基于PSO-GA-BP神经网络和分析层次过程的增强地热系统的热回收性能优化
Ling Zhou1,2, Jingchao Sun1, Yanjun Zhang3
1School of Civil Engineering, Shandong Jianzhu University, Jinan, 250101, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种通过将数值模拟与先进的人工智能模型相结合来优化增强地热系统 (EGS) 的有效方法. 优化的EGS设计预计将产生6.05-8.17兆瓦,改善地热能提取.
科学领域:
- 地热能源工程 地热能源工程
- 人工智能在能源中的作用
- 数字模拟方法 数字模拟方法
背景情况:
- 对于EGS来说,传统的数值模拟是耗时的,并且限制了最佳策略的确定.
- 在全球范围内确定EGS的最佳运营策略是具有挑战性的.
- 影响EGS性能的关键参数需要系统优化.
研究的目的:
- 为优化增强地热系统 (EGS) 发电开发一种高效准确的方法.
- 调查五个关键参数对EGS热回收性能的影响.
- 建立一个全面的EGS优化战略,结合模拟,人工智能和决策分析.
主要方法:
- 开发了优化的反向传播神经网络 (BPNN) 模型,与粒子群优化 (PSO) 和遗传算法 (GA) 集成.
- 利用数值模拟数据来训练和验证PSO-GA-BPNN模型.
- 采用分析层次流程来确定评估指数的权重,以进行全面的优化.
主要成果:
- 对于EGS绩效指数,PSO-GA-BPNN模型显示出良好的预测准确度.
- 确定了最佳参数:600米的井间距,27公斤/秒的注射速率,58°C的注射温度,1x10^-10 m2的断裂透率和100米的断裂间距.
- 预计装机容量为6.05-8.17兆瓦,电力平衡成本为0.033美元/千瓦时.
结论:
- 拟议的综合方法显著提高了EGS方案优化的准确性和计算效率.
- 优化的EGS设计提供了可行且具有成本效益的地热能源解决方案.
- 这种方法为未来地热项目的开发和最佳设计提供了宝贵的参考.
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