Carbon footprint analysis of waste plastic-to-fuel pyrolysis: implications of feedstock composition and processing conditions

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a kinetic-integrated LCA model for plastic pyrolysis that can be adapted to other feedstocks and regions.

🏢実務担当者:Enables waste management firms to optimize pyrolysis conditions for lower carbon footprint and potential carbon credit eligibility.

🏛政策担当者:Highlights how regional electricity mix affects GHG benefits of plastic-to-fuel pathways, informing waste management and energy policy integration.

Abstract Purpose The current linear approach to manage waste plastics is straining already limited fossil fuel resources. Pyrolysis offers a promising alternative, converting plastic waste into fuels while helping to reduce greenhouse gas (GHG) emissions and supporting a circular plastics economy. This study aims to evaluate the GHG emission impacts of plastic-to-fuel pyrolysis and develop a modeling approach to forecast GHG emissions under different process conditions. Methods This study presents an agile modeling framework that predicts GHG emissions based on variations in feedstock and process conditions. To address variability in pyrolysis systems, a kinetic model was integrated into the framework, allowing for the quantification of GHG emissions across different feedstock compositions, operating temperatures, vapor residence times, energy sources, co-product handling strategies, and conversion efficiencies. Results The model identified operating conditions where the plastic-to-fuel pyrolysis of mixed plastic waste composed of high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polypropylene (PP) minimized GHG emissions associated with the production of 1 MJ of produced fuel. Reducing reactor temperature from 600 to 550 °C reduced GHG emissions from 15.9 gCO 2 eq./MJ of fuel at 600 °C, VRT 2 to 15.4 gCO 2 eq./MJ of fuel at 550 °C, VRT 2, while increasing the VRT from 2s to 6s further reduced emissions to 14.8 gCO 2 eq./MJ of fuel. These reductions were primarily attributed to higher liquid fuel yields under the operating conditions. Furthermore, variations in feedstock composition from PP-rich to HDPE-rich mixtures increased the GHG emissions by an average value of 5.3 g CO 2 eq./MJ. Additionally, increasing fuel gas-to-electricity conversion efficiency was found to reduce GHG emissions by up to 41%, particularly under configurations involving surplus electricity export. Results further demonstrated strong regional dependency, where carbon-intensive electricity grids increased the benefits associated with electricity co-generation and displacement credits. Conclusions This modeling platform provides an approach for examining GHG emissions under dynamic processing conditions in plastic pyrolysis and assessing the impacts of plastic-stream composition. Variability in waste plastic feedstocks strongly influences GHG emissions. Feedstocks that require less pyrolysis energy or generate higher liquid-oil yields result in proportionally lower overall GHG emissions. System-level GHG emissions are further shaped by the regional energy supply mix and the efficiency of fuel-gas–to-electricity conversion. Advancing the dynamic simulation in plastics recycling will provide insights for guiding process improvements and policy choices toward effective waste management strategies.

• openalex https://doi.org/10.1007/s11367-026-02715-zfirst seen 2026-07-22 05:17:25