A comparative review of multi-rate moving horizon estimation schemes for bioprocess applications

Elsheikh, Mohamed; Hille, Rubin; Tătulea-Codrean, Alexandru; Krämer, Stefan

doi:10.1016/j.compchemeng.2020.107219

Cited by 18 publications

(17 citation statements)

References 50 publications

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“…With * , * and * we reconstruct the states at which can be used in the MPC. The full information estimator considers all the measurements; instead, if only the measurements in a given time window are used, one refers to a moving horizon estimator (Elsheikh et al 2021;Rawlings et al 2020).…”

Section: Reconstructing Unmeasured Cell Componentsmentioning

confidence: 99%

Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

Espinel-Ríos¹,

Bruno²,

Pohlodek³

et al. 2023

Preprint

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Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, etc. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

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Section: Reconstructing Unmeasured Cell Componentsmentioning

confidence: 99%

Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

Espinel-Ríos¹,

Bruno²,

Pohlodek³

et al. 2023

Preprint

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“…A comprehensive review of the arrival cost and stage cost for the variable structure multi-rate MHE can be found in (Elsheikh et al, 2021).…”

Section: Multi-rate Measurements and Ill-conditioning Of The Estimati...mentioning

confidence: 99%

“…Moreover, the positive definite matrix R 0 facilitates the stability of the MHE (Rawlings et al, 2017). A standard scheme for the arrival cost design is to use the covariance of the estimation error (Elsheikh et al, 2021). The covariance can be approximated with the Cramer-Rao inequality.…”

Section: Moving Horizon State and Parameter Estimationmentioning

confidence: 99%

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Kim¹,

Krausch²,

Aizpuru³

et al. 2022

Preprint

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We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput microbioreactor platform at 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with an automated liquid handling and online and atline analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges which need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and atline measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can

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“…Using MHE for state and parameter reestimation has been proposed for process engineering for some time and various examples can be found in the literature (Hedengren & Eaton, 2017; Jabarivelisdeh et al, 2020; Zavala et al, 2008). The reader is further referred to Elsheikh et al (2021) for a comprehensive review.…”

Section: Introductionmentioning

confidence: 99%

High‐throughput screening of optimal process conditions using model predictive control

Krausch

Kim

Barz

et al. 2022

Biotech & Bioengineering

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Modern biotechnological laboratories are equipped with advanced parallel mini‐bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed‐batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well‐defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini‐bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.

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A comparative review of multi-rate moving horizon estimation schemes for bioprocess applications

Cited by 18 publications

References 50 publications

Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

High‐throughput screening of optimal process conditions using model predictive control

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