Leveraging spectroscopic sensor measurements for development of models for reactions involving complex feedstocks
Creators: Srinivasan, Karthik
Publisher: name: University of Alberta Library
Year: 2024

<p>Chemically heterogeneous feedstocks are being increasingly used in process industries due to depletion of conventional feedstocks, to meet environmental demands and to recover<br>
value added products from wastes. Chemical modeling of reactive transformations of such complex feedstocks involves tracking the trajectories of multiple reactive species, typically<br>
through spectroscopic sensor measurements, to obtain atom level knowledge of the reacting species and can be challenging, especially without any human insight. Interpretation of spectroscopic signatures is an art and traditionally demands a level of domain expertise. Reaction models developed using sensor measurements also require domain expertise and<br>
are typically generated by suggesting model compounds for groups of substrates. Including human insight, however, leads to bias in modeling and does not allow for efficient exploration<br>
of the chemical space for all possible reactions in the system. Furthermore, updating these expert-guided models based on new operational data is quite cumbersome. This<br>
thesis aims to explore the usage of spectroscopic sensor measurements for automation of reaction and kinetic modeling of complex reaction systems by employing machine learning<br>
and chemometric methods. The methodologies developed are presented on hydrothermal liquefaction (HTL) of biomass as a case study by utilizing experimental Fourier Transform<br>
Infrared (FTIR) and Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy.<br>
Different methodologies for the identification of reaction networks from spectroscopic data are presented in decreasing order of human intervention required. Spectroscopic curve<br>
resolution techniques have been employed at different degrees of sensor data fusion to obtain interpretable and structurally consistent latent features of the reaction system. Signal level data fusion has been performed through Self Modeling Curve Resolution, while a higher order Joint Non-Negative Tensorial Factorization scheme has been applied at a contextual level to jointly analyze FTIR and 1H-NMR spectroscopic data. Expert knowledge has been used in determination of reactive compounds and the subsequent reaction networks. In a step towards automation, extraction of functional group signatures of the reactive species has been performed through application of convolutional operations on the resolved FTIR spectrum and partial molecular fingerprints for each reactive species have<br>
been identified. A reaction network identification methodology that maps spectroscopic signatures to candidate molecules is presented. The network generation is constrained based<br>
on the causal structure inferred using Bayesian structure learning and domain knowledge, and employs algorithmically extracted reaction rules obtained through Atom-Atom Mapping<br>
(AAM) of reactions from a database. A one-shot molecular generation methodology is presented as the next step in automation thus subverting the need for Bayesian structure<br>
learning and spectroscopic deconvolution. Employing a graph neural network based hetero-autoencoder and generative adversarial networks, the molecular generation routine<br>
generates molecules constrained by the FTIR spectrum. Localized reaction networks for the process are identified by recursive application of reaction templates. The reaction networks identified have been found to be concordant with reactive transformations recorded in the literature for HTL of biomass. Mathematical modeling of the kinetics of the system<br>
based on temporal projection of latent features of the spectroscopic deconvolution has been performed by employing chemical reaction neural networks constrained based on the<br>
adjacency information obtained via Bayesian structure learning of the resolved spectrum. Benchmarking studies comparing these neural Ordinary Differential Equations with constrained<br>
alternating least squares and basis reduction techniques such as SIND-y is also presented for a synthetic system.</p>