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Related Experiment Videos

Modeling conversion and transport phenomena in solid-state fermentation: a review and perspectives.

Yovita S P Rahardjo1, Johannes Tramper, Arjen Rinzema

  • 1Wageningen Centre for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands. Yovita.Rahardjo@wur.nl

Biotechnology Advances
|November 3, 2005
PubMed
Summary

Mathematical models are crucial for understanding solid-state fermentation (SSF) processes. Current models are insufficient, failing to capture key phenomena influencing product characteristics and yield.

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Area of Science:

  • Biotechnology
  • Biochemical Engineering
  • Process Modeling

Background:

  • Solid-state fermentation (SSF) involves complex internal gradients (concentration, temperature) essential for transport but can reduce productivity.
  • SSF processes are typically batch, leading to dynamic changes that complicate analysis and optimization.
  • Existing mathematical models for SSF often oversimplify, neglecting crucial phenomena.

Purpose of the Study:

  • To critically evaluate existing mathematical models for SSF processes.
  • To identify limitations in current modeling approaches regarding phenomena influencing SSF outcomes.
  • To discuss key issues for developing improved SSF models.

Main Methods:

  • Literature review and critical analysis of existing SSF models.

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  • Evaluation of experimental validation data for proposed models.
  • Identification of phenomena not adequately addressed by current models.

    • Main Results:

    • Current SSF models inadequately represent phenomena like coupled substrate conversion, diffusion, and microbial growth.
    • Existing models fail to explain the generation of diverse products and final product characteristics in SSF.
    • Significant discrepancies exist between model predictions and experimental observations in SSF.

    Conclusions:

    • Improved mathematical models are essential for a deeper understanding and manipulation of SSF processes.
    • Future models must incorporate a wider range of phenomena to accurately predict SSF outcomes.
    • Addressing limitations in current models is critical for advancing SSF applications and product development.