کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5012622 | 1462815 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Exergy analysis of a 1000Â MW double reheat ultra-supercritical power plant
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
This study evaluates the performance of a 1000Â MW double reheat ultra-supercritical power plant. An exergy analysis was performed to direct the energy loss distribution of this system. Based on the exergy balance equation, together with exergy efficiency, exergy loss coefficient, and exergy loss rate, the exergy distribution and efficiency of the unit were determined. Results show that the highest exergy loss in furnace is as high as 85%, which caused by the combustion of fuel and heat exchange of water wall. The VHP and the two LPs suffer the highest exergy losses, namely 1.86%, 2.04% and 2.13% respectively. The regenerative heating system has an exergy loss rate of 2.3%. The condenser suffers a heat loss of 999Â MW, but its exergy is as low as 20.49Â MW. The sensitivity variations of the unit's exergy efficiency with load, feedwater temperature, main steam temperature and pressure, the twice reheat steam temperatures, and steam exhaust pressure were also analyzed, indicating that load, feedwater temperature, and steam exhaust pressure influence the exergy efficiency of this unit than other elements. The overall exergy efficiency decreases along with the gradual increase of steam exhaust pressure at any constant outlet boiler temperature, but it increases as the load, feedwater temperature, main steam temperature and pressure, and twice reheat steam temperatures increase at fixed steam exhaust pressure.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Conversion and Management - Volume 147, 1 September 2017, Pages 155-165
Journal: Energy Conversion and Management - Volume 147, 1 September 2017, Pages 155-165
نویسندگان
Ningning Si, Zhigang Zhao, Sheng Su, Pengshuai Han, Zhijun Sun, Jun Xu, Xiaoning Cui, Song Hu, Yi Wang, Long Jiang, Yingbiao Zhou, Gang Chen, Jun Xiang,