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International Journal of Statistics and Data Science

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ArticleOpen Access http://dx.doi.org/10.26855/ijsds.2026.06.003

Wisdom of Neural Committees: A Synthetic Benchmark Study on Robust Ensemble Prediction of Two-Phase Flow Pressure Drop from Basic Fluid Properties

Mohammad Yaghoub Abdollahzadeh Jamalabadi

Department of Mechanical Engineering, Chabahar Maritime University, Chabahar 99717, Iran.

*Corresponding author: Mohammad Yaghoub Abdollahzadeh Jamalabadi

Published: June 2,2026

Abstract

Single neural networks often suffer from high variance, overfitting, and sensitivity to irrelevant inputs when trained on limited or noisy process data. This study introduces a committee neural network (CNN) ensemble that fuses three architecturally distinct base learners—multilayer perceptron (MLP), cascade forward network (CFN), and general regression neural network (GRNN)—to deliver robust, statistically stable predictions of two-phase pressure drop from five readily available fluid properties. A synthetic dataset of 800 samples, generated from an artificial benchmark function with 5% proportional Gaussian noise, provides a controlled testbed where the true input-output relationship is known. The committee aggregates the member networks via a fixed inverse-error weighting scheme, with the weights determined solely on the training data to avoid data leakage. On a held-out test set, the CNN achieves a test average absolute relative deviation (AARD) of 12.9% and a coefficient of determination (R2) of 0.912, outperforming the best single model (MLP, test AARD 16.4%) by a relative reduction of 21%. Residual analysis confirms that the ensemble substantially narrows prediction variance while preserving the base learners’ ability to disregard an intentionally irrelevant feature (surface tension). The entire workflow—data generation, normalization, training, and evaluation—is implemented in MATLAB, and the synthetic dataset generator is described to enable reproducibility. The results highlight that a simple, transparent weighted committee can serve as a practical and statistically principled tool for flow assurance, although extension to real experimental data is necessary to confirm field applicability.

Keywords

Two-phase flow; pressure drop; committee neural network; ensemble learn-ing; synthetic benchmark; artificial neural network

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How to cite this paper

Wisdom of Neural Committees: A Synthetic Benchmark Study on Robust Ensemble Prediction of Two-Phase Flow Pressure Drop from Basic Fluid Properties

How to cite this paper: Mohammad Yaghoub Abdollahzadeh Jamalabadi. (2026). Wisdom of Neural Committees: A Synthetic Benchmark Study on Robust Ensemble Prediction of Two-Phase Flow Pressure Drop from Basic Fluid Properties. International Journal of Statistics and Data Science2(1), 30-43.

DOI: http://dx.doi.org/10.26855/ijsds.2026.06.003