Optimizing Boiler Control in Real-Time with Machine Learning for Sustainability

被引：4

作者：

Ding, Yukun ^{[1
]}

Liu, Jinglan ^{[1
]}

Xiong, Jinjun ^{[2
]}

Jiang, Meng ^{[1
]}

Shi, Yiyu ^{[1
]}

机构：

[1] Univ Notre Dame, Notre Dame, IN 46556 USA

[2] IBM Thomas J Watson Res Ctr, Yorktown Hts, NY USA

来源：

CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT | 2018年

关键词：

Boiler Control; Combustion Optimization; Sustainability; GAS TEMPERATURE DEVIATION; COMBUSTION EFFICIENCY; LARGE-SCALE; OPTIMIZATION; NOX;

D O I：

10.1145/3269206.3272024

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient algorithm by integrating appropriate machine learning and optimization techniques. We obtain a large dataset collected from a real boiler for more than two months from our industry partner, and conduct extensive experiments to demonstrate the effectiveness and efficiency of the proposed algorithm.

引用

页码：2147 / 2154

页数：8

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