期刊
SUSTAINABILITY
卷 15, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/su15097366
关键词
caffeine; rice husk; activated carbon; modeling; sorption; kinetic; isotherm
One solution to water crisis problems is using agricultural residue capabilities as low-cost and abundant adsorbents to remove pollutants from water. This study evaluated the potential of activated carbon from rice husk (RHAC) for removing caffeine from water. The maximum caffeine uptake capacity was achieved under the optimum conditions of RHAC dosage, solution pH, contact time, and initial concentration. The adsorption process was best described by pseudo-first-order kinetics and Freundlich isotherm, indicating the presence of heterogeneous and varying pores in RHAC.
One of the solutions to deal with water crisis problems is using agricultural residue capabilities as low-cost and the most abundant adsorbents for the elimination of pollutants from aqueous media. This research assessed the potential of activated carbon obtained from rice husk (RHAC) to eliminate caffeine from aqueous media. For this, the impact of diverse parameters, including initial caffeine concentration (C-0), RHAC dosage (C-s), contact time (t), and solution pH, was considered on adsorption capacity. The maximum caffeine uptake capacity of 239.67 mg/g was obtained under the optimum conditions at an RHAC dose of 0.5 g, solution pH of 6, contact time of 120 min, and initial concentration of 80 mg/L. The best fit of adsorption process data on pseudo-first-order kinetics and Freundlich isotherm indicated the presence of heterogeneous and varying pores of the RHAC, multilayer adsorption, and adsorption at local sites without any interaction. Additionally, modeling the adsorption by using statistical and mathematical models, including classification and regression tree (CART), multiple linear regression (MLR), random forest regression (RFR), Bayesian multiple linear regression (BMLR), lasso regression (LR), and ridge regression (RR), revealed the greater impact of C-0 and C-s in predicting adsorption capacity. Moreover, the RFR model performs better than other models due to the highest determination coefficient (R-2 = 0.9517) and the slightest error (RMSE = 2.28).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据