Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN
Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of...
- Autores:
-
Aiesh Qaisi, Ramy Mohammed
Fouladinia, Farhad
Mohammad Mayet, Abdulilah
Grimaldo Guerrero, John William
Loukil, Hassen
Ramkumar Raja, M.
Abdul Muqeet, Mohammed
Eftekhari-Zadeh, Ehsan
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/14091
- Acceso en línea:
- https://hdl.handle.net/11323/14091
https://repositorio.cuc.edu.co/
- Palabra clave:
- 8-electrode sensor
Measuring
Temperature
Pressure
Artificial intelligence
Air-water homogenous regime
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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| dc.title.eng.fl_str_mv |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| title |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| spellingShingle |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN 8-electrode sensor Measuring Temperature Pressure Artificial intelligence Air-water homogenous regime |
| title_short |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| title_full |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| title_fullStr |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| title_full_unstemmed |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| title_sort |
Intelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANN |
| dc.creator.fl_str_mv |
Aiesh Qaisi, Ramy Mohammed Fouladinia, Farhad Mohammad Mayet, Abdulilah Grimaldo Guerrero, John William Loukil, Hassen Ramkumar Raja, M. Abdul Muqeet, Mohammed Eftekhari-Zadeh, Ehsan |
| dc.contributor.author.none.fl_str_mv |
Aiesh Qaisi, Ramy Mohammed Fouladinia, Farhad Mohammad Mayet, Abdulilah Grimaldo Guerrero, John William Loukil, Hassen Ramkumar Raja, M. Abdul Muqeet, Mohammed Eftekhari-Zadeh, Ehsan |
| dc.subject.proposal.eng.fl_str_mv |
8-electrode sensor Measuring Temperature Pressure Artificial intelligence Air-water homogenous regime |
| topic |
8-electrode sensor Measuring Temperature Pressure Artificial intelligence Air-water homogenous regime |
| description |
Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of methods exist for measuring the void fraction, and the most popular is capacitance-based sensors. Aside from being easy to use, the capacitance-based sensor does not need any separation or interruption to measure the void fraction. In addition, in the contemporary era, thanks to Artificial Neural Networks (ANN), measurement methods have become much more accurate. The same can be said for capacitance-based sensors. In this paper, a new metering system utilizing an 8-electrode sensor and a Multilayer Perceptron network (MLP) is presented to predict an air and water volume fractions in a homogeneous fluid. Some characteristics, such as temperature, pressure, etc., can have an impact on the results obtained from the aforementioned sensor. Thus, considering temperature changes, the proposed network predicts the void fraction independent of pressure variations. All simulations were performed using the COMSOL Multiphysics software for temperature changes from 275 to 370 degrees Kelvin. In addition, a range of 1 to 500 Bars, was considered for the pressure. The proposed network has inputs obtained from the mentioned software, along with the temperature. The only output belongs to the predicted void fraction, which has a low MAE equal to 0.38. Thus, based on the obtained result, it can be said that the proposed network precisely measures the amount of the void fraction. |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-08-05 |
| dc.date.accessioned.none.fl_str_mv |
2025-04-04T16:29:47Z |
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2025-04-04T16:29:47Z |
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Artículo de revista |
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Qaisi, R.M.A.; Fouladinia, F.; Mayet, A.M.; Guerrero, J.W.G.; Loukil, H.; Raja, M.R.; Muqeet, M.A.; Eftekhari-Zadeh, E. Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN. Sensors 2023, 23, 6959. https:// doi.org/10.3390/s23156959 |
