Pyrolysis of waste polyethylene in a semi-batch reactor to produce liquid fuel: Optimization of operating conditions

This study investigated the interactive effects of temperature, residence time, and carrier gas flow rate on the liquid fuel production through the pyrolysis of waste polyethylene (WPE) in a bench-scale semi-batch reactor. To enhance the liquid fuel production, fifteen experiments were conducted based on a central composite design. The adaptive neural fuzzy model was adopted to establish the relationship between liquid fuel production and operating conditions. The R-squared value of the experimental and adaptive neural fuzzy model predicted that liquid fuel production was 0.9934. Four additional experimental results verified the adaptive neural fuzzy model's applicability. Subsequently, the genetic algorithm (GA) was adopted to optimize operating conditions to maximize liquid fuel production. The GA optimized operating conditions (temperature, residence time, and carrier gas flow rate) were: 488 °C, 20 min, and 20 mL/min. The liquid fuel under the optimal operating conditions was analyzed by Fourier-transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC–MS). The liquid fuel had similar main functional groups as diesel. The components of the liquid fuel were mainly 1-alkenes and n-alkanes ranging from C7 to C36. The effects of operating conditions on liquid fuel fractions and mean molecular weight were also investigated.