Machine learning for monitoring and control of NGL recovery plants

In this contribution, the monitoring and control problem of the natural gas liquids (NGL) extraction process is addressed by exploiting a data-driven approach. The cold residue reflux (CRR) process scheme is considered and simulated by using the process simulator Aspen HYSYS®, with the main targets of the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. The respect of product quality is obtained by designing a proper control strategy that uses a data-driven approach based on a neural network to estimate the unmeasured outputs. The performance of the controlled system is assessed by simulating the process under various input conditions evaluating different control structures such as direct control and cascade control of the temperature in the column.