Multistream heat exchangers (MHEXs) facilitate simultaneous heat exchange between multiple streams and are mainly used in energy-intensive cryogenic processes. Reducing the energy consumption of existing processes with MHEXs is important, but system-wide operational optimization necessitates that the heat-transfer parameters of the MHEXs are known. However, most MHEXs are practically black-boxes due to their proprietary designs and complex geometry. In this work, we present a procedure for the operational optimization of processes with MHEXs. Our procedure involves the development of a predictive model for MHEXs as the first step, followed by the illustration of operational optimization. We begin with the development of a data-driven nonlinear programming (NLP) model to synthesize an equivalent network of simple two-stream heat exchangers that best represents the operation of an MHEX. We then demonstrate our predictive modeling procedure on the main cryogenic heat exchanger (MCHE) from an existing natural gas liquefaction plant. Finally, we use the equivalent network of two-stream exchangers in the operational optimization of an example C3MR process.

中文翻译：

---

基于数据驱动的预测模型的多流热交换器过程的操作优化

多流换热器（MHEX）促进多流之间的同时热交换，主要用于能量密集型低温过程。用MHEX降低现有过程的能耗很重要，但是系统范围内的操作优化必须知道MHEX的传热参数。但是，由于它们的专有设计和复杂的几何形状，大多数MHEX实际上都是黑匣子。在这项工作中，我们提出了使用MHEX进行流程操作优化的过程。我们的程序涉及第一步，即开发MHEX的预测模型，然后举例说明运营优化。我们从数据驱动的非线性规划（NLP）模型的开发开始，以合成最能代表MHEX运行的简单两流热交换器的等效网络。然后，我们在现有天然气液化厂的主要低温热交换器（MCHE）上展示我们的预测建模程序。最后，在示例C3MR流程的操作优化中，我们使用两流换热器的等效网络。