Using ANN and Combined Capacitive Sensors to Predict the Void Fraction for a Two-Phase Homogeneous Fluid Independent of the Liquid Phase Type
Abstract
:1. Introduction
2. Validation and Simulations
2.1. Designing and Simulation of a Concave Sensor in COMSOL Multiphysics
2.2. Designing and Simulation of a Ring Sensor in COMSOL Multiphysics
3. Artificial Neural Network
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Amount |
---|---|
The maximum element size | 0.7 cm |
The minimum element size | 0.03 cm |
The maximum element growth rate | 1.35 |
The curvature factor | 0.3 |
The resolution of narrow areas | 0.85 |
Liquid Phase Name | Void Fraction | εr | The Simulated Results of the Concave Sensor (pF) | The Simulated Results of the Ring Sensor (pF) |
---|---|---|---|---|
Crude oil | 0.00 | 1.000 | 9.337 | 5.979 |
Crude oil | 0.05 | 1.050 | 9.421 | 6.006 |
Crude oil | 0.10 | 1.100 | 9.504 | 6.033 |
Crude oil | 0.15 | 1.150 | 9.587 | 6.060 |
Crude oil | 0.20 | 1.200 | 9.669 | 6.086 |
Crude oil | 0.25 | 1.250 | 9.750 | 6.113 |
Crude oil | 0.30 | 1.300 | 9.830 | 6.139 |
Crude oil | 0.35 | 1.350 | 9.910 | 6.165 |
Crude oil | 0.40 | 1.400 | 9.989 | 6.191 |
Crude oil | 0.45 | 1.450 | 10.068 | 6.217 |
Crude oil | 0.50 | 1.500 | 10.145 | 6.243 |
Crude oil | 0.55 | 1.550 | 10.222 | 6.269 |
Crude oil | 0.60 | 1.600 | 10.299 | 6.294 |
Crude oil | 0.65 | 1.650 | 10.374 | 6.320 |
Crude oil | 0.70 | 1.700 | 10.450 | 6.345 |
Crude oil | 0.75 | 1.750 | 10.524 | 6.370 |
Crude oil | 0.80 | 1.800 | 10.598 | 6.395 |
Crude oil | 0.85 | 1.850 | 10.671 | 6.420 |
Crude oil | 0.90 | 1.900 | 10.744 | 6.445 |
Crude oil | 0.95 | 1.950 | 10.816 | 6.469 |
Crude oil | 1.00 | 2.000 | 10.888 | 6.494 |
Oil | 0.00 | 1.000 | 9.337 | 5.979 |
Oil | 0.05 | 1.060 | 9.438 | 6.011 |
Oil | 0.10 | 1.120 | 9.538 | 6.044 |
Oil | 0.15 | 1.180 | 9.636 | 6.076 |
Oil | 0.20 | 1.240 | 9.764 | 6.108 |
Oil | 0.25 | 1.300 | 9.830 | 6.139 |
Oil | 0.30 | 1.360 | 9.926 | 6.171 |
Oil | 0.35 | 1.420 | 10.021 | 6.202 |
Oil | 0.40 | 1.480 | 10.114 | 6.233 |
Oil | 0.45 | 1.540 | 10.207 | 6.264 |
Oil | 0.50 | 1.600 | 10.299 | 6.267 |
Oil | 0.55 | 1.660 | 10.390 | 6.325 |
Oil | 0.60 | 1.720 | 10.479 | 6.355 |
Oil | 0.65 | 1.780 | 10.569 | 6.385 |
Oil | 0.70 | 1.840 | 10.657 | 6.415 |
Oil | 0.75 | 1.900 | 10.744 | 6.445 |
Oil | 0.80 | 1.960 | 10.831 | 6.474 |
Oil | 0.85 | 2.020 | 10.916 | 6.503 |
Oil | 0.90 | 2.080 | 11.001 | 6.533 |
Oil | 0.95 | 2.140 | 11.086 | 6.562 |
Oil | 1.00 | 2.200 | 11.169 | 6.590 |
Gasoil | 0.00 | 1.000 | 9.337 | 5.979 |
Gasoil | 0.05 | 1.070 | 9.455 | 6.017 |
Gasoil | 0.10 | 1.140 | 9.570 | 6.054 |
Gasoil | 0.15 | 1.210 | 9.685 | 6.092 |
Gasoil | 0.20 | 1.280 | 9.798 | 6.129 |
Gasoil | 0.25 | 1.350 | 9.910 | 6.165 |
Gasoil | 0.30 | 1.420 | 10.021 | 6.202 |
Gasoil | 0.35 | 1.490 | 10.130 | 6.238 |
Gasoil | 0.40 | 1.560 | 10.238 | 6.274 |
Gasoil | 0.45 | 1.630 | 10.344 | 6.310 |
Gasoil | 0.50 | 1.700 | 10.450 | 6.345 |
Gasoil | 0.55 | 1.770 | 10.554 | 6.380 |
Gasoil | 0.60 | 1.840 | 10.657 | 6.415 |
Gasoil | 0.65 | 1.910 | 10.759 | 6.450 |
Gasoil | 0.70 | 1.980 | 10.859 | 6.484 |
