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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Iranian Journal of Oil and Gas Science and Technology
- نوع مقاله: Journal Article
- کلمات کلیدی: treatment,oil and gas,Produced water,Spray dryer
- چکیده:
- چکیده انگلیسی: The current work investigates the performance of a single-stage, bench-scale system using a spray dryer to treat produced water. The produced water is generated in three large reservoirs of Ahvaz, Maroon, and Mansouri fields, which have different compositions but the same high total dissolved solids (TDS) and total organic carbon (TOC). The results of this study indicate that the newly developed bench scale rig is able to reduce the amount of TDS in the water produced in Ahvaz, Maroon, and Mansouri reservoirs to 98.78, 98.65, and 98.90, and TOC decreases the three types of the produced water to zero. Investigating the effect of independent parameters on the performance of this system using response surface methodology shows that the most effective parameters affecting the efficiency of the produced water treatment system are the entering carrier gas temperature (TGIT), the flow rate of the produced water (QL), the carrier gas flow rate entering the spray dryer (QG), and the atomizer pore size (d). Additionally, the optimal conditions are obtained as follows: TGIT = 113.7 °C, QL = 20.8 cc/min, QG = 59.9 m3/hr., and d = 0.03 mm.
- انتشار مقاله: 06-05-1398
- نویسندگان: Mohammad Razaghiyan,Mahmood Reza Rahimi,Hajir Karimi
- مشاهده
- جایگاه : پژوهشی
- مجله: Iranian Journal of Chemistry and Chemical Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Artificial Neural Networks,Wavelet transform,Vertical liquid-liquid flow,Two–phase flow,Oil holdup,Flow pattern,Pressure fluctuations
- چکیده:
- چکیده انگلیسی: In this work, the feasibility of flow pattern and oil hold up the prediction for vertical upward oil–water two–phase flow using pressure fluctuation signals was experimentally investigated. Water and diesel fuel were selected as immiscible liquids. Oil hold up was measured by Quick Closing Valve (QCV) technique, and five flow patterns were identified using high-speed photography through a transparent test section with Inner Diameter (ID) of 0.0254 m. The observed flow patterns were Dispersed Oil in Water (D O/W), Dispersed Water in Oil (D W/O), Transition Flow (TF), Very FineDispersed Oil in Water (VFD O/W) and a new flow pattern called Dispersed Oil Slug & Water in Water (D OS& W/W). The pressure fluctuation signals were also measured by a static pressure sensor and decomposed at five levels using wavelet transform. Then, standard deviation values of decomposition levels were used as input parameters of a Probabilistic Neural Network (PNN) to train the network for predicting the flow patterns. In addition, some considered numerical values for actual flow patterns together with the signal energy value of each level were used as input parameters of a MultiLayer Perceptron (MLP) network to estimate the oil holdup. The results indicated good accuracy for recognition of the flow patterns (accuracy of 100% and 95.8% for training data and testing data, respectively) and oil holdup (AAPE=9.6%, R=0.984 for training data and AAPE=8.07%, R=0.99 for testing data).
- انتشار مقاله: 02-05-1393
- نویسندگان: Sadra Azizi,Hajir Karimi,Parviz Darvishi
- مشاهده