Optimization of acrylic dry spinning production line by using artificial neural network and genetic algorithm

Acrylic fibers are synthetic fibers with wide applications. A couple of methods can be utilized in their manufacture, one of which is the dry spinning process. The parameters in this method have nonlinear relationships, making the process very complex. To the best of the authors' knowledge, no comprehensive study has yet been conducted on the optimization of acrylic dry spinning production using computer algorithms. In this study, such parameters as extruder temperature in and around the head, solution viscosity, water content in the solution, formic acid content of the solution, and the retention time of the solution in the reactor were measured in an attempt to predict the behavior of the dry spinning process. The color index of the manufactured fibers was used as an indicator of production quality and statistical methods were employed to determine the parameters affecting the process. An artificial neural network (ANN) using the back propagation training algorithm was then designed to predict the color index. ANN parameters including the number of hidden layers, number of neurons in each layer, adaptive learning rate, activation functions, number of max fail epochs, validation and test data were optimized using a genetic algorithm (GA). The trial and error method was used to optimize the GA parameters like population size, number of generations, crossover or mutation rates, and various selection functions. Finally, an ANN with a high accuracy was designed to predict the behavior of the dry spinning process. This method is capable of preventing the manufacturing of undesired fibers. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

[1]  J. Masson.  Acrylic Fiber Technology and Applications , 1995 .

[2]  K. Sen,et al.  Structure development during dry–jet–wet spinning of acrylonitrile/vinyl acids and acrylonitrile/methyl acrylate copolymers , 2002 .

[3]  S. Tanoue,et al.  Effect of die gap width on annular extrudates by the annular extrudate swell simulation in steady-states , 1999 .

[4]  Yuan Tian,et al.  Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models , 2002 .

[5]  R. W. Diraddo,et al.  Modeling of membrane inflation in blow molding: Neural network prediction of initial dimensions from final part specifications , 1993 .

[6]  Lance D. Chambers,et al.  Practical Handbook of Genetic Algorithms , 1995 .

[7]  Silvia Curteanu,et al.  Optimization Strategy Based on Genetic Algorithms and Neural Networks Applied to a Polymerization Process , 2007 .

[8]  Hong Huang,et al.  Prediction of parison swell in plastics extrusion blow molding using a neural network method , 2002 .

[9]  Qin Sun,et al.  Application of neural networks to meltblown process control , 1996 .

[10]  Jun Wang,et al.  Artificial neural network modeling for predicting melt-blowing processing , 2006 .

[11]  L. Boullart,et al.  Using genetic algorithms to design a control strategy of an industrial process , 1998 .

[12]  Xiubao Huang,et al.  Effects of Processing Parameters on the Fiber Diameter of Melt Blown Nonwoven Fabrics , 2005 .

[13]  Hong Huang,et al.  HDPE/PA6 blends: parison formation behaviour in extrusion blow molding , 2003 .

[14]  Cláudio Augusto Oller do Nascimento,et al.  Modeling of industrial nylon‐6,6 polycondensation process in a twin‐screw extruder reactor. I. phenomenological model and parameter adjusting , 1998 .

[15]  B. V. Babu,et al.  Modified differential evolution (MDE) for optimization of non-linear chemical processes , 2006, Comput. Chem. Eng..

[16]  Song Lu,et al.  Modeling parison formation in extrusion blow molding by neural networks , 2005 .