Multi-objective combustion optimization based on data mining with full-scale working condition

ZHENG Wei; LIU Da

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Multi-objective combustion optimization based on data mining with full-scale working condition

Thermal Power Generation Issue 5, (2017)

更新时间：2025-09-28

- Multi-objective combustion optimization based on data mining with full-scale working condition
- 作者机构：
  
  1. 天津职业大学机电工程与自动化学院,天津,300410
  2. 天津国电津能热电有限公司,天津,300300
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM611
- Published Online：27 May 2017，
  
  Published：2017
- 稿件说明：
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ZHENG Wei, LIU Da. Multi-objective combustion optimization based on data mining with full-scale working condition[J]. Thermal Power Generation, 2017, (5).
DOI：

ZHENG Wei, LIU Da. Multi-objective combustion optimization based on data mining with full-scale working condition[J]. Thermal Power Generation, 2017, (5). DOI：

摘要

为了使燃煤电站锅炉燃烧达到高效低污染的目的，提出了锅炉燃烧优化的约束模糊加权规则提取算法，该算法可以快速确定燃烧过程中各变量的定量关系，并给出相应的数据挖掘方法，从锅炉全工况海量历史运行数据中提取燃烧优化规则，并构建燃烧优化知识库，实现多目标燃烧优化。通过在发电机组上的实际实验，验证了在同等工况下，利用锅炉燃烧优化规则，NOx排放质量浓度平均值下降69.47 mg/m3，飞灰含碳量平均值下降0.05%，锅炉效率平均值提升0.11%。因此，基于全工况数据挖掘的多目标燃烧优化，能够有效缓解燃煤电站节能减排的压力，也便于实施和在线应用。

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