کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1757623 1019129 2015 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of methanol content in natural gas with the GC-PR-CPA model
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
پیش نمایش صفحه اول مقاله
Prediction of methanol content in natural gas with the GC-PR-CPA model
چکیده انگلیسی


• Accurate knowledge of methanol concentration in natural gas separation unit sis important.
• A new group contribution model has been developed, combining the CPA and the PPR78 EoS.
• Methanol content and partition coefficients have been predicted for hydrocarbons mixtures.

Produced reservoir fluids are principally composed of hydrocarbons but contain also impurities such as carbon dioxide, hydrogen sulphide and nitrogen. These fluids are saturated with the formation water at reservoir conditions. During production, transportation and processing ice and/or gas hydrates formation may occur. Gas hydrate and ice formation are a serious flow assurance and inherently security issues in natural gas production, processing and transport. Therefore, inhibitors are usually injected as a hydrate inhibitor and antifreeze. For example, methanol is often used for hydrate inhibition or in some cases during start up, shut down or pipeline plug removal. Therefore impurities, water and methanol usually end up in natural gas conditioning and fractionation units. These units produce end user pipeline gas subject to local specifications and natural gas liquids like ethane, LPG or heaviers. This is why the accurate knowledge of methanol content at different operating conditions is important. In this study, a group contribution model, the GC-PR-CPA EoS (Hajiw et al., 2015) (Group Contribution – Peng-Robinson – Cubic-Plus-Association), is successfully applied for hydrocarbons systems containing methanol. Predictions of phase envelopes of binary systems as well as partition coefficients of methanol in hydrocarbons mixtures are in good agreement with experimental data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Natural Gas Science and Engineering - Volume 27, Part 2, November 2015, Pages 745–750
نویسندگان
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