络Key words
coal-fired power unit/circulating fluidized bed(CFB)/NOx emission/deep peaking regulation/artificial intelligence(AI)/whale optimization algorithm(WOA)/long short-term memory(LSTM)neural network分类
能源与动力引用本文复制引用
张鹏新,高明明,解沛然,于浩洋,张洪福,黄中..基于数据驱动的循环流化床机组深度调峰NOx预测[J].发电技术,2025,46(3):627-636,10.基金项目
国家重点研发计划项目(2022YFB4100301).Project Supported by National Key Research and Development Program(2022YFB4100301). （2022YFB4100301）
