基于SSA一SVR模型的步進(jìn)式加熱爐爐溫預(yù)測

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中圖分類號(hào)：TB9;TF31 文獻(xiàn)標(biāo)志碼：A文章編號(hào)：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，我國正在積極推進(jìn)"雙碳"政策，該政策對我國環(huán)境質(zhì)量改善和產(chǎn)業(yè)結(jié)構(gòu)轉(zhuǎn)型有著重要的指導(dǎo)作用，鋼鐵行業(yè)的綠色轉(zhuǎn)型對我國環(huán)境改善以及鋼鐵行業(yè)的高質(zhì)量發(fā)展有著重要意義[1]。（剩余7986字）