Prediction of the MMP by Using of Artificial Intelligence
Publish place: 07th International Congress on Chemical Engineering
Publish Year: 1390
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICHEC07_625
تاریخ نمایه سازی: 25 فروردین 1394
Abstract:
An important factor in the design of gas injection projects is the minimum miscibility pressure (MMP). A new genetic algorithm (GA)-based correlation and two neural network models (one of them is trained by BP algorithm and another is trained by PSO algorithm) have been developed to estimate the CO2–oil MMP. The correlation and models use the following key input parameters: reservoir temperature, molecular weight of C5+, mole percentage of the volatiles and intermediate components (for the first time, the mole percentages are used as independent variables). Then results have been validated against experimental data and are finally compared with commonly used correlations reported in the literature;The results show that the neural network model trained by BP algorithm and the correlation that has been developed by GA can be applied effectively and afford high accuracy and dependability for MMP forecasting
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Authors
a Ebrahimi
Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
h Rasouli
Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
f Rashidi
Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
e Khamehchi
Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
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