Prediction of Coal Calorific Value Based on the Combined Optimization of BP by Bionic Algorithm

Abstract

Purposes Accurate prediction and evaluation of coal heat generation is an important foundation for coal quality analysis and thermal engineering calculation. The current model of neural network prediction of coal heat generation can effectively fit the nonlinear relationship, yet there are problems such as the ease to fall into the local minimum and slow convergence speed. Methods In order to accurately predict the heat generation of coal in the combustion process of industrial boilers, a coal heat generation prediction method by bionic algorithm FA-GA joint optimization BP neural network is proposed. The industrial analysis and elemental analysis data of 774 groups of coal commonly used in coal-fired boilers are preprocessed, and the characteristic variables of coal quality indexes are screened according to the average impact value, and finally the heat generation prediction model of FA-GA-BP is established, and the optimization algorithm optimization ability and model prediction accuracy are examined in terms of the error evaluation indexes and the number of iterations. Findings The prediction accuracy of the model is improved to 0.9561 after feature variable screening; the number of iterations of the joint FA-GA algorithm is significantly reduced compared with those of the single optimization algorithms FA, GA, and PSO, and the global search ability of the FA-GA algorithm is effectively improved; the FA-GA-BP model has a higher accuracy compared with single optimization models FA-BP, GA-BP, PSO-BP, as well as the currently commonly used heat generation models MLR and SVR, and the correlation coefficient can reach 0.9845. Conclusions The FA-GA algorithm optimizes the BP model with good results in predicting the heat generation from different regions and coal types in China for coal-fired boilers, which theoretically meets the industrial error requirements. The improved coal-fired heat generation prediction model can provide a new method for effective monitoring of real-time changes in coal quality in the furnace.

Keywords

• calorific value of coal
• bp neural network
• genetic algorithm
• firefly algorithm
• mean impact value