Machine learning, a powerful tool for the prediction of BiVO 4 nanoparticles efficiency in photocatalytic degradation of organic dyes

• Published in: Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Vol. 59; no. 1; pp. 15 - 24
• Main Authors: A, Gnanaprakasam, M, Thirumarimurugan, N, Shanmathi
• Format: Journal Article
• Language: English
• Published: England 02.01.2024
• Subjects: machine learning; photocatalytic degradation; hyperparameter tuning; BiVO4
• ISSN: 1093-4529; 1532-4117
• DOI: 10.1080/10934529.2024.2319510

Wastewater pollution caused by organic dyes is a growing concern due to its negative impact on human health and aquatic life. To tackle this issue, the use of advanced wastewater treatment with nano photocatalysts has emerged as a promising solution. However, experimental procedures for identifying the optimal conditions for dye degradation could be time-consuming and expensive. To overcome this, machine learning methods have been employed to predict the degradation of organic dyes in a more efficient manner by recognizing patterns in the process and addressing its feasibility. The objective of this study is to develop a machine learning model to predict the degradation of organic dyes and identify the main variables affecting the photocatalytic degradation capacity and removal of organic dyes from wastewater. Nine machine learning algorithms were tested including multiple linear regression, polynomial regression, decision trees, random forest, adaptive boosting, extreme gradient boosting, k-nearest neighbors, support vector machine, and artificial neural network. The study found that the XGBoosting algorithm outperformed the other models, making it ideal for predicting the photocatalytic degradation capacity of BiVO . The results suggest that XGBoost is a suitable model for predicting the photocatalytic degradation of wastewater using BiVO with different dopants.