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  2. Recovery Yield And Defective Product Rate In A Bio-based Amino Acid Crystallization Process.
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  2. Recovery Yield And Defective Product Rate In A Bio-based Amino Acid Crystallization Process.

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Recovery Yield and Defective Product Rate in a Bio-Based Amino Acid Crystallization Process.

Jin Tae Hong1, Jin Joo Oh1, Jung Won Park1

  • 1CJ BIO Research Institute, CJ CheilJedang, Suwon-Si, Gyeonggi-do 16495, Republic of Korea.

ACS Omega
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Predicting crystal purity and recovery yield in biobased crystallization processes is now easier. This study introduces a new method using an effective distribution model for amino acids like l-alanine (ALA) and l-histidine (HIS).

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Area of Science:

  • Biochemical Engineering
  • Separation Processes
  • Crystallization Technology

Background:

  • Biobased crystallization processes often lack experimental data, hindering accurate prediction of crystal purity and recovery yield.
  • Challenges in designing and evaluating these processes stem from limited predictive models for amino acid crystallization.

Purpose of the Study:

  • To develop a predictive method for crystal purity and recovery yield in biobased evaporative crystallization of amino acids.
  • To establish a model describing the relationship between liquid and crystal purity.
  • To predict defective product disturbances in the crystallization process.

Main Methods:

  • An effective distribution model, using an empirically modified logistic curve, was developed to link liquid and crystal purity.
  • Mass balance principles were integrated with the effective distribution model to predict crystal purity based on feed purity and recovery yield.
  • A statistical approach was employed to forecast defective product rates.
  • Main Results:

    • The study successfully predicted crystal purity and recovery yield for l-alanine (ALA), l-histidine monohydrochloride monohydrate (HIS), and l-citrulline (CIT).
    • l-Histidine (HIS) demonstrated a higher propensity for achieving superior crystal purity compared to l-alanine (ALA).
    • Defective product rates decreased with increasing target crystal purity and decreasing minimum specification purity across all amino acids studied.

    Conclusions:

    • The developed method provides a robust framework for predicting performance in biobased amino acid crystallization.
    • Recovery yield is identified as a critical metric for the design and evaluation of downstream biobased processes.
    • The findings facilitate more accurate process design and optimization, reducing prediction inaccuracies due to data scarcity.