Development of a bio-based bifunctional catalyst for simultaneous esterification and transesterification of neem seed oil: Modeling and optimization studies

Akhabue, C. E.; Osa-Benedict, Evidence Osayi; Oyedoh, E.A.; Otoikhian, Kevin Shegun

doi:10.1016/j.renene.2020.01.103

Cited by 30 publications

(18 citation statements)

References 47 publications

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“…At room temperature, the NSO was a dark brownish colored liquid with a density of 947.9 kg/m 3 . The acid value (11.67 mg KOH/g oil) and FFA (5.94%) of the NSO differ from those reported in the literature (Akhabue et al, 2020;Chhabra et al, 2020). These may be due partly to the source of the seeds or the hydrolysis of the triglycerides.…”

Section: Physicochemical Properties Of Neem Seed Oilcontrasting

confidence: 62%

“…Previous studies have considered cheap alternatives such as waste cooking oil (Avinash and Murugesan, 2017), sorrel oil (Betiku and Ishola, 2020), rubber seed oil (Oladipo and Betiku, 2020), neem seed oil (NSO) (Merlin et al, 2015;Akhabue et al, 2020), African pear seed oil (Ofoefule et al, 2019), and Jatropha oil (Kamel et al, 2018). A key drawback of this feedstock, however, is their high free fatty acids (FFA) content that has to be reduced to ≤1% via acid-catalyzed esterification process (Gan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Although the reaction with homogeneous catalysts is fast and effective, the catalysts cannot be recycled (that is recovered and reused), which raises both environmental and high cost of biodiesel production concerns. To overcome this challenge, solid heterogeneous catalysts such as ferric sulfate (Gan et al, 2010;Betiku et al, 2017;Ighose et al, 2017;Ishola et al, 2017) and biomass-based materials such as Hura crepitans seed pod (Ogbu et al, 2018;Akhabue et al, 2020) have been proposed as possible replacements for homogeneous catalysts since they can be recovered and reused, making them environmentally benign and cost-effective in the production of biodiesel. Past reports have shown that the process input variables viz.…”

Section: Introductionmentioning

confidence: 99%

“…Its seeds contain 25-45% non-edible oil, which has potential as a feedstock for biodiesel production via transesterification (Muthu et al, 2010;Oladipo et al, 2018). However, the presence of FFA >1% means that a pretreatment step via esterification is required (Akhabue et al, 2020). While some studies separately modeled the esterification and transesterification of NSO in a two-step biodiesel production process and then determined the optimum condition for maximum FFA reduction and biodiesel yield using either RSM or ANN for each step (Chhabra et al, 2020), others lumped the two processes together in their modeling and optimization studies (Oladipo et al, 2018) and simply determined the optimum biodiesel yield.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

et al. 2020

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High free fatty acids (FFA) content in oils poses challenges such as soap formation and difficulty in the separation of by-products in direct transesterification of oil to biodiesel, which is of environmental concern and also increases the cost of production. Thus, in this study, the ferric sulfate-catalyzed esterification of neem seed oil (NSO) with an FFA of 5.84% was investigated to reduce it to the recommended level of ≤1%. The esterification process for the NSO was modeled using response surface methodology (RSM) and artificial neural network (ANN). The effect of the pertinent process input variables viz. methanol/NSO molar ratio (10:1–30:1), ferric sulfate dosage (2–6 wt%), and reaction time (30–90 min) and their interactions on the reduction of the FFA of the NSO, were examined using Box Behnken design. The optimal condition for the process for reducing the FFA content of the oil was established using RSM and ANN-genetic algorithm (ANN-GA). The results showed that the models developed described the process accurately with the coefficient of determination (R2) of 0.9656 and 0.9908 and the mean relative percent deviation (MRPD) of 6.5 and 2.9% for RSM and ANN, respectively. The ANN-GA established the optimum reduction of FFA of 0.58% with methanol/NSO molar ratio of 18.51, ferric sulfate dosage of 6 wt%, and reaction time of 62.8 min as against the corresponding values of 0.62% FFA, 23.5, 5.03, and 75 min established by the RSM. Based on the statistics considered in the study, ANN and GA outperformed RSM in modeling and optimization of the NSO esterification process.

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Section: Physicochemical Properties Of Neem Seed Oilcontrasting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

et al. 2020

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“…Sixteen experiments were generated from five factors in the process modeling and optimization of the mahua oil transesterification to obtain a biodiesel yield of 97.04 wt% 20 . For modeling and optimization investigations with BBD and CCD, a total of 29 and 30 experiments are required for four factors, respectively 12,23 and a total of 46 and 50 experiments are required for five factors, respectively 24 …”

Section: Introductionmentioning

confidence: 99%

Process optimization of microwave irradiation‐aided transesterification of kariya seed oil by Taguchi orthogonal array: pawpaw trunk as a novel biocatalyst

Fadara

Falowo

Ojumu

et al. 2021

Biofuels Bioprod Bioref

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This study investigated the potential application of an active biocatalyst from pawpaw (Carica papaya) trunk for the direct microwave irradiation‐aided transesterification of kariya (Hildegardia barteri) seed oil (KSO) with methanol to obtain biodiesel. The transesterification process involved was modeled, and the effects of the essential process input variables on the kariya seed oil methyl ester (KSOME) yield were examined, followed by the optimization of the input variables to maximize the KSOME yield, using the Taguchi orthogonal array design method. The biocatalyst was produced with a calcination process in a muffle furnace at 200–1000 °C, and each sample obtained was characterized by a scanning electron microscope equipped with energy‐dispersive X‐ray spectroscopy (SEM–EDX) to determine the best temperature. The calcined pawpaw trunk ash (CPTA) produced at the best temperature of 400 °C was further characterized using X‐ray diffraction (XRD), Fourier‐transform infrared (FTIR) spectroscopy, and physisorption analyses. The CPTA at 400 °C produced a strong catalytic activity due to its morphology, metallic composition (K (23.85%), and Ca (15.40%)), crystalline structure, the average pore size of 32.2 nm, and a surface area of 0.64 m2 g−1. The best operating conditions established for the transesterification process were a methanol / KSO molar ratio of 10.5:1, CPTA loading of 0.5 wt%, microwave heating power of 300 W, and reaction time of 2 min, with a maximum KSOME yield of 98.50 ± 0.60 wt%. The quality of the KSOME produced satisfied the American standard specifications for biodiesel. Both KSO and pawpaw trunk could serve as inexpensive feedstock for sustainable and renewable production of biodiesel. The intensification of the transesterification process using microwave irradiation drastically shortened the reaction time needed for maximum biodiesel yield. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

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Advances in Bifunctional Solid Catalysts for Biodiesel Production

Changmai¹,

Chhandama²,

Vanlalveni³

et al. 2022

Biodiesel Production

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Development of a bio-based bifunctional catalyst for simultaneous esterification and transesterification of neem seed oil: Modeling and optimization studies

Cited by 30 publications

References 47 publications

Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

Process optimization of microwave irradiation‐aided transesterification of kariya seed oil by Taguchi orthogonal array: pawpaw trunk as a novel biocatalyst

Advances in Bifunctional Solid Catalysts for Biodiesel Production

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