Study on Machine Learning-based Prediction of Lime Dosage for Mine Drainage Treatment

In this study, we predicted the lime dosage for treatment facilities (ST and HT) using machine learning (ML) to effectively design and operate mine drainage treatment facilities.After removing the bad data from the original data, the flow rate related to the lime dosage, and metal ion (Fe, Mn, Al) concentrations and the pH which are highly related to OH -supplied by lime, were selected as inputs.The convolutional neural network was used, and the data was augmented using fancy principal component analysis to compensate for the limited and imbalanced dataset while maintaining the characteristics of the original data.The test dataset prediction by the ML model demonstrated a lower mean absolute error and higher coefficient of determination (R 2 ) than that of the theoretical calculation equation.The ML application is expected to enhance operational effectiveness of treatment facilities and offer fundamental design data for new facilities.

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Publication Details

Journal
Journal of the Korean Society of Mineral and Energy Resources Engineers
Published
2024-10-31
DOI
https://doi.org/10.32390/ksmer.2024.61.5.333
Primary Topic
Mine drainage and remediation techniques
Type
article
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article

Study on Machine Learning-based Prediction of Lime Dosage for Mine Drainage Treatment

Soon Jee Seol, Ga-Hyeon Lee, Duk‐Min Kim, Juin Ko et al.
Journal of the Korean Society of Mineral and Energy Resources Engineers
Mine drainage and remediation techniques
article

Study on Machine Learning-based Prediction of Lime Dosage for Mine Drainage Treatment

Soon Jee Seol, Ga-Hyeon Lee, Duk‐Min Kim, Juin Ko, Sun-Joon Kim, Seong-Sook Park
article en

Abstract

In this study, we predicted the lime dosage for treatment facilities (ST and HT) using machine learning (ML) to effectively design and operate mine drainage treatment facilities.After removing the bad data from the original data, the flow rate related to the lime dosage, and metal ion (Fe, Mn, Al) concentrations and the pH which are highly related to OH -supplied by lime, were selected as inputs.The convolutional neural network was used, and the data was augmented using fancy principal component analysis to compensate for the limited and imbalanced dataset while maintaining the characteristics of the original data.The test dataset prediction by the ML model demonstrated a lower mean absolute error and higher coefficient of determination (R 2 ) than that of the theoretical calculation equation.The ML application is expected to enhance operational effectiveness of treatment facilities and offer fundamental design data for new facilities.

Journal of the Korean Society of Mineral and Energy Resources EngineersVol. 61(5)
Hanyang University (KR), Sangji University (KR)
Clean water and sanitation
Openalex Percentile: Top 84%
Mine drainage and remediation techniques
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Study on Machine Learning-based Prediction of Lime Dosage for Mine Drainage Treatment — Soon Jee Seol, Ga-Hyeon Lee, et al. · Journal of the Korean Society of Mineral and Energy Resources Engineers (2024) | TGRS Research Map | TGRS