https://www.selleckchem.com/products/SL327.html
Kernel extreme learning machine (KELM) and Kriging models are proposed to predict biochar adsorption efficiency of heavy metals. Both six popular ions (Pb2+, Cd2+, Zn2+, Cu2+, Ni2+, As3+) 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 R2 can reach 0.919 (KELM) and 0.980 (Kriging). In addition, stepwise regression and local sensitivity analysis show that adsorption efficiency has strong relationshi
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