‌بهينه‌سازي واحدهاي فرايندي پتروشيمي اصفهان با تغيير شدت جريان و ميزان آروماتيك‌ها در خوراك فرايند و جهت گيري در راستاي كاهش مصرف انرژي با به كارگيري الگوريتم مي‌فلاي ها

One of the most important recent problems of the petrochemical an‎d refining industries is the issue of energy supply; the increase in the price of energy carriers on the one han‎d an‎d the decrease in fossil fuel resources on the other han‎d have led the chemical industry to optimize energy consumption in process units as much as possible. The purpose of this research is to optimize energy consumption in units 200, 220, 270 an‎d 400 of the Isfahan Petrochemical Complex. For this purpose, the process in question was first simulated with Aspen Plus software an‎d then the possibility of increasing the capacity of the studied units was investigated. By increasing the inlet feed flow rate by 27% while keeping the mass percentage of the inlet flow components constant (45% BTX), the highest feed flow rate was achieved. Then, by increasing the mass percentage of BTX to 55%, the highest possible mass percentage of BTX was achieved at maximum capacity.Next, assuming a maximum feed flow rate of 45% BTX an‎d using exergy analysis, the equipment with the highest amount of exergy loss was identified. The results of the exergy analysis showed that distillation towers contain the highest amount of exergy loss an‎d malfunction due to the consumption of hot services in the reboiler an‎d cold services in the condenser; therefore, these equipment have good potential for optimization. In the next step, sensitivity analysis was performed on the distillation towers an‎d the parameters affecting the loss an‎d exergy efficiency were identified. Finally, the optimization of the desired process was performed using the Mayfly optimization algorithm an‎d with the objective functions of energy consumption, exergy an‎d utility cost. In the last step, in order to verify the accuracy of the results of the Mayfly algorithm, the results of this algorithm were compared with the DE algorithm.The results of process optimization using the Mayfly algorithm an‎d the exergy objective function showed that optimization with this function ultimately led to savings of 103 billion tomans an‎d 11.6 percent in total annual costs. These values using the DE algorithm are equal to 93 billion tomans an‎d 10.9 percent per year, respectively. The results of process optimization using the Mayfly algorithm an‎d the energy consumption objective function showed that optimization with this function ultimately led to savings of 106 billion tomans an‎d 11.98 percent in total annual costs. These values using the DE algorithm are equal to 110 billion tomans an‎d 12.41 percent per year, respectively. The results of process optimization using the Mayfly algorithm an‎d the utility cost objective function showed that optimization with this function ultimately led to savings of 105 billion tomans an‎d 11.87 percent in total annual costs.These values using the DE algorithm are equal to 110 billion Tomans an‎d 12.38 percent per year, respectively. Finally, it is concluded that the energy consumption objective function leads to better results.

Optimization , Exergy analysis , Isfahan petrochemical , Simulation

Optimization of process units in Isfahan petrochemical by changing the flow rate an‎d the amount of aromatics in the process feed an‎d orientation in order to reduce energy consumption by applying the Mayfly algorithm

مهندسي شيمي

One of the most important recent problems of the petrochemical an‎d refining industries is the issue of energy supply; the increase in the price of energy carriers on the one han‎d an‎d the decrease in fossil fuel resources on the other han‎d have led the chemical industry to optimize energy consumption in process units as much as possible. The purpose of this research is to optimize energy consumption in units 200, 220, 270 an‎d 400 of the Isfahan Petrochemical Complex. For this purpose, the process in question was first simulated with Aspen Plus software an‎d then the possibility of increasing the capacity of the studied units was investigated. By increasing the inlet feed flow rate by 27% while keeping the mass percentage of the inlet flow components constant (45% BTX), the highest feed flow rate was achieved. Then, by increasing the mass percentage of BTX to 55%, the highest possible mass percentage of BTX was achieved at maximum capacity.Next, assuming a maximum feed flow rate of 45% BTX an‎d using exergy analysis, the equipment with the highest amount of exergy loss was identified. The results of the exergy analysis showed that distillation towers contain the highest amount of exergy loss an‎d malfunction due to the consumption of hot services in the reboiler an‎d cold services in the condenser; therefore, these equipment have good potential for optimization. In the next step, sensitivity analysis was performed on the distillation towers an‎d the parameters affecting the loss an‎d exergy efficiency were identified. Finally, the optimization of the desired process was performed using the Mayfly optimization algorithm an‎d with the objective functions of energy consumption, exergy an‎d utility cost. In the last step, in order to verify the accuracy of the results of the Mayfly algorithm, the results of this algorithm were compared with the DE algorithm.The results of process optimization using the Mayfly algorithm an‎d the exergy objective function showed that optimization with this function ultimately led to savings of 103 billion tomans an‎d 11.6 percent in total annual costs. These values using the DE algorithm are equal to 93 billion tomans an‎d 10.9 percent per year, respectively. The results of process optimization using the Mayfly algorithm an‎d the energy consumption objective function showed that optimization with this function ultimately led to savings of 106 billion tomans an‎d 11.98 percent in total annual costs. These values using the DE algorithm are equal to 110 billion tomans an‎d 12.41 percent per year, respectively. The results of process optimization using the Mayfly algorithm an‎d the utility cost objective function showed that optimization with this function ultimately led to savings of 105 billion tomans an‎d 11.87 percent in total annual costs.These values using the DE algorithm are equal to 110 billion Tomans an‎d 12.38 percent per year, respectively. Finally, it is concluded that the energy consumption objective function leads to better results.

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