Hydrothermal Base Catalyzed Depolymerization and Conversion of Technical Lignin – An Introductory Review

Lignin represents the most significant potential source of sustainable aromatic compounds. Currently, the vast majority of technical lignin could be sourced from industrial paper production and in particular the Kraft process, where it is conventionally combusted for chemicals recovery and heat generation (e.g. for plant operation). While in recent years several efforts have concerned the conversion of native lignin (i.e. as found in nature) during biomass processing, there has also been significant focus on the “Base Catalyzed” conversion of technical lignin. This process is of significant interest, since it could be potentially integrated into existing Kraft mill infrastructure. The following review paper focuses on the development of the hydrothermal base catalyzed depolymerization (HBCD) of lignin, as a basis to produce valuable chemical compounds. Focus will be placed on NaOH catalyzed reactions in the aqueous phase, as this approach is considered the most promising. Focus is placed on reaction conditions and characterization of monomeric aromatic compounds from the HBCD approach. Oligomers, as largest product fraction, is also considered, however, these are seldom analyzed in detail in the literature and ideas on further use are scarce. The review also addresses findings in literature concerning the assessment of the solid, liquid, and gas product streams arising from HBCD. From this paper, process conditions for HBCD reactions can be derived and it is shown that the solid phase has a high potential for further valorization and downstream processing

Invariant Manifolds and Rate Constants in Driven Chemical Reactions

Reaction rates of chemical reactions under nonequilibrium conditions can be determined through the construction of the normally hyperbolic invariant manifold (NHIM) [and moving dividing surface (DS)] associated with the transition state trajectory. Here, we extend our recent methods by constructing points on the NHIM accurately even for multidimensional cases. We also advance the implementation of machine learning approaches to construct smooth versions of the NHIM from a known high-accuracy set of its points. That is, we expand on our earlier use of neural nets, and introduce the use of Gaussian process regression for the determination of the NHIM. Finally, we compare and contrast all of these methods for a challenging two-dimensional model barrier case so as to illustrate their accuracy and general applicability.Comment: 28 pages, 13 figures, table of contents figur