Quantifying the Environmental Benefits of a Solvent-Based Separation Process for Multilayer Plastic Films

14 November 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Food packaging often appears in the form of multilayer (ML) plastic films, which leverage the functional properties of different polymers to achieve specific food protection goals (e.g., oxygen, water, and temperature barriers). These properties are essential to enable long shelf lives, reduce refrigeration usage, mitigate food waste, and increase food accessibility. However, ML f film production processes generate large amounts of plastic waste that cannot be mechanically recycled. Recently, we have proposed a process, which we call solvent-targeted recovery and precipitation (STRAP), that enables the separation and recycling of the constituent polymers of ML films. This technology uses a series of solvent washes that selectively dissolve and precipitate target polymers. Quantifying the environmental benefits of STRAP over virgin resin production is essential for the commercial deployment of this technology. This work uses life cycle assessment (LCA) methods to evaluate these impacts in terms of carbon footprint, energy use, water use, and toxicity. We analyze three STRAP process variants that use anti-solvent and temperature-driven precipitation and that target different types of ML films. Our analysis reveals that a couple of STRAP process variants can provide environmental benefits over virgin film production and also provides valuable insight into the key components of ML films and of the STRAP process that are responsible for the highest impacts. Ultimately, we believe that the proposed analysis framework can lead to the design of more environmentally-friendly ML films and recycling processes.

Supplementary materials

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Title
SI for Quantifying the Environmental Benefits of a Solvent-Based Separation Process for Multilayer Plastic Films
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Supporting information with additional data and results.
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