TY - GEN
T1 - Prediction of Sulfur Content in Electric Furnace Matte Using Machine Learning
AU - Winoto, Gatot
AU - Santosa, Budi
AU - Anityasari, Maria
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/9/21
Y1 - 2022/9/21
N2 - Sulfur content in Electric Furnace Matte is one of the key parameters in nickel matte process control at PT Vale Indonesia Tbk. (PTVI), a nickel matte smelter in Indonesia. Currently the compliance to standard specification is relatively low due to process variability and control limitation. The sulfur content in Electric Furnace Matte depend on sulfur addition and other operating conditions. In this research, a machine learning approach is used to predict sulfur content in Electric Furnace Matte based on selected predictors. Linear and support vector regression models were built on the training data and used to predict sulfur content on testing data. The performance of each models were evaluated and compared. The linear model shows a 0.5843 coefficient correlation between test data and prediction, with a mean square error (MSE) 0.4207. The support vector regression (SVR), a non-linear model, is built with the same predictors. SVR model improve the correlation to 0.9408 and reduce the MSE to 0.0762. The research has shown the practicality of applying machine learning in nickel matte processing and open opportunity for further research.
AB - Sulfur content in Electric Furnace Matte is one of the key parameters in nickel matte process control at PT Vale Indonesia Tbk. (PTVI), a nickel matte smelter in Indonesia. Currently the compliance to standard specification is relatively low due to process variability and control limitation. The sulfur content in Electric Furnace Matte depend on sulfur addition and other operating conditions. In this research, a machine learning approach is used to predict sulfur content in Electric Furnace Matte based on selected predictors. Linear and support vector regression models were built on the training data and used to predict sulfur content on testing data. The performance of each models were evaluated and compared. The linear model shows a 0.5843 coefficient correlation between test data and prediction, with a mean square error (MSE) 0.4207. The support vector regression (SVR), a non-linear model, is built with the same predictors. SVR model improve the correlation to 0.9408 and reduce the MSE to 0.0762. The research has shown the practicality of applying machine learning in nickel matte processing and open opportunity for further research.
KW - linear regression multi variable
KW - machine learning
KW - mineral processing
KW - nickel matte
KW - support vector regression
UR - http://www.scopus.com/inward/record.url?scp=85143252475&partnerID=8YFLogxK
U2 - 10.1145/3557738.3557841
DO - 10.1145/3557738.3557841
M3 - Conference contribution
AN - SCOPUS:85143252475
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the International Conference on Engineering and Information Technology for Sustainable Industry, ICONETSI 2022
PB - Association for Computing Machinery
T2 - 2nd International Conference on Engineering and Information Technology for Sustainable Industry, ICONETSI 2022
Y2 - 21 September 2022 through 22 September 2022
ER -