Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network.

Autor: Ke B; School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China; School of Urban Construction, Wuchang University of Technology, Wuhan, 430223, China., Nguyen H; Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Viet Nam. Electronic address: nguyenhoang@humg.edu.vn., Bui XN; Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Viet Nam; Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Viet Nam. Electronic address: buixuannam@humg.edu.vn., Bui HB; Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, 100000, Viet Nam; Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, 100000, Viet Nam. Electronic address: buihoangbac@humg.edu.vn., Nguyen-Thoi T; Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address: nguyenthoitrung@tdtu.edu.vn.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2021 Sep 01; Vol. 293, pp. 112808. Date of Electronic Publication: 2021 May 23.
DOI: 10.1016/j.jenvman.2021.112808
Abstrakt: Heavy metal adsorption onto biochar is an effective method for the treatment of the heavy metal contamination of water and wastewater. This study aims to evaluate the heavy metals sorption efficiency of different biochar characteristics and propose a novel intelligence method for predicting the sorption efficiency of heavy metal onto biochar with high accuracy based on the back-propagation neural network (BPNN) and fuzzy C-means clustering algorithm (FCM), named as FCM-BPNN. Accordingly, the FCM algorithm was used to simulate the properties of metal adsorption data and divide them into clusters with similar features. The clustering results showed that the FCM algorithm simulated metal adsorption data's properties very well and classified them based on biochar characteristics and adsorption conditions. Afterward, BPNN models were well-developed based on these clusters, and their outcomes were then combined (i.e., FCM-BPNN). The results indicated that the FCM-BPNN model could predict heavy metal's sorption efficiency onto biochar with a promising result (i.e., RMSE of 0.036, R 2 of 0.987, RSE of 0.006, MAPE of 0.706, and VAF of 98.724). Whereas the BPNN model, without optimizing the FCM algorithm, was proved with lower performance (RMSE = 0.050, R 2  = 0.977, RSE = 0.011, MAPE = 0.802, and VAF = 97.662). These findings revealed that the FCM algorithm's presence impressively improved the BPNN model's accomplishment in predicting heavy metal's sorption efficiency onto biochar, and the proposed FCM-BPNN model can improve water/wastewater treatment plants' quality and provide a more efficient process for heavy metals with performance superiority.
(Copyright © 2021 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: