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Prediction and innovation of sustainable continuous flow microwave processing based on numerical simulations: A systematic review

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  • Yang, Huayu
  • Yan, Bowen
  • Chen, Wei
  • Fan, Daming

Abstract

Continuous-flow microwave heating is an emerging technology for processing liquid materials owing to its short residence time and relatively low environmental footprint. However, the uncertain transmission of microwaves makes it difficult to understand the energy input-absorption relationship using conventional experimental and theoretical methods, resulting in inefficient heating. Nevertheless, this limitation can be mitigated through numerical simulation methods. Numerical simulations can not only maintain a balance between energy conversion and heating uniformity but also help optimize reaction conditions and reactor design. The energy efficiency resulting from a resonator design can vary from 7% to 99%. Furthermore, the interference between flow paths affecting the thermal efficiency can be visualized via simulation models and scientifically optimized. The key challenge in numerical simulations is the development of models that accurately describe the reaction conditions under a microwave field. Addressing this constraint can help to design a reasonable microwave transmission method and efficiently predict the processing capability. This review introduces the concepts of numerical simulation methods and summarizes the continuous flow applications driven by these methods. Additionally, this study explains the current understanding of microwave-assisted liquid processing based on numerical simulations and emphasizes the challenges of instantaneous prediction modeling. The prospects of numerical simulations for liquid-processing development are presented.

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  • Yang, Huayu & Yan, Bowen & Chen, Wei & Fan, Daming, 2023. "Prediction and innovation of sustainable continuous flow microwave processing based on numerical simulations: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
  • Handle: RePEc:eee:rensus:v:175:y:2023:i:c:s1364032123000394
    DOI: 10.1016/j.rser.2023.113183
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