Scholarlink

<p>Accurate real-time estimation of system states and metabolic parameters is essential for effective bioprocess control. However, the dynamics of microbial adaptation—the rate at which a microorganism adapts to changes in the substrate concentration—is often overlooked, leading to early-stage plant-model mismatches and inaccurate estimation of relevant parameters, such as the biomass yield on carbon source (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(Y_{XC}\)</EquationSource> </InlineEquation>) or the maximum substrate uptake rate (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(q_S^{\text {max}}\)</EquationSource> </InlineEquation>). This work introduces a novel model-based observer for simultaneous state and parameter estimation that explicitly accounts for substrate uptake dynamics. By defining the substrate uptake rate (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(q_S\)</EquationSource> </InlineEquation>) as a state variable and introducing a random variable (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\lambda\)</EquationSource> </InlineEquation>) to represent the biomass-specific substrate uptake adaptability rate, we construct a Bayesian estimator that allows proper determination of the states and parameters in fed-batch fermentations of <i>E. coli</i> while maintaining near-zero centered residuals between the plant output and the proposed model stoichiometry. This work advances methods for robust state and adaptive parameter estimation in dynamic bioprocess environments under uncertainty.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The dynamic growth of bacterial cultures: real-time Bayesian estimation of substrate uptake rates in fed-batch fermentations of E. coli

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月8日

查看全文
  • Maximiliano Ibaceta,
  • Mark-Richard Neudert,
  • Nuno Marques,
  • Stefan Kahrer,
  • Christoph Herwig,
  • Andreas Steinboeck

摘要

Accurate real-time estimation of system states and metabolic parameters is essential for effective bioprocess control. However, the dynamics of microbial adaptation—the rate at which a microorganism adapts to changes in the substrate concentration—is often overlooked, leading to early-stage plant-model mismatches and inaccurate estimation of relevant parameters, such as the biomass yield on carbon source ( \(Y_{XC}\) ) or the maximum substrate uptake rate ( \(q_S^{\text {max}}\) ). This work introduces a novel model-based observer for simultaneous state and parameter estimation that explicitly accounts for substrate uptake dynamics. By defining the substrate uptake rate ( \(q_S\) ) as a state variable and introducing a random variable ( \(\lambda\) ) to represent the biomass-specific substrate uptake adaptability rate, we construct a Bayesian estimator that allows proper determination of the states and parameters in fed-batch fermentations of E. coli while maintaining near-zero centered residuals between the plant output and the proposed model stoichiometry. This work advances methods for robust state and adaptive parameter estimation in dynamic bioprocess environments under uncertainty.