Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study

TL;DR

This paper introduces a hybrid machine learning framework using Bayesian Neural Networks to tune first principles models of engineering systems, exemplified by multiphase flow, with uncertainty quantification for improved decision-making.

Contribution

It presents a novel hybrid modeling approach that combines mechanistic models with Bayesian neural networks for parameter tuning and uncertainty estimation.

Findings

Uncertainty estimates improve operational decision-making.

Hybrid models accurately adapt to changing process conditions.

Bayesian neural networks effectively tune first principles models.

Abstract

To operate process engineering systems in a safe and reliable manner, predictive models are often used in decision making. In many cases, these are mechanistic first principles models which aim to accurately describe the process. In practice, the parameters of these models need to be tuned to the process conditions at hand. If the conditions change, which is common in practice, the model becomes inaccurate and needs to be re-tuned. In this paper, we propose a hybrid modeling machine learning framework that allows tuning first principles models to process conditions using two different types of Bayesian Neural Networks. Our approach not only estimates the expected values of the first principles model parameters but also quantifies the uncertainty of these estimates. Such an approach of hybrid machine learning modeling is not yet well described in the literature, so we believe this paper will provide an additional angle at which hybrid machine learning modeling of physical systems can be considered. As an example, we choose a multiphase pipe flow process for which we constructed a three-phase steady state model based on the drift-flux approach which can be used for modeling of pipe and well flow behavior in oil and gas production systems with or without the neural network tuning. In the simulation results, we show how uncertainty estimates of the resulting hybrid models can be used to make better operation decisions.