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<p>Current optimization methods for engineering-controllable parameters of enhanced geothermal systems (EGS) are often constrained by high computational demands or a limited number of optimizable parameters. This study proposes a novel progressive many-objective optimization approach to efficiently optimize three or more objectives with minimal computational effort. This approach integrates the finite element method (FEM), response surface method (RSM), the non-dominated sorting genetic algorithm III (NSGA-III), and the technique for order preference by similarity to ideal solution (TOPSIS). First, the implementation procedure for the approach is described in detail. Then, a thermal–hydraulic–mechanical coupling model is developed based on the geological characteristics of an EGS in the Gonghe Basin, China. Subsequently, the suitable working fluid for this EGS is selected between H<sub>2</sub>O and CO<sub>2</sub>. Finally, the feasibility of this approach is evaluated, and it is applied to optimize the parameter of the EGS. The results show the following: (1) This approach can efficiently obtain a reasonable Pareto front with low computational cost. (2) H<sub>2</sub>O outperforms CO<sub>2</sub> as the working fluid for this EGS in the Gonghe Basin. (3) The optimal parameter scheme includes an injection rate of 56.58&#xa0;kg/s, an injection temperature of 20.88&#xa0;°C, and well spacing of 410.22 m, achieving mean electric power of over 7&#xa0;MW and a total investment cost of $92.22 million over 50&#xa0;years. This study provides an excellent example for optimizing engineering-controllable parameters in EGS projects, particularly in the Gonghe Basin. The results not only validate the feasibility and efficiency of the proposed optimization approach, but also provide a replicable optimization framework applicable to diverse EGS projects.</p>

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A Novel Progressive Many-Objective Optimization Approach for Engineering-Controllable Parameters of an Enhanced Geothermal System in the Gonghe Basin, China

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Jiajie Yang,
  • J. G. Wang,
  • Peibo Li,
  • Wei Liang,
  • Chunfai Leung

摘要

Current optimization methods for engineering-controllable parameters of enhanced geothermal systems (EGS) are often constrained by high computational demands or a limited number of optimizable parameters. This study proposes a novel progressive many-objective optimization approach to efficiently optimize three or more objectives with minimal computational effort. This approach integrates the finite element method (FEM), response surface method (RSM), the non-dominated sorting genetic algorithm III (NSGA-III), and the technique for order preference by similarity to ideal solution (TOPSIS). First, the implementation procedure for the approach is described in detail. Then, a thermal–hydraulic–mechanical coupling model is developed based on the geological characteristics of an EGS in the Gonghe Basin, China. Subsequently, the suitable working fluid for this EGS is selected between H2O and CO2. Finally, the feasibility of this approach is evaluated, and it is applied to optimize the parameter of the EGS. The results show the following: (1) This approach can efficiently obtain a reasonable Pareto front with low computational cost. (2) H2O outperforms CO2 as the working fluid for this EGS in the Gonghe Basin. (3) The optimal parameter scheme includes an injection rate of 56.58 kg/s, an injection temperature of 20.88 °C, and well spacing of 410.22 m, achieving mean electric power of over 7 MW and a total investment cost of $92.22 million over 50 years. This study provides an excellent example for optimizing engineering-controllable parameters in EGS projects, particularly in the Gonghe Basin. The results not only validate the feasibility and efficiency of the proposed optimization approach, but also provide a replicable optimization framework applicable to diverse EGS projects.