Gasoil | 0.75 | 2.050 | 10.959 | 6.518 |
Gasoil | 0.80 | 2.120 | 11.058 | 6.552 |
Gasoil | 0.85 | 2.190 | 11.155 | 6.586 |
Gasoil | 0.90 | 2.260 | 11.252 | 6.619 |
Gasoil | 0.95 | 2.360 | 11.347 | 6.652 |
Gasoil | 1.00 | 2.400 | 11.441 | 6.685 |
Gasoline | 0.00 | 1.000 | 9.337 | 5.979 |
Gasoline | 0.05 | 1.085 | 9.480 | 6.025 |
Gasoline | 0.10 | 1.170 | 9.620 | 6.070 |
Gasoline | 0.15 | 1.255 | 9.758 | 6.115 |
Gasoline | 0.20 | 1.340 | 9.894 | 6.160 |
Gasoline | 0.25 | 1.425 | 10.029 | 6.204 |
Gasoline | 0.30 | 1.510 | 10.161 | 6.248 |
Gasoline | 0.35 | 1.595 | 10.291 | 6.292 |
Gasoline | 0.40 | 1.680 | 10.420 | 6.335 |
Gasoline | 0.45 | 1.765 | 10.546 | 6.378 |
Gasoline | 0.50 | 1.850 | 10.671 | 6.420 |
Gasoline | 0.55 | 1.935 | 10.795 | 6.462 |
Gasoline | 0.60 | 2.020 | 10.916 | 6.503 |
Gasoline | 0.65 | 2.105 | 11.037 | 6.545 |
Gasoline | 0.70 | 2.190 | 11.155 | 6.586 |
Gasoline | 0.75 | 2.275 | 11.272 | 6.626 |
Gasoline | 0.80 | 2.360 | 11.388 | 6.666 |
Gasoline | 0.85 | 2.445 | 11.502 | 6.706 |
Gasoline | 0.90 | 2.530 | 11.614 | 6.746 |
Gasoline | 0.95 | 2.615 | 11.726 | 6.785 |
Gasoline | 1.00 | 2.700 | 11.835 | 6.824 |
Water | 0.00 | 1.000 | 9.337 | 5.979 |
Water | 0.05 | 5.000 | 14.381 | 7.786 |
Water | 0.10 | 9.000 | 17.492 | 9.162 |
Water | 0.15 | 13.000 | 19.643 | 10.308 |
Water | 0.20 | 17.000 | 21.226 | 11.300 |
Water | 0.25 | 21.000 | 22.444 | 12.181 |
Water | 0.30 | 25.000 | 23.411 | 12.973 |
Water | 0.35 | 29.000 | 24.197 | 13.694 |
Water | 0.40 | 33.000 | 24.849 | 14.355 |
Water | 0.45 | 37.000 | 25.399 | 14.964 |
Water | 0.50 | 41.000 | 25.869 | 15.529 |
Water | 0.55 | 45.000 | 26.276 | 16.054 |
Water | 0.60 | 49.000 | 26.631 | 16.544 |
Water | 0.65 | 53.000 | 26.943 | 17.002 |
Water | 0.70 | 57.000 | 27.221 | 17.433 |
Water | 0.75 | 61.000 | 27.469 | 17.837 |
Water | 0.80 | 65.000 | 27.692 | 18.219 |
Water | 0.85 | 69.000 | 27.893 | 18.580 |
Water | 0.90 | 73.000 | 28.077 | 18.921 |
Water | 0.95 | 77.000 | 28.244 | 19.244 |
Water | 1.00 | 81.000 | 28.397 | 19.551 |
Neural Network | MLP |
---|---|
Neurons in the input layer | 2 |
Neurons in the hidden layer | 6 |
Neurons in the output layer | 1 |
Number of epochs | 400 |
Activation function of neurons in hidden layers | Tansig |
Activation function of neurons in input and output layers | Purelin |
Method of training | Levenberg–Marquardt [56,57] |
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Chen, T.-C.; Alizadeh, S.M.; Alanazi, A.K.; Grimaldo Guerrero, J.W.; 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. https://doi.org/10.3390/pr11030940
Chen T-C, Alizadeh SM, Alanazi AK, Grimaldo Guerrero JW, Abo-Dief HM, 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(3):940. https://doi.org/10.3390/pr11030940
Chicago/Turabian StyleChen, Tzu-Chia, Seyed Mehdi Alizadeh, Abdullah K. Alanazi, John William Grimaldo Guerrero, Hala M. Abo-Dief, Ehsan Eftekhari-Zadeh, and Farhad Fouladinia. 2023. "Using ANN and Combined Capacitive Sensors to Predict the Void Fraction for a Two-Phase Homogeneous Fluid Independent of the Liquid Phase Type" Processes 11, no. 3: 940. https://doi.org/10.3390/pr11030940