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Record W6999729326

Development of novel solid acid catalysts for biodiesel production from green seed canola oil through alcoholysis process

2023· dissertation· en· W6999729326 on OpenAlexfundno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsCatalysisTransesterificationMesoporous materialBiodieselBiodiesel productionAlkali metal
DOInot available

Abstract

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Biodiesel as a renewable energy source is conventionally produced from vegetable oils using homogeneous alkaline catalysts owing to their fast reaction rate. However, due to undesirable side reactions resulted from alkaline-catalyzed transesterification reaction, tremendous efforts have been made to find a remedy to prevent undesirable reactions. Also, water washing of biodiesel product to remove alkali from biodiesel requires large amount of water. Heterogeneous solid acid catalysts can provide the opportunity to facilitate transesterification and esterification reactions simultaneously using low quality and non-edible feedstocks including green seed canola oil without any side reactions. In the first phase, three solid acid catalysts, namely mesoporous aluminophosphate supported 12-tugstophosphoric heteropoly acid (HPW/MAP), mesoporous aluminosilicate supported 12-tugstophosphoric heteropoly acid (HPW/MAS), and γ-Al2O3 supported 12-tugstophosphoric heteropoly acid (HPW/γ-Al2O3) were prepared and characterized. Mesoporous aluminophosphate (MAP) and mesoporous aluminosilicate (MAS) were synthesized via sol-gel and hydrothermal methods respectively, and 25 wt. % of 12-tugstophosphoric heteropoly acid (HPW) was immobilized on support materials using the wet impregnation method. The features of the catalysts were comprehensively investigated using various techniques such as BET, XRD, NH3-TPD, TGA, and TEM. The surface area of supported catalysts decreased after HPW impregnation according to BET results which indicates that HPW was loaded inside of the pores of the supports successfully. The density and strengths of acid sites of support materials and catalysts before reaction and after regeneration were determined by the NH3-TPD technique. Accordingly, an increase in acidity was observed after HPW immobilization on all support materials. The catalytic performance of the catalysts was studied through the alcoholysis reaction using unrefined green seed canola oil (UGSC) as the feedstock. The maximum biodiesel yield of 82.3 % was obtained using 3 wt. % of HPW/MAS, with methanol to oil molar ratio of 20:1 at 200 °C and 4 MPa during 7 h. The reusability study of HPW/MAS showed that it can maintain 80% of its initial activity after 5 runs. In the second phase, response surface methodology (RSM) based on the central composite design (CCD) approach was implemented to study the effects of catalyst loading, methanol to oil (M/O) molar ratio, and reaction time on biodiesel yield using HPW/MAS as the best catalyst. A polynomial quadratic model was developed as a suitable model to correlate the reaction parameters to the response. M/O molar ratio indicated to have the most influence on biodiesel yield owing to the reversible nature of this reaction, while catalyst loading had minor effect on it. The highest biodiesel yield of 89 % was obtained at optimal 5.9 wt. % of catalyst loading, 27.2 M/O molar ratio, at 200 °C and 4 MPa for 8 h. The formation of biodiesel was verified using HPLC, NMR and GC-MS analyses for their functional groups corresponding to esters. Kinetic study was carried out with pseudo first order assumption at various reaction temperatures in the range of 160 to 200 °C. From the Arrhenius equation, the pre-exponential factor and activation energy were found to be 9.1 × 102 min-1 and 36.34 kJ/mol, respectively. In the third phase, MAS was functionalized successfully using 3-aminopropyltriethoxysilane (APTES) and 3-mercaptopropyltriethoxysilane (MPTS) through two different techniques of post and direct functionalization to improve the performance of the catalyst with respect to activity and reusability. Afterwards, HPW was immobilized on the functionalized carriers, the attachment of carriers with HPW was studied for transesterification reaction to produce biodiesel. The synthesized catalysts were analyzed in terms of textural properties, stability, acidity, chemical state and composition by BET, XRD, Pyridine FTIR, XPS, Raman, TGA, ICP, and NH3-TPD as well as 29Si and 31P MAS NMR. BET, XRD, and Raman results confirmed the well dispersion of HPW over the carriers. Successful functionalization of carriers leading to a stronger HPW attachment to minimize leaching was evaluated through reusability study and ICP analysis. HPW supported amino functionalized mesoporous aluminosilicate catalyst obtained via direct synthesis method demonstrated the best catalytic activity with 93.6% biodiesel yield under optimum reaction conditions of 5.9 wt. % of catalyst, methanol to oil (M/O) molar ratio of 27.2, for 8 h at 200 ◦C and 4 MPa. This catalyst retained 93% of its initial activity after 5 cycles revealing its significant reusability and its potential of being applied at industrial scale. Lastly, Machine Learning (ML) methods were employed for modeling the biodiesel process. Various ML methods of Linear Regression (LR), Decision Trees (DT), Random Forests (RF), and K-Nearest Neighbors (KNN) were used to obtain the most suitable model for prediction of biodiesel yield obtained from UGSC using HPW/MAS. The accuracy of the models was then evaluated and compared with respect to coefficient of determination (R2) and root mean squared error (RMSE). Accordingly, all the models showed acceptable accuracy, however, the DT model demonstrated the best performance for prediction of biodiesel yield with R2 and RMSE of 0.97 and 0.89, respectively.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.205
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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