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| Energy Consumption Prediction of Tin Smelting Based on Grey Wolf Optimized Support Vector Machine Regression and SHAP Values |
| Received:October 23, 2023 Revised:November 02, 2023 |
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| DOI：doi：10.3969/j.issn.1007-7545.2024.02.001 |
| KeyWord:tin smelting prediction models; model interpretability; support vector machine regression; grey wolf optimization algorithm |
| Author | Institution |
| MA Chaojun |
云南锡业集团控股有限责任公司研发中心 |
| PENG Juzhang |
云南锡业集团控股有限责任公司研发中心 |
| YUAN Haibin |
云南锡业集团控股有限责任公司研发中心 |
| ZHENG Guangfa |
昆明理工大学信息工程与自动化学院 |
| MU Changhui |
云南锡业集团控股有限责任公司研发中心 |
| ZHANG Xiabing |
云南锡业集团控股有限责任公司研发中心 |
| FENG Zao |
昆明理工大学信息工程与自动化学院 |
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| Abstract: |
| The comprehensive energy consumption of tin smelting process accounts for 90% of the entire tin production process, which has great energy-saving potential. Address to the difficulty in establishing the comprehensive energy consumption mechanism model of tin smelting process and the low prediction accuracy, the Gray Wolf Optimization Support Vector Machine Regression (GWO-SVR) model was proposed to predict the comprehensive energy consumption of tin smelting process. Taking a tin smelter as an example, the proposed model was compared with the SVR, RF (Random Forest), BP (Back Propagation Neural Network) and LR (Linear Regression) models. The results show that the GWO-SVR model yields the most desirable prediction results, and has great advantages over other machine learning algorithms in terms of prediction accuracy. Furthermore, using SHAP values to explain the GWO-SVR model from both global interpretation and single-sample interpretation and visualize the contribution of features to the output increases the interpretability of GWO-SVR, and thus develops a reliable energy-saving strategy. |
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