Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

• Published in: Expert systems 2019-06, Vol.36 (3), p.n/a
• Main Authors: Khayat, Omid, Afarideh, Hossein
• Format: Article
• Language: English
• Subjects: adaptive neuro‐fuzzy inference system; Artificial neural networks; Back propagation; Basis functions; Clustering; Flow measurement; flow regime; Fuzzy logic; Fuzzy systems; Inference; Iterative methods; Linear functions; Machine learning; Mathematical models; Measurement; Metering; Microprocessors; multilayer perceptron; Multilayer perceptrons; Multiphase flow; multiphase flow measurement; Neural networks; Parameter identification; Performance evaluation; Prediction models; Radial basis function; Void fraction; water–liquid ratio; Wavelet analysis; wavelet transform
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.12386

Application of predictive models in industrial multiphase flow metering has attracted an increasing attention recently. Void fraction (VF), water–liquid ratio (WLR), and flow regime are key parameters, measured by oil/water/gas multiphase flow metres (MPFM) in petroleum industry. Artificial neural networks and fuzzy inference systems (FIS) are reliable and efficient computational models, which can be simply implemented on microprocessors of MPFMs, having the advantages of trainability, adaptability, and capability to model non‐linear functions. In this paper, a wavelet‐based adaptive neuro‐FIS (WANFIS) is introduced and validated by the prediction of multiphase flow measurement critical parameters including flow regime, VF, and WLR. The performance of the proposed WANFIS model is then compared with multilayer perceptron (MLP), radial basis function (RBF) network, and an FIS trained by fuzzy c‐means and a subtractive clustering method in the prediction of flow parameters in a customized designed structure of oil/water/gas MPFM. Structural parameters of all predictive models are first optimized to yield the most efficient structure for the available dataset. Comparison is then made between the optimized predictive models, in terms of mean squared error of parameter prediction, computation time, and repeatability of the prediction process. According to the obtained results, MLP model using Levenberg–Marquardt training algorithm and WANFIS model using gradient‐based back propagation dynamical iterative learning algorithm are the most efficient models, which give the best performance compared with other used models. All predictive models can predict the flow regime with 100% accuracy, whereas the highest inaccuracy is related to the prediction of WLR. The results of this study can be used to select and develop the most appropriate predictive model applicable in predicting and identifying flow measurement parameters in industrial MPFMs.