Prediction and Optimization of Fish Biodiesel Characteristics Using Permittivity Properties

Authors
M. Zarein 1
M. H. Khoshtaghaza 1
B. Ghobadian 1
H. Ameri Mahabadi 2
1 Department of Mechanical and Biosystems Engineering, Tarbiat Modares University, Tehran, Islamic Republic of Iran.
2 Department of Electrical Engineering, University of Malaya (UM), Kuala Lumpur, Malaysia
Abstract
The purpose of this research was to predict and optimize the fish biodiesel characteristics using its permittivity properties. The parameters of biodiesel permittivity properties such as έ, dielectric constant, and ε″, loss factor at microwave frequencies of 434, 915, and 2,450 MHz, were used as input variables. The fish biodiesel characteristics, as Fatty Acid Methyl Ester (FAME) content and flash point at three different levels of reaction time 3, 9, and 27 min and catalyst concentrations 1, 1.5, and 2% w woil-1, were selected as output parameters for the models. Linear Regression (LR), the Multi-Layer Perceptron (MLP), and the Radial Basis Function (RBF) as the methods of Artificial Neural Networks (ANN), and the response surface methodology were compared for prediction and optimization of FAME content and flash point. A comparison of the results showed that the RBF recorded higher coefficient of determination at frequency of 2,450 MHz as 0.999 and 0.988 and lower root mean square error as 0.009 and 0.023 for FAME content and flash point, respectively. The optimum condition was obtained using RSM by FAME content of 89.88% and flash point of 152.7°C with desirability of 0.998.

Keywords

Subjects

1. Abedin, M. Masjuki, H. H. Kalam, M. A. Sanjid, A. Ashraful Rahman, S. M. and Rizwanul Fattah, I. M. 2014. Performance, Emissions, and Heat Losses of Palm and Jatropha Biodiesel Blends in a Diesel Engine. Ind. Crops Prod., 59: 96-104.
2. Alberta, N. A., Frederik, R., Voort, V. D. and Benjamin, K. 2009. FTIR Determination of Free Fatty Acids in Fish Oils Intended for Biodiesel Production. Ind. Crops Prod., 59: 96-104.
3. Amid, S. and Mesri Gundoshmian, T. 2016. Prediction of Output Energy Based on Different Energy Inputs on Broiler Production Using Application of Adaptive Neural-Fuzzy Inference System. Agri. Sci. Dev., 5: 14-21.
4. Balabin, R. M., Safieva, R. Z. and Lomakina, E. I. 2010. Gasoline Classification Using Near Infrared (NIR) Spectroscopy Data: Comparison of Multivariate Techniques. Analy. Chimi. Acta, 671: 27-35.
5. Balabin, R. M., Lomakina, E. I. and Safieva, R. Z. 2011. Neural Network (ANN) Approach to Biodiesel Analysis: Analysis of Biodiesel Density, Kinematic Viscosity, Methanol and Water Contents using Near Infrared (NIR) Spectroscopy. Fuel, 90: 2007-2015.
6. Betiku, E. and Ajala, S. O. 2014. 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: 314–322.
7. Chakraborty, R. and Sahu, H. 2014. Intensification of Biodiesel Production from Waste Goat Tallow using Infrared Radiation: Process Evaluation through Response Surface Methodology and Artificial Neural Network. Appl. Ener., 114: 827–836.
8. Cohen, S. and Intrator, N. 2003. A Study of Ensemble of Hybrid Networks with Strong Regularization. Multi. Classi. Sys., 4: 227–235.
9. Corach, J., Sorichetti, P. A. and Romano, S.D. 2012. Electrical Properties of Mixtures of Fatty Acid Methyl Esters from Different Vegetable Oils. Int. J. Hydro. Ener., 37: 14735–14739.
