Organic Rankine cycles (ORCs) offer a high structural design flexibility. The best process structure can be identified via the optimization of a superstructure, which considers design alternatives simultaneously. In this contribution, we apply deterministic global optimization to a geothermal ORC superstructure, thus guaranteeing to find the best solution. We implement a hybrid mechanistic data-driven model, employing artificial neural networks as thermodynamic surrogate models. This approach is beneficial as we optimize the problem in a reduced space using the optimization solver MAiNGO. We further introduce redundant constraints that are only considered for the lower-bounding problem of the branch-and-bound algorithm. We perform two separate optimizations, one maximizing power output and one minimizing levelized cost of electricity. The optimal solutions of both objectives differ from each other, but both have three pressure levels. Global optimization is necessary as there exist suboptimal local solutions for both flowsheet configuration and design with fixed configurations.

有机朗肯循环（ORC）具有高度的结构设计灵活性。可以通过优化上部结构来确定最佳的过程结构，该结构同时考虑了设计方案。在此贡献中，我们将确定性全局优化应用于地热ORC上部结构，从而保证找到最佳解决方案。我们采用人工神经网络作为热力学替代模型，实现了一种混合的机械数据驱动模型。这种方法是有益的，因为我们使用优化求解器MAiNGO在缩小的空间中优化了问题。我们进一步介绍了仅在分支定界算法的下界问题中考虑的冗余约束。我们执行两个单独的优化，一个最大化功率输出，另一个最小化平均电力成本。两个目标的最佳解决方案互不相同，但是都具有三个压力水平。全局优化是必要的，因为对于流程图配置和具有固定配置的设计都存在次优的局部解决方案。