Using Hybrid Artificial Neural Network-Particle Swarm Optimization for Prediction of HIPS Mechanical Properties

Document Type : Research Paper

Authors

1 Chemical Engineering Department, Islamic Azad University, Ilkhchi Branch, Ilkhchi, Iran

2 Faculty of Chemical Engineering, Urmia University of Technology, Urmia, Iran

Abstract

Artificial neural networks (ANN) have emerged as a useful artificial intelligence concept in various engineering applications. Also, ANN only recently has been used in modeling the mechanical behavior of polymers. This study aims to show the applicability of ANNs, to predict and determine the mechanical properties of High Impact Polystyrene (HIPS). Moreover, Izod, Vicat, and MFI of pure HIPS were considered, and the effect of 25 different parameters on them investigated. By using ANN, a black-box model is considered as a calculator of mechanical properties. It is not easy to predict the accurate value of these properties without experimental works for two reasons: (1) the nonlinear behavior of the polymerization process and (2) the inaccuracy of experimental results of measurement of each of the properties. To overcome these problems, an alternative prediction model was proposed for calculating properties using a hybrid ANN-PSO model. All parameters in various cases have been gathered from the industrial DCS system and laboratory for the training of the ANN. First, by using the ANN model, the sensitivity analysis of parameters has been performed. It is then filtered using the effective ANN-PSO hybrid parameters. Out of 25 proposed parameters, 14, 10, 11 of them were selected in MFI, Izod, Vicat, respectively, and used to predict new parameters in the modified ANN. Ultimately, according to the obtained results, it was found that the hybrid ANN-PSO model was powerful in predicting properties with an average relative error of 6, 5, and 1% for predicting considered properties between industrial and computed ANN-PSO hybrid model data.

Keywords


  1. Yousef BF, Mourad A-HI, Hilal-Alnaqbi A (2011) Prediction of the mechanical properties of PE/PP blends using artificial neural networks, Procedia Engineering, 10: 2713-2718. ##
  2. Alfarraj A, Bruce Nauman E (2004) Super HIPS: improved high impact polystyrene with two sources of rubber particles, Polymer, 45: 8435-8442. ##
  3. Cunha FR, Costa JM, Nele M, Folly ROM, Souza Jr MB, Pinto JC (2013) Influence of reaction operation conditions on the final properties of High Impact Polystyrene (hips), Brazilian Journal of Chemical Engineering, 30: 575-587. ##
  4. Duce C, Micheli A, Starita A, Tiné MR, Solaro R (2006) Prediction of polymer properties from their structure by recursive neural networks, Macromolecular Rapid Communications, 27: 711-715. ##
  5. El Kadi H (2006) Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks—A review, Composite Structures, 73: 1-23. ##
  6. Özcanli Y, Kosovali ÇavuĊŸ F, Beken M (2016) Comparison of mechanical properties and artificial neural Networks Modeling of PP/PET Blends, ACTA PHYSICA POLONICA A, 130, 1: 444-446. ##
  7. Fernandes FAN, Lona LMF (2005) Neural network applications in polymerization processes, Brazilian Journal of Chemical Engineering, 22: 401-18. ##
  8. Hanai T, Ohki T, Honda H, Kobayashi T (2003) Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm, Computers and Chemical Engineering, 27: 1011-1019. ##
  9. Krothapally M, Palanki S (1997) A neural network strategy for batch process optimization, Computers andChemical Engineering, 21:S463-S8. ##
  10. Nascimento CAO, Giudici R, Guardani R (2000) Neural network based approach for optimization of industrial chemical processes, Computers and Chemical Engineering, 24: 2303-14. ##
  11. Syu M-J, Tsao GT (1993) Neural network modeling of batch cell growth pattern, Biotechnology and Bioengineering, 42:376-80.
  12. Tian Y, Zhang J, Morris J (2002) Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models, Chemical Engineering and Processing: Process Intensification, 41: 531-8. ##
  13. Tsen AY-D, Jang SS, Wong DSH, Joseph B (1996) Predictive control of quality in batch polymerization using hybrid ANN models, AIChE Journal, 42: 455-65. ##
  14. Zhang J (1999) Inferential estimation of polymer quality using bootstrap aggregated neural networks, Neural Networks, 12: 927-38. #3
  15. El-Shorbagy  MA, Hassanien AE (2018) Particle swarm optimization from theory to applications, International Journal of Rough Sets and Data Analysis, 5, 2: 1-24. ##
  16. Mousavi SM, Panahi P, Niaei A, Farzi A, Salari D (2012) Modeling and simulation of styrene monomer reactor: mathematical and artificial neural network model, International Journal of Scientific and Engineering Research, 3: 153-159. ##