Modeling of glycolysis of flexible polyurethane foam wastes by artificial neural network methodology

Abstract

The glycolysis process as a useful approach to recycling flexible polyurethane foam wastes is 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 (Sol/Foam) ratios, on the efficiency of the glycolysis reaction were investigated individually and simultaneously. For the continuous prediction of process behavior and interactive effects of parameters, an artificial neural network (ANN) model as an efficient statistical-mathematical method has been developed. 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 the studied parameters, where the minimum hydroxyl value was obtained at a medium amount of the assigned ratio. For the consumed polyurethane foam, a higher value of this ratio led to an increase in the hydroxyl value and isocyanate conversion.

Single‐phase product obtained via crude glycerine depolymerisation of polyurethane elastomer: structure characterisation and rheological behaviour

“…In feedstock recycling of PUs, the urethane, urea and biuret groups are treated through chemical reaction with a special decomposing agent. The most well‐known chemical recycling methods include hydrolysis (chemical agent: water), glycolysis (glycols), alcoholysis (alcohols), acidolysis (organic acids) and aminolysis (amines) . Glycerine has also been proposed as a decomposing agent with promising results…”

The influence of different glycerine purities on chemical recycling process of polyurethane waste and resulting semi‐products

“…The low conversion for GL62 can also be associated with the formation of more low‐molecular‐weight carbamates, as was evident from the GPC results (Table ). However, the conversion rate probably could be increased by conducting the reaction for a longer time, as was reported in the literature …”

Modeling and closed‐loop control of particle size and initial burst of PLGA biodegradable nanoparticles for targeted drug delivery

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