Sustainable freshwater/energy supply through geothermal-centered layout tailored with humidification-dehumidification desalination unit; Optimized by regression machine learning techniques

Shuguang Li, Yuchi Leng, Rishabh Chaturvedi, Ashit Kumar Dutta, Barno Sayfutdinovna Abdullaeva and Yasser Fouad

Energy, 2024, vol. 303, issue C

Abstract: The application of regression machine learning techniques is crucial for the analysis and optimization of energy systems based on geothermal energy to produce freshwater, power, and heating. This study applied regression machine learning techniques to investigate and optimize a geothermal tri-generation energy system that combines double-flash geothermal energy, humidification-dehumidification desalination, and transcritical carbon dioxide Rankine cycle. The goal was to produce freshwater, power, and heating. The algorithms performed remarkably well, with R-squared values surpassing 96 %. It is worth mentioning that for specific parameters like freshwater production, heating capacity, and efficiency, the R-squared values exceeded an impressive 99 %. The optimum conditions include maintaining a Rankine pressure ratio of 3, a mass flow rate of 30 kg/s, a geothermal temperature of 220 °C, and a turbine pressure ratio of 2.2. By following these ideal parameters, it is possible to achieve the highest level of system performance. This will lead to the production of 7.88 kg/s of freshwater, the generation of 1283 kW of power, a heating capacity of 30.8 kg/s, and an impressive system efficiency of 24.98 %. The findings reveal that the integration of regression machine learning algorithms in geothermal energy systems for freshwater, power, and heating production holds great promise for a sustainable and efficient future.

Keywords: Geothermal energy; Desalination; Tri-generation system; Artificial intelligence; Machine learning; Optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422401692X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:303:y:2024:i:c:s036054422401692x

DOI: 10.1016/j.energy.2024.131919

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().