ArticleScientific reports2025
Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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2 authors.
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Abstract
The increasing demand for sustainable waste management has driven innovation in the production of activated carbon (AC) from waste. AC's textural properties, including its surface area (SA), total pore volume (TPV), and micropore volume (MPV), are critical for applications such as gas purification and wastewater treatment. However, the traditional assessment methods are expensive and complex. This study employed machine learning (ML) models to predict AC's properties and optimize its production process. Random Forest (RF), Decision Tree (DT), Gradient Boosting Regressor (GBR), support vector machines (SVM), and Artificial Neural Networks (ANN) were applied along with key input parameters, including raw material type, particle size, and activation conditions. A genetic algorithm (GA) integrated with the GBR model optimizes the synthesis process. The ML models, particularly RF and GBR, accurately predicted SA with R
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