1. Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment.
- Author
-
Iftikhar S, Ishtiaq R, Zahra N, Ruba F, Lam SM, Abbas A, and Jaffari ZH
- Subjects
- Adsorption, Machine Learning, Neural Networks, Computer, Bayes Theorem, Ions chemistry, Charcoal chemistry, Phosphates chemistry, Phosphates analysis, Water Pollutants, Chemical chemistry, Water Pollutants, Chemical analysis
- Abstract
Phosphate (PO
4 (III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO4 (III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R2 (0.95) on new adsorbents. SHapely Additive exPlanations analysis showed that adsorption experimental conditions had the most significant impact (43%), followed by synthesis conditions (29%) and adsorbent characteristics (28%). Optimized conditions included an initial PO4 (III) concentration of 125 mg/L, carbon content of 11.5%, oxygen content of 23%, a contact time of 1440 min, a heating rate of 5 °C/min, 200 rpm, and a surface area of 410 m2 /g, using Ra-LDO adsorbent synthesized from rape cabbage feedstock. This study developed and presented a practical online framework for predicting PO4 (III) removal onto biochar using a web-based graphical user interface., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)- Published
- 2025
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