10. Corach, J., Sorichetti, P. A. and Romano, S. D. 2014. Electrical Properties of Vegetable Oils between 20 Hz and 2 MHz. Int. J. Hydro. Ener., 39: 8754–8758.
11. Corach, J., Sorichetti, P. A. and Romano, S. D. 2015. Electrical and Ultrasonic Properties of Vegetable Oils and Biodiesel. Fuel, 139: 466–471.
12. Dawson, C. W. and Wilby, R. 1998. An Artificial Neural Network Approach to Rainfall-Runoff Modeling. Hydrolo. Sci. J., 43: 47–66.
13. Deh Kiani, M. K., Ghobadian, B., Tavakoli, T., Nikbakht, A. M. and Najafi, G. 2010. Application of Artificial Neural Networks for the Prediction of Performance and Exhaust Emissions in SI Engine using Ethanol- Gasoline Blends. Energy, 35: 65–69.
14. Duren, I. V., Voinov, A., Arodudu, O. and Firrisa, M. T. 2015. Where to Produce Rapeseed Biodiesel and Why? Mapping European Rapeseed Energy Efficiency. Renew. Ener., 74: 49-59.
15. Flores, I. S., Godinho, M. S., De Oliveira, A. E., Alcantara, G. B., Monteiro, M. R., Menezes, S. M. C. and Lião, L. M. 2012. Discrimination of Biodiesel Blends with 1H NMR Spectroscopy and Principal Component Analyses. Fuel, 99: 40-44.
16. Foody, G. M. 2004. Supervised Image Classification by MLP and RBF Neural Networks with and without an Exhaustively Defined Set of Classes. Int. J. of Remote Sens., 25: 3091–3104.
17. García-Moreno, P. J., Khanum, M., Guadix, A. and Guadix, E. M. 2014. Optimization of Biodiesel Production from Waste Fish Oil. Renew. Ener., 68: 618-624.
18. Garg, A., Vijayaraghavan, V., Tai, K., Singru, P. M., Jain, V. and Krishnakumar, N. 2015. Model Development Based on Evolutionary Framework for Condition Monitoring of a late Machine. Measurement, 73: 95–110.
19. Ghazali, W. N. M. W. Mamat, R. Masjuki, H. H. and Najafi, G. 2015. Effects of Biodiesel from Different Feedstocks on Engine Performance and Emissions: A Review. Renew. Sustain. Ener. Rev., 51: 585-602.
20. Ghobadian, B., Tavakoli Hashjin, T. and Rahimi, H. 2008. Production of Bioethanol and Sunflower Methyl Ester and Investigation of Fuel Blend Properties. J. Agri. Sci. Tech., 10: 225-232
21. Gonzalez, P. L., Sorichetti, P. A. and Romano, S. D. 2008. Electric Properties of Biodiesel in the Range from 20 Hz to 20 MHz. Comparison with Diesel Fossil Fuel. Int. J. Hydro. Ener., 33: 3531–3537.
22. Halim, S. F. A., Kamaruddin, A. H. and Fernando, W. J. N. 2009. Continuous Biosynthesis of Biodiesel from Waste Cooking Palm Oil in a Packed Bed Reactor: Optimization using Response Surface Methodology (RSM) and Mass Transfer Studies. Biores. Tech., 100: 710-716.
23. Joshi, A., Pund, S., Nivsarkar, M., Vasu, K. and Shishoo, C. 2008. Dissolution Test for Site-Specific Release Ionized Pellets in USP Apparatus 3 (Reciprocating Cylinder): Optimization using Response Surface Methodology. Eur. J. Pharm. Biopharm., 69: 769–775.
24. Khoshnevisan, B., Rafiee, S., Omid, M. and Mousazadeh, H. 2014. Prediction of Potato Yield Based on Energy Inputs Using Multi-Layer Adaptive Neuro-Fuzzy Inference System. Measurement, 47: 521–530.
