Analysis of molecular proxies of a peat core by thermally assisted hydrolysis and methylation-gas chromatography combined with multivariate analysis

Younes, Khaled; Grasset, Laurent

doi:10.1016/j.jaap.2016.11.014

Cited by 26 publications

(26 citation statements)

References 43 publications

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“…After data standardization, PCA results were obtained using XLSTAT 2014 software, following the strategy adopted by Younes et al [31][32][33][34]. Some data in Tables S1 and S2 were not discussed.…”

Section: Principal Component Analysismentioning

confidence: 99%

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal

et al. 2021

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Nowadays, acquiring a water supply for urban and industrial uses is one of the greatest challenges facing humanity for ensuring sustainability. Membrane technology has been considered cost-effective, encompasses lower energy requirements, and at the same time, offers acceptable performance. Electrospun nanofibrous membranes (ENMs) are considered a novel and promising strategy for the production of membranes that could be applied in several treatment processes, especially desalination and ion removal. In this study, we apply an unsupervised machine-learning strategy, the so-called principal component analysis (PCA), for the purpose of seeking discrepancies and similarities between different ENMs. The main purpose was to investigate the influence of membrane fabrication conditions, characteristics, and process conditions in order to seek the relevance of the application of different electrospun nanofibrous membranes (ENMs). Membranes were majorly classified into single polymers/layers, from one side, and dual multiple layer ENMs, from another side. For both classes, variables related to membrane fabrication conditions were not separated from membrane characterization variables. This reveals that membranes’ characteristics not only depend on the chemical composition, but also on the fabrication conditions. On the other hand, the process conditions of ENM fabrication showed an extensive effect on membranes’ performance.

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Section: Principal Component Analysismentioning

confidence: 99%

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal

et al. 2021

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“…This division of peat core presents a source of discrepancy in the molecular composition of OM. For the investigated samples, the prevalence has been identified for all three layers [ 9 , 22 , 26 , 29 ]. Fresh OM (therefore, lignin) is found in the acrotelm due to the continuous input of vegetation debris from the uppermost vegetation.…”

Section: Methodsmentioning

confidence: 99%

“…The investigated two methods are the CuO–NaOH oxidation of lignin and the TMAH thermochemolysis. Both methods have been extensively explained in previous studies performed on the same samples [ 22 , 26 ]. The alkaline oxidation of lignins, in the presence of copper (II) oxide, has been proposed by Hedges and Ertel [ 19 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…In this study, our aim is to shed light on two lignin degradation methods: CuO–NaOH oxidation [ 19 , 20 ] and the tetramethyl ammonium hydroxide (TMAH) thermochemolysis methods [ 9 , 21 , 22 ]. To our knowledge, these techniques have only been implemented for analytical uses as they showed a high capacity to extract a large molecular fingerprint [ 19 , 21 , 22 ]. Herein, we will try to seek the capacity of each technique by the application of an unsupervised machine learning technique, the so-called principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…Hence, the theory is well developed. Other studies have used PCA as a tool to reveal some proxies in Geochemistry [12][13][14][15], Energy [16] and Biomass characterization [17,18].…”

Section: Introductionmentioning

confidence: 99%

The application of unsupervised machine learning to optimize water treatment membrane selection

Younes¹,

Mouhtady²,

Chaouk³

2021

Ann Robot Automation

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Artifi cial intelligence technologies have been extensively used to decipher water quality and characterization. Fewer studies have employed these techniques in the purpose of optimizing a water treatment process. Here, we apply unsupervised machine learning techniques for the optimization of the choice of membranes, following the different constraints and conditions encountered. The adopted data analysis techniques are the Principal Component Analysis (PCA) and the Hierarchical Cluster Analysis (HCA). Both methods showed their capacity to reveal resemblance and discrepancies between different membrane types and based on several properties. PCA is more appreciated than HCA as it removes any intercorrelation between factors and it helps in a better understanding of different trends of the dataset by establishing a Scores-Factors relation.

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Analysis of molecular proxies of a peat core by thermally assisted hydrolysis and methylation-gas chromatography combined with multivariate analysis

Cited by 26 publications

References 43 publications

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal

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

The application of unsupervised machine learning to optimize water treatment membrane selection

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