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1424-8220 |
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https://hdl.handle.net/11323/14091 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/s23156959 |
| dc.identifier.instname.none.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Qaisi, R.M.A.; Fouladinia, F.; Mayet, A.M.; Guerrero, J.W.G.; Loukil, H.; Raja, M.R.; Muqeet, M.A.; Eftekhari-Zadeh, E. Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN. Sensors 2023, 23, 6959. https:// doi.org/10.3390/s23156959 1424-8220 10.3390/s23156959 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/14091 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Sensors |
| dc.relation.references.none.fl_str_mv |
Karimi, H.; Boostani, M. Heat transfer measurements for oil–water flow of different flow patterns in a horizontal pipe. Exp. Therm. Fluid Sci. 2016, 75, 35–42. [CrossRef] Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Setayeshi, S.; Zadeh, E.E.; Fatehi, A. Optimization of a method for iden-tifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. Int. J. Hydrog. Energy 2016, 41, 7438–7444. [CrossRef] Steven, R.N. Wet gas metering with a horizontally mounted Venturi meter. Flow Meas. Instrum. 2002, 12, 361–372. [CrossRef] Wang, D.; Liang, F.; Peng, Z.; Wang, Y.; Lin, Z. Gas–liquid two-phase flow measurements by full stream batch sampling. Int. J. Multiph. Flow 2012, 40, 113–125. [CrossRef] Banowski, M.; Beyer, M.; Szalinski, L.; Lucas, D.; Hampel, U. Comparative study of ultrafast X-ray tomography and wire-mesh sensors for vertical gas–liquid pipe flows. Flow Meas. Instrum. 2017, 53, 95–106. [CrossRef] Salgado, C.M.; Dam, R.S.; Puertas, E.J.; Salgado, W.L. Calculation of volume fractions regardless scale deposition in the oil industry pipelines using feed-forward multilayer perceptron artificial neural network and MCNP6 code. Appl. Radiat. Isot. 2022, 185, 110215. [CrossRef] [PubMed] Iliyasu, A.M.; Bagaudinovna, D.K.; Salama, A.S.; Roshani, G.H.; Hirota, K. A Methodology for Analysis and Prediction of Volume Fraction of Two-Phase Flow Using Particle Swarm Optimization and Group Method of Data Handling Neural Network. Mathematics 2023, 11, 916. [CrossRef] Al-Lababidi, S.; Addali, A.; Yeung, H.; Mba, D.; Khan, F. Gas Void Fraction Measurement in Two-Phase Gas/Liquid Slug Flow Using Acoustic Emission Technology. J. Vib. Acoust. 2009, 131, 501–507. [CrossRef] Xie, C.G.; Stott, A.L.; Plaskowski, A.; Beck, M.S. Design of capacitance electrodes for concentration measurement of two-phase flow. Meas. Sci. Technol. 1990, 1, 65–78. [CrossRef] Abdulkadir, M.; Hernandez-Perez, V.; Lowndes, I.; Azzopardi, B.; Brantson, E. Detailed analysis of phase distributions in a vertical riser using wire mesh sensor (WMS). Exp. Therm. Fluid Sci. 2014, 59, 32–42. [CrossRef] Koyama, S.; Lee, J.; Yonemoto, R. An investigation on void fraction of vapor–liquid two-phase flow for smooth and microfine tubes with R134a at adiabatic condition. Int. J. Multiph. Flow 2004, 30, 291–310. [CrossRef] Shahsavari, M.H.; Veisi, A.; Roshani, G.H.; Eftekhari-Zadeh, E.; Nazemi, E. An Experimental and Simulation Study for Comparison of the Sensitivity of Different Non-Destructive Capacitive Sensors in a Stratified Two-Phase Flow Regime. Electronics 2023, 12, 1284. [CrossRef] Strazza, D.; Demori, M.; Ferrari, V.; Poesio, P. Capacitance sensor for hold-up measurement in high-viscous-oil/conductive-water core-annular flows. Flow Meas. Instrum. 