关键词: 加热炉, 迁移学习, 时间卷积网络, 知识蒸馏

Abstract: To use accurate control strategy to optimize the combustion control of heating furnace for solving the core problem of combustion device optimization control in metallurgical enterprises and making the accurate prediction of furnace temperature,and to study the applicability of neural network in furnace temperature prediction,a prediction method of furnace temperature based on transfer learning and knowledge distillation was proposed.The temperature prediction model of the source zone based on the temporal convolution network was established,and the model transfer was completed by using the generative adversarial loss for domain adaptation,so as to realize the accurate prediction of the temperature in all heating zones.A distillation network based on multi-task learning was further established,which solved the shortcomings of the high delay of the deep transfer network by the way of teacher assisting student.The experimental results showed that the proposed transfer learning network could significantly improve the accuracy of furnace temperature prediction,the distillation network could significantly reduce network parameters and greatly improve the timeliness of furnace temperature prediction.

Key words: heating furnace, transfer learning, temporal convolution network, knowledge distillation