Global statistical predictor model for characteristic adsorption energy of organic vapors–solid interaction: Use in dynamic process simulation

Adsorption of Volatile Organic Compounds (VOCs) is one of the best remediation techniques for controlling industrial air pollution. In this paper, a quantitative predictor model for the characteristic adsorption energy (E) of the Dubinin–Radushkevich (DR) isotherm model has been established with R2 value of 0.94. A predictor model for characteristic adsorption energy (E) has been established by using Multiple Linear Regression (MLR) analysis in a statistical package MINITAB. The experimental value of characteristic adsorption energy was computed by modeling the isotherm equilibrium data (which contain 120 isotherms involving five VOCs and eight activated carbons at 293, 313, 333, and 353 K) with the Gauss–Newton method in a statistical package R-STAT. The MLR model has been validated with the experimental equilibrium isotherm data points, and it will be implemented in the dynamic adsorption simulation model PROSIM. By implementing this model, it predicts an enormous range of 1200 isotherm equilibrium coefficients of DR model at different temperatures such as 293, 313, 333, and 353 K (each isotherm has 10 equilibrium points by changing the concentration) just by a simple MLR characteristic energy model without any experiments.

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► Dynamic simulation of adsorption requires a huge set of experimental isotherm data.
► Experimental measures can be expressed by Multiple Linear Regression (MLR) model.
► The MLR is for characteristic adsorption energy of Dubinin–Raduskevich model.
► The MLR model covers the 120 adsorption isotherms for four VOCs–eight activated carbons.
► This MLR model will be replaced to make efficient dynamic simulation PROSIM.