2011, 22, 360–369. [CrossRef] An, Z.; Ningde, J.; Lusheng, Z.; Zhongke, G. Liquid holdup measurement in horizontal oil–water two-phase flow by using concave capacitance sensor. Measurement 2014, 49, 153–163. [CrossRef] Ortiz, J.; Masek, V. Cyclonic capacitive sensor for multiphase composition measurement. Sens. Transducers 2015, 191, 1–11. Li, J.; Kong, M.; Xu, C.; Wang, S.; Fan, Y. An Integrated Instrumentation System for Velocity, Concentration and Mass Flow Rate Measurement of Solid Particles Based on Electrostatic and Capacitance Sensors. Sensors 2015, 15, 31023–31035. [CrossRef] Elkow, K.; Rezkallah, K. Void fraction measurements in gas-liquid flows under 1-g and µ-g conditions using capacitance sensors. Int. J. Multiph. Flow 1997, 23, 815–829. [CrossRef] Li, H.; Yang, D.; Cheng, M.X. Sensitivity analysis of capacitance sensor with helical shaped surface plates. CIESC J 2011, 62, 2292–2297. Tollefsen, J.; Hammer, E.A. Capacitance sensor design for reducing errors in phase concentration measurements. Flow Meas. Instrum. 1998, 9, 25–32. [CrossRef] Jaworek, A.; Krupa, A. Gas/liquid ratio measurements by rf resonance capacitance sensor. Sens. Actuators A Phys. 2004, 113, 133–139. [CrossRef] dos Reis, E.; Cunha, D.d.S. Experimental study on different configurations of capacitive sensors for measuring the volumetric concentration in two-phase flows. Flow Meas. Instrum. 2014, 37, 127–134. [CrossRef] Kendoush, A.A.; Sarkis, Z.A. Improving the accuracy of the capacitance method for void fraction measurement. Exp. Therm. Fluid Sci. 1995, 11, 321–326. [CrossRef] Abouelwafa, M.S.A.; Kendall, E.J.M. The Use of Capacitance Sensors for Phase Percentage Determination in Multiphase Pipelines. IEEE Trans. Instrum. Meas. 1980, 29, 24–27. [CrossRef] Ahmed, H. Capacitance Sensors for Void-Fraction Measurements and Flow-Pattern Identification in Air–Oil Two-Phase Flow. IEEE Sens. J. 2006, 6, 1153–1163. [CrossRef] Roshani, M.; Phan, G.T.; Nazemi, E.; Eftekhari-Zadeh, E.; Phan, N.H.; Corniani, E.; Tran, H.N.; Duong, V.H.; Roshani, G.H. Performance comparison of capacitance-based flowmeter with gamma-ray attenuation-based two-phase flowmeter for deter-mining volume fractions in an annular flow regime’s component. Eur. Phys. J. Plus. 2021, 136, 24–27. [CrossRef] Wang, X.; Chen, Y.; Wang, B.; Tang, K.; Hu, H. Sectional void fraction measurement of gas-water two-phase flow by using a capacitive array sensor. Flow Meas. Instrum. 2020, 74, 101788. [CrossRef] Krupa, A.; Lackowski, M.; Jaworek, A. Capacitance sensor for measuring void fraction in small channels. Measurement 2021, 175, 109046. [CrossRef] He, D.; Chen, S.; Bai, B. Void fraction measurement of stratified gas-liquid flow based on multi-wire capacitance probe. Exp. Therm. Fluid Sci. 2019, 102, 61–73. [CrossRef] Zych, M.; Petryka, L.; K ˛epi ´nski, J.; Hanus, R.; Bujak, T.; Puskarczyk, E. Radioisotope investigations of compound two-phase flows in an open channel. Flow Meas. Instrum. 2014, 35, 11–15. [CrossRef] Chen, X.; Zheng, J.; Jiang, J.; Peng, H.; Luo, Y.; Zhang, L. Numerical Simulation and Experimental Study of a Multistage Multiphase Separation System. Separations 2022, 9, 405. [CrossRef] Rushd, S.; Gazder, U.; Qureshi, H.J.; Arifuzzaman, M. Advanced Machine Learning Applications to Viscous Oil-Water Mul-tiPhase Flow. Appl. Sci. 2022, 12, 4871. [CrossRef] Veisi, A.; Shahsavari, M.H.; Roshani, G.H.; Eftekhari-Zadeh, E.; Nazemi, E. Experimental Study of Void Fraction Measurement Using a Capacitance-Based Sensor and ANN in Two-Phase Annular Regimes for Different Fluids. Axioms 2023, 12, 66. [CrossRef] Mao, Z.; Peng, Y.; Hu, C.; Ding, R.; Yamada, Y.; Maeda, S. Soft computing-based predictive modeling of flexible electrohydrodynamic pumps. Biomim. Intell. Robot. 