Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine

CUI Shiwen; TIE Zhixin; DING Chengfu; ZHAO Feng

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Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine

Thermal Power Generation Issue 3, (2017)

更新时间：2025-09-28

- Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine
- 作者机构：
  
  1. 浙江理工大学信息学院,浙江,杭州,310018
  2. 聚光科技（杭州）股份有限公司,浙江,杭州,310052
- 作者简介：
- 基金信息：
- DOI：
  CLC： X773
- Published Online：27 March 2017，
  
  Published：2017
- 稿件说明：
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CUI Shiwen, TIE Zhixin, DING Chengfu, et al. Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine[J]. Thermal Power Generation, 2017, (3).
DOI：

CUI Shiwen, TIE Zhixin, DING Chengfu, et al. Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine[J]. Thermal Power Generation, 2017, (3). DOI：

摘要

为了能够更好地反映电厂湿法脱硫中的过程参数与脱硫效率之间的关系，本文利用偏最小二乘回归法（PLS）对影响烟气湿法脱硫效率的过程因素进行分析，提取出对湿法脱硫效率影响较大的因素作为主成分，将提取的主成分采用支持向量机（ SVM）进行预测，降低了 SVM的输入维数，建立了基于偏最小二乘支持向量机（ PLS-SVM）的烟气湿法脱硫效率预测模型，并选取某机组石灰石 -石膏湿法脱硫设施运行监控数据进行模型的训练和预测。预测分析结果显示， PLS-SVM的预测数据最大绝对误差小于 0.65%，平均绝对误差在0.3%左右，说明该模型的预测效果较好，与 SVM预测模型相比，提高了预测效率和精度。

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