Three-objective optimization of boiler combustion process based on multi-objective teaching-learning based optimization algorithm and ameliorated extreme learning machine

作     者：Ma, Yunpeng Wang, Heqi Zhang, Xinxin Hou, Likun Song, Jiancai 

作者机构：Tianjin Univ Commerce Sch Informat Engn CO-300134 Tianjin Peoples R China 

出 版 物：《MACHINE LEARNING WITH APPLICATIONS》 (Mach. Learn. Appl.)

年 卷 期：2021年第5卷

核心收录：

基　　金：Innovation Guide Foundation of Tianjin, China [20YDT-PJC00320] College Students' Innovative Entrepreneurial Training Plan Program, China 

主　　题：Multi-objective optimization Model Teaching-learning-based optimization Extreme learning machine Boiler combustion optimization 

摘      要：The combustion optimization problem of Circulation Fluidized Bed Boiler (CFBB) can be regarded as a constrained dynamic multi -objective optimization problem, so it has become a hot research to solve the problem for saving energy and reducing polluting gas. However, it is difficult to optimize the combustion process based on traditional optimization method due to a variety of complex characteristics of boiler, such as non -linearity, strong coupling , large lag. In order to address the boiler combustion optimization problem, a kind of multi -objective modified teaching-learning-based optimization (namely MMTLBO) is proposed. For the MMTLBO, a constrained mechanism is firstly introduced into MMTLBO. Finally, the MMTLBO and ameliorated extreme learning machine (AELM) are utilized to optimize the CFBB s combustion process for increasing the thermal efficiency and reducing the NOx/SO 2 emissions concentration. The AELM is used to establish the comprehensive model of the thermal efficiency and NOx/SO 2 emissions. The model accuracy and standard deviation can arrive 10 -2 and 10 -4 , separately. So the model shows high generalization ability and good stability. Based on the model, the MMTLBO is applied to optimize the boiler s combustion process parameters. Experiment results show that the MMTLBO can find several groups reasonable combustion parameters which increase the thermal efficiency and reduce the NOx/SO 2 emissions concentration. Therefore, the AELM and MMTLBO are the effective artificial intelligence algorithms.