207 Investigation of Mathematical Modeling with Two Approaches for Catalytic Pyrolysis of Plastic Wastes


Abstract

Catalytic pyrolysis of plastic wastes is recently deemed as an efficacious way to tackle the severe issue of plastic waste accumulation in the globe. In order to extend the pyrolysis process to industrial scale and optimize its operational conditions, development of suitable models seems crucial. In current study, time series modeling has been investigated for a semi  industrial pyrolysis employing two distinctive multivariate modeling methods namely, Least  Square Support Vector Machine (LS-SVM)  and Artificial Neural Network (ANN). Sufficient experimental data sets were utilized to form both LS-SVM and ANN models effectively. After proper training of both models, two unseen test data sets were applied to measure the predicting performance of developed models. In both training and prediction sections,  accuracy of both models was analyzed using two statistical error measuring methods namely,  the Mean Squared Error (MSE) and the coefficient of determination (R2). Both methods achieved the R2 of more than 0.99 and the MSE of less than 0.1 in training part. In prediction part, the resulted R2 values for both models were analogous to values obtained in training part while the MSE values were slightly more than those values were achieved in training part. Finally, obtained outcomes from training and prediction parts revealed that both ANN and LS-SVM methods are quite reliable and precise tools for time series modeling of pyrolysis process.

 

Keyword: Plastic Wastes, Catalytic Pyrolysis, Time Series Modelling, Artificial Neural Network, Least Square-Support Vector Machine

 

 

References:

[1] K. Gobin, G. Manos. Polymer degradation to fuels over microporous catalysts as a novel tertiary plastic recycling method. Polymer Degradation and Stability 83 (2004):267–279.

[2] Ch. Tang, Yu.Wang. Catalytic effect of Al–Zn composite catalyst on the degradation of PVC-containing polymer mixtures into pyrolysis oil. Polymer Degradation and Stability 81

(2003):89–94.

[3] Donahue W.S., Brandt J.C.Pyrolysis: Types, Processes, and Industrial Sources and Products.Nova Science Pub Inc, (2009).

[4] Ding W.B., Liang J., Anderson L.L., “Thermal and catalytic degradation of high density polyethylene and commingled post-consumer plastic waste”, Fuel Processing Technology, Vol 51(, 1997): 47–62.

[5] Manos G, Garforth A, Dwyer J. Catalytic Degradation of high-density polyethylene on an ultra-stable Y zeolite. Nature of initial polymer reactions, pattern of formation of gas and liquid products, temperature effects. Ind Eng Chem Res 39(2000):1203–8.

[6] Manos G, Garforth A, Dwyer J. Catalytic Degradation of High Density Polyethylene over different Zeolitic structures. Ind Eng Chem Res 39(2000):1198–202.

[7] Demirbas A., “Pyrolysis of municipal plastic waste for recovery of gasoline range hydrocarbons”, Journal of Analytical and Applied Pyrolysis. 72(2004): 97–102.

[8] Hernandez M. D., Garcia A. N., Marcilla A. Study of the gases obtained in thermal and catalytic flash pyrolysis of HDPE in a fluidized  bed  reactor. Journal of Analytical and Applied Pyrolysis.73 (2005): 314–22.

[9] Miskolczi N., Bartha L., Deak G., Jo ver B., Kallo D. Thermal and thermo-catalytic degradation of high-density polyethylene waste. Journal of Analytical and Applied Pyrolysis.72 (2004): 235–42.

[10] Murata  K., Sato K., Sakata Y. Effect of pressure on thermal degradation of polyethylene. Journal of Analytical and Applied Pyrolysis. 71(2004):  569–89.

[11] Sakata Y., Uddin M.A., Muto A. Degradation of polyethylene and polypropylene into fuel oil by using solid acid and non-acid  catalysts. Journal of Analytical and Applied Pyrolysis. 51(1995): 135–55.

[12] Ohkita H., Nishiyama R., Tochihara Y., Mizushima T., Kakuta N., Morioka Y. Acid properties of silica-alumina catalysts and catalytic degradation of polyethylene. Industrial & Engineering Chemistry Research. 32(1993): 3112–6.

[13] Lin Y. H., Yang M. H. Catalytic reactions of post-consumer polymer waste over fluidised cracking catalysts for producing hydrocarbons. Journal of Molecular Catalysis A Chemical. 231(2005): 113–22.

[14] Wall L.L., Madorsky S.L., Brown D.W., Straus S. The depolymerization of poly methylene and polyethylene. Journal of the American Chemical Society, Vol 76, pp.343–7, 1957.Int. J Sci. Emerging Tech .5(2013):263.

[15] Zeng G.M., Yuan X.Z., Yin Y.Y., Hu T.J., Yan G. Manufacture of liquid fuel by catalytic cracking waste plastics in a fluidized bed. Energy Sources. 25(2003): 577–90.

[16] Anders G., Burkhardt I., Illgen U., Schultz I.W., Scheve J. The influence of HZSM-5 zeolite on the product composition aftercracking of high boiling hydrocarbon Fractions. Applied Catalysis A General. 62(1990): 271–8.

[17] Al-Salem SM, Lettieri P., Baeyens J.  Recycling and recovery routes of plastic solid waste (PSW): A review.  Waste Manage29 (2009): 2625-43.

[18] A. Lopez-Urionabarreneche, I. de Marco. Empiric model for the prediction of packaging waste pyrolysis yields. Applied Energy 98 (2012):524–532.

