Optimasi proses pemulihan tembaga dan nikel dari limbah PCB menggunakan machine learning
Abstract
The rapid advancement of information and communication technology has led to an increase in electronic waste (e-waste), including printed circuit board (PCB) waste. PCB waste contains valuable metals such as copper (Cu) and nickel (Ni), which have high economic value. However, conventional methods for metal recovery from PCB waste, such as pyrometallurgy and acid-based hydrometallurgy, have drawbacks such as high energy consumption and negative environmental impacts. Therefore, more efficient and environmentally friendly methods are needed. Artificial Neural Network (ANN) offers a solution by modeling complex relationships between process variables and metal recovery outcomes, thereby improving the efficiency and accuracy of the recycling process. This study aims to explore the application of ANN in predicting metal recovery efficiency from PCB waste and supporting the circular economy.
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References
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