The application of machine learning methods for prediction of metal sorption onto biochars

生物炭 吸附 吸附 废水 阳离子交换容量 化学 金属 环境科学 土壤水分 环境工程 环境化学 材料科学 土壤科学 冶金 热解 有机化学
作者
Xinzhe Zhu,Xiaonan Wang,Yong Sik Ok
出处
期刊:Journal of Hazardous Materials [Elsevier BV]
卷期号:378: 120727-120727 被引量:296
标识
DOI:10.1016/j.jhazmat.2019.06.004
摘要

The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regression models were trained and optimized to predict the adsorption capacity according to biochar characteristics, metal sources, environmental conditions (e.g. temperature and pH), and the initial concentration ratio of metals to biochars. The RF model showed better accuracy and predictive performance for adsorption efficiency (R2 = 0.973) than ANN model (R2 = 0.948). The biochar characteristics were most significant for adsorption efficiency, in which the contribution of cation exchange capacity (CEC) and pHH2O of biochars accounted for 66% in the biochar characteristics. However, surface area of the biochars provided only 2% of adsorption efficiency. Meanwhile, the models developed by RF had better generalization ability than ANN model. The accurate predicted ability of developed models could significantly reduce experiment workload such as predicting the removal efficiency of biochars for target metal according to biochar characteristics, so as to select more efficient biochar without increasing experimental times. The relative importance of variables could provide a right direction for better treatments of heavy metals in the real water and wastewater.
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