Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization (PSO), fuzzy c-means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate-co-adipate) (PBSA), respectively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.

[1]  Yudong Zhang,et al.  Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization , 2011, Sensors.

[2]  Gade Pandu Rangaiah,et al.  Author's Personal Copy Fluid Phase Equilibria Evaluation of Integrated Differential Evolution and Unified Bare-bones Particle Swarm Optimization for Phase Equilibrium and Stability Problems , 2022 .

[3]  Chengye Zhao,et al.  Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy , 2012 .

[4]  Sumarno,et al.  Solubilities and diffusion coefficients of carbon dioxide and nitrogen in polypropylene, high-density polyethylene, and polystyrene under high pressures and temperatures , 1999 .

[5]  D. Ouazar,et al.  Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting , 2010 .

[6]  L. Hua,et al.  A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters , 2011 .

[7]  Taghi Khayamian,et al.  Solubility prediction of 21 azo dyes in supercritical carbon dioxide using wavelet neural network , 2007 .

[8]  Seyed Taghi Akhavan Niaki,et al.  A hybrid method of artificial neural networks and simulated annealing in monitoring auto-correlated multi-attribute processes , 2011 .

[9]  M. Lashkarbolooki,et al.  Comparison the capability of artificial neural network (ANN) and EOS for prediction of solid solubil , 2011 .

[10]  K. Movagharnejad,et al.  A comparison between neural network method and semi empirical equations to predict the solubility of , 2011 .

[11]  J. Segovia-Hernández,et al.  A comparative study of particle swarm optimization and its variants for phase stability and equilibrium calculations in multicomponent reactive and non-reactive systems , 2010 .

[12]  Alejandro A. Pérez Ponce,et al.  Application of particle swarm optimization to model the phase equilibrium of complex mixtures , 2012 .

[13]  Zhang Yahui,et al.  APTox: Assessment and Prediction on Toxicity of Chemical Mixtures , 2012 .

[14]  K. Weale,et al.  Solution and diffusion of gases in polystyrene at high pressures. , 1948, Journal of the Chemical Society.

[15]  Mojca Škerget,et al.  Solubility and diffusivity of CO2 in carboxylated polyesters , 2010 .

[16]  W. Du,et al.  Development of a Free Radical Kinetic Model for Industrial Oxidation of p-Xylene Based on Artificial Neural Network and Adaptive Immune Genetic Algorithm , 2012 .

[18]  M. Ahmadi Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

[19]  Dong-Yuan Ge,et al.  Testing of Rounded Corner for Micro-Drill on Hybrid of BP Neural Network and Adaptive Particle Swarm Optimization , 2012, J. Comput..

[20]  Jui-Chung Hung,et al.  Modified particle swarm optimization structure approach to direction of arrival estimation , 2013, Appl. Soft Comput..

[21]  Jianfei Luo,et al.  Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm , 2012 .

[22]  R. Smith,et al.  Solubility, swelling degree and crystallinity of carbon dioxide-polypropylene system , 2007 .

[23]  Qi-yu Zheng,et al.  Synthesis and Properties of Porous Organic Polymers from a Rigid Macrocyclic Building Block , 2013 .

[24]  Xinggao Liu,et al.  Melt index prediction by RBF neural network optimized with an MPSO-SA hybrid algorithm , 2011, Neurocomputing.

[25]  Aboozar Khajeh,et al.  Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network , 2010, Expert Syst. Appl..

[26]  Agílio A. H. Pádua,et al.  Simultaneous measurement of the solubility of nitrogen and carbon dioxide in polystyrene and of the associated polymer swelling , 2001 .

[27]  Qingyou Zhang,et al.  Classification Prediction of Photochemical Ractions Based on MOLMAP , 2012 .

[28]  Babak Rezaee,et al.  Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers , 2009, Expert Syst. Appl..

[29]  Wai Keung Wong,et al.  A hybrid particle swarm optimization and its application in neural networks , 2012, Expert Syst. Appl..

[30]  Rahib Hidayat Abiyev,et al.  Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction , 2011, Neural Computing and Applications.

[31]  Juan A. Lazzús,et al.  Estimation of solid vapor pressures of pure compounds at different temperatures using a multilayer network with particle swarm algorithm , 2010 .

[32]  Xiaoyan Zhao,et al.  New Soluble Polyimides with High Optical Transparency and Light Color Containing Pendant Trifluoromethyl and Methyl Groups , 2012 .

[33]  Haralambos Sarimveis,et al.  Radial Basis Function Network Training Using a Nonsymmetric Partition of the Input Space and Particle Swarm Optimization , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Ali Eslamimanesh,et al.  Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids , 2011 .

[35]  Wei-Chiang Hong,et al.  Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm , 2013, Neurocomputing.

[36]  Mitsuhiro Imaizumi,et al.  Solubility and Diffusion Coefficient of Carbon Dioxide in Biodegradable Polymers , 2000 .

[37]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..

[38]  W. Yuan,et al.  Solubility and Diffusivity of Carbon Dioxide in Solid-State Isotactic Polypropylene by the Pressure−Decay Method , 2009 .

[39]  Yan Wu,et al.  Solubility Prediction of Gases in Polymers based on Chaotic Self-adaptive Particle Swarm Optimization Artificial Neural Networks , 2013 .

[40]  Hassan Pahlavanzadeh,et al.  Experimental analysis and modeling of CO2 solubility in AMP (2-amino-2-methyl-1-propanol) at low CO2 partial pressure using the models of Deshmukh–Mather and the artificial neural network , 2011 .

[41]  Yousef Bakhbakhi,et al.  Neural network modeling of ternary solubilities of 2-naphthol in supercritical CO2: A comparative study , 2012, Math. Comput. Model..

[42]  Leon P.B.M. Janssen,et al.  Supercritical carbon dioxide as a green solvent for processing polymer melts: Processing aspects and applications , 2006 .

[43]  H. S. Wang,et al.  Cost estimation of plastic injection molding parts through integration of PSO and BP neural network , 2013, Expert Syst. Appl..

[44]  Devid Desfreed Kennedy,et al.  Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data , 2011 .

[45]  H. Masuoka,et al.  Solubilities of carbon dioxide and nitrogen in polystyrene under high temperature and pressure , 1996 .

[46]  Yan Wu,et al.  Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory , 2013 .

[47]  Ali Eslamimanesh,et al.  Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide , 2011 .

[48]  H. Masuoka,et al.  Solubilities and diffusion coefficients of carbon dioxide in poly(vinyl acetate) and polystyrene , 2001 .

[49]  T. Lu,et al.  New Crystalline Forms of Mebendazole with n-Alkyl Carboxylic Acids: Neutral and Ionic Status , 2013 .