Thermo-economic optimization of an enhanced geothermal system (EGS) based on machine learning and differential evolution algorithms

Xue, Zhenqian; Yao, Shuo; Ma, Haoming; Zhang, Chi; Zhang, Kai; Chen, Zhangxin

doi:10.1016/j.fuel.2023.127569

Cited by 24 publications

(2 citation statements)

References 58 publications

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“…Moreover, economic challenges in hot dry rock exploitation have led to optimization frameworks considering levelized cost of electricity (LCOE) as an economic performance indicator. By employing Artificial Neural Network and Differential Evolution optimization, one study achieved a promising LCOE, demonstrating the potential of these methods to significantly reduce operation time and enhance the economic viability of EGS [152].…”

Section: Geothermal Energy Extraction Technologies: a Modern Perspectivementioning

confidence: 99%

Green Energy Technologies: A Key Driver in Carbon Emission Reduction

Wenten,

Khoiruddin,

Siagian

2024

J. Eng. Technol. Sci.

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This paper explores the vital role of green energy technologies in mitigating carbon emissions and advancing sustainable energy transition. It emphasizes the significance of green energy in reducing the carbon footprint, delves into the environmental consequences of carbon emissions, and analyzes the mechanisms through which green energy contributes to carbon reduction. This paper discusses technological advancements across various renewable energy sources, including solar, wind, hydroelectric, biomass, geothermal, tidal, wave, nuclear, osmotic, and salinity-powered energy generation. It also examines emerging green energy technologies, identifies barriers to adoption, offers an Indonesian perspective, and provides recommendations for a greener energy future. Overall, this paper offers a comprehensive exploration of green energy's transformative potential in combatting climate change and promoting sustainable development.

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Section: Geothermal Energy Extraction Technologies: a Modern Perspectivementioning

confidence: 99%

Green Energy Technologies: A Key Driver in Carbon Emission Reduction

Wenten,

Khoiruddin,

Siagian

2024

J. Eng. Technol. Sci.

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“…In the process of solving positioning, the search starts from string set, which has a large coverage and is conducive to global search, and the output results can be obtained quickly with good real-time performance. In literature [12], differential evolution algorithm was adopted to reduce positioning errors and achieve accurate positioning. Literature [13] adopts an adaptive particle swarm positioning algorithm, which adaptively adjusts the cross probability and variation factors of each generation to better search the global optimal location.…”

Section: Introductionmentioning

confidence: 99%

Optimal Differential Control for Accurate Positioning of Medical Electronic Wristband

2024

Teh. vjesn.

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In this paper, an improved differential evolution algorithm is proposed to improve the positioning accuracy of electronic wristband system. The wristband system is composed of STM32F103 microcontroller, Beidou positioning module, LORA communication module and so on. In the traditional differential evolution algorithm, premature convergence to local optimal will reduce the accuracy of the algorithm. To solve this problem, the method introduces the distance information between known and unknown points and between all unknown points in the fitness function and overall initialization. This reduces the premature convergence of the algorithm and improves the optimization ability of the algorithm. Experimental results using the CICDDoS2019 dataset show that the localization accuracy of the proposed algorithm is improved by 7.6% compared with traditional differential evolution. The method also has good stability. This provides an effective way to improve the positioning accuracy of the wristband, which can be used for applications such as patient monitoring.

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Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Al‐Fakih,

Abdulraheem,

Kaka

2024

Deep Underground Science and Engineering

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This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

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Thermo-economic optimization of an enhanced geothermal system (EGS) based on machine learning and differential evolution algorithms

Cited by 24 publications

References 58 publications

Green Energy Technologies: A Key Driver in Carbon Emission Reduction

Green Energy Technologies: A Key Driver in Carbon Emission Reduction

Optimal Differential Control for Accurate Positioning of Medical Electronic Wristband

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

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