Optimization of boiler real-time operation based on pattern-matching of agent model

A novel framework for modeling coal-fired power plant boiler operations was proposed based on pattern matching with an agent model (PMAM) in order to enhance the effectiveness and real-time performance. A new method for calculating the lag of the main steam flow rate was proposed. An improved pattern-matching optimization model was introduced to calculate the optimal operational database for historical optimization. A three-level scheme optimization mechanism was incorporated in order to ensure the effectiveness of the pattern-matching approach. The mechanism includes attention parameters, state parameter interval frequency and regulation minimum. An agent model for boiler operation optimization was constructed offline by using a neural network algorithm, and pattern-matching steps were represented based on the agent model to enable online applications. The case results show that the proposed pattern-matching optimization model can effectively find the optimized boiler operation scheme, and the similarity of working conditions is more than 95%, which can improve the boiler efficiency by 1.92% in practice. The mean square error of the trained agent model is less than 0.35%. The method avoids the influence of generalization error caused by optimization solutions compared with traditional methods, and has high reliability and real-time performance while improving boiler efficiency. © 2023 Zhejiang University. All rights reserved.