On-line combustion optimization framework for coal-fired boiler combining improved cultural algorithm, deep learning, multi-objective evolutionary algorithm with improved case-based reasoning technology

Abstract

It is important to gain the on-line combustion adjustment strategy under ensuring the running working condition of boiler unchanged. Because it can not only significantly improve the whole combustion environment of boiler, but can ensure the stability and safety of boiler in optimization. This article proposes a kind of new-style on-line combustion optimization framework for boiler combustion system, and this study is to quickly acquire the combustion adjustment strategies to improve the whole combustion environment of boiler under ensuring the operating working condition of boiler unchanged. First, improved culture algorithm based on improved particle swarm optimization and double mutation evolution strategy (CIPSODM) and long-short term memory neural network (LSTM) are combined to establish the boiler dynamic combustion model with self-adaptive capability. The improvement of CIPSODM involves two aspects: introducing clustering analysis to the population space of culture algorithm and adding the double mutation thought to the belief space of culture algorithm. And then domain maximum substitution strategy (DMS) is introduced into the decomposition-based multiple-objective evolutionary algorithm (DMS- D/MOEA) to obtain a great deal of combustion optimization cases offline, and an optimization cases base is established by gathering all optimization cases. Whereafter, mutation strategy is added to the case-based reasoning (MSCBR) to online obtain the combustion optimization adjustment strategy with the same running working condition as the current running working condition of the boiler for stable and safety combustion. Meanwhile, to illustrate the availability of the combination of CIPSODM-LSTM, DMS-D/ MOEA and MSCBR, several different on-line optimization approaches are adopted to acquire the combustion adjustment strategies online for comparison. The experiment results illustrated that the thermal efficiency could be increased by 0.588 % and NOx emission could be reduced 14.183 mg/m3 using proposed online combustion optimization approaches. Furthermore, the difference in operating load between adjustment strategy and current boiler reached the minimum value 0.696 MW in all optimization results. Consequently, proposed the on-line combustion optimization framework can gain the on-line combustion adjustment strategies to maximize thermal efficiency and decrease NOx emission so as to improve the whole running environment of boiler.