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Process parameter optimization for waste polyethylene terephthalate bottle depolymerization using neutral hydrolysis
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Researchers optimized process parameters for the neutral hydrolysis depolymerization of waste polyethylene terephthalate (PET) bottles as a chemical recycling strategy to address growing plastic waste accumulation in landfills. The study identified key conditions that improve depolymerization efficiency, offering a pathway to recover high-quality monomers from post-consumer PET.
Abstract The global surge in plastic production has led to a concerning accumulation of durable plastic waste in landfills and the environment. To address this issue, the depolymerization of waste polyethylene terephthalate (PET) through neutral hydrolysis has been proposed as a chemical recycling solution. Despite its potential environmental benefits, the endothermic nature of this process at high temperatures has raised doubts about its commercial feasibility. In response, this study was conducted to assess optimal conditions for waste PET depolymerization using neutral hydrolysis in a continuous stirred tank reactor with zinc acetate as a catalyst. Process simulation, aimed to manufacture pure terephthalic acid (TPA) and ethylene glycol from pelletized post-consumer PET bottles, was conducted with Aspen Plus Version 11. Sensitivity analysis explored the impact of factors such as reaction temperature, reaction time, PET flake size, and catalyst to PET ratio on both PET conversion and TPA yield. The study found that PET depolymerization increased with decreasing particle size, longer reaction times, increasing catalyst to PET ratio and reaction temperatures within the range of 200–240 ºC. Optimizing the process through response surface modelling revealed that key parameters for neutral hydrolysis considering a mean particle size of 20 mm were the ratio of water to PET, temperature, pressure, and reaction time with optimal values of 5:1, 225 ºC, 30 bar, and 67.5 min respectively. The model's reliability was confirmed through variance analysis, emphasizing the significance of main and interaction effects in the regression model.
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