[19] Mjolsness E., DeCoste D. Machine Learning for Science: State of the Art and Future Prospects. Science 293 (2001): 2051-2055.

[20] Kecman V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge, (2001):11255-8.

[21] Chang Park T., Kim U.S. Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems. Korean J. Chem. Eng. 27(2010):1063-1071.

[22] Bas, D., F. C. Dudak. Modeling and optimization III: Reaction rate estimation using artificial neural network (ANN) without a kinetic model. Journal of Food Engineering. 79(2007): 622-628.

[23] Chang, C. W., W. C. Yu. A study on the enzymatic hydrolysis of steam exploded napiergrass with alkaline treatment using artificial neural networks and regression analysis. Journal of the Taiwan Institute of Chemical Engineers. 42(2011): 889-894.

[24] Basheer, I. A., M. Hajmeer. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43(2000): 3-31.

[25] Ramadhas AS, Jayaraj S, Muraleedharan C, Padmakumari K. Artificial neural networks used for the prediction of the cetane number of biodiese. Renewable Energy. 31(2006):2524–33.

[26] Yuste AJ, Dorado MP. A neural network approach to simulate biodiesel production from waste olive oil. Energ Fuel. 20 (2006)399–402.

[27] V. Vapnik. Statistical learning theory. John Wiley, New York (1998).

[28] V. Vapnik. The nature of statistical learning theory. Springer Verlag, New York (1995).

[29] Thissen U., Van Brakel R. Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems 69 (2003):35– 49.

[30] Balabin R. M., Lomakina E. I. Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 136 (2011):1703–1712.

[31] W. Haifeng, H. Dejin.Comparison of SVM and LS-SVM for regression, Neural Netw. Brain (2005): 279–283.

[32] Suykens J.A.K., Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9 (1999): 293–300.

[33] Suykens J.A.K., Van Gestel T., De Brabanter J. Least Squares Support Vector Machines. World Scientific in press .ISBN 981-238-151-1.

[34] Vapnik V., Suykens J.A.K., Vandewalle J. Nonlinear Modeling: Advanced Black-Box techniques. Kluwer Academic Publishers (1998).

[35] Hertz, J., Krogh, A., and Palmer, R. G., Introduction to the Theory of Neural Computation. Addison-Wesley, Reading, MA (1991).

[36] Parberry I. A primer on the complexity theory of neural networks, in Formal Techniques in Artificial Intelligence: A Sourcebook. Banerji R. B., Ed., Elsevier Science Publishers B. V. (North-Holland),Amsterdam( 1990): 217.

[37] Kawato M., Furukawa K., and Suzuki, R. A hierarchical neural network model for control and learning of voluntary movement’. Biol. Cybern. 57(1987): 169.

[38] Siegelmann H., Sontag E. D. On the computational power of neural nets .JCSS.50 (1995): 132.

[39] Rasul Jan M., Shah J., Gulab H. Catalytic degradation of waste high-density polyethylene into fuel products usingBaCO3 as a catalyst. Fuel Processing Technology 91 (2010):1428–1437.

[40] Rajasekaran S, Pai GAV. Neural networks fuzzy logic and genetic algorithms –synthesis and applications. Third ed. Prentice-Hall of India Private Limited (2004).

[41] Sh. Rafiee-Taghanakia, M. Arabloob, A. Chamkalani. Implementation of SVM framework to estimate PVT properties of reservoir oil. Fluid Phase Equilibria 346 (2013): 25– 32.

[42] L. Xie, Y. Ying, T. Ying. Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics. Journal of Food Engineering 94 (2009):34–39.

[43] X. De Souza, S., Suykens, J. A. K., Vandewalle, J., Boll´e, D. Coupled Simulated Annealing. IEEE Transactions on Systems, Man and Cybernetics. 40 (2010): 320–335.

[44] K. Pelckmans, J.A.K. Suykens, T. Van Gestel, D. De Brabanter, L. Lukas, B. Hamers, B. De Moor, J.Vandewalle. LS‐SVMlab: A Matlab/C Toolbox for Least Squares Support Vector Machines. In, Internal Report 02‐44. ESAT‐SISTA.  K. U. Leuven: Leuven, Belgium, (2002).

[45] J.K. Koo, S.W. Kim. Reaction kinetic model for optimal pyrolysis of plastic waste mixtures. Waste Management and Research 11 (1993):515-529.

[46] J.M. Encinara, J.F. Gonzálezb. Pyrolysis of synthetic polymers and plastic wastes: Kinetic study. Fuel Processing Technology. 89 (2008):678 – 686.

[47] J.F. Mastral, C. Berrueco, J. Ceamanos. Modeling of the pyrolysis of high-density polyethylene product distribution in a fluidized bed reactor. J. Anal. Appl. Pyrolysis. 79 (2007): 313–322.

[48] Y.-H. Lin, W.-H. Hwu, M.-D. Ger, T.-F. Yeh, J. Dwyer. Combined kinetic and mechanistic modeling of the catalytic degradation of polymers. Journal of Molecular Catalysis A: Chemical. 171 (2001):143–151.

[49] Wei-Chiang Huang, Mao-Suan Huang, Chiung-Fang Huang. Thermochemical conversion of polymer wastes into hydrocarbon fuels over various fluidizing cracking catalysts. Fuel. 89 (2010):2305–2316.

[50] S.M.F Hoseini, M. Dastanian. Predicting Pyrolysis Products of PE, PP, and PET Using NRTL Activity Coefficient Model. Journal of Chemistry (2013). Article ID: 487676.