Abstract We present an optimal cost and design prediction of an underground gas storage (UGS) system, which is proposed to be constructed from one or more lined rock caverns. The adaptive network based fuzzy inference system ANFISUGS was generated to predict minimal investment costs and optimal UGS design. Since a safe and impermeable UGS system requires a rigorous calculation, three steps were proposed to solve this task: the first is solving the geotechnical engineering problem for different UGS designs, the second is the cost/design optimization of the UGS structures, and the last is the generation of an ANFIS system for optimal cost and design prediction of the UGS. While the geotechnical problem was solved with a series of finite element analyses in order to define special geotechnical constraints to be put into the optimization models, a parameter non-linear programming (NLP) optimization approach was used for a variety of different UGS design parameters. The ANFISUGS system was then constructed on the basis of data sets defined from previous NLP optimization results. A case study demonstrates the effectiveness and the prediction capability of the proposed ANFISUGS system.

中文翻译：

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ANFIS 地下储气库的优化成本和设计

摘要 我们提出了地下储气库 (UGS) 系统的最佳成本和设计预测，该系统建议由一个或多个带衬砌的岩洞建造。生成基于自适应网络的模糊推理系统 ANFISUGS 以预测最小投资成本和最佳 UGS 设计。由于安全且不渗透的 UGS 系统需要严格的计算，因此提出了三个步骤来解决此任务：第一是解决不同 UGS 设计的岩土工程问题，第二是 UGS 结构的成本/设计优化，最后是是生成 ANFIS 系统以优化 UGS 的成本和设计预测。虽然岩土工程问题是通过一系列有限元分析来解决的，以便定义要放入优化模型的特殊岩土工程约束，参数非线性规划 (NLP) 优化方法用于各种不同的 UGS 设计参数。然后，ANFISUGS 系统是根据之前 NLP 优化结果定义的数据集构建的。案例研究证明了所提出的 ANFISUGS 系统的有效性和预测能力。