Browsing by Author "Meiirbekov, Arshyn"

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    Challenges in cost modelling of recycling carbon fiber composites
    (IOS, 2020-07-10) Shehab, Essam; Meiirbekov, Arshyn; Sarfraz, Shoaib
    The use of carbon fiber composites (CFCs) has become broad in many industries due to its superior properties compared to conventional materials. However, the increased demand coupled with environmental regulations has led to the development of different recycling methods for CFCs such as mechanical, thermal and chemical processes. Each recycling method has its own requirements and outputs along with some economic implications which need to be justified through cost modelling. This paper aims to identify current challenges associated with cost modelling of different processes for CFC recycling. The main challenges identified are grouped into three main categories such as technical issues, supply chain and market challenges
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    ItemOpen Access
    Environmental assessment of recycling carbon fibre-reinforced composites: current challenges and future opportunities
    (Springer, 2022-11-19) Meiirbekov, Arshyn; Amantayeva, Akniyet; Tokbolat, Serik; Suleimen, Aidar; Sarfraz, Shoaib; Shehab, Essam
    The increasing application of carbon fiber reinforced polymer composites (CFRP) across different industries raises environmental concerns. It requires focusing on the end-of-life phase of the product/material. The environmental benefits of CFRP recycling over conventional ways of treatment are apparent. However, estimating the environmental impacts is followed up with various challenges. In this study, the aspects of environmental assessment of CFRP recycling and their respective challenges are examined. CFRP recycling methods such as mechanical treatment, pyrolysis, fluidized bed process, and solvolysis have been previously studied in the context of energy and environmental assessment under the Life-Cycle-Assessment (LCA) framework. This study focused on the identification of challenges associated with variability of applied methods used, comparability, scaling results, data, uncertainty, and resource-demanding process of LCA. Recommendations on overcoming the identified challenges are provided and discussed.