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钢铁是当今社会的重要生产资料,钢铁生产力水平是国家生产力水平的重要标志。在钢铁生产中炉温的控制是钢铁生产的重要环节,传统上主要依靠工人经验来进行炉温控制,误差较大,效率低下。针对以上问题本文提出了基于小波变换的炉温预测模型。首先建立起了铁水硅含量时间序列模型,在此基础上通过Mallat算法实现小波分解,完成了炉温预测模型的设计。
Steel is an important means of production in today’s society. The level of steel productivity is an important symbol of the national productivity level. The control of furnace temperature in the production of steel is an important link in the production of steel. Traditionally, the control of furnace temperature mainly depends on the experience of the workers, resulting in large errors and inefficiencies. In view of the above problems, this paper presents a temperature prediction model based on wavelet transform. Firstly, the time series model of molten iron silicon content was established. Based on this, the Mallat algorithm was used to realize the wavelet decomposition, and the design of furnace temperature prediction model was completed.