You can view the full text of this article for free using the link below.

Title: Research on NOx emission of coal-fired unit based on multi-model clustering ensemble

Authors: Chenggang Zhen; Huaiyuan Liu; Hanyong Hao

Addresses: School of Control and Computer Engineering, North China Electric Power University, Baoding, 071000, China ' School of Control and Computer Engineering, North China Electric Power University, Baoding, 071000, China ' State Grid Corporation of China, Beijing, 100031, China

Abstract: The predictive control of NOx emission generated by coal-fired units has an important impact on economic benefits of power station and control of environmental pollution. In order to enhance the accuracy of prediction model, a modelling method of boiler NOx emission based on voting multi-model soft clustering ensemble (VMSC) is proposed. The data space is divided into three sub-space according to the level of NOx emission, and the variables that participate in clustering are determined by using variable weight based on relevant analysis and hierarchical clustering utilised information entropy. The proposed algorithm VMSC is used to obtain new membership degree matrix of each sub-space. The multiple least squares support vector machine (LSSVM) models of each subspace are compromised by the least-squares method fused membership degree. The simulation results show that the VMSC algorithm which merges soft fuzzy C-means clustering (SFCM) and genetic algorithm-soft fuzzy C-means clustering (GA-SFCM) improve the accuracy of clustering, and the simulation performance is better than other selected models. The integrated model VMSC-LSSVM can achieve accurate prediction for NOx emission of utility boiler and effectively solve the problem that the model used single method to modelling is weak generalisation.

Keywords: NOx emission; soft clustering; cluster ensemble; SFCM; GA-SFCM; multi LSSVM; ensemble model.

DOI: 10.1504/IJSPM.2020.106967

International Journal of Simulation and Process Modelling, 2020 Vol.15 No.1/2, pp.30 - 39

Received: 23 Aug 2018
Accepted: 08 Apr 2019

Published online: 21 Apr 2020 *

Full-text access for editors Access for subscribers Free access Comment on this article