Assessment of the Efficiency of Chemical and Thermochemical Depolymerization Methods for Lignin Valorization: Principal Component Analysis (PCA) Approach

• Published in: Polymers Vol. 14; no. 1; p. 194
• Main Authors: Younes, Khaled, Moghrabi, Ahmad, Moghnie, Sara, Mouhtady, Omar, Murshid, Nimer, Grasset, Laurent
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04-01-2022; MDPI
• Subjects: Alternative energy; Ammonium hydroxide; Biodiesel fuels; Biofuels; Catalytic oxidation; Chemical reactions; CuO–NaOH oxidation; Data analysis; Degradation; Demand analysis; Depolymerization; Efficiency; Fine chemicals; Investigations; Lignin; lignin valorization; Machine learning; Oxidation; peatland; Peatlands; Polymers; principal component analysis; Principal components analysis; thermochemolysis; United States > US; thermochemolysis; CuO–NaOH oxidation; lignin valorization; principal component analysis; peatland
• ISSN: 2073-4360; 2073-4360
• DOI: 10.3390/polym14010194

Energy demand and the use of commodity consumer products, such as chemicals, plastics, and transportation fuels, are growing nowadays. These products, which are mainly derived from fossil resources and contribute to environmental pollution and CO2 emissions, will be used up eventually. Therefore, a renewable inexhaustible energy source is required. Plant biomass resources can be used as a suitable alternative source due to their green, clean attributes and low carbon emissions. Lignin is a class of complex aromatic polymers. It is highly abundant and a major constituent in the structural cell walls of all higher vascular land plants. Lignin can be used as an alternative source for fine chemicals and raw material for biofuel production. There are many chemical processes that can be potentially utilized to increase the degradation rate of lignin into biofuels or value-added chemicals. In this study, two lignin degradation methods, CuO-NaOH oxidation and tetramethyl ammonium hydroxide (TMAH) thermochemolysis, will be addressed. Both methods showed a high capacity to produce a large molecular dataset, resulting in tedious and time-consuming data analysis. To overcome this issue, an unsupervised machine learning technique called principal component analysis (PCA) is implemented.