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.
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.
J Environ Manage. 2021 Sep 1;293:112808. doi: 10.1016/j.jenvman.2021.112808. Epub 2021 May 23.
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 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 = 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.
重金属吸附到生物炭上是处理水和废水重金属污染的有效方法。本研究旨在评估不同生物炭特性对重金属吸附效率，并提出一种新的智能方法，基于反向传播神经网络（BPNN）和模糊 C 均值聚类算法（FCM），即 FCM-BPNN，以高精度预测重金属吸附到生物炭上的吸附效率。因此，使用 FCM 算法模拟金属吸附数据的特性，并将其分为具有相似特征的聚类。聚类结果表明，FCM 算法很好地模拟了金属吸附数据的特性，并根据生物炭特性和吸附条件对其进行分类。随后，基于这些聚类建立了 BPNN 模型，并将其结果进行了组合（即 FCM-BPNN）。结果表明，FCM-BPNN 模型可以很好地预测重金属吸附到生物炭上的吸附效率（即 RMSE 为 0.036、R 为 0.987、RSE 为 0.006、MAPE 为 0.706 和 VAF 为 98.724）。而未优化 FCM 算法的 BPNN 模型则表现出较低的性能（RMSE=0.050、R=0.977、RSE=0.011、MAPE=0.802 和 VAF=97.662）。这些发现表明，FCM 算法的存在显著提高了 BPNN 模型预测重金属吸附到生物炭上的吸附效率的能力，所提出的 FCM-BPNN 模型可以提高水/废水处理厂的质量，并为重金属处理提供更高效的过程，具有性能优势。
