Prediction of SO2 emission from dual-alkali desulfurization tower based on RBF neural network

SU Xiangpeng; DIAO Yongfa; YANG Qingjie

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Prediction of SO2 emission from dual-alkali desulfurization tower based on RBF neural network

更新时间：2025-09-28

- Prediction of SO2 emission from dual-alkali desulfurization tower based on RBF neural network
- Thermal Power Generation Issue 9, (2017)
- 作者机构：
  
  1. 东华大学环境科学与工程学院,上海,201600
  2. 河南潜合自动化科技有限公司,河南,新乡,453000
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK39
- Published Online：30 September 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
SU Xiangpeng, DIAO Yongfa, YANG Qingjie. Prediction of SO2 emission from dual-alkali desulfurization tower based on RBF neural network[J]. Thermal power generation, 2017, (9).
DOI：

SU Xiangpeng, DIAO Yongfa, YANG Qingjie. Prediction of SO2 emission from dual-alkali desulfurization tower based on RBF neural network[J]. Thermal power generation, 2017, (9). DOI：

摘要

采用传统公式估算脱硫塔出口SO2排放量的方法难以推广，现场实测又耗时耗财，对此，本文提出一种基于RBF神经网络的脱硫塔出口SO2排放量预测方法，并以传统F-R修正的变梯度BP神经网络和以LM算法优化的BP神经网络（LM-BP神经网络）为对照模型，对双碱法脱硫塔在线监测数据进行预测。结果发现：RBF神经网络最大偏差和平均偏差均为0，优于变梯度BP神经网络和LM-BP神经网络。基于此，采用RBF神经网络作为预测模型，以平均烟气温度为101?℃、平均脱硫塔清液pH=8.40、平均进塔清液流量与烟气流量之比（液气比）1.130 0 L/m3为输入参数，连续改变其中某一输入参数，分别预测其他参数对脱硫塔出口SO2质量浓度折算值的影响。结果表明：脱硫塔出口SO2质量浓度折算值随进口烟气温度升高而增大，在烟气温度高于110?℃后出现波动；当烟气温度为101?℃、液气比为1.130 0 L/m3时，最优脱硫塔清液pH为8.60；当温度为101?℃、pH=8.40时，脱硫塔出口SO2质量浓度折算值随液气比增大而减小。因此，基于RBF神经网络的脱硫塔出口SO2排放量预测方法不仅能够有效预测双碱法脱硫塔出口SO2排放量，而且能够对SO2排放量进行优化控制。

Abstract

关键词

Keywords

references

Views

365

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Numerical simulation on influencing factors of flow field inside a desulfurization spray tower

Design and operation characteristics of limestone-gypsum desulfurization double-tower series system

NOx emission prediction for coal-fired boilers based on RBF Neural network and BP Neural network

Numerical simulation on demister performance of spray tower desulfurization system

COLDSTATE EXPERIMENTAL STUDY ON DUALALKALINE METHOD FOR FLUE GAS DESULFURATION BY USING TURBULENT BALL TOWER

Related Author

FAN Xu

CAO Chenjun

LI Xinghua

HE Yudong

YU Zhao

LIU Ran

YU Tingfang

GUO Haoran

Related Institution

Guangdong Electric Power Design Institute,China Energy Engineering Group Co.,Ltd.

Huayang Tongchuan-Zhaojin Power Plant

Xi'an Thermal Power Research Institute Co.,Ltd.

School of Mechanical & Electrical Engineering,Nanchang University

Xi'an Aerospace Chemical Power Plant

AI问答

⁰