Prediction of Degradation Temperature of Polyamide by Biofuels Using Artificial Neural Network

Document Type : Research Article

Authors

1 Department of Chemical Engineering, Payam Noor University, Tehran, I.R. IRAN

2 Polymer Engineering Department, Vienna Technical University, Vienna, Austria

3 Faculty of Polymer and Paint Engineering, Amirkabir University of Technology, Tehran, I.R. IRAN

Abstract

The aim of this study was to apply an artificial neural network to predict the degradation temperature of polyamide 12 under the influence of several factors such as environment, biofuels, and temperature over time. In this modeling, a three-layer perceptron (MLP) neural network is used. The perceptron neural network consists of three input layers, a hidden layer, and an output layer. The inputs of this network are three variables related to ethanol concentration, temperature, and time and its output is the temperature of polyamide degradation. The perceptron neural network was designed with a linear transfer function at the output layer for modeling. A comparison of experimental results and network modeling results showed R2 = 0.99. Also, using experimental data, the mean squared error and absolute percentage error of system error were 0.09 and 0.03, respectively. Then, the degradation temperature and the effects of several factors such as ethanol percent and temperatures (20, 40,  and 60) degrees Celsius on the physical properties of polyamide 12 during times (0, 900, 5000, 3000, 6000, and 7000) hours are predicted and the results show the high accuracy of the neural network in estimating the degradation temperature.

