Abstract: In this paper, an improved RBF prediction model was proposed to solve the problems of cumbersome calculation and many influencing factors in the process of coking energy consumption. In the energy consumption prediction model, target flue temperature, flue suction, moisture, volatile matter and coking time were taken as input variables, and coking energy consumption as output variables. Because RBF network had many shortcomings such as poor learning ability and slow convergence speed, an energy consumption prediction model based on improved differential evolution algorithm was proposed. By using the differential evolution algorithm with strong global search ability, the optimal values of the center value, width and output weight of the basis function in RBF network were selected as the center value, width and output weight of RBF neural network. The results show that the improved RBF prediction has high accuracy, stability and training speed, which is of great significance for reducing coking energy consumption, increasing coke output and improving economic benefits of enterprises.

Key words: RBF neural network, Differential evolutionary algorithm, Coking energy consumption, Prediction model

摘要： 针对炼焦能耗计算繁琐、影响因素众多的问题，以目标火道温度、烟道吸力、水分、挥发分和炼焦时间为输入变量，以炼焦能耗为输出变量，提出基于差分进化算法改进的RBF预测模型。由于RBF网络存在学习能力差、收敛速度慢等多个缺点，针对性地改进了差分进化算法优化的能耗预测模型。利用具有强大全局搜索能力的差分进化算法，选择RBF网络中基函数的中心值、宽度和输出权重的计算最优值，以此作为RBF神经网络的中心值、宽度和输出权重。结果表明，改进后的RBF预测模型具有较高的精度、稳定性和训练速度，对降低炼焦能耗、提高焦炭产量和提高企业经济效益具有重要意义。

关键词: RBF神经网络, 　差分进化算法, 　炼焦能耗, 　预测模型