Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Flow Rate Estimation using Dynamic Artificial Neural Networks with Ultrasonic Level Measurements Linköping University Electronic Press Conference Proceedings
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Title:
Flow Rate Estimation using Dynamic Artificial Neural Networks with Ultrasonic Level Measurements
Author:
Khim Chhantyal: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway Minh Hoang: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway Håkon Viumdal: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway Saba Mylvaganam: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway
DOI:
10.3384/ecp17142561
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
082
Pages:
561-567
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Accurate estimation of ?ow in drilling operations at in?ow and out?ow positions can lead to increased safety, optimized production and improved cost ef?ciency. In this paper, Dynamic Arti?cial Neural Network (DANN) is used to estimate the ?ow rate of non-Newtonian drilling ?uids in an open channel Venturi-rig that may be used for estimating out?ow. Flow in the Venturi-rig is estimated using ultrasonic level measurements. Simulation study looks into fully connected Recurrent Neural Network (RNN) with three different learning algorithms: Back Propagation Through Time (BPTT), Real-Time Recurrent Learning (RTRL) and Extended Kalman Filter (EKF). The simulation results show that BPTT and EKF algorithms converge very quickly as compared to RTRL. However, RTRL gives more accurate results, is less complex and computationally fastest among these three algorithms. Hence, in the experimental study RTRL is chosen as the learning algorithm for implementing Dynamic Arti?cial Neural Network (DANN). DANN with RTRL learning algorithm is compared with Support Vector Regression (SVR) and static ANN models to assess their performance in estimating ?ow rates. The comparisons show that the proposed DANN is the most accurate model among three models as it uses previous inputs and outputs for the estimation of current output.

Keywords: drilling operations, open channel venture ?ume, non-Newtonian ?uid, ?ow rate estimation, ultrasonic level measurements, recurrent neural network, real-time recurrent learning

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Khim Chhantyal, Minh Hoang, Håkon Viumdal, Saba Mylvaganam
Title:
Flow Rate Estimation using Dynamic Artificial Neural Networks with Ultrasonic Level Measurements
DOI:
http://dx.doi.org/10.3384/ecp17142561
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Khim Chhantyal, Minh Hoang, Håkon Viumdal, Saba Mylvaganam
Title:
Flow Rate Estimation using Dynamic Artificial Neural Networks with Ultrasonic Level Measurements
DOI:
https://doi.org10.3384/ecp17142561
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