Artificial neural networks modelling for biodiesel production from waste cooking oil

Öz

The objective of the present work is to develop models inculcating the effect of operating conditions of waste cooking oil methyl esters production in the reactive distillation column, namely waste cooking oil (WCO) flow rate, methanol/WCO molar ratio, reboiler heat duty and feed inlet temperature on the estimation of parameters like the biodiesel conversion by using Artificial Neural Networks technique. In our study, at the maximum biodiesel conversion of 99.48% and at steady state time of 1.69 hour were determined as WCO flow rate of 2.90 ml/min, methanol/oil molar ratio of 8.19 and reboiler heat duty of 0.419 kW. Experiments were conducted in the laboratory and the results obtained were used to develop the ANN model using MATLAB. The developed model was in good agreement with the experimental values.

Anahtar Kelimeler

• Biodiesel
• artificial neural network
• Simulation
• Waste cooking oil

Kaynakça

1. Ray S.K. and Prakash O., 2017. “Bio-Diesel Derived from Waste Vegetable Oil as an Alternative Fuel for Diesel Engine: A Review”, Renewable Energy and Its Innovative Technologies, Proceedings of ICEMIT 2017, Vol:1, 210-231.
2. Zhang Y., Dube M.A., McLean D.D. and Kates M. 2003. “Biodiesel production from waste cooking oil: 1. Process design and technological assessment”. Bioresource Technology, 89, 1-16.
3. Sharma, Y.C., Singh, B. and Upadhyay, S.N. 2008. “Advancements in development and characterization of biodiesel: a review”. Fuel, 87, 2355–2373.
4. Liu, X., He, H., Wang, Y., Zhu, S. and Piao, X. 2008. “Transesterification of soybean oil to biodiesel using CaO as a solid base catalyst”. Fuel, 87, 216–221.
5. Kouzu, M., Kasuno, T., Tajika, M., Sugimoto, Y., Yamanaka, S. and Hidaka, J. 2008. “Calcium oxide as a solid base catalyst for transesterification of soybean oil and its application to biodiesel production”. Fuel, 87, 2798–2806.
6. DiSerio, M., Tesser R., Pengmei, L. and Santacesaria, E. 2008. “Heterogeneous catalysts for biodiesel production”. Energy and Fuels, 22:201–17.
7. Zabeti, M., Daud, WMAW and Aroua, MK. 2009. “Activity of solid catalysts for biodiesel production: a review”. Fuel Process Technol, 90:770–7.
8. Helwani, Z., Othman, MR., Aziz, N., Kim, J. and Fernando WJN. 2009. “Solid heterogeneous catalysts for transesterification of triglycerides with methanol: a review”. Appl Catal A Gen., 363:1–10.

9. Yan, S., DiMaggio, C., Mohan, S., Kim, M., Salley, SO. and Ng, KYS. 2010. “Advancements in heterogeneous catalysis for biodiesel synthesis”. Top Catal, 53:721–36.
10. Zhang, Y., Dubé, M.A., McLean, D.D. and Kates, M. 2008. “Biodiesel production from waste cooking oil” vol: 1. Process design and technology assessment. Bioresour. Technol., 99, 1131–1140.
11. Festel, G.W. 2008. “Biofuels—economic aspects, chemical engineering & technology”. Chem. Eng. Technol., 31, 715–720.
12. Kulkarni, M.G. and Dalai, A.K. 2006. “Waste cooking oil – an economical source for biodiesel: a review”. Ind. Eng. Chem. Res., 45, 2901–2913.
13. Anderson J.A, 1999. An Introduction to Neural Networks Prentice-Hall of India, Pvt Ltd New Delhi.
14. Rumelhart D. E. & McClleland, 1986. Back Propagation Training Algorithm Processing, M.I.T Press, Cambridge Massachusetts.
15. Machavaram R., Prakash C. J., Hifjur R 2009. “Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA”. Fuel, 88 (5), pg. 868-875.
16. Atiya B., Devyani V., Surendra K., Payal C., Gupta V. K. 2017. “Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation”. Int J Ind Chem, 8:253–262.
17. Kumar R.S., Sureshkumar K., Velraj R. 2015. “Optimization of biodiesel production from Manilkara zapota (L.) seed oil using Taguchi method”. Fuel, 140, 90‒96.
18. Banarjee A., Varshney D., Kumar S., Chaudhary P., Gupta V. K.(2017) Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation. Int J Ind Chem 8:253–262
19. Xiaoyun Yue, Yehua Chen, And Guoyang Chang (2018) Accurate Modeling Of Biodiesel Production From Castor Oil Using ANFIS. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2018, vol. 40, no. 4, 432–438.
20. Soares I. P., Rezende T. F., Silva R. C., Castro E. V. R. and Fortes I. C. P., “Multivariate Calibration by Variable Selection for Blends of Raw Soybean Oil/Biodiesel from Different Sources Using Fourier Transform Infrared Spectroscopy (FTIR) Spectra Data”, Energy Fuels, 22: 2079–83, (2008).
21. Dube M. A., Zheng S., Mclean D. D. and Kates M. J. A., “A Comparison of Attenuated Total Reflectance–FTIR Spectroscopy and GPC for Monitoring Biodiesel Production”, J. Am. Oil. Chem. Soc., 81: 599–603, (2004).
22. Mahamuni N. N. and Adewuyi Y. G., “Fourier Transform Infrared Spectroscopy (FTIR) Method to Monitor Soy Biodiesel and Soybean Oil in Transesterification Reactions, Petrodiesel–Biodiesel Blends, and Blend Adulteration with Soy Oil”, Energy & Fuels, 23: 3773–82, (2009).
23. Sabrina N. R., Vany P. F., Leandro S. O. and Adriana S. F., “FTIR Analysis for Quantification of Fatty Acid Methyl Esters in Biodiesel Produced by Microwave–Assisted Transesterification”, Int. J. of Environmental Science and Development, 6: 964–969, (2015).
24. Matlab 11, The MathWorks, Inc., Apple Hill Drive, Natick, MA.,2016.