نوع مقاله : مقاله پژوهشی
1 دانشجوی دکترا، گروه مهندسی شیمی، واحد ماهشهر، دانشگاه آزاد اسلامی، ماهشهر، ایران
2 استادیار، گروه مهندسی شیمی، واحد ماهشهر، دانشگاه آزاد اسلامی، ماهشهر، ایران
عنوان مقاله [English]
In recent years, with the sudden rise in water prices, many industries have decided to treat their effluent and reuse this water. In order to treat the wastewater produced by Abadan Oil Refining Company and turn it into water that can be used in this industrial, nanofiltration membrane treatment or more advanced processes are necessary. The nanofiltration membrane has a limit of hydrocarbon compounds in its input feed up to a maximum amount of 3 mg/L, but the effluent used in this research contains 5.1 mg/L of hydrocarbons. In this research, the pre-treatment process was done by anthracite adsorbent in a fixed bed, and the variables of the adsorption process, including reactor flow rate, adsorbent service time, and pH were optimized to maximize the amount of adsorption. The optimum flow rate of the reactor is 8.48 L/min, time 92.9 minutes, pH: 6.36 and the highest percentage of hydrocarbon removal is 57.62%. The output of the adsorption process is 2.16 mg/L of hydrocarbon compounds, which is considered as the feed of the nanofiltration process. The module used in this process is disk type and polyamide membrane. In the nanofiltration process, the optimal value of the variable pressure was 9.58 barg, temperature was 18.04 °C, pH: 4.62 and the highest removal percentage was 81.35%. Combining the two processes of adsorption and nanofiltration, they were able to produce hydrocarbon compounds at the output of the aqueous nanofiltration stage with a value of 0.4 mg/L, which can be used in several internal networks of the refinery. Each process has three variables, each of which is examined in 5 levels and the number of experiments in each step is 20 and the optimization of variables in all stages using Design Expert software using the response surface methodology based on the principles of central composite design Mathematical model and optimization have been used for data design.