Summary of The Application Of Artificial Neural Network Model to Predicting the Acid Mine Drainage From Long-term Lab Scale Kinetic Test, by Muhammad Sonny Abfertiawan et al.
The Application of Artificial Neural Network Model to Predicting the Acid Mine Drainage from Long-Term Lab Scale Kinetic Test
by Muhammad Sonny Abfertiawan, Muchammad Daniyal Kautsar, Faiz Hasan, Yoseph Palinggi, Kris Pranoto
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning model is proposed to predict acid mine drainage (AMD) using artificial neural networks (ANNs). The approach aims to reduce the inefficiencies of traditional lab-scale kinetic tests, which require a long procedure and large amounts of chemical reagents. By training an ANN on 83 weeks’ worth of data from lab-scale kinetic tests with 100% potential acid-forming rock, the model is able to accurately predict the results, with an overall Nash-Sutcliffe Efficiency (NSE) of 0.99 on training and validation data. This study highlights the potential for ANNs to learn patterns, trends, and seasonality from past data, enabling accurate forecasting and contributing significantly to solving AMD problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AMD is a common environmental problem in the coal mining industry that can be predicted using machine learning models like artificial neural networks (ANNs). A team of researchers developed an ANN model that can predict acid mine drainage by looking at patterns in lab-scale kinetic test data. They used 83 weeks’ worth of data from tests with rocks that have a high chance of producing acid. The model was able to accurately predict the results, which could help reduce the cost and time it takes to do these tests. |
Keywords
* Artificial intelligence * Machine learning
