Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production

Samuel, Olusegun David; Okwu, Modestus O.

doi:10.1080/15567036.2018.1539138

Cited by 83 publications

(31 citation statements)

References 31 publications

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“…Moreover, the separation of the BD from glycerin during settlement becomes very hard. [47][48][49] It was observed that increasing the reaction temperature enhanced the BD yield as a fact that the separation of glycerin may be accelerated by raising the reaction temperature. 41 Similar findings were also announced by Eevera and Pazhanichamy 42 and Verma et al 43 Alcohol is a basic reactant during the alcoholysis reaction because it owns a great impact on the conversion.…”

Section: Alcoholysis Of Mixed Ro and Ako With Different Alcoholsmentioning

confidence: 99%

“…Besides, the TE of the RO-AKO blend with ethanol and mixed methanol/ethanol had to be achieved at temperatures close to the boiling point of ethanol, because many researchers have found that maximum yield of EBD could be obtained at temperatures close to the boiling point of ethanol. [47][48][49] It was observed that increasing the reaction temperature enhanced the BD yield as a fact that the separation of glycerin may be accelerated by raising the reaction temperature. The conversion of mixed RO-AKO to MBD and MEBD was maximum at 60°C, whereas 65°C was the best temperature that produced the highest yield of EBD.…”

Section: Alcoholysis Of Mixed Ro and Ako With Different Alcoholsmentioning

confidence: 99%

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Valorization of mixed radish seed oil and Prunus armeniaca L. oil as a promising feedstock for biodiesel production: Evaluation and analysis of biodiesels

Fadhil

Nayyef

Sedeeq

2019

Asia-Pacific J Chem Eng

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A blend of radish oil (RO) and apricot kernel oil (AKO) was transesterified with methanol, ethanol, and their equivalent mixture, to yield methylic biodiesel (MBD), ethylic biodiesel (EBD), and methylic/ethylic biodiesel (MEBD), respectively with the refining of the so-developed fuels through adsorption onto the activated carbon prepared by ZnCl 2 activation of apricot shells. The best conversion of RO-AKO blend to MBD (96.12 ± 1.25%) and MEBD (94.23 ± 2.22) was attained at KOH concentration, 0.75% w/w of oil, alcohol/oil molar ratio, 6/1, mixing intensity, 600 rpm, reaction temperature, 60°C, and 45-min reaction period, whereas the best conditions that produced the highest yield of EBD (95.19 ± 2.0 %) were KOH concentration, 1.0 KOH % w/w of oil, ethanol/oil molar ratio, 8/1, mixing intensity, 600 rpm, reaction temperature, 65°C, and 75-min reaction period. The Fourier-transform infrared spectroscopy and 1 H nuclear magnetic resonance confirmed formation of the alkyl esters from the RO-AKO blend. The quality of MBD, EBD, and MEBD obtained from the RO-AKO blend was evaluated and found comparable with the American Society for Testing and Materials (ASTM) D6751 standards. Additionally, blending of MEBD with petrodiesel was possible, and properties of the blends were in accordance with those established by ASTM D7467-17. The methanolysis of the RO-AKO mixture followed the pseudo-first-order kinetics with activation energy of 27.27 kJ mol −1 . In conclusion, the RO-AKO mixture could be lowcost feedstock for biodiesel production. Moreover, apricot shells could be exploited as a priceless and suitable adsorbent for the adsorption of impurities found in the crude alkyl esters for its high surface area.KEYWORDS radish oil-apricot kernel oil blend, alcoholysis reaction, purification by activated carbon, fuel properties assessment, identification of biodiesel, kinetics

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Section: Alcoholysis Of Mixed Ro and Ako With Different Alcoholsmentioning

confidence: 99%

Section: Alcoholysis Of Mixed Ro and Ako With Different Alcoholsmentioning

confidence: 99%

Valorization of mixed radish seed oil and Prunus armeniaca L. oil as a promising feedstock for biodiesel production: Evaluation and analysis of biodiesels

Fadhil

Nayyef

Sedeeq

2019

Asia-Pacific J Chem Eng

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“…Feedforward and feedback neural network architectures are the more preferred network topology for reasons of processing speed, scalability, fault tolerance, and performance (Mozaffari et al, 2019). Its application to the modeling and optimization of biodiesel production has been investigated (Samuel and Okwu, 2019;Selvaraj et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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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“…Specifically, intermediate ketonic acids formed via the transamination of amino acids are decarboxylated by yeast hydrase leading to the production of BC aldehydes which are subsequently reduced to corresponding alcohols (Derrick & Large, 1993). BCAA, such as isoamyl acetate (banana‐like aroma) and 2‐phenylethyl acetate (rose petal‐like aroma) are important aroma components of wine and are formed by the esterification of BC alcohols and acyl CoA in the presence of alcohol acyltransferase (Samuel & Okwu, 2019; Wei, Zhang, Yuan, Dai, & Yue, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…aroma) are important aroma components of wine and are formed by the esterification of BC alcohols and acyl CoA in the presence of alcohol acyltransferase (Samuel & Okwu, 2019;Wei, Zhang, Yuan, Dai, & Yue, 2019).…”

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confidence: 99%

Nitrogen metabolism of branched‐chain alcohols acetates in jujube wine assessed by ¹³ C‐labeling

Xia

Liu

Zhao

et al. 2020

J. Food Process. Preserv.

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Winemakers employ a variety of techniques and tools to produce wines with specific flavor profiles. These volatile metabolites, which are derived from sugar and metabolism of amino acid by yeasts, include esters, carbonyls, volatile fatty acids, sulphur compounds, and higher alcohols. Higher alcohols, also known as fusel alcohols, are quantitatively the largest group of aroma compounds in many alcoholic beverages (Amerine et al., 1980; Lilly, Bauer, & Styger, 2006). They are identified by a strong, pungent smell, and taste and can have a significant effect on the sensorial quality and character of wine and brandy

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Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production

Cited by 83 publications

References 31 publications

Valorization of mixed radish seed oil and Prunus armeniaca L. oil as a promising feedstock for biodiesel production: Evaluation and analysis of biodiesels

Valorization of mixed radish seed oil and Prunus armeniaca L. oil as a promising feedstock for biodiesel production: Evaluation and analysis of biodiesels

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

Nitrogen metabolism of branched‐chain alcohols acetates in jujube wine assessed by ¹³ C‐labeling

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Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production

Cited by 83 publications

References 31 publications

Valorization of mixed radish seed oil and Prunus armeniaca L. oil as a promising feedstock for biodiesel production: Evaluation and analysis of biodiesels

Valorization of mixed radish seed oil and Prunus armeniaca L. oil as a promising feedstock for biodiesel production: Evaluation and analysis of biodiesels

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

Nitrogen metabolism of branched‐chain alcohols acetates in jujube wine assessed by 13 C‐labeling

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Nitrogen metabolism of branched‐chain alcohols acetates in jujube wine assessed by ¹³ C‐labeling