Modeling of Nitrogen Removal from Natural Gas in Rotating Packed Bed Using Artificial Neural Networks

• Main Authors: Surmi, A., Shariff, A.M., Lock, S.S.M.
• Format: Article
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37500/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165985672&doi=10.3390%2fmolecules28145333&partnerID=40&md5=588335d55ccd6950c50ed838cff2b8c2
• Institution: Universiti Teknologi Petronas

Novel or unconventional technologies are critical to providing cost-competitive natural gas supplies to meet rising demands and provide more opportunities to develop low-quality gas fields with high contaminants, including high carbon dioxide (CO2) fields. High nitrogen concentrations that reduce the heating value of gaseous products are typically associated with high CO2 fields. Consequently, removing nitrogen is essential for meeting customers� requirements. The intensification approach with a rotating packed bed (RPB) demonstrated considerable potential to remove nitrogen from natural gas under cryogenic conditions. Moreover, the process significantly reduces the equipment size compared to the conventional distillation column, thus making it more economical. The prediction model developed in this study employed artificial neural networks (ANN) based on data from in-house experiments due to a lack of available data. The ANN model is preferred as it offers easy processing of large amounts of data, even for more complex processes, compared to developing the first principal mathematical model, which requires numerous assumptions and might be associated with lumped components in the kinetic model. Backpropagation algorithms for ANN Lavenberg�Marquardt (LM), scaled conjugate gradient (SCG), and Bayesian regularisation (BR) were also utilised. Resultantly, the LM produced the best model for predicting nitrogen removal from natural gas compared to other ANN models with a layer size of nine, with a 99.56 regression (R2) and 0.0128 mean standard error (MSE). © 2023 by the authors.