论文信息 - Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods

Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods

Abstract A new methodology combining the advanced extreme learning machine (ELM) and harmony search (HS) was proposed to model and optimize the operational parameters of the boiler for the control of NO X emissions in a 700 MW pulverized coal-fired power plant. About five days’ worth of real data were obtained from supervisory information system (SIS) of the power plant to build the ELM NO X model. HS was employed to optimize the operational parameters of the boiler to minimize NO X emissions based on the prediction of NO X by ELM. Compared with the widely used learning method such as ANN and SVR, ELM performed better both in accuracy and computing time for the modeling of NO X emission. The proposed comprehensive methodology can provide desired and feasible optimal solutions within one second, which is acceptable for the online optimization.

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