کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5476657 1399206 2016 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Cascade refrigeration systems in integrated cryogenic natural gas process (natural gas liquids (NGL), liquefied natural gas (LNG) and nitrogen rejection unit (NRU))
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
پیش نمایش صفحه اول مقاله
Cascade refrigeration systems in integrated cryogenic natural gas process (natural gas liquids (NGL), liquefied natural gas (LNG) and nitrogen rejection unit (NRU))
چکیده انگلیسی
Heavy components in the natural gas itself can feed downstream units and also due to the low temperature process may be formed solid. Therefore heavy components separation is a necessity and can produce useful products. Virtually all natural gases are containing nitrogen that would lower the heating value of natural gas. This study investigates design and optimization of integrated process recovery of natural gas liquids, natural gas liquefaction, and nitrogen remove unit. In this integrated process, design of low temperature processes is started from the core process and continued by heat exchangers network design and cooling system based on MFC. Design and integration processes of units at the same time reduces the number of required equipment and energy consumption. The results show that the new integrated process has specific power around 0.343-0.33 (kW-h/kg-LNG) and its thermal efficiency equal to 62.82%, compared to other integrated systems have the lowest and highest values. Exergy analysis shows that towers has the highest Exergy destruction among other equipment. Sensitivity analysis shows that the structure of the integrated process capable of removing nitrogen from natural gas at a concentration of between 5% and 15%. By analyzing the operating parameters shows reduction in the Total specific power from 19.5% to 24% and the Specific power from 2.57% to 11%, yet surging in the Ethane recovery from 2.5% to 17%. Sensitivity analysis is the method to identification of the Decision variables, finally Genetic Algorithm used to identify optimum of objective function (minimization of Specific Power) and reduction of it to 6%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 115, Part 1, 15 November 2016, Pages 88-106
نویسندگان
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