2023, 3, 100114. [CrossRef] Mao, Z.; Iizuka, T.; Maeda, S. Bidirectional electrohydrodynamic pump with high symmetrical performance and its application to a tube actuator. Sens. Actuators A Phys. 2021, 332, 113168. [CrossRef] Peng, Y.; Yamaguchi, H.; Funabora, Y.; Doki, S. Modeling Fabric-Type Actuator Using Point Clouds by Deep Learning. IEEE Access 2022, 10, 94363–94375. [CrossRef] Fernandez, D.P.; Mulev, Y.; Goodwin, A.R.H.; Levelt-Sengers, J.M.H. A formulation for the static permittivity of water and steam at temperatures from 238 K to 873 K at pressures up to 1200 MPa, including derivatives and Debye–Hückel coefficients. J. Phy. Chem. Ref. Data 1995, 24, 33. [CrossRef] Quist, A.S.; Marshall, W.L.J. Estimation of the Dielectric Constant of Water to 800. Phy. Chem. 1965, 9, 3165. [CrossRef] Fernandez, D.P.; Goodwin, A.R.H.; Lemmon, E.W.; Levelt-Sengers, J.M.H.; Williams, R.C.J. A Formulation for the Static Permittivity of Water and Steam at Temperatures from 238 K to 873 K at Pressures up to 1200 MPa, Including Derivatives and Debye–ückel Coefficients. Phy. Chem. Ref. Data 1997, 26, 1125. Chen, T.-C.; Alizadeh, S.M.; Alanazi, A.K.; Guerrero, J.W.G.; Abo-Dief, H.M.; Eftekhari-Zadeh, E.; Fouladinia, F. Using ANN and Combined Capacitive Sensors to Predict the Void Fraction for a Two-Phase Homogeneous Fluid Independent of the Liquid Phase Type. Processes 2023, 11, 940. [CrossRef] Syah, R.B.Y.; Veisi, A.; Hasibuan, Z.A.; Al-Fayoumi, M.A.; Daoud, M.S.; Eftekhari-Zadeh, E. A Novel Smart Optimized Capacitance-Based Sensor for Annular Two-Phase Flow Metering With High Sensitivity. IEEE Access 2023, 11, 60709–60716. [CrossRef] Mayet, A.M.; Ilyinichna, G.E.; Fouladinia, F.; Daoud, M.S.; Ijyas, V.T.; Shukla, N.K.; Habeeb, M.S.; Alhashim, H.H. An artificial neural network and a combined capacitive sensor for measuring the void fraction independent of temperature and pressure changes for a two-phase homogeneous fluid. Flow Meas. Instrum. 2023, 93, 102406. [CrossRef] Iliyasu, A.M.; Fouladinia, F.; Salama, A.S.; Roshani, G.H.; Hirota, K. Intelligent Measurement of Void Fractions in Homo-geneous Regime of Two-Phase Flows Independent of the Liquid Phase Density Changes. Fractal Fract. 2023, 7, 179. [CrossRef] Dong, J.; Hu, S. The progress and prospects of neural network research. Inf. Control. 1997, 26, 360–368. Jenkins, B.K.; Tanguay, A.R. Handbook of Neural Computing and Neural Networks; MIT Press: Boston, MA, USA, 1995. Bnlsabi, A. Some analytical solutions to the general approximation problem for feed forward neural networks. Neural Netw. 1993, 6, 991–996 Luo, Z.H.; Xie, Y.; Zhu, C. The study of convergence of CMAC learning process. Acta Autom. Sin. 1997, 23, 455–461 Daneshfar, F.; Jamshidi, M. An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse. Neural Netw. 2023, 163, 108–121. [CrossRef] Shafiei, A.; Jamshidi, M.; Khani, F.; Talla, J.; Peroutka, Z.; Gantassi, R.; Baz, M.; Cheikhrouhou, O.; Hamam, H. A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing. Math. Probl. Eng. 2021, 2021, 1–14. [CrossRef] Jamshidi, M.; Yahya, S.I.; Roshani, S.; Chaudhary, M.A.; Ghadi, Y.Y.; Roshani, S. A Fast Surrogate Model-Based Algorithm Using Multilayer Perceptron Neural Networks for Microwave Circuit Design. Algorithms 2023, 16, 324. [CrossRef] Roshani, G.; Feghhi, S.; Mahmoudi-Aznaveh, A.; Nazemi, E.; Adineh-Vand, A. Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement 2014, 51, 34–41. [CrossRef] Gallant, A.R.; White, H. On learning the derivatives of an unknown mapping with multilayer feedforward networks. Neural Netw. 1992, 5, 129–138. [CrossRef] Salgado, C.M.; Brandão, L.E.; Schirru, R.; Pereira, C.M.; da Silva, A.X.; Ramos, R. Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. Appl. Radiat. Isot. 2009, 67, 1812–1818. [CrossRef] [PubMed] Levenberg, K. A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 1944, 2, 164–168. [CrossRef] Marquardt, D.W. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J. Soc. Ind. Appl. Math. 1963, 11, 431–441. [CrossRef] |
