eprintid: 17237
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/72/37
datestamp: 2023-12-19 03:23:40
lastmod: 2023-12-19 03:23:40
status_changed: 2023-12-19 03:07:42
type: conference_item
metadata_visibility: show
creators_name: Mustafa, M.F.
creators_name: Talib, A.M.
creators_name: Rashid, R.Z.J.A.
creators_name: Ismail, I.
creators_name: Awang, A.
creators_name: Saad, M.N.M.
creators_name: Zahari, M.S.
title: Development of Gas Flow Characteristic Prediction for Industrial Flow Meter using Long Short-Term Memory (LSTM)
ispublished: pub
keywords: Balancing; Brain; Flow measurement; Flow of gases; Flowmeters; Gas industry; Long short-term memory; Mean square error, Flow characteristic; Flow meter; Industrial flows; Maintenance optimization; Mass balancing; Memory units; Memory-based modeling; Oil and Gas Industry; Production flows; Production optimization, Forecasting
note: cited By 1; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671
abstract: Prediction of production flow rates of industrial flow meter will bring significance value in terms of production and maintenance optimization, and mass balancing in oil and gas industry. This paper proposes a long short-Term memory-based model to predict production flow of an industrial flow meter. Besides, this paper also discusses the significance of training sample size and hyperparameter of machine learning model upon the accuracy of the prediction. This paper found that with simpler model architecture (32 LSTM units and 8 Rectified Linear Units) has produced a prediction with 1.4 Root Mean Square Error, that has similar performance of a more complex model configuration (64 LSTM units). © 2022 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149141692&doi=10.1109%2fICFTSC57269.2022.10039824&partnerID=40&md5=8233009e139612f812397b9863da6d0c
id_number: 10.1109/ICFTSC57269.2022.10039824
full_text_status: none
publication: 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
pagerange: 214-217
refereed: TRUE
isbn: 9798350334548
citation:   Mustafa, M.F. and Talib, A.M. and Rashid, R.Z.J.A. and Ismail, I. and Awang, A. and Saad, M.N.M. and Zahari, M.S.  (2022) Development of Gas Flow Characteristic Prediction for Industrial Flow Meter using Long Short-Term Memory (LSTM).  In: UNSPECIFIED.