Sensitivity Analysis of Adsorption Isotherms Subject to Measurement Noise in the Data

Reflecting the importance of adsorption as a major water purification method, the main objective of this research is to perform a sensitivity analysis on some of the common adsorption isotherms subject to measurement noise in data. Even though most of adsorption isotherms have been derived based on some theoretical assumptions about the adsorption mechanism, they involve model parameters that need to be estimated from experimental measurements of the process variables. Specifically, for the Langmuir isotherm, which can be expressed in three linearized forms, it was sought to determine which of these three forms would give the highest accuracy of the adsorption model parameters ? maximum amount of adsorbate per unit weight of the adsorbent and the constant related to the affinity between the adsorbent and adsorbate . Another objective was to estimate the adsorption parameters using the nonlinear Langmuir model, and to compare their accuracy to the ones estimated using the best linear form. To achieve these objectives, the effect of different levels of noise on the accuracy of model (linear and nonlinear) parameters was investigated by varying the noise content (variance). The simulation of this study was performed using MATLAB. The results of this work could be summarized as follows: One of the linearized forms of Langmuir model showed normal distribution and provided most accurate estimation of both model parameters. In addition, it was shown that when the noise content (standard deviation) increased on the data, less accurate estimates were obtained for both adsorption parameters. Finally, the estimation accuracy was more sensitive to the magnitude of the affinity constant than to the maximum amount of adsorbate in adsorbent; larger values of affinity constant result in higher estimation accuracy of both model parameters.