当前位置: X-MOL 学术Steel Res. Int. › 论文详情
Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace
Steel Research International ( IF 1.810 ) Pub Date : 2020-03-24 , DOI: 10.1002/srin.202000053
Leo Stefan Carlsson; Peter Bengt Samuelsson; Pär Göran Jönsson

Machine learning (ML) is a promising modeling framework that has previously been used in the context of optimizing steel processes. However, many of the more advanced ML models, capable of providing more accurate predictions to complex problems, are often impossible to interpret. This makes the domain experts in the steel industry, to a large extent, hesitant to adopt these models. The valuable increase in model accuracy is diminished by the lack of model interpretability. Herein, Shapley additive explanations (SHAP) is applied to an advanced ML model, predicting the electrical energy (EE) consumption of an electric arc furnace (EAF). The insights from SHAP reveal the contributions from each input variable on the EE for every single heat in the prediction domain. These contributions are then evaluated based on process metallurgical experience.
更新日期:2020-03-24

 

全部期刊列表>>
胸部和胸部成像专题
自然科研论文编辑服务
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
鲁照永
华东师范大学
苏州大学
南京工业大学
南开大学
中科大
唐勇
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
广东实验室
南京大学
王杰
南科大
刘尊峰
湖南大学
清华大学
王小野
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug