Reliable prediction of the heterogeneous solid adsorbents pore size distribution (PSD) is the key element in the design and operation of all adsorption processes. Direct measurement of PSD is usually expensive and in many practical applications may not be feasible. Complex theories and sophisticated models are required to obtain a faithful estimation of PSD from a set of noisy measured isotherms. Various shortcomings of the traditional PSD recovery techniques (e.g. forward and regularization methods) are discussed. A robust method based on linear regularization theory is developed to extract PSD of heterogeneous solid adsorbents from highly noisy condensation isotherms with almost no a priori assumption. The performance of the proposed method is tested using various synthetic examples and different experimental data. The predicted results clearly demonstrate that the proposed method can successfully filter out the noise and recovers the proper PSD from a set of noisy condensation data with almost no initial assumption. The optimum level of regularization is crucial for appropriate recovery of pore size distribution, especially for lower orders of regularization technique. To the best of our knowledge the proposed method has not been addressed previously.
Impressive performance of the new proposed method (NEW1) with other available procedures.