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March 3, 2026Journal of Process Control0 citationsOpen Access

Observer design for lactic-acid bacteria population balances with non-uniformly delayed measurements

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ALArthur LepsienLHLucas HoltorfASAlexander Schaum

Key Points

  • The estimation strategy improves the mean normalized root mean squared error by approximately 41.6% through the use of two extended Kalman filters for non-uniform measurements.
  • Experimental data from batch experiments with Streptococcus thermophilus demonstrates that the strategy effectively balances biomass concentrations and associated measurements.
  • The approach combines a cell population balance model with measurements taken at different time scales to enhance accuracy in lactic-acid fermentation analysis.
  • A cascade structure is used in the model, highlighting the relationship between nonlinear and linear subsystems handling varying measurement delays.

Abstract

The paper addresses the problem estimating the cell-mass distribution density, glucose and lactate concentration, as well as of the total biomass concentration in lactic-acid fermentation. The estimate is based on the combination of a cell population balance model with the available measurements. The model shows a cascade structure of a nonlinear finite-dimensional subsystem and a linear infinite-dimensional subsystem. The measurements are available on different time scales. On a quasi-continuous time scale optical density and conductivity are measured. The cell-size distribution is measured with a considerably lower frequency and is furthermore subject to non-uniform delays. The proposed estimation strategy exploits the cascade structure and consists of two cascaded discrete-time extended Kalman filters (EKFs). The performance of the proposed approach is demonstrated using experimental data from batch experiments with Streptococcus thermophilus . The estimation strategy improves the mean normalized root mean squared error of the distribution by approximately 41.6 % compared to a pure simulation.

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Cite This Study

Lepsien et al. (2026) studied this question.

synapsesocial.com/papers/69a75aa4c6e9836116a20bd3https://doi.org/10.1016/j.jprocont.2026.103639
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Also Consider

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  5. 5Incorporating delayed and infrequent measurements in Extended Kalman Filter based nonlinear state estimation2010 · 137 citations