Machine Learning-Based Predictive Modeling of Infrared Spectroscopic Data from Thermal Conversion of Athabasca Bitumen.

ABSTRACT: This study explores the use of machine learning (ML) techniques to predict Fourier-transform infrared (FTIR) intensities of products from the thermal cracking of Athabasca bitumen, aiming to develop a reliable soft-sensor. The ultimate goal is to obtain the FTIR spectra of the thermally cracked products online to reduce process time from slow physical measurements. Various ML models, including Linear Regression (LinR), partial least squares regression (PLSR), support vector regression (SVR), K-nearest neighbors (k-NN), random forest (RF), and gradient boosting regression (GBR), were implemented to enhance the predictive accuracy and efficiency of FTIR spectroscopy, aiming to reduce the need for traditional physical measurements which are often slow compared to the rapid predictions

SUBMITTER: Mansoori NA 

PROVIDER: S-EPMC12268744 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature