Browse by author
Lookup NU author(s): Dr Zainal Ahmad, Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
In dynamic simulators, mathematical models are applied in order to study the time dependentbehaviour of a system, meaning the system process units and the corresponding control units. Absorption and stripping are the unit operations which are widely used in the natural gas processing industries. Many attempts have been made to define an average absorption factor method to short-cut the time consuming rigorous calculation procedures. One of the options for this complex engineering modelling problem is artificial intelligent (AI) approach. Artificial neural networks (ANN) havebeen shown to be able to approximate any continuous non-linear functions and have been used to build data base empirical models for non-linear processes. In this study, feedforward neural networks (FANN) models were used to model the absorption efficiency. The mean square error (MSE), residue analysis and coefficient determination based on the observed and prediction output is chosen as the performance criteria of model. It was found that the developed feedforward neural networks (FANN) models provided satisfactory model with the MSE and coefficient determination of 0.0003 and0.9998 for new unseen data from literature respectively.
Author(s): Ahmad Z, Zhang J, Kashiwao T, Bahadori A
Publication type: Article
Publication status: Published
Journal: Petroleum Science and Technology
Year: 2016
Volume: 34
Issue: 2
Pages: 105-113
Online publication date: 22/02/2016
Acceptance date: 17/11/2015
Date deposited: 15/12/2015
ISSN (print): 1091-6466
ISSN (electronic): 1532-2459
Publisher: Taylor & Francis Inc.
URL: http://dx.doi.org/10.1080/10916466.2015.1122628
DOI: 10.1080/10916466.2015.1122628
Altmetrics provided by Altmetric
