Data-driven prediction of copper leaching yield from brass waste using stacking ensemble learning
Yazarlar (2)
Dr. Öğr. Üyesi Sercan BASİT
Kırşehir Ahi Evran Üniversitesi, Türkiye
Bursa Uludağ Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Separation and Purification Technology |
| Dergi ISSN | 1383-5866 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2025 |
| Cilt No | 378 |
| Sayı | 1 |
| Sayfalar | 134691 / 0 |
| DOI Numarası | 10.1016/j.seppur.2025.134691 |
| Makale Linki | https://doi.org/10.1016/j.seppur.2025.134691 |
| Özet |
| The recovery of valuable metals from industrial waste has become increasingly important due to the diminishing supply of primary resources and growing environmental concerns. This paper presents a stacking ensemble learning method for predicting copper leaching yield from brass melting slag under different hydrometallurgical conditions. Instead of conducting time-consuming and complex experiments, a machine-learning approach was built using a large dataset previously collected through controlled laboratory research. Six experimental variables were used as input features, including leaching time, acid concentration, hydrogen peroxide concentration, stirring speed, temperature, and solid-to-liquid ratio. The proposed model combines three tuned base learners, namely Gaussian process regression, least-squares boosting, and support vector regression, with a linear regression meta-learner. The stacking model achieved all individual models in predictive performance, yielding the lowest RMSE of 0.853, MAE of 0.668, and MAPE of 8.347 %, resulting in the highest R2 value of 0.994. The results demonstrate that the proposed method is reliable and practically feasible for predicting leaching yield and optimizing the recovery process of copper from brass waste. Furthermore, it has significant potential for supporting resource management objectives. |
| Anahtar Kelimeler |
| Brass waste | Copper leaching | Hydrometallurgical recovery | Machine learning prediction | Stacking ensemble learning |
Science Direct
Data-driven prediction of copper leaching yield from brass waste using stacking ensemble learning
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 1 |
| Google Scholar | 1 |