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

Abstract

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/SO2 emissions concentration. The AELM is used to establish the comprehensive model of the thermal efficiency and NOx/SO2 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/SO2 emissions concentration. Therefore, the AELM and MMTLBO are the effective artificial intelligence algorithms.

Keywords

• Multi-objective optimization
• Model
• Teaching–learning-based​ optimization
• Extreme learning machine
• Boiler combustion optimization