Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: A case of artificial neural network vs. response surface methodology

Betiku, Eriola; Ajala, Sheriff Olalekan

doi:10.1016/j.indcrop.2013.12.046

Cited by 240 publications

(135 citation statements)

References 25 publications

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“…In a separate study, both RSM and artificial neural network (ANN) were used to optimize the transesterification reaction of yellow oleander oil and methanol using unripe plantain peel ash calcined at 500 • C as catalyst. The study showed that 95% yield of biodiesel can be achieved in this process [8]; the study also showed that ANN was better than RSM in terms of accuracy and predictive capabilities. Table 1 shows a brief review of various base heterogeneous catalysts previously investigated together with the vegetable oils, conditions of transesterification, and biodiesel yields obtained.…”

Section: Introductionmentioning

confidence: 84%

“…The sigmoid transfer function (Equation (1)) and pure-linear transfer function (Equation (2)) were selected for the hidden and output layers, respectively. Each neural network was trained using a default stopping criteria of 100,000 iterations [8,22]. Other parameters were chosen as the default values of the software.…”

Section: Model Development and Optimizationmentioning

confidence: 99%

“…Although we have previously shown the potential of unripe plantain fruit peel ash as catalyst for yellow oleander biodiesel production [8], it could be argued that catalyst development from the plantain may have a negative impact on food security from this source as it was earlier highlighted that plantain are generally consumed when ripened. Thus, this present work focused on the development of a catalyst from ripe plantain fruit peels with emphasis on the catalyst characterization which was hither to not reported in our previous study but may be a useful resource with respect to future development of this catalyst.…”

Section: Introductionmentioning

confidence: 96%

“…One key advantage of ANN is that it can be used to predict outputs of a new input data set once a data set has been trained well by a neural network. ANNs have been successfully applied as a modeling tool in many research areas, including esterification [19] and transesterification [8,20,21] processes. In the work of Betiku and Ajala [8] in which the efficacy of ANN was tested, the authors used calcined unripe plantain peel as a heterogeneous catalyst, yellow oleander oil and methanol for the transesterification reaction.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs have been successfully applied as a modeling tool in many research areas, including esterification [19] and transesterification [8,20,21] processes. In the work of Betiku and Ajala [8] in which the efficacy of ANN was tested, the authors used calcined unripe plantain peel as a heterogeneous catalyst, yellow oleander oil and methanol for the transesterification reaction. Whereas, Avramovic et al [20] applied ANN to the transesterification reaction in which synthetic CaO as a heterogeneous catalyst, sunflower oil and ethanol were used.…”

Section: Introductionmentioning

confidence: 99%

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Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

et al. 2018

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The present work was aimed at assessing the possible use of ripe plantain fruit peel as a green-base catalyst in synthesizing Azadirachta indica oil methyl esters (AIOME). The free fatty acid content of the oil (5.81 wt %) was initially reduced to 0.90 wt % using methanol: oil at 2.19 v/v, Fe 2 (SO 4 ) 3 at 6 wt %, time of 15 min and temperature of 65 • C. The pretreated oil was converted to AIOME in a transesterification process with calcined ripe plantain peel ash (CRPPA) at 700 • C as catalyst. The process was modeled by artificial neural network and optimized using genetic algorithm. The effectiveness of the developed CRPPA is ascribable to its high K content and microstructural transformation. The reliability of the model obtained was confirmed with a high coefficient of determination (R 2 ) of 0.996 and a low mean relative percentage deviation (MRPD) of 8.10%. The best operating variables combination for the process was methanol:oil of 0.73 v/v, CRPPA of 0.65 wt % and time of 57 min while the temperature was kept constant at 65 • C with a corresponding AIOME yield of 99.2 wt %. The results of this work demonstrated the potentials of ripe plantain peels and neem oil as cheap feedstocks for biodiesel production.

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Section: Introductionmentioning

confidence: 84%

Section: Model Development and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

et al. 2018

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Non‐catalytic and heterogeneous acid/base‐catalyzed biodiesel production: Recent and future developments

Tran‐Nguyen

Ong

et al. 2020

Asia-Pacific J Chem Eng

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Biodiesel production technologies, which are non-catalytic and those involving the use of heterogeneous catalyst(s), have gained much interest owing to its high biodiesel and pure glycerol throughput. During the past 15 years, many studies were devoted to explore new catalysts as well as improve non-catalytic technologies with aims to ultimately industrialize such biodiesel production processes. This review elucidates the main process parameters that influences non-catalytic and heterogeneous-catalyzed biodiesel production processes. The common parameters among these technologies include the process temperature, alcohol/oil molar ratio, pressure, catalyst loading, and feedstock quality.Optimization of these process parameters is discussed according to the use of conventional method (one factor at a time [OFAAT]) or the use of statistical methods such as response surface methodology (RSM), artificial neural network (ANN), and Taguchi method. In addition, recent development on noncatalytic process and those involving the use of heterogeneous catalyst(s), such as glycerol-free processes under supercritical conditions of alkyl donors and strategies to improve properties of heterogeneous catalyst, are also reviewed.Finally, insight based on the published techno-economical evaluations is also tackled, and future prospects and research directions on these technologies are also discussed.

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Multi‐objective optimization of transesterification in biodiesel production catalyzed by immobilized lipase

Karimi

Jenkins

Stroeve

2016

Biofuels Bioprod Bioref

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In order to comply with criteria of green energy concepts and sustainability, a multi-objective analysis was performed for the transesterification of waste cooking oil (WCO) using immobilized lipase. Response surface methodology and artificial neural networks, followed by multiple response optimization through a desirability function approach were applied to individually and simultaneously evaluate the fatty acid methyl esters (FAME) content and the exergy effi ciency. Reaction time and concentrations of methanol, immobilized lipase and water were considered as the design variables in maximizing FAME content and exergy effi ciency. The maximum individual desirability of FAME content was predicted to be 95.7% corresponding to a methanol to WCO molar ratio of 6.7, catalyst concentration of 45%, water content of 9% and reaction time of 25 h. However, based on the simultaneously optimization of both the FAME content and the exergy effi ciency, the maximum overall desirability was found at a methanol to WCO molar ratio of 6.7, catalyst concentration of 35%, water content of 12% and reaction time of 20 h to achieve FAME content of 88.6% and exergy effi ciency of 80.1%, respectively.

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Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: A case of artificial neural network vs. response surface methodology

Cited by 240 publications

References 25 publications

Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

Non‐catalytic and heterogeneous acid/base‐catalyzed biodiesel production: Recent and future developments

Multi‐objective optimization of transesterification in biodiesel production catalyzed by immobilized lipase

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