Classification of biomass through their pyrolytic bio-oil composition using FTIR and PCA analysis

Lazzari, Eliane; Schena, Tiago; Marcelo, Marcelo Caetano Alexandre; Primaz, Carmem Tatiane; Silva, Aline Nunes; Ferrão, Marco Flôres; Bjerk, Thiago Rodrigues; Caramão, Elina Bastos

doi:10.1016/j.indcrop.2017.11.005

Cited by 148 publications

(56 citation statements)

References 27 publications

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“…The vibrational stretch of the C=O bond, characteristic of carbonyls of lipids, esters and carboxylic acids can be observed in the range of 1700-1750 cm -1 . The region between around 1500 cm -1 corresponds to C=C, while region between 1000 and 1100 cm -1 corresponds to the C-O and C-C-O bonds and has been attributed to the structure of cellulose, hemicellulose and lignin, according to Lazzari et al 28 On the other hand, Figure 3c shows the FTIR spectrum for biochar and it is possible to observe the absence of all these signals indicating the majority presence of mineral materials or coke, concluding that the pyrolysis was efficient. This spectrum and the representations of each band corroborate with the results found by other authors who analyzed the coffee grounds by FTIR.…”

Section: Characterization Of the Solid Residue From The Soxhlet Extramentioning

confidence: 99%

“…Related to biomass, thermogravimetric analysis (TGA) allows the prediction of the kinetic parameters of a thermochemical process, such as pyrolysis, 27 while Fourier transform infrared spectroscopy (FTIR) aiming an initial analysis of its chemical composition. 28 With regard to vegetable oil and bio-oil, gas chromatography coupled to the mass spectrometry (GC-MS) provides information about the identity of the sample, making possible to prospect for future applications, in the chemical or energy industry. 29 Considering these parameters and techniques, the goal of this work is the production of vegetable oil (Soxhlet extraction) and bio-oil (fast pyrolysis) from the SCG (industrial residue from the production of soluble coffee) and characterize them, aiming their exploration and possible application for energy and industrial purposes.…”

Section: Introductionmentioning

confidence: 99%

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Production and Characterization of the Bio-Oil Obtained by the Fast Pyrolysis of Spent Coffee Grounds of the Soluble Coffee Industry

Krause

Moitinho

Ferreira

et al. 2019

J. Braz. Chem. Soc.

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Agro-industrial wastes are abundant and low-cost sources of energy and chemicals. Biomass account for 14% of the world's energy production. Industrial residues of production of soluble coffee (spent coffee grounds -SCG) have great potential due to its large-scale production and can be transformed by pyrolysis, in liquid, solid and gaseous products with applications from energy to chemicals. This work has the objective of producing bio-oil from the pyrolysis of industrial SCG, characterize it by chromatography and indicate its possible applications. As SGC contains a large amount of glycerides, they were extracted from SGC before the pyrolysis, aiming to obtain a better bio-oil from the residue. The yield in vegetable oil was 5.2% and its analyses showed that 50% are saturated acids (mainly palmitic 27.6%) and other 50% are unsaturated ones (mainly linoleic acid 35.3%). This composition qualifies this oil for biodiesel purposes. The residue (SGC after extraction) was submitted to pyrolysis, yielding 30% in liquid products, being 6% bio-oil. The bio-oil was analyzed by gas chromatography being identified free fatty acids, hydrocarbons, phenols and N-compounds. The heteroatomic compounds limit the use as biofuel but can be interesting for the pharmaceutical, agrochemical and fine chemicals industries.

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Section: Characterization Of the Solid Residue From The Soxhlet Extramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Production and Characterization of the Bio-Oil Obtained by the Fast Pyrolysis of Spent Coffee Grounds of the Soluble Coffee Industry

Krause

Moitinho

Ferreira

et al. 2019

J. Braz. Chem. Soc.

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“…All studies point that coconut fiber biooils are composed essentially by phenols (65%), followed by ketones (15%), this last class presents greater number of tentatively identified compounds [10,27]. These results prove that bio-oil identity was not lost by switch the conventional analyze method to the fast one [10,[24][25][26][27].…”

Section: Real Sample Analysismentioning

confidence: 87%

Fast two‐dimensional gas chromatography applied in the characterization of bio‐oil from the pyrolysis of coconut fibers

Schena

Farrapeira

Bjerk

et al. 2019

Separation Science Plus

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The goal of this paper is the evaluation of fast two‐dimensional gas chromatography applied to bio‐oil samples. Bio‐oils are complex matrixes that usually are analyzed by conventional gas chromatography, involving long columns, long time of analysis due to slow heating rates, and consequently, high cost associated to time consumed. Fast gas chromatography techniques are based on the use of narrow capillary columns that allow the achievement high‐speed separations for complex samples, maintaining excellent resolution. Firstly, the two‐dimensional gas chromatography method was optimized varying the heating rate (10, 15 and 20°C min−1) and achieving the optimal separation at 15°C min−1. This method allies a good separation of bio‐oil constituents with shorter time analysis. The developed method and the traditional conventional two‐dimensional gas chromatography method (used in previous studies) were applied in the analysis of a mixture of 30 standard compounds. Despite coelutions of short retention time peaks (compounds with very similar physical‐chemical properties), the fast two‐dimensional gas chromatography method showed an increase in chromatographic signal and a noise reduction. Good results were also obtained in the real bio‐oil sample. Fast two‐dimensional gas chromatography maintained all the chromatographic information from conventional two‐dimensional gas chromatography, reducing drastically the total time of analysis.

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“…PCA is a statistical feature extraction algorithm. It seems to be logical to use PCA in the presence of large datasets of variables where a small set of data contains the determinative information [15][16][17][18]. In addition, it is possible to apply PCA in some applications where the training samples are much smaller than the number of features.…”

Section: Pcamentioning

confidence: 99%

Fault detection and classification in smart grids using augmented K-NN algorithm

Hosseinzadeh

Masoodzadeh

Roshandel³

2019

SN Appl. Sci.

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The ability of artificial intelligence and machine learning techniques in classification and detection of the types of data in large datasets lead to their popularity among scientists and researchers. Because of the presence of different load at different times in power systems, it is hard to provide an accurate mathematical model for such systems. On the other hand, most of the available protection devices in power grids work based on the estimated mathematical models of the grid. For this reason, power system utilizers usually suffer from the low accuracy of the available protection systems in fault detection and diagnosis. In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes. The applied PCA reduces the dataset sizes and eliminates the possible singularity of the datasets. The LDA method is applied to the outputs data of the PCA to minimize the within class distance of the dataset and maximize the distance between classes. Finally, the well-known K-nearest neighbor technique is applied to detect the fault and determine its classes. The paper results demonstrate the effectiveness and robustness of the proposed algorithm in the determination of the fault class in smart grids.

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Classification of biomass through their pyrolytic bio-oil composition using FTIR and PCA analysis

Cited by 148 publications

References 27 publications

Production and Characterization of the Bio-Oil Obtained by the Fast Pyrolysis of Spent Coffee Grounds of the Soluble Coffee Industry

Production and Characterization of the Bio-Oil Obtained by the Fast Pyrolysis of Spent Coffee Grounds of the Soluble Coffee Industry

Fast two‐dimensional gas chromatography applied in the characterization of bio‐oil from the pyrolysis of coconut fibers

Fault detection and classification in smart grids using augmented K-NN algorithm

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