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Hyperbranched polymers (HBPs) are highly branched, three-dimensional and polydisperse macromolecules and have been employed for modification of poly(ethylene terephthalate) (PET) fabrics. The PET fabrics treatment process parameters, like HBP concentration, temperature and time, play a major role in treatment yield and dyeability of treated PET fabrics by acid dyes. Two different quantitative models, comprising response surface methodology (RSM) and artificial neural networks (ANN), were developed for predicting color strength (K/S value) of treated fabrics. The experiments were conducted based on central composite design (CCD) and a mathematical model was developed. A comparison of the predicted color strength using RSM and ANN was studied. The results obtained indicated that both RSM and ANN models show a very good relationship between the experimental and predicted response values. However, the ANN model shows more accurate results than the RSM model.

Modification of PET fabrics by hyperbranched polymer: a comparative study of artificial neural networks (ANN) and statistical approach