Statistical Optimization of Pyrolysis Process for Thermal Destruction of Plastic Waste Based on Temperature-Dependent Activation Energies and Pre-Exponential Factors

Abstract

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.

Sensitivity Analysis of Thermal Degradation of Plastic Waste Using Statistically Assumed Exponential Factors and Activation Energies

“…Equation 2 is applied to the supposed series factors a m and b m in the R software, and rate constants k(1)−k( 9) are predicted. 15 In these equations, k is the rate constant, A o is the frequency factor, E a is the activation energy, and(…”

“…Equation is used to suppose two sets of E a and A o for predicting the rate constants. Equation is applied to the supposed series factors a m and b m in the R software, and rate constants k (1)– k (9) are predicted . In these equations, k is the rate constant, A o is the frequency factor, E a is the activation energy, and­( ) is the absolute temperature.…”

“…Equation 2 is applied to the supposed series factors a m and b m in the R software, and rate constants k (1)– k (9) are predicted. 15 In these equations, k is the rate constant, A o is the frequency factor, E a is the activation energy, and( ) is the absolute temperature. The rate constant number (1–9) is denoted with M , and supposed values of E a and A o are denoted with m .…”

Response Surface Methodology Analysis of Pyrolysis Reaction Rate Constants for Predicting Efficient Conversion of Bulk Plastic Waste into Oil and Gaseous Fuels

“…It directly impacts the processing time and operating parameters. At high temperatures, the formation of gases increases while the creation of waxes reduces rapidly [29]. High-working temperatures lead to a buildup of small radicals, which accelerates the process and promotes the evaporation of long chains.…”

“…To better understand the effectiveness of large-scale conversion of plastics, statistical rate constants are the best choice to estimate petroleum products and gases. Our recent publications [29,30] show various ways to forecast the statistical rate constants. In this work, we estimated rate constants at 500 • C and 550 • C by utilizing RSMin the Design Expert software.…”