25. Kouzu, M. and Hidaka, J. S. 2012. Transesterification of Vegetable Oil into Biodiesel Catalyzed by CaO: A Review. Fuel, 93: 1-12.
26. Leevijit, T., Tongurai, C., Prateepchaikul, G. and Wisutmethangoon, W. 2008. Performance Test of a 6-Stage Continuous Reactor for Palm Methyl Ester Production. Bioresour. Tech., 99: 214-221.
27. Liptak, B. G. 2003. Instrument Engineers ’Handbook: Process Measurement and Analysis. 4th Edition, Section 8.52, Chapter 8, Vol. I, CRC Press.
28. Maghami, M., Sadrameli, S. M. and Ghobadian, B. 2015. Production of Biodiesel from Fishmeal Plant Waste Oil using Ultrasonic and Conventional Methods. App. Ther. Eng., 75: 575-579.
29. Mejia, J. D., Salgado, N. and Orrego, C. E. 2013. Effect of Blends of Diesel and Palm-Castor Biodiesels on Viscosity, Cloud point and Fash Point. Ind. Crops Prod., 43: 791– 797.
30. Moorthi, N. S. V., Franco, P. A. and Ramesh, K. 2015. Application of Design of Experiments and Artificial Neural Network in Optimization of Ultrasonic Energy Assisted Transesterification of Sardinella Longiceps Fish Oil to Biodiesel. J. Chin. Inst. Eng., 38: 731–741.
31. Muley, P. D. and Boldor, D. 2013. Investigation of Microwave Dielectric Properties of Biodiesel Components. Bioresour. Tech., 127: 165–174.
32. Oliveira, L. S., Franca, A. S., Camargos, R. R. and Ferraz, V. P. 2008. Coffee Oil as a Potential Feedstock for Biodiesel Production. Bioresour Tech., 99: 3244-3250.
33. Parkar, P. A., Choudhary, H. A. and Moholkar, V. S. 2012. Mechanistic and Kinetic Investigations in Ultrasound Assisted Acid Catalyzed Biodiesel Synthesis. Chem. Eng. J., 187: 248–260.
34. Rajkovic, K. M., Avramovic, J. M., Milic, P. S., Stamenkovic, O. S. and Veljkovic, V. 2013. Optimization of Ultrasound-Assisted Base-Catalyzed Methanolysis of Sunflower Oil using Response Surface and Artificial Neural Network Methodologies. Chem. Eng. J., 215: 82–89.
35. Ramadhas, A. S., Jayaraj, S., Muraleedharan, C. and Padmakumari, K. 2006. Artificial Neural Networks Used for the Prediction of the Cetane Number of Biodiesel. Renew. Ener., 31: 2524–2533.
36. Ramos, M. J., Fernández, C. M., Casas, A., Rodríguez, L. and Pérez, Á. 2009. Influence of Fatty Acid Composition of Raw Materials on Biodiesel Properties. Bioresour Tech., 100: 261-268
37. Rodriguez, R. P., Melo, E. A., Pérez, L. G. and Verhelst, S. 2014. Conversion of By-Products from the Vegetable Oil Industry into Biodiesel and Its Use in Internal Combustion Engines: A Review. Ener. Braz. J. Chem. Eng., 65: 255–261.
38. Romano, S. D. and Sorichetti, P. A. 2011. Dielectric Relaxation Spectroscopy in Biodiesel Production and Characterization. 1st Edition, Springer Verlag, London.
39. Santos, F. F. P., Rodrigues, S. and Fernandes, F. A. N. 2009. Optimization of the Production of Biodiesel from Soybean Oil by Ultrasound Assisted Methanolysis. Fuel Process. Technol., 90: 312–316.
40. Sarve, A. N., Varma, M. N. and Sonawane, S. S. 2015. Response Surface Optimization and Artificial Neural Network Modeling of Biodiesel Production from Crude Mahua (Madhuca indica) Oil Under Supercritical Ethanol Conditions Using CO2 as Co-Solvent. Royal Soci. Chem., 5: 69702–69713.
