Global statistical predictor model for characteristic adsorption energy of organic vapors-solid interaction: use in...

Area of Science:

• Environmental Chemistry
• Adsorption Science
• Chemical Engineering

Background:

• Industrial air pollution control relies heavily on adsorptive remediation techniques.
• Volatile Organic Compounds (VOCs) pose significant environmental and health risks, necessitating effective removal strategies.
• The Dubinin-Radushkevich (DR) isotherm model is crucial for understanding adsorption phenomena.

Purpose of the Study:

• To develop a quantitative predictive model for the characteristic adsorption energy (E) of the DR isotherm model.
• To establish a computationally efficient method for predicting adsorption behavior without extensive experimentation.
• To validate the predictive model against experimental data and integrate it into dynamic simulation.

Main Methods:

• Multiple Linear Regression (MLR) analysis was employed using MINITAB software to build the predictive model.
• Experimental characteristic adsorption energy values were computed using the Gauss-Newton method in R-STAT, analyzing 120 isotherms across five VOCs and eight activated carbons at various temperatures (293-353 K).
• The MLR model was validated using experimental equilibrium isotherm data.

Main Results:

• A quantitative predictor model for characteristic adsorption energy (E) was successfully established with a high R² value of 0.94.
• The model demonstrates strong predictive capability, correlating well with experimental data.
• The validated MLR model can predict 1200 DR isotherm equilibrium coefficients across a range of temperatures and concentrations without requiring new experiments.

Conclusions:

• The developed MLR model provides a reliable and efficient method for predicting the characteristic adsorption energy (E) in the DR isotherm model.
• This computational approach significantly reduces the experimental burden for characterizing adsorption processes.
• The model's integration into dynamic adsorption simulation models like PROSIM offers enhanced capabilities for industrial air pollution control strategy development.