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Articles

Comparative Machine Learning-Based Prediction of Gold Enrichment in a Sulphide-Hosted Orogenic System Using Multielement Geochemistry

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By Gilbert Yaw Bimpong, Justina Senam Lotsu, Kwaku Boakye
| preprints.org
You are already at the latest version Accurate prediction of gold enrichment is critical for mineral exploration and resource evaluation, particularly in data-limited environments where only geochemical information is available. This study evaluates machine learning (ML) models against linear baselines for predicting relative gold enrichment defined as ln(Au/G10), where G10 represents the geometric mean of ten predictor elements (S, Fe, Al, Si, Mn, Sr, Ni, Cu, K, Ti).
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A Scalable Python-Based Optimization Model for Sustainable Open-Pit Mine Production Scheduling

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By Kwaku Boakye, Justina Senam Lotsu, Gilbert Yaw Bimpong
| preprints.org
This version is not peer-reviewed. Justina Senam Lotsu  *,Gilbert Yaw Bimpong,Kwaku Boakye Justina Senam Lotsu  *,Gilbert Yaw Bimpong,Kwaku Boakye This version is not peer-reviewed. Open-pit mine production scheduling plays a critical role in maximizing economic returns while meeting environmental and operational constraints.
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