The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (Ao) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370–410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the reduced frequency factor. In both assumed series of Ea and Ao, a significantly larger quantity of oil (99%) was predicted; however, the number of byproducts increased in the first series analysis compared to the second series analysis. It was revealed that an appropriate combination of Ea, Ao, and the predicted rate constants could significantly enhance the efficiency of the pyrolysis process. The major oil recovery in the first assumed series occurred at 390 °C to 400 °C, whereas the second assumed series of Ea and Ao occurred at 380 °C to 390 °C. In the second series at 390 °C to 400 °C, the predicted kinetic rate constants behaved aggressively after 120 min of the pyrolysis process. The second assumed series and anticipated rate constants at 380 °C to 390 °C can be applied commercially to improve oil production while saving energy and heat.

The surge in plastic waste production has forced researchers to work on practically feasible recovery processes. Pyrolysis is a promising and intriguing option for the recycling of plastic waste. Developing a model that simulates the pyrolysis of high-density polyethylene (HDPE) as the most common polymer is important in determining the impact of operational parameters on system behavior. The type and amount of primary products of pyrolysis, such as oil, gas, and waxes, can be predicted statistically using a multiple linear regression model (MLRM) in R software. To the best of our knowledge, the statistical estimation of kinetic rate constants for pyrolysis of high-density plastic through MLRM analysis using R software has never been reported in the literature. In this study, the temperature-dependent rate constants were fixed experimentally at 420 °C. The rate constants with differences of 0.02, 0.03, and 0.04 from empirically set values were analyzed for pyrolysis of HDPE using MLRM in R software. The added variable plots, scatter plots, and 3D plots demonstrated a good correlation between the dependent and predictor variables. The possible changes in the final products were also analyzed by applying a second-order differential equation solver (SODES) in MATLAB version R2020a. The outcomes of experimentally fixed-rate constants revealed an oil yield of 73% to 74%. The oil yield increased to 78% with a difference of 0.03 from the experimentally fixed rate constants, but light wax, heavy wax, and carbon black decreased. The increased oil and gas yield with reduced byproducts verifies the high significance of the conducted statistical analysis. The statistically predicted kinetic rate constants can be used to enhance the oil yield at an industrial scale.

The rise in the production of plastic waste has prompted the exploration of various recovery options instead of landfilling, burning, and other unethical ways of decomposing. The experimentally generated rate constants for the thermal processing of plastic waste do not yield enough liquid fuels and gases for commercial-scale usage. It is imperative to predict kinetic rate constants statistically using an appropriate combination of activation energies (E a) and frequency factors (A o) for the optimized thermal valorization of plastic waste. This approach also assists in controlling the selectivity and quantity of the pyrolysis products. A statistical kinetic model was tested to find the best combination of rate constants from different combinations of E a and A o to pyrolyze the high-density polyethylene. Two series of E a and A o were first assumed using R software. These series were then used to predict kinetic rate constants and analyze their sensitivity independently using MATLAB. The rate constants were varied from their originally predicted values during the sensitivity analysis. It was found that the rate constant k(7) dominated the other predicted rate constants where high oil and gas yields were concerned. The gas yield increased from lower to higher extreme positions in the range of 60%–74% with the first series and from 65% to 81% with the second series. The maximum oil content was found around 74% and 65% with the first series and second series, respectively.

The growing production of plastic waste and improper dumping after use has become a worldwide challenge. This waste is a substantial source of petroleum and can be effectively converted into pyrolytic oil and other useful products. A statistical prediction of the rate constants is essential for optimizing pyrolysis process parameters, such as activation energy (Ea), frequency factor (Ao), temperature (T), and kinetic rate constants (k). In this research, we utilized Box–Behnken using RSM with Design Expert software to predict statistical rate constants at 500 °C and 550 °C. The efficiency of the predicted rate constants was investigated and compared to the findings of experimental rate constants extracted from the literature. At 500 °C, the estimated rate constants did not reveal a significant rise in the oil output since these constants promoted high gas yield. Compared to the experimental rate constants, statistically predicted rate constants at 550 °C demonstrated substantially high-oil output with only 1% byproducts. The experimental rate constants yielded 32% oil at 550 °C, whereas the predicted rate constants yielded 85% oil. The statistically predicted rate constants at 550 °C could be used to estimate commercial-scale extraction of liquid fuels from the pyrolysis of high-density plastics. It was also concluded that Ea, Ao, and T must be analyzed and optimized according to the reactor type to increase the efficiency of the expected rate constants.