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Statistical and Mathematical Modeling for Predicting Caffeine Removal from Aqueous Media by Rice Husk-Derived Activated Carbon

Author

Listed:
  • Mehdi Bahrami

    (Department of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 74616-86131, Iran
    These authors contributed equally to this work.)

  • Mohammad Javad Amiri

    (Department of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 74616-86131, Iran
    These authors contributed equally to this work.)

  • Mohammad Reza Mahmoudi

    (Department of Statistics, Faculty of Science, Fasa University, Fasa 74616-86131, Iran)

  • Anahita Zare

    (Department of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 74616-86131, Iran)

Abstract

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).

Suggested Citation

  • Mehdi Bahrami & Mohammad Javad Amiri & Mohammad Reza Mahmoudi & Anahita Zare, 2023. "Statistical and Mathematical Modeling for Predicting Caffeine Removal from Aqueous Media by Rice Husk-Derived Activated Carbon," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7366-:d:1136004
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    References listed on IDEAS

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    1. Bedoui, Adel & Lazar, Nicole A., 2020. "Bayesian empirical likelihood for ridge and lasso regressions," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
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