فهرست مطالب شکوفه طیبی

Petroleum coke calcination is a chemical process during which the petroleum coke loses moisture and volatile combustible materials due to the increase in temperature and ultimately improves the physical properties of the calcined coke. In this study, A 2-Dim model was developed for the petroleum coke calcination process via rotary kiln using the CFD approach. Understanding the temperature, concentration, and fluid movement behavior are the main goals for developing the simulation model, by using which the rotary kiln control and design can be performed.

Comsol Multiphysics was applied to develop the simulation model. Petroleum coke rotary kiln calcination consists of two solid and gas phases, which cross each other counter-currently. All governing physics in the system, including chemical reactions, heat transfer via conduction, convection, and radiation, intra-phase and interphase mass transfer, evaporation or evolution of components from the solid phase into the gas phase, fluid turbulency and all complex relationships were considered. Using the finite element method, the governing equations of the model were solved, and consequently, the variation of temperature, components concentration, and fluid velocity was calculated.

It is concluded that tertiary air injection significantly affects the temperature profile and combustion reactions in the bed (About 100 degrees increase in temperature). In addition, the maximum temperature of 1910 °C has been achieved in the kiln. Concentration changes of components in the gas phase were also seen mainly in the bed entrance and in the areas near the tertiary air injection. Comparing the results with similar works showed the high accuracy of the developed model

In recent decades, hybrid optimizations methods based on natural phenomenon have placed special position according to their capabilities in finding optimal solutions without expensive computational loads and disassociation on choosing initial points. Artificial Neural Network is used as one of the powerful tools of Artificial Intelligence for process simulation. The employment of the neural network in the modeling of m-Cresol alkylation process of with isopropanol as well as meta-heuristic methods in obtaining the optimal conditions for the catalyst and the reaction can prepare an effective step towards a high efficiency process.

 In the present study, the artificial neural network is applied to model alkylation of m‐Cresol with isopropanol process. In addition, the bee colony is employed in order to optimize the process yield. To verify its performance, the proposed method is used in prediction of the m‐Cresol conversion and Thymol selectivity of the alkylation process with isopropanol 120 data. In this process, the input variables are Weight Hourly Space Velocity (WHSV), pressure and temperature; m-cresol conversion and thymol selectivity are considered as the output variables of the neural network. Five hidden neurons are considered for the proposed neural network. 120 data is used to train the neural network. The meta-heuristic approach based on bee colony (BC) is applied to maximize the yield of the process.

The results confirm that the proposed method develops the accurate model with an R2 value of greater than 97.5%. The maximum yield is obtained 28.9% by bee colony algorithm with adjustable variables that are WHSV of 0.062 hr-1, the pressure of 1.5 bar and the temperature of 300oC. In addition, in order to achieve the better performance of the optimization algorithm, the appropriate values of acceleration coefficient and population size are chosen 100 and 10 during the trial-and-error phase.