Joint Laboratory of Northeast Agricultural University and Max Planck Institute of Colloids and Interfaces (NEAU-MPICI), Harbin 150030, China; School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China.
Joint Laboratory of Northeast Agricultural University and Max Planck Institute of Colloids and Interfaces (NEAU-MPICI), Harbin 150030, China; School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China.
Bioresour Technol. 2021 Jun;329:124876. doi: 10.1016/j.biortech.2021.124876. Epub 2021 Feb 23.
Kernel extreme learning machine (KELM) and Kriging models are proposed to predict biochar adsorption efficiency of heavy metals. Both six popular ions (Pb, Cd, Zn, Cu, Ni, As) and single ion are considered to test the accuracy of KELM and Kriging models. Two ways (data selection and fix output value) are attempted to improve the model fitting accuracy and the best R can reach 0.919 (KELM) and 0.980 (Kriging). In addition, stepwise regression and local sensitivity analysis show that adsorption efficiency has strong relationship with pH and T. Moreover, the most sensitive parameters are T, pH, r, C and pH. The accurate KELM and Kriging models identify the most important controlling factors on metal adsorption, and ultimately provide some sort of predictive framework that will be useful in selecting appropriate biochar for particular treatment scenarios. This, in turn, will reduce the number of metal-biochar adsorption experiments needed going forward.
核极端学习机（KELM）和克里金模型被提出用于预测重金属的生物炭吸附效率。考虑了六种常见离子（Pb、Cd、Zn、Cu、Ni、As）和单离子，以测试 KELM 和克里金模型的准确性。尝试了两种方法（数据选择和固定输出值）来提高模型拟合精度，最佳 R 可达 0.919（KELM）和 0.980（克里金）。此外，逐步回归和局部灵敏度分析表明，吸附效率与 pH 和 T 密切相关。此外，最敏感的参数是 T、pH、r、C 和 pH。准确的 KELM 和克里金模型确定了金属吸附的最重要控制因素，最终提供了一种预测框架，这将有助于在特定处理场景下选择合适的生物炭。这反过来又将减少未来金属-生物炭吸附实验的数量。
