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

2023· dissertation· en· W6999729326 sur OpenAlexfundno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueBiodiesel Production and Applications
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
Mots-clésCatalysisTransesterificationMesoporous materialBiodieselBiodiesel productionAlkali metal
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,192
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,205
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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