论文标题
工程系统的混合机器学习建模 - 在多相流建模案例研究上测试的概率观点
Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study
论文作者
论文摘要
为了以安全可靠的方式运行流程工程系统，在决策中经常使用预测模型。在许多情况下，这些是机械的第一原理模型，旨在准确描述该过程。实际上，这些模型的参数需要调整为手头的过程条件。如果情况在实践中很常见，则该模型将变得不准确，需要重新调整。在本文中，我们提出了一个混合建模机学习框架，该框架允许使用两种不同类型的贝叶斯神经网络来调整第一原理模型。我们的方法不仅估计了第一原理模型参数的预期值，而且还量化了这些估计值的不确定性。这种混合机器学习建模的方法尚未在文献中得到很好的描述，因此我们认为本文将提供一个额外的角度，可以考虑可以考虑物理系统的混合机学习建模。例如，我们选择了一个多相管道流动过程，我们为其构建了基于漂流方法的三相稳态模型，该模型可用于建模带有或没有神经网络调整的石油和天然气生产系统中的管道和井流动行为。在仿真结果中，我们显示了如何使用混合模型的不确定性估计来做出更好的操作决策。
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.