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Atribución 4.0 Internacional (CC BY 4.0)© 2023 by the authors.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aiesh Qaisi, Ramy MohammedFouladinia, FarhadMohammad Mayet, AbdulilahGrimaldo Guerrero, John WilliamLoukil, HassenRamkumar Raja, M.Abdul Muqeet, MohammedEftekhari-Zadeh, Ehsan2025-04-04T16:29:47Z2025-04-04T16:29:47Z2023-08-05Qaisi, R.M.A.; Fouladinia, F.; Mayet, A.M.; Guerrero, J.W.G.; Loukil, H.; Raja, M.R.; Muqeet, M.A.; Eftekhari-Zadeh, E. Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN. Sensors 2023, 23, 6959. https:// doi.org/10.3390/s231569591424-8220https://hdl.handle.net/11323/1409110.3390/s23156959Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of methods exist for measuring the void fraction, and the most popular is capacitance-based sensors. Aside from being easy to use, the capacitance-based sensor does not need any separation or interruption to measure the void fraction. In addition, in the contemporary era, thanks to Artificial Neural Networks (ANN), measurement methods have become much more accurate. The same can be said for capacitance-based sensors. In this paper, a new metering system utilizing an 8-electrode sensor and a Multilayer Perceptron network (MLP) is presented to predict an air and water volume fractions in a homogeneous fluid. Some characteristics, such as temperature, pressure, etc., can have an impact on the results obtained from the aforementioned sensor. Thus, considering temperature changes, the proposed network predicts the void fraction independent of pressure variations. All simulations were performed using the COMSOL Multiphysics software for temperature changes from 275 to 370 degrees Kelvin. In addition, a range of 1 to 500 Bars, was considered for the pressure. The proposed network has inputs obtained from the mentioned software, along with the temperature. The only output belongs to the predicted void fraction, which has a low MAE equal to 0.38. Thus, based on the obtained result, it can be said that the proposed network precisely measures the amount of the void fraction.15 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerlandhttps://www.mdpi.com/1424-8220/23/15/6959/notesIntelligent measuring of the volume fraction considering temperature changes and independent pressure variations for a two-phase homogeneous fluid using an 8-electrode sensor and an ANNArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85SensorsKarimi, H.; Boostani, M. Heat transfer measurements for oil–water flow of different flow patterns in a horizontal pipe. Exp. Therm. Fluid Sci. 2016, 75, 35–42. [CrossRef]Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Setayeshi, S.; Zadeh, E.E.; Fatehi, A. Optimization of a method for iden-tifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. Int. J. Hydrog. Energy 2016, 41, 7438–7444. [CrossRef]Steven, R.N. Wet gas metering with a horizontally mounted Venturi meter. Flow Meas. Instrum. 2002, 12, 361–372. [CrossRef]Wang, D.; Liang, F.; Peng, Z.; Wang, Y.; Lin, Z. Gas–liquid two-phase flow measurements by full stream batch sampling. Int. J. Multiph. Flow 2012, 40, 113–125. [CrossRef]Banowski, M.; Beyer, M.; Szalinski, L.; Lucas, D.; Hampel, U. Comparative study of ultrafast X-ray tomography and wire-mesh sensors for vertical gas–liquid pipe flows. Flow Meas. Instrum. 2017, 53, 95–106. [CrossRef]Salgado, C.M.; Dam, R.S.; Puertas, E.J.; Salgado, W.L. Calculation of volume fractions regardless scale deposition in the oil industry pipelines using feed-forward multilayer perceptron artificial neural network and MCNP6 code. Appl. Radiat. Isot. 2022, 185, 110215. 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[CrossRef]15115238-electrode sensorMeasuringTemperaturePressureArtificial intelligenceAir-water homogenous regimePublicationORIGINALIntelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN.pdfIntelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN.pdfapplication/pdf4378926https://repositorio.cuc.edu.co/bitstreams/042651e0-ff2e-4199-af3a-9d3f98d4af4a/download9b641e43762832d79bfcd7edf97fb397MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/f18ac581-e2ff-4e46-b939-5d03fc22b93c/download73a5432e0b76442b22b026844140d683MD52TEXTIntelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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