College of Engineering, Tunghai University, Taichung 407, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan.
Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan; Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan; Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
J Hazard Mater. 2024 Mar 5;465:133154. doi: 10.1016/j.jhazmat.2023.133154. Epub 2023 Dec 2.
Using bone char for contaminated wastewater treatment and soil remediation is an intriguing approach to environmental management and an environmentally friendly way of recycling waste. The bone char remediation strategy for heavy metal-polluted wastewater was primarily affected by bone char characteristics, factors of solution, and heavy metal (HM) chemistry. Therefore, the optimal parameters of HM sorption by bone char depend on the research being performed. Regarding enhancing HM immobilization by bone char, a generic strategy for determining optimal parameters and predicting outcomes is crucial. The primary objective of this research was to employ artificial neural network (ANN) technology to determine the optimal parameters via sensitivity analysis and to predict objective function through simulation. Sensitivity analysis found that for multi-metals sorption (Cd, Ni, and Zn), the order of significance for pyrolysis parameters was reaction temperature > heating rate > residence time. The primary variables for single metal sorption were solution pH, HM concentration, and pyrolysis temperature. Regarding binary sorption, the incubation parameters were evaluated in the following order: HM concentrations > solution pH > bone char mass > incubation duration. This approach can be used for further experiment design and improve the immobilization of HM by bone char for water remediation.
利用骨炭处理受污染废水和土壤修复是一种有趣的环境管理方法，也是一种环保的废物回收方式。骨炭修复受重金属污染废水的策略主要受骨炭特性、溶液因素和重金属（HM）化学性质的影响。因此，骨炭对 HM 的最佳吸附参数取决于正在进行的研究。关于通过骨炭增强 HM 的固定化，确定最佳参数和预测结果的通用策略至关重要。本研究的主要目的是利用人工神经网络（ANN）技术通过敏感性分析确定最佳参数，并通过模拟预测目标函数。敏感性分析发现，对于多金属吸附（Cd、Ni 和 Zn），热解参数的重要性顺序为反应温度>加热速率>停留时间。单一金属吸附的主要变量是溶液 pH 值、HM 浓度和热解温度。对于二元吸附，孵育参数的评估顺序为：HM 浓度>溶液 pH 值>骨炭质量>孵育时间。这种方法可用于进一步的实验设计，并提高骨炭对水修复中 HM 的固定化。
