Sensitivity Analysis of Thermal Degradation of Plastic Waste Using Statistically Assumed Exponential Factors and Activation Energies
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https://hdl.handle.net/10037/28945Dato
2023-04-04Type
Journal articleTidsskriftartikkel
Peer reviewed
Forfatter
Un Nabi, Rao Adeel; Hussain, Hammad; Naz, Yasin; Shukrullah, Shazia; Khawaja, Hassan; Irfan, Muhammad; Rahman, Saifur; Jazem Ghanim, Abdulnour AliSammendrag
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 (Ea) and frequency factors (Ao) 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 Ea and Ao to pyrolyze the high-density polyethylene. Two series of Ea and Ao 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.
Forlag
ACS PublicationsSitering
Un Nabi RA, Hussain H, Naz Y, Shukrullah S, Khawaja HA, Irfan M, Rahman S, Jazem Ghanim. Sensitivity Analysis of Thermal Degradation of Plastic Waste Using Statistically Assumed Exponential Factors and Activation Energies. ACS Omega. 2023Metadata
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