Understanding the Molecular Structure of Lignin Macromolecule Using Data Analytics and Computational Simulation
Washington State University
Doctor of Philosophy (PhD), Washington State University
2022
:
https://doi.org/10.7273/000005281
Lignin macromolecule is a complex polymer made by cross-linking phenolic precursors with a variety of chemical bonds. The precise molecular structure, physical dimension and chemical functionality/reactivity of lignin remain elusive despite multiple studies on lignin structural characterization. As the properties of any biological component depend critically on its molecular geometry, it will be vital to understand the structure of lignin macromolecules to utilize its vast potential. As part of this research, we have attempted to delineate the structural patterns of lignin using data analytics by utilizing experimental data from literature. Data analytic techniques facilitate the implementation of data-driven discovery pipelines (e.g., knowledge extraction and data mining) and the development of computational models capable of linking molecular structure with its physicochemical properties. The research focus on developing a data-driven approach to better understand lignin macromolecular assembly and structural variations from different sources and processes. The thesis presents a new methodology for computational simulation of lignin structure, generated novel lignin polymer dataset and demonstrated a lignin depolymerization workflow to provide insight on the structural transformation of lignin when using deep eutectic solvent pretreatment. The software developed provides a hands-on tool to create and visualize lignin structures based on experimental analysis which is not available currently. The explanations obtained in these simulation approaches helped in identifying the structural variations of desired wood type and separation method. Also, demonstrated a machine learning model in utilizing data driven strategies to predict the reaction chemistry of hydrotreating bioprocess for lignocellulosic biomass. The model interpretation techniques implemented in this study can also be used as a guide for future experiments to gain novel insights from computational approaches.
- Understanding the Molecular Structure of Lignin Macromolecule Using Data Analytics and Computational Simulation
- Sudha Cheranma Devi Eswaran
- Xiao Zhang (Advisor)Manuel Garcia-Perez (Committee Member)John Miller (Committee Member)Robert Rallo (Committee Member)
- Washington State University
- Chemical Engineering and Bioengineering, School of
- Doctor of Philosophy (PhD), Washington State University
- Washington State University
- 195
- 99901019638901842
- English
- Dissertation