太原理工大学学报2024，Vol.55Issue(2)：287-295,9.DOI:10.16355/j.tyut.1007-9432.20230315
基于仿生算法联合优化BP的燃煤发热量预测
Prediction of Coal Calorific Value Based on the Combined Optimization of BP by Bionic Algorithm
摘要
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 net-work 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 opti-mization algorithms FA,GA,and PSO,and the global search ability of the FA-GA algorithm is ef-fectively 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 algo-rithm 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 monitor-ing of real-time changes in coal quality in the furnace.关键词
燃煤发热量/BP神经网络/遗传算法/萤火虫算法/平均影响值Key words
calorific value of coal/BP neural network/genetic algorithm/firefly algorithm/mean impact value分类
信息技术与安全科学引用本文复制引用
张艺,姚素玲,董宪姝,付元鹏,樊玉萍,马晓敏..基于仿生算法联合优化BP的燃煤发热量预测[J].太原理工大学学报,2024,55(2):287-295,9.基金项目
山西省重点研发计划(2022ZDYF049) （2022ZDYF049）
山西省基础研究计划项目(202103021223045) （202103021223045）
