Evaluation and Optimization of Heat Extraction Strategies Based on Deep Neural Network in the Enhanced Geothermal System
Publication: Journal of Energy Engineering
Volume 149, Issue 1
Abstract
Production strategies and parameters control the efficiency of geothermal energy extraction related to the thermal stability and economic benefits of a geothermal system. The optimization strategies of geothermal energy extraction play a critical role in engineering and are generally determined through a numerical simulation approach. Considering the correlation among production parameters, numerical simulation requires numerous runs and manual adjustments, resulting in lower calculation efficiency and limited or local optimizations. This study proposes a high-efficiency network based on a three-dimensional heterogeneity model in the Gonghe Basin in China to achieve a high-efficiency and high-precision production strategy. The neural network was successfully established as a surrogate of the numerical model for the repetitive forward simulation. Meanwhile, the neural network is integrated with the Harris Hawks algorithm to optimize extraction strategies for sustainable heat extraction. This paper focuses on the effects of human-controlled operational parameters on geothermal systems. Results indicated that the maximum electrical power can be guaranteed 5.2 MW during a 50-year production period at an injection temperature of 60°C, an injection rate of , and a well spacing of 380 m. The study provides important operational guidance for sustainable utilization in the Gonghe Basin. This simulation-optimization approach can be applied to other geothermal sites for sustainable energy production.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Key R&D Program of China (2018YFB1501803) and the Graduate Innovation Fund of Jilin University. Thanks are given to Zhenjiao Jiang, Han Yu, and Chenghao Zhong at Jilin University for their help with the numerical modeling tools.
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Journal of Energy Engineering
Volume 149 • Issue 1 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 28, 2022
Accepted: Sep 1, 2022
Published online: Nov 7, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 7, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Basins
- Bodies of water (by type)
- Business management
- Computer programming
- Computing in civil engineering
- Energy engineering
- Energy sources (by type)
- Engineering fundamentals
- Mathematics
- Models (by type)
- Neural networks
- Numerical models
- Parameters (statistics)
- Practice and Profession
- Renewable energy
- Simulation models
- Statistics
- Sustainable development
- Thermal power
- Three-dimensional models
- Water and water resources
- Water management
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