relation: https://khub.utp.edu.my/scholars/17237/
title: Development of Gas Flow Characteristic Prediction for Industrial Flow Meter using Long Short-Term Memory (LSTM)
creator: Mustafa, M.F.
creator: Talib, A.M.
creator: Rashid, R.Z.J.A.
creator: Ismail, I.
creator: Awang, A.
creator: Saad, M.N.M.
creator: Zahari, M.S.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149141692&doi=10.1109%2fICFTSC57269.2022.10039824&partnerID=40&md5=8233009e139612f812397b9863da6d0c
relation: 10.1109/ICFTSC57269.2022.10039824
identifier: 10.1109/ICFTSC57269.2022.10039824