期刊
FLUID PHASE EQUILIBRIA
卷 356, 期 -, 页码 11-17出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.fluid.2013.07.017
关键词
Solubility prediction; Gas in polymers; Artificial neural network; Chaos; Particle swarm optimization
资金
- National Natural Science Foundation of China [51163011, 61202313, 51102131]
- Nanchang University [cx2012011]
A novel prediction method based on chaos theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using chaos theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N-2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R-2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good. (C) 2013 Elsevier B.V. All rights reserved.
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