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:: Volume 8, Issue 1 (11-2020) ::
jehe 2020, 8(1): 1-16 Back to browse issues page
Prioritization of Air Pollutant Removal (VOC) Scenarios from Refinery R.O.P Units Using Artificial Neural Network Model (Case Study: Abadan Oil Refinery)
Ladan Khajeh Hoseini, Reza Jalilzadeh Yengejeh * , Maryam Mohammadi Rouzbahani, Sima Sabz alipour
Department of Environmental Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
Abstract:   (424 Views)
Oil is vital in many industries and is the most important source of energy supply internationally, accounting for 32% of energy supply in Europe and Asia and more than 53% in the Middle East. Given the position that the petrochemical industry has found today, its damage to human health and the environment should not be overlooked. Refineries today emit millions of pounds of pollutants into the air, which poses a serious threat to human health and the environment, and seriously damages the quality of life of people living in industrial communities. Therefore, in this study, using the logic and algorithm used in the artificial neural network model, the weight prioritization of the strategies and the prediction of future conditions governing the plan have been done, and finally the final ranking was done with the TOPSIS model.
Methods: In this study, air pollutants were measured by gas chromatography and using artificial neural network ANN (Artificial Neural Networks) to prioritize the scenarios of removal of volatile organic pollutants from ROP (Recovery Oil Compound) of Abadan Oil Refinery It was done in 2019 to 2020. The method of using artificial neural network in the present study was MLP (Multi Layer Perceptron). The final ranking was done with TOPSIS model.
Results: Based on the results obtained from the measurement of air pollutants adjacent to the ROP unit of the refinery, the highest emission of volatile organic compounds compared to the amount announced by WHO belongs to benzene emission with values of 8865.88 µg/m3 in spring, 1701.04 µg/m3 in summer, 8061.72  µg/m3 in autumn and 8447.62 µg/m3 was in winter.
Conclusion: Based on the model outputs and its ranking with the TOPSIS model, minimization of production effluent in the factory through more effective control of water consumption, optimization of production processes, reuse of condensate water of indirect converters, control of leakage in connections, valves and equipment The refinery with an impact factor of 1 purity was the first priority and the most ideal. hen return the sludge from the aeration tank to provide a sufficient number of microorganisms to prevent anaerobic activation of the sludge, as well as increase the hydraulic retention time of wastewater every two hours with 0.7798 second priority and use of uniformity ponds with 0.6964 to the ideal state. The third strategy was identified.
Keywords: Volatile Organic Compounds, Oil Refinery, Air Pollution, Artificial Neural Network
Full-Text [PDF 1552 kb]   (164 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/08/11 | Accepted: 2020/11/1 | Published: 2021/01/4
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Khajeh Hoseini L, Jalilzadeh Yengejeh R, Mohammadi Rouzbahani M, Sabz alipour S. Prioritization of Air Pollutant Removal (VOC) Scenarios from Refinery R.O.P Units Using Artificial Neural Network Model (Case Study: Abadan Oil Refinery). jehe. 2020; 8 (1) :1-16
URL: http://jehe.abzums.ac.ir/article-1-802-en.html


Volume 8, Issue 1 (11-2020) Back to browse issues page
مجله مهندسی بهداشت محیط Journal of Environmental Health Enginering
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