JOURNAL ARTICLE
A study on geothermal electricity systems for Tibet geothermal fields considering thermal performance, economic analysis, and CaCO3 scaling.
Published In: Journal of Renewable & Sustainable Energy, 2023, v. 15, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yu, Hao; Lu, Xinli; Ma, Fei; Zhang, Wei; Liu, Jiali; Li, Chenchen 3 of 3
Abstract
This article focuses on the thermodynamic performance, economic analysis, and calcite (CaCO₃) scaling assessment of seven geothermal electricity production systems—organic Rankine cycle (ORC), dual-stage ORC (DSORC), triple-stage ORC (TSORC), single-flash (SF), double-stage flash (DSF), single-flash ORC (SFORC), and double-stage-flash ORC (DSFORC)—applied to Tibetan geothermal fields. The study finds that TSORC yields the highest net power output, followed by DSFORC and DSORC, with flash systems becoming more suitable as geofluid temperature and dryness increase. Thermoeconomic analysis indicates that ORC-based systems generally have lower electricity production costs and shorter payback periods compared to flash and combined systems. Scaling analysis reveals that calcite precipitation primarily occurs at the wellhead for ORC systems, while flash systems experience additional scaling within system components, with DSF showing the highest scaling rate. A selection map based on geofluid temperature and dryness is provided to guide optimal system choice, and the findings aim to support the design and operation of geothermal power plants in Tibet.
Additional Information
- Source:Journal of Renewable & Sustainable Energy. 2023/01, Vol. 15, Issue 1, p1
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2023
- ISSN:1941-7012
- DOI:10.1063/5.0133895
- Accession Number:162171320
- Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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