We present a data-driven approach to identifying the reaction network of the dominant chemistry in complex mixtures using model compounds representative of cellulose and lignin chemistry that are processed using hydrous pyrolysis. We present two methods for the identification of pseudocomponents: self-modeling multivariate curve resolution, which is a non-negative matrix factorization method, and Bayesian hierarchical clustering. The pseudocomponents are identified from spectroscopic data from two sources: Fourier transform infrared spectroscopy and 1H NMR spectroscopy. The data from the two sources is combined using a simple data combination method. Once pseudocomponents have been identified, Bayesian networks are used to identify directed pathways between the components, resulting in a proposed hypothesis for the reaction network or mechanism. We validate the methods by showing consistency of the derived reaction networks with the known chemistry of cellulose, lignin, and their derivatives and demonstrate the importance of data fusion in developing believable reaction networks.

In this work, we analyze the hydrous pyrolysis of a physical mixture of the model components representing cellulose (levoglucosan) and lignin (2-phenoxyethyl benzene). Fourier transform infrared (FTIR) and proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy was used to characterize the products of the reaction. The main objective of the work was to use datadriven methods to develop a reaction network for this system based on the spectroscopic data. This was achieved using Bayesian hierarchical clustering to identify pseudocomponents and Bayesian networks to develop a reaction network between these pseudocomponents. The data-driven reaction network was shown to be consistent with the known chemistry of the pyrolysis of cellulose and lignin, and the chemistry of the physical mixture incorporated/combined elements of the reaction mechanisms of cellulose and lignin.

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The amount of dangerous goods (DG) transported by rail within Canada has increased by an average of approximately 25% since 2004, with a 42.5% increase in transported fuels and chemicals between 2011 and 2017. Further, movement of DG by rail is forecasted to continue increasing. Sustainable growth in the transport of dangerous goods (TDG) by rail will require acceptable safety levels. This study provides insight into key occurrence types for TDG and their causes, to better focus on risk control strategies, including measurement and control of leading and lagging safety indicators. This work also reviews current safety performance and Canadian railway incident occurrence databases. The results of the analyses suggested that the performance against lagging indicators currently being reported is adequate, including derailments and collisions (main and nonmain track), serious injuries (including fatalities), DG leakers, and releases. Furthermore, a list of the rail accidents with the greatest number of fatalities was used to calculate a crude estimate of societal risk associated with rail transportation. According to UK Health and Safety Executive (HSE) recommendations, this analysis indicated that the estimated rail transport risks would be considered acceptable when assessed at a milepost scale. However, there are opportunities for further enhancing safety reporting, management, and performance.