Operation optimization of a cryogenic NGL recovery unit using deep learning based surrogate modeling

Abstract

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 case studies show the capabilities of the proposed optimization framework in terms of optimizing a cold residue reflux (CRR) NGL recovery unit.