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Sensors 2017, 17(11), 2458; https://doi.org/10.3390/s17112458

Soft Sensing of Non-Newtonian Fluid Flow in Open Venturi Channel Using an Array of Ultrasonic Level Sensors—AI Models and Their Validations

Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway, Kjølnes Ring 56, 3918 Porsgrunn, Norway
These authors contributed equally to this work.
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Received: 27 September 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 26 October 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Abstract

In oil and gas and geothermal installations, open channels followed by sieves for removal of drill cuttings, are used to monitor the quality and quantity of the drilling fluids. Drilling fluid flow rate is difficult to measure due to the varying flow conditions (e.g., wavy, turbulent and irregular) and the presence of drilling cuttings and gas bubbles. Inclusion of a Venturi section in the open channel and an array of ultrasonic level sensors above it at locations in the vicinity of and above the Venturi constriction gives the varying levels of the drilling fluid in the channel. The time series of the levels from this array of ultrasonic level sensors are used to estimate the drilling fluid flow rate, which is compared with Coriolis meter measurements. Fuzzy logic, neural networks and support vector regression algorithms applied to the data from temporal and spatial ultrasonic level measurements of the drilling fluid in the open channel give estimates of its flow rate with sufficient reliability, repeatability and uncertainty, providing a novel soft sensing of an important process variable. Simulations, cross-validations and experimental results show that feedforward neural networks with the Bayesian regularization learning algorithm provide the best flow rate estimates. Finally, the benefits of using this soft sensing technique combined with Venturi constriction in open channels are discussed. View Full-Text
Keywords: soft sensing in open channels; non-Newtonian flow; ultrasonic scanning of open channel flow; neural networks; Bayesian regularization learning; fuzzy logic; support vector regression soft sensing in open channels; non-Newtonian flow; ultrasonic scanning of open channel flow; neural networks; Bayesian regularization learning; fuzzy logic; support vector regression
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Chhantyal, K.; Viumdal, H.; Mylvaganam, S. Soft Sensing of Non-Newtonian Fluid Flow in Open Venturi Channel Using an Array of Ultrasonic Level Sensors—AI Models and Their Validations. Sensors 2017, 17, 2458.

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