Combustion Characteristic Prediction of a Supercritical CO2 Circulating Fluidized Bed Boiler Based on Adaptive GWO-SVM

The development of a new and efficient supercritical carbon dioxide (S-CO2) power cycle system is one of the important technical ways to break through the bottleneck of coal power development, improve the efficiency of power generation, and realize energy saving and emission reduction. In order to simplify the complicated workload and save the huge time cost of numerical simulations on combustion characteristics, it is of great significance to accurately make the combustion characteristic prediction according to the operating performance of the S-CO2 CFB boiler. This study proposed a combustion characteristic prediction model corresponding to the S-CO2 CFB boiler based on the adaptive gray wolf optimizer support vector machine (AGWO-SVM). The parameters of the gray wolf optimizer algorithm were processed adaptively first combined with the boiler characteristics, and then the adaptive gray wolf optimizer algorithm was integrated with the support vector machine to solve the imbalance of local and global search problems of particles being easy to gather in a certain position in the process of pattern recognition. The novel method effectively predicts the boiler in the scaling process from the aspect of boiler capacity, optimizes the combustion characteristic expression by numerical simulations, greatly saves time cost and applicability of enlarged design by altering complex numerical simulations, and lays the application foundation of the S-CO2 CFB boiler in the industrial field with acceptable operation accuracy.