Document Type : Research Article
Authors
- Gholam Hossein Roshani ^{1}
- Alimohammad Karami ^{2}
- Ehsan Nazemi ^{3}
- Cesar Marques Salgado ^{} ^{4}
^{1} Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran
^{2} Mechanical Engineering Department, Razi University, Kermanshah, Iran
^{3} Nuclear Science and Technology Research Institute, Tehran, Iran
^{4} Instituto de Engenharia Nuclear, CNEN/IEN, P.O. Box 68550, 21945-970 Rio de Janeiro, Brazil
Abstract
The used metering technique in this study is based on the dual energy (Am-241 and Cs-137) gamma ray attenuation. Two transmitted NaI detectors in the best orientation were used and four features were extracted and applied to the model. This paper highlights the application of Adaptive Neuro-fuzzy Inference System (ANFIS) for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems. In fact, the aim of the current study is to recognize the flow regimes based on dual energy broad-beam gamma-ray attenuation technique using ANFIS. In this study, ANFIS is used to classify the flow regimes (annular, stratified, and homogenous) and predict the value of volume fractions. To start modeling, sufficient data are gathered. Here, data are generated numerically using MCNPX code. In the next step, ANFIS must be trained.
According to the modeling results, the proposed ANFIS can correctly recognize all the three different flow regimes, and other ANFIS networks can determine volume fractions with MRE of less than 2% according to the recognized regime, which shows that ANFIS can predict the results precisely.
Highlights
- The MCNPX code provided data for training of ANFIS has been used.
- The methodology is based on dual energy broad-beam gamma-ray attenuation.
- ANFIS is used to classify the flow regimes and predict the volume fractions in multiphase systems.
- Water, gas and oil percentages in three flow regimes are obtained precisely with two detectors.
Keywords
Abouelwafa, M. and Kendall, E. (1980). The measurement of component ratios in multiphase systems using alpha-ray attenuation. Journal of Physics E: Scientific Instruments, 13(3):341.
Aghakhani, M., Ghaderi, M., Karami, A., et al. (2014). Combined effect of TiO2 nanoparticles and input welding parameters on the weld bead penetration in submerged arc welding process using fuzzy logic. The International Journal of Advanced Manufacturing Technology, 70(1-4):63–72.
Aghakhani, M., Jalilian, M. M., and Karami, A. (2012). Prediction of weld bead dilution in GMAW process using fuzzy logic. In Applied Mechanics and Materials, volume 110, pages 3171–3175. Trans Tech Publ.
Amiri, A., Karami, A., Yousefi, T., and Zanjani, M. (2012). Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders. Polish Journal of Chemical Technology, 14(4):46–52.
Bishop, C. M. and James, G. D. (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 327(2-3):580–593.
Gulley, N. and Jang, J.-S. R. (1995). Fuzzy logic toolbox user’s guide. The MathWorks, Inc, 24.
Jang, J., Sun, C., and Mizutani, E. (1997). Neuro-fuzzy and soft computing, (1997). PTR Prentice Hall.
Jang, J.-S. and Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3):378–406.
Karami, A., Akbari, E., Rezaei, E., et al. (2013a). Neurofuzzy modeling of the free convection from vertical arrays of isothermal cylinders. Journal of Thermophysics and Heat Transfer, 27(3):588–592.
Karami, A., Rezaei, E., Rahimi, M., et al. (2013b). Modeling of heat transfer in an air cooler equipped with classic twisted tape inserts using adaptive neuro-fuzzy inference system. Chemical Engineering Communications, 200(4):532–542.
Karami, A., Rezaei, E., Shahhosseni, M., et al. (2012). Fuzzy logic to predict the heat transfer in an air cooler equipped with different tube inserts. International Journal of Thermal Sciences, 53:141–147.
Karami, A., Yousefi, T., Ebrahimi, S., et al. (2013c). Adaptive neuro-fuzzy inference system (ANFIS) to predict the forced convection heat transfer from a v-shaped plate. Heat and Mass Transfer, 49(6):789–798.
Karami, A., Yousefi, T., Harsini, I., et al. (2015). Neurofuzzy modeling of the free convection heat transfer from a wavy surface. Heat Transfer Engineering, 36(9):847–855.
Karami, A., Yousefi, T., Mohebbi, S., et al. (2014). Prediction of free convection from vertical and inclined rows of horizontal isothermal cylinders using ANFIS. Arabian Journal for Science and Engineering, 39(5):4201–4209.
Karami, A., Yousefi, T., Rezaei, E., et al. (2016). Modeling of the free convection heat transfer from an isothermal horizontal cylinder in a vertical channel via the fuzzy logic. The International Journal of Multiphysics, 6(1).
Khorsandi, M., Feghhi, S., Salehizadeh, A., et al. (2013). Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network. Radiation Measurements, 59:183–187.
Nazemi, E., Roshani, G., Feghhi, S., et al. (2016). Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. International Journal of Hydrogen Energy, 41(18):7438–7444.
Pelowitz, D. B. et al. (2005). MCNPXTM user’s manual. Los Alamos National Laboratory, Los Alamos.
Rezaei, E., Karami, A., Yousefi, T., et al. (2012). Modeling the free convection heat transfer in a partitioned cavity using ANFIS. International Communications in Heat and Mass Transfer, 39(3):470–475.
Roshani, G., Feghhi, S., Mahmoudi-Aznaveh, A., et al. (2014). Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement, 51:34–41.
Roshani, G., Feghhi, S., and Setayeshi, S. (2015). Dual modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network. Radiation Measurements, 82:154–162.
Roshani, G., Nazemi, E., and Roshani, M. (2017a). Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Computing and Applications, 28(1):1265–1274.
Roshani, G., Nazemi, E., and Roshani, M. (2017b). Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement, 100:122–130.
Salgado, C. M., Brand˜ao, L. E., Schirru, R., et al. (2009). Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. Applied Radiation and Isotopes, 67(10):1812–1818.
Salgado, C. M., Pereira, C. M., Schirru, R., et al. (2010). Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks. Progress in Nuclear Energy, 52(6):555–562.
Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, (1):116–132.
Thorn, R., Johansen, G., and Hammer, E. (1997). Recent developments in three-phase flow measurement. Measurement Science and Technology, 8(7):691.
Yousefi, T., Karami, A., Rezaei, E., et al. (2012). Fuzzy modeling of the forced convection heat transfer from a V-shaped plate exposed to an air slot jet. Heat Transfer–Asian Research, 41(5):430–443.