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

Abstract

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.

A data-driven multi-criteria decision-making approach for assessing new product conceptual designs

“…For more information on this method, the readers can refer to Sharafi et al, 37 Das et al 38 and Khayat and Afarideh. 39…”

“…In the second stage, the weight of output neurons is set by utilizing a delta rule. 41 For a better understanding of the RBF method, the studies of Vahdani et al, 42 Nguyen et al, 43 and Khayat and Afarideh 39 are recommended.…”

“…For more information on this method, the readers can refer to Sharafi et al, 37 Das et al 38 and Khayat and Afarideh. 39 The RBF method was introduced by Broomhead and Lowe, 40 in which three layers, including input, hidden, and output layers, form this model of ANN. The structure of the RBF method is more straightforward compared to the MLP method, which results in a faster learning process.…”

Solving predictive control problem of fast‐varying multivariable systems by incorporating unknown active dynamics generated by real‐time adaptive learning machine

“…Recent advances in identification algorithms have led to incorporation of fuzzy logic and wavelet analysis into neural‐networks, benefitting from inference capabilities of fuzzy systems and localization characteristics of wavelet analysis in order to speed up the procedure of model generation. Fuzzy‐wavelet neural network (FWNN) models boast superior estimation qualities (Hsu, 2011; Khayat & Afarideh, 2019; Lu, 2011). However, the problem of ensuring closed‐loop stability and preventing performance deterioration during the convergence period at each discontinuity should still be addressed accordingly.…”