Optimization of process parameters of catalytic pyrolysis using natural zeolite and synthetic zeolites on yield of plastic oil through response surface methodology.

This study aims to reach a sustainable solution for waste management of medical plastics through value-added product extraction. It uses the DOE technique to examine the effect of natural zeolite and synthetic AlO and SiO as catalysts. A small lab-scale pyrolysis setup was used for medical plastic waste management treatment. Pyrolysis of medical plastics with temperature range (350-450 °C), three catalysts, and wt.% are examined. This process is designed for 3 factors and 3 levels, such as type of catalyst, catalyst wt.%, and temperature, to create an L9 orthogonal array. At the same time, the heating rate and residence time are maintained constant at 5 °C/min and 75 minutes, respectively. Furthermore, this study analyzed the input variables in catalytic pyrolysis using response surface methodology. As a result of the study, generating the regression equation for oil yield, F and P values assure the model is significant. Optimization result shows the type of catalyst, temperature, and catalyst concentration values are found as aluminum oxide, 376 °C, and 6.6 wt.%, respectively. HDPE and LDPE oil yield a value of 58.3648 and 61.2051 wt%, respectively, under the optimum variables condition. For oil yield prediction, HDPE and LDPE's correlation coefficient (R) were 0.9949 and 0.9943, respectively. Authentication of the model response using a regression equation validated with the experimental result shows good agreement. The produced medical plastic oil has a density, viscosity, flash & fire point, carbon residue, and cetane number 904 kg/m, 2.3 cSt, 42 & 45 °C, 7.1 wt.% and 51 respectively. Finally, this study concludes that plastic oil extraction from medical waste through catalytic pyrolysis can be a potential source of alternative fuels in IC engines. Priority to optimization and low-cost catalysts highlights medical plastics waste management under the socio-economic model.