At present, most plastic recycling in Europe is done mechanically, a process with high energy requirements, which does not allow a high level of contaminant removal, and results in lower-quality products. Solvent-based polymer dissolution is emerging as an alternative, with the potential to decrease CO2 emissions by 65-75% per ton of plastic waste when compared to incineration [2].
Polyolefins, including polyethylene and polypropylene, are commonly found in heavily contaminated multilayer plastic films [3]. However, they are not suitable for chemical recycling (polymer to monomer) because of their highly unreactive carbon-carbon bonds [4]. Consequently, they are strong candidates for the investigation of novel dissolution and precipitation polymer-to-polymer processes.
In such processes, the systematic selection of solvent(s) is critical as it significantly affects both the economics of the process and its environmental impact. However, in previous studies of dissolution/precipitation approaches, solvents have often been chosen primarily based on empirical Hansen Solubility Parameters and predictions or measurements of polymer solubility, without simultaneously considering the overall design of the process. This may lead to suboptimal process performance. Predictive thermodynamic models such as COSMO-RS have already been used to aid dissolution/precipitation process design [5], although the solvent selection and flowsheet design have typically been considered sequentially.
Computer-aided molecular and process design (CAMPD) combines material selection with process design to enhance plant design decisions [6]. It has recently been applied to identify optimal solvents and process conditions for pharmaceutical manufacturing In this study, we present a novel simultaneous CAMPD formulation [9] for selecting optimal solvents for polymer recycling via dissolution and precipitation.
Solubility plays a crucial role in determining the performance of the process but experimental data are only available for a limited number of solvents and temperatures. For the first time, we utilize the predictive SAFT-γ Mie [10] equation of state to describe polymer-solvent mixtures in the context of plastic recycling. SAFT-γ Mie is used to predict polyethylene solubility across multiple solvents and to assess polymer-solvent miscibility. We compare SAFT-γ Mie predictions of polyethylene solubility with experimental data and find that this thermodynamic model, with its group contribution approach, can accurately describe various solvent systems with a minimal number of parameters and experimental data.
We then formulate a CAMPD problem that makes it possible to explore a range of organic solvents with diverse molecular structures, including aromatic molecules like toluene and p-xylene, bi-cyclic compounds, such as decalin, and ketones and acetates (e.g., methyl ethyl ketone (MEK) and ethyl acetate). Additionally, bioderived solvents such as cymene and dibutoxymethane are included in the design space.
Our framework is applied to multiple solvent-based dissolution and precipitation recycling case studies of contaminated polyethylene, each involving different types of input waste. The case studies involve a technoeconomic criterion to evaluate the performance of the solvent and process conditions to identify optimal designs and highlight the extent to which the high-performing solvents and the process conditions depend on the type of waste being treated. This motivates the further development of the CAMPD framework to take into more detailed models of the process units and other solvent effects such as the dependence of the dissolution rate on the chosen medium.
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[8]Y. S. Lee, A. Galindo, G. Jackson, and C. S. Adjiman, ‘Enabling the direct solution of challenging computer-aided molecular and process design problems: Chemical absorption of carbon dioxide’, Comput. Chem. Eng., vol. 174, p. 108204, Jun. 2023, doi: 10.1016/j.compchemeng.2023.108204.
[9]C. S. Adjiman and A. Galindo, ‘Challenges and opportunities for computer-aided molecular and process design approaches in advancing sustainable pharmaceutical manufacturing’, Curr. Opin. Chem. Eng., vol. 47, p. 101073, Mar. 2025, doi: 10.1016/j.coche.2024.101073.
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