The reliable estimation of pore size distribution (PSD) of the heterogenous solid adsorbents is the key element in the design and operation of all adsorption processes. Direct measurement of PSD is not practically feasible. Complex theories and sophisticated models are required to obtain a faithful estimation of PSD from a set of noisy measured isotherms. A robust method based on linear regularization theory is developed to extract PSD from highly noisy condensation isotherms with almost no a priori assumption. The performance of the proposed method was compared with the traditional regularization techniques, using various synthetic examples. The predicted results clearly demonstrate that the proposed method can successfully filter out the noise and recovers the exact PSD from a set of noisy training data with almost no initial assumption. The optimum level of regularization is crucial for proper 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.

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