The glycolysis process as a useful approach to recycling of flexible polyurethane foam wastes has been modeled in this work. To obtain high quality recycled polyol, the effects of influential processing and material parameters, i.e., process time, process temperature, catalyst-to-solvent (Cat/Sol) and solvent-to-foam (Cat/Foam) ratio, on the efficiency of glycolysis reaction have been investigated individually and simultaneously. For the continuous prediction of process behavior and interactive effects of parameters, Artificial Neural Network (ANN) model as an efficient statistical-mathematical method has been developed and put into prediction. The results of modeling for the criteria that determine the glycolysis process efficiency including the hydroxyl value of the recycled polyol and isocyanate functional group conversion prove that the adopted ANN model successfully anticipates the recycling process responses over the whole range of experimental conditions. The Cat/Sol ratio showed the strongest influence on the quality of the recycled polyol among studied parameters, where the minimum hydroxyl value has been obtained at the medium amount of the assigned ratio. For the consumed polyurethane foam, higher quantity of this ratio led to an increase in the hydroxyl value and isocyanate conversion.

Keywords