基于SSA一SVR模型的步进式加热炉炉温预测

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中图分类号：TB9;TF31 文献标志码：A文章编号：1674-5124（2025）07-0064-08

Abstract： The prediction and temperature control of heating furnace temperature is of great significance to improve the quality of billet， energy saving and consumption reduction. Aiming at the problems such as low accuracy of heating furnace temperature prediction， a furnace temperature prediction model （SSA-SVR） based on the combination of Sparrow Search Algorithm （SSA） and Support Vector Machine Regression （SVR） is proposed from the data-driven point of view.By comparing this prediction model with five other prediction models，the results show that the SSA-SVR model has the smallst mean square error （MSE） index and the highest goodness of fit （r2） ， and the accuracy of the model is significantly improved compared with the SVR model， with the mean square error index significantly reduced and the goodness of fit improved by 0.0283. It provides a powerful support for the improvement of the temperature control accuracy of the furnace， and provides a powerful support for the improvement of the furnace temperature control accuracy of the steel furnace. Provide strong support for improving the control accuracy of the furnace temperature， and provide a more reliable basis for billet rolling.

Keywords： heating furnace roling; sparrow search optimization algorithm; support vector machine; furnace temperature prediction

0 引言

目前，我国正在积极推进"双碳"政策，该政策对我国环境质量改善和产业结构转型有着重要的指导作用，钢铁行业的绿色转型对我国环境改善以及钢铁行业的高质量发展有着重要意义[1]。（剩余7986字）