使用改进的鱼优化算法和分类增强,预测多个渣系统中的CaO活性
Zi-Cheng Xin1,2, Jiang-Shan Zhang3, Qing Liu4
1State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing, 100083, China.
Scientific reports
|March 20, 2025
概括
这项研究引入了一种新型模型,使用改进的鱼优化算法 (IWOA) 和分类提升 (CatBoost) 来准确预测渣成分活性 (a(CaO)). IWOA-CatBoost模型显著优于现有的热力学属性分析方法.
科学领域:
- 金热力学是一种金属热力学.
- 材料科学是一种材料科学.
- 计算化学是一种计算化学.
背景情况:
- 污泥成分活动对于理解热力学性质至关重要.
- 精确预测活动对于金工艺过程至关重要.
研究的目的:
- 开发一种可靠的模型,用于预测渣中氧化 (a(CaO)) 的活性.
- 为了将模型的性能与既定方法进行比较.
主要方法:
- 利用了一个改进的鱼优化算法 (IWOA) 与分类提升 (CatBoost) 集成.
- 分析了渣渣成分 (w(CaO),w(SiO2),w(MgO),w(Al2O3)) 和温度对a(CaO) 的影响.
主要成果:
- IWOA-CatBoost模型实现了高精度,其中R2 = 0.9200,RMSE = 0.0042和MAE = 0.0030.
- 与Factsage,离子和分子共存理论以及GA-BPNN模型相比,表现出卓越的性能.
- 确定了w(CaO) 作为最有影响力的变量,其次是w(SiO2),温度,w(MgO) 和w(Al2O3).
结论:
- 开发的IWOA-CatBoost模型提供了一个非常准确的方法来预测a(CaO).
- 这种方法在分析废渣的热力学特性方面取得了重大进展.
- 该方法可适应预测其他渣成分活动和各种金应用.
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