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Optimization of boiler real-time operation based on pattern-matching of agent model |
Wei ZHONG1,2(),Xue-ru LIN1,2,Xiao-jie LIN1,3,*(),Yi ZHOU1 |
1. Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Zhejiang University, Hangzhou 310027, China 2. College of Energy Engineering, Zhejiang University, Hangzhou 310027, China 3. Jiaxing Research Institute, Zhejiang University, Jiaxing 314024, China |
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Abstract A novel framework for modeling coal-fired power plant boiler operations was proposed based on pattern matching with an agent model (PMAM) in order to enhance the effectiveness and real-time performance. A new method for calculating the lag of the main steam flow rate was proposed. An improved pattern-matching optimization model was introduced to calculate the optimal operational database for historical optimization. A three-level scheme optimization mechanism was incorporated in order to ensure the effectiveness of the pattern-matching approach. The mechanism includes attention parameters, state parameter interval frequency and regulation minimum. An agent model for boiler operation optimization was constructed offline by using a neural network algorithm, and pattern-matching steps were represented based on the agent model to enable online applications. The case results show that the proposed pattern-matching optimization model can effectively find the optimized boiler operation scheme, and the similarity of working conditions is more than 95%, which can improve the boiler efficiency by 1.92% in practice. The mean square error of the trained agent model is less than 0.35%. The method avoids the influence of generalization error caused by optimization solutions compared with traditional methods, and has high reliability and real-time performance while improving boiler efficiency.
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Received: 09 August 2022
Published: 17 July 2023
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Fund: 国家重点研发计划资助项目(2019YFE0126000);国家自然科学基金资助项目(51806190) |
Corresponding Authors:
Xiao-jie LIN
E-mail: zhongw@zju.edu.cn;xiaojie.lin@zju.edu.cn
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基于代理模型模式匹配的锅炉实时操作优化
为了提高电厂燃煤锅炉操作优化的有效性与实时性,提出新的基于代理模型模式匹配(PMAM)的建模框架. 提出主蒸汽流量的滞后性计算方法. 采用改进的模式匹配优化模型,计算历史优化操作库. 引入工况注意力机制参数、状态参数区间频率法及调控最小的3层方案优化机制,确保模式匹配方案的有效性. 采用神经网络算法预建模构建锅炉操作优化的代理模型,基于代理模型表征模式匹配步骤,使得本文方法可以适用于在线应用. 案例结果表明,利用提出的模式匹配优化模型,能够有效地寻找优化的锅炉操作方案,工况相似度大于95%,可以使得锅炉效率提升1.92%;训练的代理模型均方误差小于0.35%. 与传统方法相比,本文方法避免了优化求解带来的泛化误差影响,在提升锅炉效率的同时,具有高可靠性及实时性.
关键词:
模糊C均值聚类,
数据驱动,
模式匹配,
操作优化,
在线优化,
电厂锅炉
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