A new approach for finding smooth optimal feeding profiles in fed-batch fermentations

Ochoa, Silvia

doi:10.1016/j.bej.2015.09.004

Cited by 39 publications

(36 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This model has been used as a benchmark tool for in silico ethanol production optimization, as presented in the works of several authors such as Banga et al Ochoa, Rocha et al [14,15,18], among others.…”

Section: Fermentation Modelingmentioning

confidence: 99%

“…Usually, this function is defined trough the summation of the costs of the process input and output streams of interest, pondered by their cost expressed in an intensive form (e.g., kg −1 or L −1 ). Although the utilization of a steady-state consideration for the process would expressively simplify the optimization task, the consideration of the transient response is crucial for the achievement of good optimization performance [15,33,44]. This aspect is even more relevant for biotechnological processes, as they exhibit frequently a complex non-linear behavior, especially when subjected to disturbances in the operational parameters.…”

Section: Drto Approachmentioning

confidence: 99%

“…Several methodologies are studied in the literature for the feeding profile parametrization, such as sinusoidal [15], linear piecewise [14] and pure analytical (no parametrization) [32]. In the work of Ochoa [15], the advantages of the sinusoidal feeding profile are discussed, in terms of the continuous form of its mathematical expression, ensuring for the minimization of inhibitory shocks occurrence due to substrate overfeeding. Thus, studies in respect to the functional form for the feed rate employed in the present work are appropriately discussed in the Section 2.4 below.…”

Section: Dynamic Optimization Problemsmentioning

confidence: 99%

“…It is important to emphasize that a productivity based objective function is appropriately for the control or optimization of a processes in which there is disturbance occurrence, instead of a defined operation point, as it can induce an unsatisfactory performance [29]. The parametrization of the feed rate was performed using a periodic function based in a non-linear mathematical expression based in cosinoidal terms, as presented in the work of Ochoa [15], an exponential and linear functions. The referred mathematical expressions are outlined in the following equations.…”

Section: Drto Of the Fermentative Processmentioning

confidence: 99%

“…The frequent non-linear behavior of microbial metabolism, and the strong relation between this process and the bioreactor environmental and operational characteristics, make the development of descriptive mathematical models for bioprocesses and the process control itself a laborious task [12,13]. Despite the inherent difficulties, the model-based optimization of biotechnological process has been the subject of several researches [14,15]. In this sense, the fed-batch fermentation bioreactors represent an important topic, given the widespread utilization of this class of reactors in the biochemical industrial field, what can be explained by the avoidance of substrate inhibition due to overfeeding (when compared to batch-mode operated reactors), and the preservation of a sterile environment inside the fermenter (when compared to continuous equipments) [16].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Freitas

Olivo²,

Andrade³

2017

Energies

View full text Add to dashboard Cite

Due to growing worldwide energy demand, the search for diversification of the energy matrix stands out as an important research topic. Bioethanol represents a notable alternative of renewable and environmental-friendly energy sources extracted from biomass, the bioenergy. Thus, the assurance of optimal growth conditions in the fermenter through operational variables manipulation is cardinal for the maximization of the ethanol production process yield. The current work focuses in the determination of optimal control scheme for the fermenter feed rate and batch end-time, evaluating different parametrization profiles, and comparing evolutionary computation techniques, the genetic algorithm (GA) and differential evolution (DE), using a dynamic real-time optimization (DRTO) approach for the in silico ethanol production optimization. The DRTO was able to optimize the reactor feed rate considering disturbances in the process input. Open-loop tests results obtained for the algorithms were superior to several works presented in the literature. The results indicate that the interaction between the intervals of DRTO cycles and parametrization profile is more significant for the GA, both in terms of ethanol productivity and batch time. In general lines, the present work presents a methodology for control and optimization studies applicable to other bioenergy generation systems.

show abstract

Section: Fermentation Modelingmentioning

confidence: 99%

Section: Drto Approachmentioning

confidence: 99%

Section: Dynamic Optimization Problemsmentioning

confidence: 99%

Section: Drto Of the Fermentative Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Freitas

Olivo²,

Andrade³

2017

Energies

View full text Add to dashboard Cite

show abstract

Identification of optimal flow rate for culture media, cell density, and oxygen toward maximization of virus production in a fed‐batch baculovirus‐insect cell system

Sharma,

Mahadevan,

Giri

et al. 2023

Biotech & Bioengineering

View full text Add to dashboard Cite

In recent times, it has been realized that novel vaccines are required to combat emerging disease outbreaks, and faster optimization is required to respond to global vaccine demands. Although, fed‐batch operations offer better productivity, experiment‐based optimization of a new fed‐batch process remains expensive and time‐consuming. In this context, we propose a novel computational framework that can be used for process optimization and control of a fed‐batch baculovirus‐insect cell system. Since the baculovirus expression vector system (BEVS) is known to be widely used platforms for recombinant protein/vaccine production, we chose this system to demonstrate the identification of optimal profile. Toward this, first, we constructed a mathematical model that captures the time course of cell and virus growth in a baculovirus‐insect cell system. Second, the proposed model was used for numerical analysis to determine the optimal operating profiles of control variables such as culture media, cell density, and oxygen based on a multiobjective optimal control formulation. Third, a detailed comparison between batch and fed‐batch culture was perfromed along with a comparison between various alternatives of fed‐batch operation. Finally, we demonstrate that a model‐based quantification of controlled feed addition in fed‐batch culture is capable of providing better productivity as compared to a batch culture. The proposed framework can be utilized for the estimation of optimal operating regions of different control variables to achieve maximum infected cell density and virus yield while minimizing the substrate/media, uninfected cell, and oxygen consumption.

show abstract

Fermentation Process Control and Optimization

et al. 2022

View full text Add to dashboard Cite

Process control and optimization is important in ensuring a process in sustaining profitable income while maintaining the required quality. The fermentation process has attracted attention from food processing, pharmaceutical energy, and waste treatment industries due to its lower impact on the environment and lower operating cost than the conventional chemical process. However, its control and monitoring are relatively complex as living cell activity is not easily comprehended and highly nonlinear. Various control techniques applied in the fermentation process are reviewed, including a summary of the control objective, manipulated and controlled variables chosen, and their strengths and limitations in determining the optimal fermentation process.

show abstract

A new approach for finding smooth optimal feeding profiles in fed-batch fermentations

Cited by 39 publications

References 40 publications

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Identification of optimal flow rate for culture media, cell density, and oxygen toward maximization of virus production in a fed‐batch baculovirus‐insect cell system

Fermentation Process Control and Optimization

Contact Info

Product

Resources

About