Online optimization of boiler operation based on information integration and case-based reasoning

Abstract

ABSTRACT Boiler combustion optimization is a crucial method to deal with the profound load changing of coal-fired units, improve thermal power plant flexibility, and ensure unit operation safety, energy-saving, and environmental protection. However, the strong coupling, non-linearity, time-variation and timeliness of boiler combustion systems bring challenges to the online application of boiler combustion optimization technology in practical engineering. This work proposes a new multi-objective online combustion optimization framework based on information integration and case-based reasoning (II_CBR), and establishes the II_CBR system platform to serve a 330-MW coal-based power plant. The framework is combined offline database construction and online combustion optimization. The offline database integrates simulation feature information and operation parameter information, which provides richer informative knowledge. The online approach achieves the online calculation of the simulated feature information and combustion optimization. The results show that boiler efficiency increased by 0.31%, and the maximum reduction in NOx generation was 41.22 mg/Nm3, which indicates the proposed method is suitable for online real-time combustion optimization and supports power plant operators for flexible optimization. In addition, it improves boiler efficiency, reduces nitrogen oxide emissions, and provides new directions for deep load-changing coal-fired units.