sorood zahedi abghari

The delayed Coker process as an upgrading process has the main impact on the productivity of the Refinery Complexes. To determine the impact of different operating conditions on the product yield distribution of the delayed coking process, several experiments were designed and conducted in a prefabricated pilot plant. The experiments were conducted on different Iranian vacuum residues at temperatures ranging from 420°C to 480°C and at atmospheric pressure. Reaction times were within the range of 5-120 minutes. A four lumps kinetic model has been developed based on the experimental results. The lumps—which included Volatile products, coke, feed, and an intermediate phase between coke and feed—were defined to precisely monitor the yield distribution of products throughout the reaction time. The feedstocks utilized were three different vacuum residues and their blends. The mixtures were produced by using different mixing ratios of the three vacuum residues. The Statistical analysis shows that this model has R-squared, RMSE, SSE, and MRE equal to 0.99, 0.022, 0.08, and 3.537%, respectively. This shows that the developed model is sufficiently accurate. The experimental and modeling results in this research reveal that by increasing the temperature, the yield of coke and gas is abated. However, the yield of the distillate is escalated. This investigation illustrates that the production of an intermediate reaction has the highest amount of activation energy in comparison with the other reactions. Also, the results indicate that the production reaction rate of coke has the highest amount compared to other reactions.

Determination of wax appearance temperature is a crucial specification in the transportation and storage of crude oil or heavy refinery products same as fuel oils. In this work, to determine wax appearance temperature different applicable methods were tested and developed. At first, the microscopic experiments were applied to different samples. Then the variation of viscosity versus temperature was monitored. To determine a feasible method to be applied in the fields, the variation of viscosity versus temperature was monitored. It was declared that at the wax appearance temperature a deep change in viscosity happened. Eventually, the wax appearance temperature of different crude oil including Iranian or heavy crude oil can be determined by the viscosimetry method. In the related experiments, the negative effect of water sediments and wax crystals was eliminated. It was also declared that the wax appearance temperature could be varied by variation of density, wax content, pour point, and asphaltenes of the samples. To correlate between input and output variables three different modeling methods were tested and implemented. The Artificial neural networks, Neuro-fuzzy, and regression models were tested to derive the most accurate correlation. The statistical studies approved that the regression models have suitable accuracy. The results of the regression model declared that the interaction effect of density and pour point and the effect of asphaltenes as one of the main input variables have the greatest impact on the wax appearance temperature.

Fluid catalytic cracking is one of the most crucial refinery processes to upgrade heavy hydrocarbon feedstock to light higher qualified products. Due to the high impact of this process, many scientists investigated different aspects of this process. In this paper, a comprehensive review of the related studies is carried out. The main focus of the review is on three categories including kinetic networks, steady and unsteady modeling, and process optimization. A review of researches since 1970, reveals that kinetic investigations were majorly based on the lumping methodology in which the reactive mixture is divided into main groups with different categories that may be defined based on carbon number of species or kind of components. Mainly the related number of lumps was limited to three to nineteen. Moreover, in the process modeling researches mainly two main equipment including riser-reactor and regenerator were considered. The primary regenerator model includes single-phase regenerator models, simple contact with plug flow and dissipated reactions with series tanks that were also considered in the researches. Also, different studies were carried out to consider more equipment. Also, the application of the momentum equation besides mass and energy equations in the regenerator was considered in some studies. The main challenge in the development of the related model was the determination of the parameters. Investigation of the impact of input catalyst temperature and catalyst to oil ratio were also carried out in the other researches. The optimization of the process was usually in the steady-state. However, dynamic optimization was applied less. Various algorithms, including genetics and particle swarm, were evaluated and compared. It was found that the particle swarm algorithm is adjusted easier than the genetic algorithm and the handling is better.

Fluid catalytic cracking (FCC) process is a vital refinery process which majorly produces gasoline. In this research, a hybrid algorithm which was constituted of Adaptive Neuro-Fuzzy Inference System (ANFIS) and firefly optimization algorithm was developed to model the process and optimize the operating conditions. To conduct the research, industrial data of Abadan refinery FCC process were carefully gathered along a definite period. Then a model based on ANFIS was developed to investigate the effect of operating variables including reactor temperature, feed flow rate, temperature of top of main column, and the temperature of bottom of the debutanizer tower on quality and quantity of gasoline, LPG flow rate, and process conversion. Moreover, statistical parameters comprising R2, RMSE, and MRE approved the accuracy of the model. Eventually, validated ANFIS model and firefly algorithm as an evolutionary optimization algorithm were applied to optimize the operating conditions. Also, three different optimization cases including maximization of Research Octane Number (RON) , gasoline flow rate, and conversion were considered. In addition, to obtain maximum target output variables, inlet reactor temperature, temperature of top of main column, temperature of  the bottom of debutanizer column, and feed flow rate should be respectively set at 523, 138, 183 ‎°C and 40731 bbl/day. Also, the sensitivity analysis between the input and output variables was carried out to derive some effective rules of thumb to facilitate operation of the process under unsteady state conditions. Finally, the obtained result introduces a methodology to compensate for the negative effect of undesirable variation of some of the operating variables by manipulating the others.

Fluid Catalytic Cracking (FCC) process is a vital unit to produce gasoline. In this research, a feed forward ANN model was developed and trained with industrial data to investigate the effect of operating variables containing reactor temperature feed flow rate, the temperature of the top of the main column and the temperature of the bottom of the debutanizer tower on quality and quantity of gasoline, LPG flow rate and process conversion. Eventually, validated ANN model and firefly algorithm which is an evolutionary optimization algorithm were applied to optimize the operating conditions. Three different optimization cases including maximization of RON (as the parameter which demonstrates the quality of the gasoline), gasoline flow rate and conversion were investigated. In order to obtain the maximum level of targeted output variables, inlet reactor temperature, temperature of the top of the main column, temperature of the bottom of debutanizer column and feed flow rate should respectively set at 525,138, 169ºC and 43000 bbl/day. Also, sensitivity analysis between the input and output variables were carried out to derive some effective rule-of- thumb to facilitate the operation of the process under unsteady state conditions. The result introduces a methodology to compensate for the negative effect of undesirable variation in some operating variables by manipulating the others.