Recovery of Carbon Fibres From Aged Epoxy Matrix Composites Using H2O2 as an Oxidant: A Thermodynamic and Technoeconomic Analysis
There is an effort to use hydrogen peroxide for recycling carbon fibre from epoxy matrix composites because it is an ecofriendly material, and the related technology is feasible. However, there is little information on the technoeconomic impact of this method, thus whether it is economically better...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/je/5807390 |
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| Summary: | There is an effort to use hydrogen peroxide for recycling carbon fibre from epoxy matrix composites because it is an ecofriendly material, and the related technology is feasible. However, there is little information on the technoeconomic impact of this method, thus whether it is economically better than current techniques. Therefore, in this paper, we discuss the technoeconomic analysis of recycling using hydrogen peroxide. The analysis also includes a thermodynamic model to calculate the amount of energy required to decompose the epoxy matrix. Various financial indicators, including the payback period, net present value (NPV), internal rate of return (IRR) and profitability index (PI), were used. The technoeconomic assessment revealed favourable outcomes across all key financial indicators, demonstrating the viability and potential benefits of the process. A capital investment of $17.34M over 10 years was required. The NPV of $15.56M with a 15% minimum discounted rate of return (WACC) was computed. The project is more likely to succeed with an annual production cost of $176.5 million for 50,000 tons in the first year, with this amount subject to annual inflation. A sensitivity analysis was also performed to assess the effect of input variables. In the sensitivity analysis, we calculated between 25,000 and 100,000 tons. The price of hydrogen peroxide and recovered carbon fibre are essential variables that have a high effect on the model. |
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| ISSN: | 2314-4912 |