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References

[1] Gagnard C., Germain Y., Keraudren P., Barrière B., Permeability of Semicrystalline Polymers to Toluene/Methanol Mixture, J. Appl. Polym. Sci, 90: 27 (2009).
[2] Kallio K.J., Hedenqvist M.S., Effects of Ethanol Content and Temperature on the Permeation of Fuel through Polyamide-12-based Pipes, Polymer Testing, 29: 3-6 (2010).
[3] Kallio K.J., Hedenqvist M.S., Ageing Properties of Polyamide-12 Pipes Exposed to Fuels with and Without Ethanol, Polymer Degradation and Stability, 93: 18-46 (2008).
[4] Baena L., Jaramillo F., Calderón J.A., Aggressiveness of a 20% Bioethanol 80% Gasoline Mixture on Autoparts: II Behavior of Polymeric Materials, Fuel, 95: 3-12 (2012).
[5] Khonakdar H.A., Morshedian J., Yazdani H., Investigation of Thermal, Rheological and Mechanical Properties of Interfacially Modified PP/Mica Composites, e-Polymers, 8(1): (2008).
[6] Agarwal K., Biofuels (Alcohols And Biodiesel) Applications as Fuels for Internal Combustion Engines, Progress in Energy and Combustion Science, 33: 233-271 (2007).
[7] Ozgoli H.A., Yazdani H., Integration of a Vanadium Redox Flow Battery with a Proton Exchange Membrane. Fuel Cell as an Energy Storage System, Iranian Journal of Hydrogen & Fuel Cell, 4(1): 53-68 (2017).
[8] Yazdani H., Ozgoli H.A., Transient Model and Technical Analysis of Vanadium Redox Flow Battery with Polymer Membrane, Journal of Management, 5(3): 36-49 (2015).
[9] Berlanga-Labari C., Albistur-Goñi A., Barado-Pardo I., Gutierrez-Peinado M., Compatibility Study of High-Density Polyethylene with Bioethanol–Gasoline Blends, Materials and Design, 32: 4-41 (2011).
[10] Kallio K.J., Hedenqvist M.S., Effects of Ethanol Content and Temperature on the Permeation of Fuel through Polyamide-12-based Pipes, Polym.Test, 29: 41-48 (2010).
[11] Berlanga-Labari C., Albistur-Goñi A., Barado-Pardo I., Gutierrez-Peinado M., Compatibility Study of High Density Polyethylene with Bioethanol–Gasoline Blends, Materials and Design, 32: 4-41 (2011).
[12] Defilippi R., Zasa M., Multi-Layer Tube for Conducting Fuel in a Motor Vehicle, European Patent Specification. EP, 66(2): 4-16 (2010).
[13] Özdemir U., Özbay B., Veli S., Zor S., Modeling Adsorption of Sodium Dodecyl Benzene Sulfonate (SDBS) onto Polyaniline (PANI) by using Multi Linear Regression and Artificial Neural Networks, Chem. Eng. J, 178: 183-190 (2011).
[14] Shanmugaprakash M., Sivakumar V., Development of Experimental Design Approach and ANN-based Models for Determination of Cr (VI) Ions Uptake Rate from Aqueous Solution onto the Solid Biodiesel Waste Residue, Bioresource, Technol, 148: 550-559 (2013).
[15] Mirvand M., Ghasemiye H., Akbari M., Sadatinejad S., Simulation of Underground Water Quality Changes with Artificial Neural Network Model (Case Study: Kashan Aquifer), Civil Engineering Journal and Environment Engineering Journal of Tabriz University, 68: 159-171 (2015).
[16] Beigzadeh R., Rahimi M., Prediction of Heat Transfer and Flow Characteristics in Helically Coiled Tubes using Artificial Neural Networks, International Communications in Heat and Mass Transfer, 39: 1279-1285 (2012).
[17] Balcilar M., Dalkilic A.S., Wongwises S., Artificial Neural Network Techniques for the Determination of Condensation Heat Transfer Characteristics during Downward Annular Flow of R134a Inside a Vertical Smooth Tube, International Communications in Heat and Mass Transfer, 38(1): 75-84 (2011).
[18] Yoon H., Jun S.C., Hyun Y., Bae G.O., Lee K.K., A Comparative Study of Artificial Neural Networks and Support Vector Machines for Predicting Groundwater Levels in a Coastal Aquifer, Journal of Hydrology, 396: 128 (2011).
[19] Technical Data Sheet, EMS-Chemie GmbH, (2014).
[20] Standard SAE J 1737, Test Procedure to Determine the Hydrocarbon Losses from Fuel Tubes, Hoses, Fittings, and Fuel Line Assemblies by Recirculation, j1737 201305 (2013).
[21] Standard SAE J 1737, Test Procedure to Determine the Hydrocarbon Losses from Fuel Tubes, Hoses, Fittings, and Fuel Line Assemblies by Recirculation, Access Data, 2: (2014).
[22] Proposed Fuel Quality Standard – Ethanol (E85) Automotive Fuel, Australian Government, Department of Sustainability, Environment, Water, Population and Communities, 4 (2011).
[23] Salehi F., Razavi S.M.A., Dynamic Modeling of Flux and Total Hydraulic Resistance in Nanofiltration Treatment of Regeneration Waste Brine using Artificial Neural Network, Desalination and Water Treatment, 21(1): 41-104 (2012).
[24] Rafiee J., Arvani F., Harifi A., Sadeghi M.H., Intelligent Condition Monitoring of a Gearbox using Artificial Neural Network, Mechanical Systems and Signal Processing, 36(4): 1746-1754 (2007).
[25] Chatterjee S., Reifler F.A., Chu B.T., Hufenus R., Investigation of Crystalline and Tensile Properties of Carbon Nanotube-Filled Polyamide-12 Fibers Melt-Spun by Industry-Related Processes, Journal of Engineered Fibers and Fabrics, 82: (2012).
[26] Darwish N.A, Hilal N., Al-Zoubi H., Mohammad A.W., Neural Networks Simulation of the Filtration of Sodium Chloride and Magnesium Chloride Solutions using Nanofiltration Membranes, Institution of Chemical Engineers, 85: 417-430 (2011).
[27] Abbas A., Al-Bastaki N., Modeling of an RO Water Desalination Unit using Neural Networks. Chemical Engineering Journal, 114(1-3): 139-143 (2005).
[28] DIN EN ISO 11358, Plastics - Thermogravimetry (TG) of Polymers - General Principles, 1997, Access Date, (2014).
[29] Baena L., Jaramillo F., Calderón J.A., Aggressiveness of a 20% Bioethanol–80% Gasoline Mixture on Autoparts: I Behavior of Metallic Materials and Evaluation of their Electrochemical Properties, Fuel, 95: 322 (2012).
[30] Yang X., Li Q., Chen Z., Han H., Fabrication and Thermal Stability Studies of Polyamide 6,6 Containing Triaryl Phosphine Oxide, Bull Mater Sci, 32: 375-380 (2009).
[31] Halary J.L., Laupretre F., Monnerie L., “Polymer Materials: Macroscopic Properties and Molecular Interpretations”, John Wiley & Sons Ltd, Canada, (2011).
[32] Sullivan K., Electronic Fuel Injection Overview, (2014).
[33] Fuel systems, Available from: http://www.weindex.info/pdf/FuelSystem/9.pdf, Access date: (2014).
[34] Dupont Viton fluoroelastomer, Viton Excelling in Modern Automotive Fuel Systems, (2014).
[35] Cerruti P., Carfagna C., Thermal-Oxidative Degradation of Polyamide 6,6 Containing Metal Salts, Polymer Degradation and Stability, 95: 2405 (2010).
[36] Ferruccio B., Massimo M., Oligomeric Sterically Hindered Amines and their use as Polymer Stabilizers, Patent EP2112141 B1: 1-3 (2013).

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