采用最优MVs决策模型的电站锅炉燃烧优化

基于锅炉燃烧模型的非线性寻优和基于历史运行工况的数据挖掘是两种常见的锅炉燃烧优化技术,且各有利弊。前者可得到全局最优解,但算法复杂度较高;后者计算较为简易,但只能实现局部最优。结合两种方案的优点,提出基于离线非线性寻优所得最优知识库,采用模糊关联规则挖掘算法,建立最优操作变量(manipulated variables,MVs)决策模型,实现高效、稳定的锅炉燃烧优化。关联规则挖掘中,提出基于k-均值聚类的语言变量非均等模糊分割,以提高所得规则库的可信度;并基于改进的支持度和置信度概念实现规则库的精简。仿真结果表明,基于该文最优MVs决策模型的锅炉燃烧优化结果与全局寻优结果接近,且算法复杂度低、稳定性高,适合于在线实时优化与自适应更新。

An Optimal MVs Decision-model for Boiler Combustion Optimization

There are two typical boiler combustion optimization techniques.One is global searching with evolutionary algorithm based on the boiler combustion model and the other is employing data-mining technology to the historical operating data.Theoretically,the global optimum can be achieved with the first method;however,the relevant computation is very complex.The computation of the second technology is much simpler but only with local optimum.A new boiler combustion optimization proposal was presented by combining the different advantages of the existing two methods.It was based on the optimal manipulated variables(MVs) decision-model,which was established by employing fuzzy-association-rule-mining method to the optimal knowledge base;and the optimal knowledge base was the results of global searching.In the process of mining association rules,fuzzy language variables were determined by unequal partition based on the k-mean clustering algorithm,so as to improve the confidence of the achieved fuzzy rules.Modified definitions of support and confidence were adopted for the rules reduction.The final numerical experiment indicates that the optimizing results based on the optimal MVs decision-model built as the algorithm in this paper is close to the global optimum,and moreover,the calculation of the new optimization technology is much less and more stable than the evolutionary algorithm;thus it is more suitable for online use and easy for timely updating when the system feature is timevarying.