Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil

Ultrasonics Sonochemistry

Volumn 23, Issue , 2015, Pages 192-200

  Prakash Maran, J ^a   Priya, B ^b  

^a KONGU ENGINEERING COLLEGE   (India)

^b SRM INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

Author keywords

ANN; Biodiesel; Muskmelon oil; RSM; Two step in situ process; Ultrasound

Indexed keywords

BIODIESEL; CATALYSTS; DATA FLOW ANALYSIS; MEAN SQUARE ERROR; MESOPOROUS MATERIALS; METHANOL; NEURAL NETWORKS; POTASSIUM HYDROXIDE; SURFACE PROPERTIES; ULTRASONICS;

ABSOLUTE AVERAGE DEVIATION; ARTIFICIAL NEURAL NETWORK APPROACH; CENTRAL COMPOSITE ROTATABLE DESIGN; COEFFICIENT OF DETERMINATION; FATTY ACID METHYL ESTER; MUSKMELON OIL; RESPONSE SURFACE METHODOLOGY; ROOT MEAN SQUARE ERRORS;

FATTY ACIDS;

BIODIESEL; FATTY ACID; FATTY ACID ESTER; GLYCEROL; LINOLEIC ACID; METHANOL; MUSKMELON OIL; OIL; PALMITIC ACID; STEARIC ACID; UNCLASSIFIED DRUG; BIOFUEL; ESTER; SULFURIC ACID; VEGETABLE OIL;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOFUEL PRODUCTION; ESTERIFICATION; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; INTERMETHOD COMPARISON; NONHUMAN; PREDICTION; REACTION ANALYSIS; REACTION TIME; RESPONSE SURFACE METHOD; SIGNAL NOISE RATIO; TEMPERATURE; TRANSESTERIFICATION; ULTRASOUND; CHEMICAL MODEL; CHEMISTRY; COMPARATIVE STUDY; CUCUMIS;

BIOFUELS; CUCUMIS; ESTERIFICATION; ESTERS; FATTY ACIDS; MODELS, CHEMICAL; NEURAL NETWORKS (COMPUTER); PLANT OILS; SULFURIC ACIDS; ULTRASONICS;

EID: 84912055247     PISSN: 13504177     EISSN: 18732828     Source Type: Journal    
DOI: 10.1016/j.ultsonch.2014.10.019     Document Type: Article

References (40)

