Prediction of Acid Mine Drainage Generation Potential of A Copper Mine Tailings Using Gene Expression Programming-A Case Study
Publish place: Journal of Mining and Environment، Vol: 11، Issue: 4
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 199
This Paper With 14 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JMAE-11-4_014
تاریخ نمایه سازی: 21 اردیبهشت 1400
Abstract:
This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH have been suggested by applying the gene expression programming (GEP) algorithms. For this, after gathering an appropriate database, some of the most significant parameters such as the tailing particle depths, initial remaining pyrite and chalcopyrite fractions, and concentrations of bicarbonate, nitrite, nitrate, and chloride are considered as the input data. Then ۳۰% of the data is chosen as the training data randomly, while the validation data is included in ۷۰% of the dataset. Subsequently, the relationships are proposed using GEP. The high values of correlation coefficients (۰.۹۲, ۰.۹۱, ۰.۸۶, and ۰.۸۹) as well as the low values of RMS errors (۰.۱۴۰, ۰.۰۱۴, ۱۵۰.۳۰۱, and ۰.۵۴۳) for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH prove that these relationships can be successfully validated. The results obtained also reveal that GEP can be applied as a new-fangled method in order to predict the AMD generation process.
Keywords:
Authors
B. Jodeiri Shokri
Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
H. Dehghani
Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
R. Shamsi
Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
F. Doulati Ardejani
School of Mining, College of Engineering, University of Tehran, Tehran, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :