Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method

Abstract

This paper focuses on modeling and reducing NOX emissions for a coal-fired boilers with advanced machine learning approaches. The novel ELM (extreme learning machine) model was introduced to model the correlation between operational parameters and NOX emissions of the boiler. Approximately ten days of real data from the SIS (supervisory information system) of a 700 MW coal-fired power plant were acquired to train and verify the ELM-based NOX model. Based on the NOX model, HS (harmony search) algorithm was then employed to optimize the operational parameters to finally realize NOX emission reduction. The modeling results indicated that the ELM model was more precise and faster in modeling NOX emissions than the popular artificial neural network and support vector regression. The searching process of HS was convergent and consumed only 0.7 s of CPU (Central Processing Unit) time on a personal computer. 16.5% and 19.3% NOX emission reductions for the two selected cases were achieved according to the simulation result. Additionally, the simulation result was experimentally justified, which demonstrated that the experimental results corresponded well with the computational: the experimental NOX reduction percentages were 14.8% and 15.7%, respectively. The proposed integrated method was capable of providing desired and feasible solutions within 1 s.