基于多模型智能组合算法的锅炉炉膛温度建模

唐振浩; 张宝凯; 曹生现; 王恭; 赵波

doi:10.11949/0438-1157.20190037

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基于多模型智能组合算法的锅炉炉膛温度建模

过程系统工程 | 更新时间：2020-06-24

- 基于多模型智能组合算法的锅炉炉膛温度建模
- Furnace temperature modeling based on multi-model intelligent combination algorithm
- 化工学报 2019年70卷第S2期页码：301-310
- 作者机构：
  
  东北电力大学自动化学院，吉林省吉林市 132012
- 作者简介：
  
  唐振浩（1985—），男，博士，副教授，tangzhenhao@neepu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61503072;51606035);国家重点研发计划项目(2018YFB1500803);吉林省科技厅自然科学基金项目(20190201095JC;20190201098JC)
- DOI：10.11949/0438-1157.20190037
  中图分类号： TK 227
- 收稿：2019-01-09，
  
  修回：2019-06-04，
  
  纸质出版：2019-09-05
- 稿件说明：
移动端阅览
唐振浩, 张宝凯, 曹生现, 王恭, 赵波. 基于多模型智能组合算法的锅炉炉膛温度建模[J]. 化工学报, 2019, 70(S2): 301-310

TANG Zhenhao, ZHANG Baokai, CAO Shengxian, WANG Gong, ZHAO Bo. Furnace temperature modeling based on multi-model intelligent combination algorithm[J]. CIESC Journal, 2019, 70(S2): 301-310
唐振浩, 张宝凯, 曹生现, 王恭, 赵波. 基于多模型智能组合算法的锅炉炉膛温度建模[J]. 化工学报, 2019, 70(S2): 301-310 DOI： 10.11949/0438-1157.20190037.

TANG Zhenhao, ZHANG Baokai, CAO Shengxian, WANG Gong, ZHAO Bo. Furnace temperature modeling based on multi-model intelligent combination algorithm[J]. CIESC Journal, 2019, 70(S2): 301-310 DOI： 10.11949/0438-1157.20190037.

摘要

炉膛温度是表征锅炉燃烧状态的重要参数，但是影响炉膛温度的参数多、机理复杂，导致难以建立准确的预测模型。针对这一问题，提出一种多模型智能组合算法(multi-model intelligent combination algorithm

MICA)实现对炉膛温度的建模预测。首先，对实际运行生产数据进行小波降噪，并结合机理分析和分类回归树（classification and regression tree，CART）算法选取预测模型输入参数。然后，通过多种数据驱动方法构建锅炉炉膛温度预测模型。最后，采用决策树C4.5算法建立多模型智能组合预测模型。基于实际生产数据的实验结果表明，所提出算法能够建立准确的炉膛温度预测模型。

Abstract

Furnace temperature is an important parameter which can reflect boiler combustion status. However

furnace temperature is affected by many parameters and the mechanism is complicated. Therefore

it is difficult to establish accurate prediction model. To solve this problem

a multi-model intelligent combination algorithm (MICA) is proposed to construct an accurate prediction model. First

the actual production data is pre-processed by wavelet denoising algorithm

and the model input variables were selected based on classification and regression trees algorithm and mechanism analysis. Then

several furnace temperature prediction models are constructed by many data-driven algorithms. Finally

a C4.5 algorithm is applied to combine these models into a multi-model intelligent combination model. The experimental results illustrate that the proposed algorithm can construct an accurate furnace temperature prediction model through actual operating data.

关键词

Keywords

references

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