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A general dynamic model is developed for separation of air over a carbon molecular sieve and a zeolite adsorbent for production of nitrogen and oxygen. The proposed model is validated using experimental data from working laboratory scale N 2 -PSA and laboratory scale O 2 -PSA systems. Simulations studies are performed to investigate the effect of changing various process variables, such as the duration of PSA steps, bed length and feed inlet velocity.

To investigate the efficiency of a Co-Mo catalyst in HDS process, a set of experiments were designed and carried out based on central composite design (CCD) methodology in an HDS pilot plant. The designed variables included temperature, LHSV and pressure. However, the hydrogen over fresh feed ratio remained constant. The ranges of these variables were, respectively, equal to 335-361 o C, 1.06-1.8 1/hr and 46.8-53.2 bar. The outcomes of experiments were employed to determine the coefficients of statistical models. For the clarification of the accuracy of the model, several statistical tests like ANOVA (Analysis of Variance), Lack-of-Fit test and residual squares were carried out. To optimize the operating conditions to achieve maximum sulfur removal, an optimization algorithm was employed. The outcomes revealed that the minimum sulfur content, which is 23.65 ppm in the final product, is attained at 355 o C, 1.2 1/ hr and 49.2 bar.

Knowledge of accurate process measurements in the form of Flow, temperature and pressure strongly affect product quality, process real time optimization and control, plant safety and plant profitability. The paper reports an experience with online data filtering in Naphtha Hydrotreater setup. First, pilot plant data is analyzed for detecting and removing faulty data and gross errors. To remove noise hidden in the process data, a fast and adaptive data denoising technique is proposed. The proposed technique is based on the recursive least square to identify the pilot plant model and the Kalman filter to reconcile noisy data. This technique offers competitive advantages over conventional approaches: Independent and adaptive model and less computation time. From several pilot runs, the proposed technique has shown good performance in terms of accuracy and speed.

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