Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials
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 purif...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-95061-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850265289886793728 |
|---|---|
| author | Ahmed Farid Ibrahim Mohamed Abdrabou Hussein |
| author_facet | Ahmed Farid Ibrahim Mohamed Abdrabou Hussein |
| author_sort | Ahmed Farid Ibrahim |
| collection | DOAJ |
| description | 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 R2 values exceeding 0.96. In contrast, the linear regression models were inadequate, with R2 values below 0.6, emphasizing the non-linear relationship between the inputs and outputs. Sensitivity analysis showed that the activation temperature, ratio of the activating agent to carbon, and particle size significantly affected the AC properties. Optimal properties were achieved under activation temperatures between 800 and 900 °C and activating-agent to the carbon ratio 3.8. This approach provides a scalable solution for enhancing AC production sustainability, while addressing critical waste management challenges. |
| format | Article |
| id | doaj-art-8d78cba353ab40ebbb69ed28091c29c2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8d78cba353ab40ebbb69ed28091c29c22025-08-20T01:54:29ZengNature PortfolioScientific Reports2045-23222025-04-0115112510.1038/s41598-025-95061-3Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materialsAhmed Farid Ibrahim0Mohamed Abdrabou Hussein1Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & MineralsInterdisciplinary Research Center for Advanced Materials, King Fahd University of Petroleum & MineralsAbstract 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 R2 values exceeding 0.96. In contrast, the linear regression models were inadequate, with R2 values below 0.6, emphasizing the non-linear relationship between the inputs and outputs. Sensitivity analysis showed that the activation temperature, ratio of the activating agent to carbon, and particle size significantly affected the AC properties. Optimal properties were achieved under activation temperatures between 800 and 900 °C and activating-agent to the carbon ratio 3.8. This approach provides a scalable solution for enhancing AC production sustainability, while addressing critical waste management challenges.https://doi.org/10.1038/s41598-025-95061-3Porous carbonActivated carbonMachine learningSurface areaSustainable waste management |
| spellingShingle | Ahmed Farid Ibrahim Mohamed Abdrabou Hussein Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials Scientific Reports Porous carbon Activated carbon Machine learning Surface area Sustainable waste management |
| title | Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials |
| title_full | Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials |
| title_fullStr | Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials |
| title_full_unstemmed | Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials |
| title_short | Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials |
| title_sort | leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials |
| topic | Porous carbon Activated carbon Machine learning Surface area Sustainable waste management |
| url | https://doi.org/10.1038/s41598-025-95061-3 |
| work_keys_str_mv | AT ahmedfaridibrahim leveragingmachinelearningforpredictionandoptimizationoftexturepropertiesofsustainableactivatedcarbonderivedfromwastematerials AT mohamedabdrabouhussein leveragingmachinelearningforpredictionandoptimizationoftexturepropertiesofsustainableactivatedcarbonderivedfromwastematerials |