Data-driven Online Adaptation of the Scenario-tree in Multistage Model Predictive Control

Thombre, Mandar; Krishnamoorthy, Dinesh; Jäschke, Johannes

doi:10.1016/j.ifacol.2019.06.105

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…All these parameters can affect the oxygen consumption and hence easily lead to constraint violation. Choosing the right parameters in the scenario tree could be supported by using a subset selection method with sensitivity analysis or further data-driven approaches like PCA (Thombre et al, 2019). Many studies have shown the advantage of advanced control methods such as MPC, but have mainly been studying the nominal case for optimization (Chang et al, 2016; Ulonska et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

Krausch

Kim

Lucia

et al. 2021

Preprint

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Bioprocess development is commonly characterized by long development times, especially in the early screening phase. After promising candidates have been pre-selected in screening campaigns, an optimal operating strategy has to be found and verified under conditions similar to production. Cultivating cells with pulse-based feeding and thus exposing them to oscillating feast and famine phases has shown to be a powerful approach to study microorganisms closer to industrial bioreactor conditions. In view of the large number of strains and the process conditions to be tested, high-throughput cultivation systems provide an essential tool to sample the large design space in short time. We have recently presented a comprehensive platform, consisting of two liquid handling stations coupled with a model-based experimental design and operation framework to increase the efficiency in High Throughput bioprocess development. Using calibrated macro-kinetic growth models, the platform has been successfully used for the development of scale-down fed-batch cultivations in parallel mini-bioreactor systems. However, it has also been shown that parametric uncertainties in the models can significantly affect the prediction accuracy and thus the reliability of optimized cultivation strategies. To tackle this issue, we implemented a multi-stage Model Predictive Control (MPC) strategy to fulfill the experimental objectives under tight constraints despite the uncertainty in the parameters and the measurements. Dealing with uncertainties in the parameters is of major importance, since constraint violation would easily occur otherwise, which in turn could have adverse effects on the quality of the heterologous protein produced. Multi-stage approaches build up scenario tree, based on the uncertainty that can be encountered and computing optimal inputs that satisfy the constrains despite of such uncertainties. Using the feedback information gained through the evolution along the tree, the control approach is significantly more robust than standard MPC approaches without being overly conservative. We show in this study that the application of multi-stage MPC can increase the number of successful experiments, by applying this methodology to a mini-bioreactor cultivation operated in parallel.

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Section: Resultsmentioning

confidence: 99%

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

Krausch

Kim

Lucia

et al. 2021

Preprint

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“…In addition, they are sometimes worse at generalizing for unknown strains. Furthermore, the use of data-driven approaches such as principal component analysis (Thombre et al, 2019) could be supported. In contrast, other approaches in MPC such as multistage MPC or stochastic MPC would likely predict more cautious feeding rates so that they do not violate constraints even in the presence of large uncertainties (Lucia et al, 2013).…”

Section: Control Under Uncertaintymentioning

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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“…As such, the discretization of the PDF can be chosen according to the required confidence interval. In [31,32], a data-driven approach is proposed for scenario selection when the uncertain parameters exhibit interdependencies. Such correlations in the uncertain parameters can be exploited using multivariate data-analysis techniques like PCA, which we employ in this work.…”

Section: Data-driven Selection Of Scenarios In Multistage Mpcmentioning

confidence: 99%

Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters

2020

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Industrial waste heat recovery is an attractive option having the simultaneous benefits of reducing energy costs as well as carbon emissions. In this context, thermal energy storage can be used along with an optimal operation strategy like model predictive control (MPC) to realize significant energy savings. However, conventional control methods offer little robustness against uncertainty in terms of daily operation, where supply and demand of energy in the cluster can vary significantly from their predicted profiles. A major concern is that ignoring the uncertainties in the system may lead to the system violating critical constraints that affect the quality of the end-product of the participating processes. To this end, we present a method to make optimal energy storage and discharge decisions, while rigorously handling this uncertainty. We employ multivariate data analysis on historical industrial data to implement a multistage nonlinear MPC scheme based on a scenario-tree formulation, where the economic objective is to minimize energy costs. Principal component analysis (PCA) is used to detect outliers in the industrial data on heat profiles, and to select appropriate scenarios for building the scenario-tree in the multistage MPC formulation. The results show that this data-driven robust MPC approach is successfully able to keep the system from violating any operating constraints. The solutions obtained are not overly conservative, even in the presence of significant deviations between the predicted and actual heat profiles. This leads to an energy-efficient utilization of the storage unit, benefiting all the stakeholders involved in heat-exchange in the cluster.

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Data-driven Online Adaptation of the Scenario-tree in Multistage Model Predictive Control

Cited by 10 publications

References 12 publications

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

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

Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters

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