Optimization of pigment dyeing process of high performance fibers using feed-forward bottleneck neural networks mapping technique Original Research Article

Process optimization involves the minimization (or maximization) of an objective function, that can be established from a technical and (or) economic viewpoint taking into account safety of process. The basic idea of the optimization method using neural network (NN) is to replace the model equations (which traditionally obtained using, for example, the surface response design or others methods) by an equivalent NN. The feed-forward bottleneck neural network (FFBN) as a mapping technique is described and evaluated. From the 2D maps the optimal parameters of pigment dyeing of high performance fibers on the bases of poly-amide benzimidazole (PABI) and polyimide (arimid) are discussed. The studied fibers were treated in 32 experiments under the conditions as proposed by the Design of Experiment (DOE), varying five influencing factors. Neural network mapping method enables visualization of process and shows the influence of different factors on different output responses. Optimum parameters were selected upon compromise decision.