41. Silva, L. M., Alves Filho, E. G., Simpson, A. J., Monteiro, M. R., Cabral, E., Ifa, D. and Venâncio, T. 2017. DESI-MS Imaging and NMR Spectroscopy to Investigate the Iinfluence of Biodiesel in the Structure of Commercial Rubbers. Talanta, 173: 22-27.
42. Singh, T. N., Kanchan, R., Verma, A. K. and Singh, S. 2003. An Intelligent Approach for Prediction of Triaxial Properties using Unconfined Uniaxial Strength. Min. Eng. J., 5: 12–16.
43. Sorichetti, P. A. and Romano, S. D. 2005. Physico-Chemical and Electrical Properties for the Production and Characterization of Biodiesel. Phys. Chem. Liq., 43: 37–48.
44. Stamenkovic, O. S., Velickovic, A. V., Kostic, M. D., Jokovic, N. M., Rajkovic, K., Milic, P. S. and Veljkovic, V. B. 2015. Optimization of KOH-Catalyzed Methanolysis of Hempseed Oil. Ener. Conv. Manage., 103: 235–243.
45. Talebian-Kiakalaieh, A. Amin, N. A. S. and Mazaheri, H. 2013. A Review on Novel Processes of Biodiesel Production from Waste Cooking Oil. Appl. Ener., 104: 683-710.
46. Tan, K., Lee, K. and Mohamed, A. 2011. Potential of Waste Palm Cooking Oil for Catalyst-Free Biodiesel Production. Energy, 36: 2085-2088.
47. Venkatesan, P. and Anitha, S. 2006. Application of a Radial Basis Function Neural Network for Diagnosis of Diabetes Mellitus. Curr. Sci., 91: 1195–1199.
48. Vicente, G. Martinez, M. and Aracil, J. 2007. Optimisation of Integrated Biodiesel Production. Part I. A Study of the Biodiesel Purity and Yield. Bioresour Tech., 98: 1724-1733.
49. Yahyaee, R., Ghobadian, B. and Najafi, G. 2013. Waste Fish Oil Biodiesel as a Source of Renewable Fuel in Iran. Renew. Sust. Ener. Rev., 17: 312–319.
50. Yatish, K. V., Lalithamba, H. S., Suresh, R., Arun, S. B. and Kumar, P. V. 2016. Optimization of Scum Oil Biodiesel Production by using Response Surface Methodology. Proc. Saf. Env. Prot., 102: 667–672.
51. Yilmaz, I. and Kaynar, O. 2011. Multiple Regression, ANN (RBF, MLP) and ANFIS Models for Prediction of Swell Potential of Clayey Soils. Exp. Sys. with Appl., 38: 5958–5966.
52. Yilmaz, I. and Yuksek, G. 2009. Prediction of the Strength and Elasticity Modulus of Gypsum using Multiple Regression, ANN, and ANFIS Models. Int. J. Rock Mech. Mining Sci., 46: 803–810.
53. Yin, X., Ma, H., You, Q., Wang, Z. and Chang, J. 2012. Comparison of Four Different Enhancing Methods for Preparing Biodiesel through Transesterification of Sunflower Oil. Appl. Ener., 91: 320-325.
54. Ying, Y., Shao, P., Jiang, S. and Sun, P. 2009. Artificial Neural Network Analysis of Immobilized Lipase Catalyzed Synthesis of Biodiesel from Rapeseed Soapstock. IFIP Adv. Info. Commu. Tech., 294: 1239-1249.
55. Zheng, S., Kates, M., Dube, M. A. and McLean, D. D. 2006. Acid-Catalyzed Production of Biodiesel from Waste Frying Oil. Biom. Bioene., 30: 267-272.
Journal of Agricultural Science and Technology
Volume 21, Issue 2
March and April 2019
Pages 309-322