Self-Modeling Multivariate Curve Resolution Model for Online Monitoring of Bitumen Conversion Using Infrared Spectroscopy

Abstract

For the efficient real-time monitoring of reaction chemistry in a complex mixture using online spectroscopy, it is essential to develop a mathematical tool that can automatically resolve the spectra so that either the spectral or the concentration profile of the changing species can be tracked easily. While self-modeling multivariate curve resolution (SMCR) is a well-suited tool when initial profiles are known beforehand, it is not straightforward to use when dealing with complex mixtures. In this study, a multivariate data analysis algorithm was designed for use with online infrared spectroscopy to provide an instant best estimate of the reaction chemistry of a complex mixture with no additional user input. The investigated process is thermal conversion of oil sands bitumen, and the study employed 43 infrared spectra from samples, collected offline, of products treated at different temperatures and time periods. The resolved spectral and concentration profiles can be used to understand the reaction mechanism of the system. In addition to the concentration and spectral profile, simple parameters were devised to monitor the changes in the key regions of the spectral profiles. In general, the results described the possible reaction mechanism of the investigated system and were consistent with other experimental findings in the literature. Computationally, the algorithm requires only a few seconds to converge and is therefore suitable for online monitoring.

Chemoinformatic Investigation of the Chemistry of Cellulose and Lignin Derivatives in Hydrous Pyrolysis

“…Figure shows the pseudoconcentrations of the pseudocomponents for the different experimental runs, and it is clear that these concentrations change with different experimental conditions. In a continuously operating process, these concentration signatures could be used with pseudokinetics , derived from the BNs for on-line monitoring of the process.…”

“…While there is a long history of applying chemometric methods in the interpretation and analysis of spectroscopic data, with principal component analysis (PCA) being the most common method, , we focus on methods that aim to deconvolve the spectra into different pseudocomponents or to group elements of the spectra. We refer to our previous work for details of our implementation of these methods: self-modeling multivariate curve resolution (SMCR) and Bayesian hierarchical clustering and only provide short descriptions here.…”

“…The main contributions of this work are the validation of our previously developed , methods based on self-modeling multivariate curve resolution (SMCR) and Bayesian hierarchical clustering (BHC) for pseudocomponent identification on the better-understood chemistry of the pyrolysis of model compounds representative of biomass (we have previously demonstrated the application of these methods on the partial upgrading of bitumen), the extension of the SMCR method to develop candidate reaction networks based on its pseudocomponents, and the development of a methodology to hypothesize reaction networks (using the Bayesian network (BN) methodology based on SMCR and BHC-generated pseudocomponents) by fusing data from multiple sources. We demonstrate the chemical feasibility of the data-driven hypotheses generated for reaction networks and also highlight the impact of data fusion/combination on modifying these hypotheses.…”

Data‐driven hypotheses of reaction networks for thermochemical conversion of a physical mixture of levoglucosan and 2‐phenoxyethyl benzene

“…The methods used in this work have been explained in detail in our previous work [1,9,10] ; consequently, we provide only brief descriptions of the specific techniques used here. Note that the headings of the subsections provide both the names of the methods and their purpose.…”

A Perspective on the Impact of Process Systems Engineering on Reaction Engineering

“…Curve resolution in spectral data is a factor analytical decomposition that works by resolving the data into concentration and spectral profiles either bilinearly or multilinearly using the self-modeling multivariate curve resolution–alternating least-squares (SMCR-ALS) , and the parallel factor analysis (PARAFAC) , models, respectively. The initial estimates for the decision variables can be obtained by evolving factor analysis (EFA) on a row-wise augmented data matrix both in the forward and backward directions if the data has an intrinsic order or by use of a global search technique in the feasible space using particle swarm optimization (PSO) .…”