Optimization of Bioreactor Using Metabolic Control Analysis Approach

Konde, Kakasaheb; Modak, Jayant M.

doi:10.1021/bp0602911

Cited by 4 publications

(3 citation statements)

References 40 publications

(51 reference statements)

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“…Optimization criteria are set according to the bioprocess model or the modeling equations that have been developed for the process wherein the process models play an important role in terms of the kind of model being proposed which may be structured or unstructured [33]. Identifying optimal operational strategies for metabolic products is difficult.…”

Section: Optimization Strategymentioning

confidence: 99%

Bioreactor control systems in the biopharmaceutical industry: a critical perspective

Mitra

Murthy

2021

Syst Microbiol and Biomanuf

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Industrial-scale bioprocessing underpins much of the production of pharmaceuticals, nutraceuticals, food, and beverage processing industries of the modern world. The profitability of these processes increasingly leverages the economies of scale and scope that are critically dependent on the product yields, titers, and productivity. Most of the processes are controlled using classical control approaches and represent over 90% of the industrial controls used in bioprocessing industries. However, with the advances in the production processes, especially in the biopharmaceutical and nutraceutical industries, monitoring and control of bioprocesses such as fermentations with GMO organisms, and downstream processing has become increasingly complex and the inadequacies of the classical and some of the modern control systems techniques is becoming apparent. Therefore, with increasing research complexity, nonlinearity, and digitization in process, there has been a critical need for advanced process control that is more effective, and easier process intensification and product yield (both by quality and quantity) can be achieved. In this review, industrial aspects of a process and automation along with various commercial control strategies have been extensively discussed to give an insight into the future prospects of industrial development and possible new strategies for process control and automation with a special focus on the biopharmaceutical industry.

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Section: Optimization Strategymentioning

confidence: 99%

Bioreactor control systems in the biopharmaceutical industry: a critical perspective

Mitra

Murthy

2021

Syst Microbiol and Biomanuf

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“…It should be mentioned that because of the dynamic nature of fermentation and complexity of cellular metabolism, mathematical modeling of fed-batch bioprocesses is a very complex challenge (Hjersted and Henson, 2006). Some structured and mechanistic models have been developed that are able to describe the behavior of the microorganisms more accurately than the traditional unstructured models (Hodgson et al, 2004;Konde and Modak, 2007;Varma and Palsson, 1994;Varner and Ramkrishna, 1999). Much attention has been paid to structured models for modeling and simulation of batch and fed-batch cultivations (Hjersted and Henson, 2006;Henson, 2006).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fed-Batch Recombinant Yeast Fermentation for Ethanol Production Using a Reduced Dynamic Flux Balance Model Based on Artificial Neural Networks

Eslamloueyan

Setoodeh

2011

Chemical Engineering Communications

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In this work, a reduced form of dynamic flux balance model based on artificial neural networks for batch and fed-batch fermentation of xylose-utilizing engineered Saccharomyces cerevisiae RWB 218 is developed. The intracellular description of carbon metabolism in the model is simulated by a multilayer-perceptron (MLP) network. First, this hybrid model is compared to the full mechanistic dynamic flux balance analysis (DFBA) in terms of accuracy and computational time regarding available experimental data on anaerobic batch cultivation. Afterwards, it is used in a model-based sequential dynamic optimization procedure in order to maximize ethanol productivity. The initial liquid volume charged in the bioreactor, the feed flow rates in aerobic and anaerobic conditions, the final batch time, and the switching time from aerobic to anaerobic conditions are considered as decision variables. Differential evolution (DE), as a robust and efficient optimization method, is employed to solve the problem for several glucose mass fractions in the feed stream. The results show that in similar conditions a small deviation in each operating parameter from its optimal value may lead to considerable decrease in ethanol productivity.

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“…The optimisation of bioreactors now implies the manipulation of both microbial culture and the environmental factors involved, i.e. a multivariable optimisation of the process (Konde and Modak, 2007). Model-based optimisation is therefore a vital tool in determining the batch operating strategies (Hjersted and Henson, 2006).…”

mentioning

confidence: 99%

Multi-scale Models for the Optimization of Batch Bioreactors

Liew

Nandong

Samyudia

2012

IFAC Proceedings Volumes

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Process models play an important role in the bioreactor design, optimisation and control. In previous work, the bioreactor models have mainly been developed by considering the microbial kinetics and the reactor environmental conditions with the assumption that the ideal mixing occurs inside the reactor. This assumption is relatively difficult to meet in the practical applications. In this paper, we propose a new approach to the bioreactor modelling by expanding the so-called Herbert's Microbial Kinetics (HMK) model so that the developed models are able to incorporate the mixing effects via the inclusion of the aeration rate and stirrer speed into the microbial kinetics. The expanded models of Herbert's microbial kinetics allow us to optimize the bioreactor's performances with respects to the aeration rate and stirrer speed as the decision variables, where this optimisation is not possible using the original HMK model of microbial kinetics. Simulation and experimental studies on a batch ethanolic fermentation demonstrates the use of the expanded HMK models for the optimisation of bioreactor's performances. It is shown that the integration of the expanded HMK model with the Computational Fluid Dynamics (CFD) model of mixing, which we call it as a Kinetics Multi-Scale (KMS) model, is able to predict the experimental values of yield and productivity of the batch fermentation process accurately (with less than 5% errors).

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Optimization of Bioreactor Using Metabolic Control Analysis Approach

Cited by 4 publications

References 40 publications

Bioreactor control systems in the biopharmaceutical industry: a critical perspective

Bioreactor control systems in the biopharmaceutical industry: a critical perspective

Optimization of Fed-Batch Recombinant Yeast Fermentation for Ethanol Production Using a Reduced Dynamic Flux Balance Model Based on Artificial Neural Networks

Multi-scale Models for the Optimization of Batch Bioreactors

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