Increased interpretation of deep learning models using hierarchical cluster-based modelling.

Linear prediction models based on data with large inhomogeneity or abrupt non-linearities often perform poorly because relationships between groups in the data dominate the model. Given that the data is locally linear, this can be overcome by splitting the data into smaller clusters and creating a local model within each cluster. In this study, the previously published Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) procedure was extended to deep learning, in order to increase the interpretability of the deep learning models through local modelling.

Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data.

Crude oils are among the world's most complex organic mixtures containing a large number of unique components and many analytical techniques lack resolving power to characterize. Fourier transform ion cyclotron resonance mass spectrometry offers a high mass accuracy, making a detailed analysis of crude oils possible. Infrared (IR) spectroscopic methods such as Fourier transform IR spectroscopy (FT-IR) and near-IR, can also be used for crude oil characterization.

Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra.

The blockages of pipelines caused by agglomeration of gas hydrates is a major flow assurance issue in the oil and gas industry. Some crude oils form gas hydrates that remain as transportable particles in a slurry. It is commonly believed that naturally occurring components in those crude oils alter the surface properties of gas hydrate particles when formed.