Prediction of melter cold‐cap topology from plenum temperatures with computational fluid dynamics and machine learning

Abstract

A computational fluid dynamics (CFD) model of a pilot‐scale waste‐glass melter was used to generate input data for several different machine‐learning models to predict the cold‐cap coverage from plenum temperatures. This methodology could serve as useful to provide nonvisual feedback for operational control. The machine‐learning models tested include an artificial neural network (ANN), a convolutional neural network (CNN), a random forest (RF) algorithm, and a support vector machine (SVM) method. CFD simulations with randomized cold‐cap coverage were run to generate plenum‐temperature distributions. The topology of the cold cap was predicted with each method using an input layer of selected temperature locations. The ANN was used to predict the cold‐cap coverage percentage with an accuracy of 1.2%. The accuracy of the various machine‐learning algorithms was dependent on the filter resolution employed. When using a low resolution (16‐cm filter), the CNN and ANN methods produced the best accuracy, with errors of 6.98% and 6.58%, respectively. At finer levels of resolution (4‐cm filter), the RF method produced the best accuracy, with an error of 8.12%. The ANN and SVM methods required less computational time to train than either the CNN or RF methods.

Young’s Modulus of Calcium-Alumino-Silicate Glasses: Insight From Machine Learning

“…In addition to traditional experimental and simulation techniques, deep learning (DL) and machine learning (ML) have emerged as a popular method for predicting the mechanical properties of glasses [10], [11], [12]. Particularly when compared to Molecular Dynamics (MD) simulations, ML and DL methods offer accurate results albeit with significantly reduced computational time [13].…”