In this work, the operation of a cryogenic expansion unit for the extraction of NGL is optimized through the implementation of data-driven techniques. The proposed approach is based on an optimization framework that integrates dynamic process simulations with two deep learning based surrogate models. The first model discloses the dynamics involved in the process using a long short-term memory (LSTM) layout with bidirectional recurrent neural network (RNN) structure and attention mechanism. The error maximization sampling strategy is adopted to improve the model accuracy. The second regression model is built to generate profit predictions of the process. Results from two proposed case studies show the capabilities of the proposed optimization framework in terms of optimizing a cold residue reflux (CRR) NGL recovery unit.

在这项工作中，通过实施数据驱动技术优化了用于提取NGL的低温膨胀单元的操作。所提出的方法基于一个优化框架，该框架将动态过程仿真与两个基于深度学习的替代模型集成在一起。第一个模型使用具有双向递归神经网络（RNN）结构和注意力机制的长短期记忆（LSTM）布局，揭示了过程中涉及的动力学。采用误差最大化采样策略来提高模型的准确性。建立第二个回归模型以生成过程的利润预测。来自两个拟议案​​例研究的结果表明，该拟议优化框架具有优化冷残渣回流（CRR）NGL回收装置的能力。