Removal of N-Nitrosodiphenylamine from contaminated water: A novel modeling framework using metaheuristic-based ensemble models.

Autor: Alam MS; Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia. Electronic address: mdshafiul.alam@kfupm.edu.sa., Akinpelu AA; Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia., Nazal MK; Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia., Rahman SM; Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Aug; Vol. 365, pp. 121503. Date of Electronic Publication: 2024 Jun 21.
DOI: 10.1016/j.jenvman.2024.121503
Abstrakt: Investigating the complex interactions among physicochemical variables that influence the adsorptive removal of pollutants is a challenge for conventional one-variable-at-a-time (OVAT) batch methods. The adoption of machine learning-based chemometric prediction models is expected to be more accurate than the conventional method. This study proposed a novel modeling framework for predicting and optimizing the adsorptive removal of N-Nitrosodiphenylamine (NDPhA). Initially, models were trained by using OVAT data, with their hyperparameters subsequently fine-tuned through Bayesian optimization. In the second phase, the particle swarm optimization (PSO) technique was adopted to identify optimal parameters, specifically time, concentration, temperature, pH, and dose, to ensure the highest removal. The adopted analytical method enhances both prediction accuracy and removal efficiency. Utilizing OVAT data for NDPhA removal, the XGBoost regressor significantly outperformed other models. With a correlation coefficient of 0.9667 in the testing dataset, the XGBoost model exhibited its accuracy, emphasized by its low mean squared errors of 28.45 and mean absolute errors of 0.0982. Feature importance analysis consistently identified time and concentration as the most critical factors across all models.
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.)
Databáze: MEDLINE
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