Supervised machine learning-based categorization and prediction of uranium adsorption capacity on various process parameters
Niken Siwi Pamungkas, Zico Pratama Putra, Hendra Adhi Pratama, Muhammad Yusuf
Abstract
• ML effectively predicts and classifies uranium adsorption capacity (Qe). • CNN achieved 98% accuracy; Random Forest excelled in Qe prediction. • Initial concentration most influences Qe per permutation importance analysis. • ML models capture complex input-Qe relations, guiding adsorption system design. • Future work: expand datasets, add adsorbents, and use real-time data for models. Existing uranium poses significant dangers to the environment and the general population's health. Within the scope of this study, machine learning techniques were utilized to assess the characteristics that influence uranium adsorption. The adsorbent material, pH, temperature, solid-liquid ratio (SLR), and initial concentration (C i ) values were imported as the feature to evaluate using ML models in this study. The finding concluded that the Convolutional Neural Network (CNN) model performed excellently by attaining an accuracy of 98%, which is very high in classifying low and high levels of adsorption predictions. The Random Forest model was also the best performer for Q e prediction, with the lowest MSE value of 334.45 and the highest Squared-R score of 0.92 for the uranium adsorption process. Permutation Importance analysis indicated that initial concentration had the strongest impact on Q e . Knowing that a good interaction between uranyl ions and adsorbent surface would result in more active sites for trapping radioactive metal, optimizing this parameter can be quite helpful for designing new materials capable of capturing uranium species strongly from solutions. ML model excels at predicting Q e in uranium adsorption and can directly accommodate large datasets and non-linear input parameter-Q e interactions. As noted, the innovation poised to drive ML research has been made possible by recent technological developments in machine learning and can improve while offering new ways of addressing difficult environmental problems.