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

Abstract

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.

Metal Oxide Hydrogel Composites for Remediation of Dye-Contaminated Wastewater: Principal Component Analysis

“…Principal component analysis (PCA) with several parameters was used to assess the adsorption efficiency of composite hydrogels containing MONPs in wastewater dye removal. PCA is generally used to reduce the parameters of a dataset by generating linear combinations of the original parameters, and thus to identify the main parameters required to enhance and improve a given process [ 29 , 30 , 31 ]. Following the huge number of parameters affecting the effectiveness of composite hydrogels containing MONPs for wastewater treatment, a PCA study can be implemented to pursue intercorrelation in parameters associated with adsorption efficiency.…”

Graphene Functionalized with Imidazole Derivatives as Nanotransducers for Electrochemically Detecting Isothiazolinone Biocides

“…CGNH3 yielded different Δ I values for each chemical species at the same concentration, and the overall response profiles in the concentration range depended on the type of chemical species. PCA, − a statistical technique to identify covariances among multivariate data, was performed to examine if the responses of the CGNHs could differentiate only the target OIT among the interferences. As a preliminary test, the Δ I magnitudes were examined for OIT and DCOIT (with subtle differences in the chemical structure) at four potentials: −0.75, 0.08, 0.30, and 1.30 V (vs Ag/AgNO 3 ).…”

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach

“…To this end, principal component analysis (PCA) is applied before applying the CVA algorithm, because PCA is probably the most recognized unsupervised algorithm for dimensionality reduction [30]. PCA allows calculation of a reduced number of uncorrelated and orthogonal latent variables, known as principal components (PCs) [31]. The PCs concentrate the significant information included in the original variables [32], while explaining most of the variance in the whole set of samples.…”