Natural minerals as potential catalysts for the pyrolysis of date kernels: effect of catalysts on products yield and bio-oil quality

2021 ◽  
pp. 1-9
Author(s):  
Rima A. Aljeradat ◽  
Salah H. Aljbour ◽  
Nabeel A. Jarrah
Keyword(s):  
Oil Quality ◽  
Natural Minerals ◽  
Bio Oil

scholarly journals Impact of ZSM-5 Deactivation on Bio-Oil Quality during Upgrading of Straw Derived Pyrolysis Vapors

2018 ◽  
Vol 33 (1) ◽  
pp. 397-412 ◽  
Author(s):  
Andreas Eschenbacher ◽  
Peter Arendt Jensen ◽  
Ulrik Birk Henriksen ◽  
Jesper Ahrenfeldt ◽  
Chengxin Li ◽  
...  
Keyword(s):  
Oil Quality ◽  
Bio Oil

Physical, chemical, thermal and biological pre-treatment technologies in fast pyrolysis to maximize bio-oil quality: A critical review

2019 ◽  
Vol 128 ◽  
pp. 105333 ◽  
Author(s):  
Brenda J. Alvarez-Chavez ◽  
Stéphane Godbout ◽  
Joahnn H. Palacios-Rios ◽  
Étienne Le Roux ◽  
Vijaya Raghavan
Keyword(s):  
Critical Review ◽  
Oil Quality ◽  
Fast Pyrolysis ◽  
Physical Chemical ◽  
Bio Oil ◽  
Treatment Technologies ◽  
Pre Treatment

Investigation of operating parameters of water extraction processes for improving bio-oil quality

2014 ◽  
Vol 31 (12) ◽  
pp. 2229-2236 ◽  
Author(s):  
Boonyawan Yoosuk ◽  
Jiraporn Boonpo ◽  
Parncheewa Udomsap ◽  
Sittha Sukkasi
Keyword(s):  
Oil Quality ◽  
Water Extraction ◽  
Operating Parameters ◽  
Bio Oil ◽  
Extraction Processes

Improving Bio-oil Quality through Co-Pyrolysis of Corn Cobs and Polypropylene in a Stirred Tank Reactor

2016 ◽  
Vol 7 (8) ◽  
pp. 1381 ◽  
Author(s):  
Dijan Supramono ◽  
Jonathan Jonathan ◽  
Haqqyana Haqqyana ◽  
Setiadi Setiadi ◽  
Mohammad Nasikin
Keyword(s):  
Oil Quality ◽  
Stirred Tank ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Corn Cobs ◽  
Bio Oil

scholarly journals Catalytic Rapid Pyrolysis ofQuercus variabilisover Nanoporous Catalysts

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Hyeon Koo Kang ◽  
In-Gu Lee ◽  
Kyong-Hwan Lee ◽  
Beom-Sik Kim ◽  
Tae Su Jo ◽  
...  
Keyword(s):  
Oil Quality ◽  
Strong Acid ◽  
Abundant Species ◽  
Acid Sites ◽  
Weak Acid ◽  
Nanoporous Materials ◽  
Bio Oil ◽  
Strong Acid Sites ◽  
Rapid Pyrolysis ◽  
Mcm 41

Catalytic rapid pyrolysis ofQuercus variabilis, a Korean native tree species, was carried out using Py-GC/MS. Mesoporous MFI, which has both nanopores and micropores, and three nanoporous materials, Al-MCM-41, Al-SBA-15, andγ-Al2O3, were used as the catalyst. The acid sites of mesoporous MFI were strong Brønsted acid sites, whereas those of nanoporous materials were mostly weak acid sites. The composition of the product bio-oil varied greatly depending on the acid characteristics of the catalyst used. Phenolics were the most abundant species in the bio-oil, followed by acids and furanics, obtained over Al-MCM-41 or Al-SBA-15 with weak acid sites, whereas aromatics were the most abundant species produced over mesoporous MFI with strong acid sites, followed by phenolics. Aromatics, phenolics, and furanics are all important chemicals contributing to the improvement of bio-oil quality.

scholarly journals Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Pressurized Hydrogen

2021 ◽  
Vol 12 (1) ◽  
pp. 46
Author(s):  
Jingliang Wang ◽  
Shanshan Wang ◽  
Jianwen Lu ◽  
Mingde Yang ◽  
Yulong Wu
Keyword(s):  
Oil Quality ◽  
Fixed Bed ◽  
Hydrogen Atmosphere ◽  
Nitrogen Atmosphere ◽  
Fixed Bed Reactor ◽  
Heating Value ◽  
Pine Sawdust ◽  
Bio Oil ◽  
Ethyl Hexadecanoate ◽  
Area Percentage

The pyrolysis of pine sawdust was carried out in a fixed bed reactor heated from 30 °C to a maximum of 700 °C in atmospheric nitrogen and pressurized hydrogen (5 MPa). The yield, elemental composition, thermal stability, and composition of the two pyrolysis bio-oils were analyzed and compared. The result shows that the oxygen content of the bio-oil (17.16%) obtained under the hydrogen atmosphere was lower while the heating value (31.40 MJ/kg) was higher than those of bio-oil produced under nitrogen atmosphere. Compounds with a boiling point of less than 200 °C account for 63.21% in the bio-oil at pressurized hydrogen atmosphere, with a proportion 14.69% higher than that of bio-oil at nitrogen atmosphere. Furthermore, the hydrogenation promoted the formation of ethyl hexadecanoate (peak area percentage 19.1%) and ethyl octadecanoate (peak area percentage 15.42%) in the bio-oil. Overall, high pressure of hydrogen improved the bio-oil quality derived from the pyrolysis of pine biomass.

scholarly journals Auto Classification of Biomass through Characterization of their Pyrolysis Behaviors by using Thermogravimetric Analysis with Support Vector Machine Algorithm: Case Study for Tobacco

2020 ◽  
Author(s):  
Chao Yin ◽  
Xiaohua Deng ◽  
Zhiqiang Yu ◽  
Ruting Chen ◽  
Hongxiang Zhong ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Thermogravimetric Analysis ◽  
Oil Quality ◽  
Support Vector ◽  
Analysis Method ◽  
Biomass Pyrolysis ◽  
Svm Algorithm ◽  
Bio Oil

Abstract Background: During the biomass-to-bio-oil conversion process, many researches focus on the study of the association between the biomass and the bio-products by using near infrared spectra (NIR) and chemical analysis method. However, the characterization of biomass pyrolysis behaviors by using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar and gases) released by tobacco pyrolysis reactions decide the sensory quality, which is similar to the use of biomass as a renewable resource through the pyrolysis process. Results: Support vector machine (SVM) has been employed to automatically classify the planting area and growing position of tobacco leaves by using thermogravimetric analysis data as the information source for the first time. 88 single-grade tobacco samples belonging to 4 grades and 8 categories were split into the training, validation and blind testing set. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower and middle positions. Error only occurs in the classification of subgrades of the middle position. Conclusions: Our results not only validated the feasibility of using thermogravimetric analysis with SVM algorithm as an objective and rapid method for automatic classification of tobacco planting area and growing position, but also showed this new analysis method would be a promising way to exploring bio-oil quality prior to biomass pyrolysis production.

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