Modeling caffeine adsorption by multi-walled carbon nanotubes using multiple polynomial regression with interaction...

Area of Science:

• Environmental Science
• Materials Science
• Chemical Engineering

Background:

• Effective environmental monitoring requires efficient pollutant prediction methods.
• Wastewater treatment relies on understanding pollutant adsorption mechanisms.
• Multi-walled carbon nanotubes (MWCNTs) show promise for adsorbing organic pollutants like caffeine.

Purpose of the Study:

• To investigate the efficiency of multiple polynomial regression for predicting caffeine adsorption capacity (q).
• To identify key operating variables influencing caffeine adsorption onto MWCNTs.
• To evaluate the suitability of multiple polynomial regression as a predictive tool for wastewater treatment.

Main Methods:

• Characterization of MWCNTs using scanning electron microscopy, Fourier transform infrared spectroscopy, and point of zero charge.
• Experimental batch mode adsorption of caffeine using MWCNTs.
• Development and validation of a multiple polynomial regression model using parameters: pH, reaction time (t), adsorbent mass (M), temperature (T), and initial pollutant concentration (C).

Main Results:

• MWCNTs demonstrated high caffeine uptake capacity.
• The best predictive model utilized initial pollutant concentration (C), adsorbent mass (M), and reaction time (t), achieving R² = 0.996 and normalized RMSE = 0.0916.
• Sensitivity analysis revealed that predicted adsorption capacity (q) was most sensitive to C, followed by M and t.
• pH and temperature showed no significant impact on caffeine adsorption capacity in batch mode.

Conclusions:

• Multiple polynomial regression is an accurate and efficient method for predicting caffeine adsorption onto MWCNTs.
• The developed model provides a faster alternative to complex, time-consuming prediction methods.
• This approach can aid in optimizing wastewater treatment processes for caffeine removal.