Exploring nontraditional LSTM architectures for modeling demethanizer column operations

Abstract

Digital twins have recently attracted attention as a new technology that can facilitate the digital transformation of process industries. It may provide live, or near real-time, information and insights into the process and may be used for monitoring, control and optimization purposes. In this study, a digital twin has been developed for modelling the demethanizer column of a NGL separation plant. Based on a non-conventional Long Short-Term Memory (LSTM) neural network arrangement, the surrogate model has been trained and validated using data obtained by the process simulator Aspen HYSYS®. Model prediction can be obtained using only readily available variables as input data, ensuring easy and cost-effective implementation. Measurement noises have been considered in order to mimic real-world measurements in a real plant. In both steady-state and transient conditions, the developed demethanizer digital twin accurately reconstructs the separation operation, including compositions, temperatures, and pressures in the reboiler and all column stages.