1. A. Kumar, S. Sharma, Potential non-edible oil resources as biodiesel feedstock: an Indian perspective, Renew. Sust. Energy Rev. 15 (2011) 1791-1800.
2. A. Dhar, R. Kevin, A.K. Agarwal, Production of biodiesel from high-FFA neem oil and its performance, emission and combustion characterization in a single cylinder DICI engine, Fuel Process. Technol. 97 (2012) 118-129.
3. U. Rashid, F. Anwar, B.R. Moser, G. Knothe, Moringa oleifera oil: a possible source of biodiesel, Bioresour. Technol. 99 (2008) 8175-8179.
4. A.S. Ramadhas, S. Jayaraj, C. Muraleedharan, Biodiesel production from high FFA rubber seed oil, Fuel 84 (2005) 335-340.
5. S.K. Karmee, A. Chadha, Preparation of biodiesel from crude oil of Pongamia pinnata, Bioresour. Technol. 96 (2005) 1425-1429.
6. H. Jaliliannosrati, N.A.S. Amin, A. Talebian-Kiakalaieh, I. Noshadi, Microwave assisted biodiesel production from Jatropha curcas L. seed by two-step in situ process: optimization using response surface methodology, Bioresour. Technol. 136 (2013) 565-573.
7. X. Deng, Z. Fang, Y.-H. Liu, C.-L. Yu, Production of biodiesel from Jatropha oil catalyzed by nanosized solid basic catalyst, Energy 36 (2011) 777-784.
8. B.R. Moser, F.V. Steven, Evaluation of alkyl esters from Camelina sativa oil as biodiesel and as blend components in ultra low-sulfur diesel fuel, Bioresour. Technol. 101 (2010) 646-653.
9. B.R. Moser, F.V. Steven, Coriander seed oil methyl esters as biodiesel fuel: unique fatty acid composition and excellent oxidative stability, Biomass Bioenergy 34 (2010) 550-558.
10. L. Lin, Z. Cunshan, S. Vittayapadung, S. Xiangqian, D. Mingdong, Opportunities and challenges for biodiesel fuel, Appl. Energy 88 (2011) 1020-1031.
11. X. Yin, H. Ma, Q. You, Z. Wang, J. Chang, Comparison of four different enhancing methods for preparing biodiesel through transesterification of sunflower oil, Appl. Energy 91 (2012) 320-325.
12. I. Worapun, K. Pianthong, P. Thaiyasuit, Optimization of biodiesel production from crude palm oil using ultrasonic irradiation assistance and response surface methodology, J. Chem. Technol. Biotechnol. 87 (2) (2012) 189-197.
13. N. Shihani, B.K. Kumbhar, M. Kulshreshtha, Modeling of extrusion process using response surface methodology and artificial neural networks, J. Eng. Sci. Technol. 1 (2006) 31-40.
14. K.M. Desai, S.A. Survase, P.S. Saudagar, S.S. Lele, R.S. Singhal, Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan, Biochem. Eng. J. 41 (2008) 266-273.
15. M.B. Kasiri, H. Aleboyeh, A. Aleboyeh, Heterogeneous photo-fenton process with response surface methodology and artificial neural networks, Environ. Sci. Technol. 42 (2008) 7970-7975.
16. M.G. Moghaddam, M. Khajeh, Comparison of response surface methodology and artificial neural network in predicting the microwave-assisted extraction procedure to determine zinc in fish muscles, Food Sci. Nutr. 2 (2011) 803-808.
17. R. Chakraborty, H. Sahu, Intensification of biodiesel production from waste goat tallow using infrared radiation: process evaluation through response surface methodology and artificial neural network, Appl. Energy 114 (2014) 827-836.
18. E. Betiku, S.O. Ajala, 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, Ind. Crops Prod. 53 (2014) 314-322.
19. K.M. Rajković, J.M. Avramović, P.S. Milić, O.S. Stamenković, V.B. Veljković, Optimization of ultrasound-assisted base-catalyzed methanolysis of sunflower oil using response surface and artificial neural network methodologies, Chem. Eng. J. 215-215 (2013) 82-89.
20. L.C. Meher, D.V. Sagar, S.N. Naik, Technical aspect of biodiesel production by transesterification-a review, Renew. Sust. Energy Rev. 10 (2006) 248-268.
21. M. Holčapek, P. Jandera, J. Fischer, B. Prokeš, Analytical monitoring of the production of biodiesel by high-performance liquid chromatography with various detection methods, J. Chromatogr. A 858 (1999) 13-31.
22. J.P. Maran, V. Mekala, S. Manikandan, Modeling and optimization of ultrasound-assisted extraction of polysaccharide from Cucurbita moschata, Carbohydr. Polym. 92 (2013) 2018-2026.
23. J. Prakash Maran, S. Manikandan, Response surface modeling and optimization of process parameters for aqueous extraction of pigments from prickly pear (Opuntia ficus-indica) fruit, Dyes Pigm. 95 (2012) 465-472.
24. J. Prakash Maran, V. Sivakumar, K. Thirugnanasambandham, R. Sridhar, Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L, Alex. Eng. J. 52 (2013) 507-516.
25. M.Y. Rafiq, G. Bugmann, D.J. Easterbrook, Neural network design for engineering applications, Comput. Struct. 79 (17) (2001) 1541-1552.
26. J.P. Maran, S. Manikandan, K. Thirugnanasambandham, C. Vigna Nivetha, R. Dinesh, Box-Behnken design based statistical modeling for ultrasoundassisted extraction of corn silk polysaccharide, Carbohydr. Polym. 92 (2013) 604-611.
27. J.P. Maran, B. Priya, S. Manikandan, Modeling and optimization of supercritical fluid extraction of anthocyanin and phenolic compounds from Syzygium cumini fruit pulp, J. Food Sci. Technol. 51 (2014). 1983-1946.
28. J. Prakash Maran, S. Manikandan, V. Mekala, Modeling and optimization of betalain extraction from Opuntia ficus indica using Box-Behnken design with desirability function, Ind. Crops Prod. 49 (2013) 304-311.
29. J.P. Maran, B. Priya, Ultrasound-assisted extraction of polysaccharide from Nephelium lappaceum L fruit peel, Int. J. Biol. Macromol. 70 (2014) 530-536.
30. J.P. Maran, S. Manikandan, C. Vigna Nivetha, R. Dinesh, Ultrasound assisted extraction of bioactive compounds from Nephelium lappaceum L. fruit peel using central composite face centered response surface design, Arab. J. Chem. (2013). 10/1016/j.arabjc.2013.002.007.
31. J. Prakash Maran, S. Manikandan, B. Priya, P. Gurumoorthi, Box-Behnken design based multi-response analysis and optimization of supercritical carbon dioxide extraction of bioactive flavonoid compounds from tea (Camellia sinensis L.) leaves, J. Food Sci. Technol. (2013), doi: 10.1007/s13197-013-0985-z.
32. P. Balasubramani, P.T. Palaniswamy, R. Visvanathan, V. Thirupathi, A. Subbarayan, J. Prakash Maran, Microencapsulation of garlic oleoresin using maltodextrin as wall material by spray drying technology, Int. J. Biol. Macromol. 72 (2015) 210-217.
33. D.D. Pukale, G.L. Maddikeri, P.R. Gogate, A.B. Pandit, A.M. Pratap, Ultrasound assisted transesterification of waste cooking oil using heterogeneous solid catalyst, Ultrason. Sonochem. 22 (2015) 278-286.
34. D.S. Badkar, K.S. Pandey, G. Buvanashekaran, Development of RSM and ANNbased models to predict and analyze the effects of process parameters of laserhardened commercially pure titanium on heat input and tensile strength, Int. J. Adv. Manuf. Technol. 65 (2013) 1319-1338.
35. S.M. Ghoreishi, E. Hedari, Extraction of Epigallocatechin-3-gallate from green tea via supercritical fluid technology: neural network modeling and response surface optimization, J. Supercrit. Fluids 74 (2013) 128-136.
36. S. Haykin, Neural Networks: A Comprehensive Foundation, second ed., Prentice Hall International, New Jersey, 1999.
37. K. Swingler, Applying Neural Networks: A Practical Guide, Academic Press, London, 1996.
38. D. Bingöl, M. Hercan, S. Elevli, E. Kilic¸, Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin, Bioresour. Technol. 112 (2012) 111-115.
39. F. Geyikc¸i, E. Kilic¸, S. Çoruh, S. Elevli, Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN, Chem. Eng. J. 183 (2012) 53-59.
40. J.P. Maran, B. Priya, Ultrasound-assisted extraction of pectin from sisal waste, Carbohydr. Polym. (2014). 10/1016/j.carbpol.2014.07.058.