TY - JOUR
T1 - Chemoinformatic Investigation of the Chemistry of Cellulose and Lignin Derivatives in Hydrous Pyrolysis
AU - Sattari, Fereshteh
AU - Tefera, Dereje
AU - Sivaramakrishnan, Kaushik
AU - Mushrif, Samir H.
AU - Prasad, Vinay
N1 - Publisher Copyright:
© 2020 American Chemical Society.
PY - 2020/6/24
Y1 - 2020/6/24
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.iecr.0c01592
DO - 10.1021/acs.iecr.0c01592
M3 - Article
AN - SCOPUS:85087609956
SN - 0888-5885
VL - 59
SP - 11582
EP - 11595
JO - Industrial and Engineering Chemistry Research
JF - Industrial and Engineering Chemistry Research
IS - 25
ER -