Dynamic optimization of bioprocesses: Efficient and robust numerical strategies

Banga, Julio R.; Balsa‐Canto, Eva; Moles, Carmen G.; Alonso, Antonio A.

doi:10.1016/j.jbiotec.2005.02.013

Cited by 183 publications

(171 citation statements)

References 38 publications

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“…Since the optimality criteria used in both stages are of different nature, this synergy can yield a more comprehensive approach to the investigation of metabolic pathways. However, it must be pointed out that given the large scale of real metabolic networks, the use of dynamic optimization as a practical tool still requires the development of appropriate computational techniques that can efficiently cope with high-dimensional problems, perhaps in the spirit of some of the recent work in the field (Mahadevan et al, 2002;Uygun et al, 2006;Banga et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Sequential Activation of Metabolic Pathways: a Dynamic Optimization Approach

et al. 2009

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The regulation of cellular metabolism facilitates robust cellular operation in the face of changing external conditions. The cellular response to this varying environment may include the activation or inactivation of appropriate metabolic pathways. Experimental and numerical observations of sequential timing in pathway activation have been reported in the literature. It has been argued that such patterns can be rationalized by means of an underlying optimal metabolic design. In this paper we pose a dynamic optimization problem that accounts for time-resource minimization in pathway activation under constrained total enzyme abundance. The optimized variables are time-dependent enzyme concentrations that drive the pathway to a steady state characterized by a prescribed metabolic flux. The problem formulation addresses unbranched pathways with irreversible kinetics. Neither specific reaction kinetics nor fixed pathway length are assumed.In the optimal solution, each enzyme follows a switching profile between zero and maximum concentration, following a temporal sequence that matches the pathway topology. This result provides an analytic justification of the sequential activation previously described in the literature. In contrast with the existent numerical approaches, the activation sequence is proven to be optimal for a generic class of monomolecular kinetics. This class includes, but is not limited to, Mass Action, Michaelis-Menten, Hill, and some Power-law models. This suggests that sequential enzyme expression may be a common feature of metabolic regulation, as it is a robust property of optimal pathway activation.

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Section: Discussionmentioning

confidence: 99%

Sequential Activation of Metabolic Pathways: a Dynamic Optimization Approach

et al. 2009

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“…Now, for a numerical solution of this problem using the mentioned MatLab toolbox of Banga et al (2005), piecewise constant controls are considered. More precisely, for…”

Section: Optimal Equilibrium Controlmentioning

confidence: 99%

Equilibrium control of nonlinear verticum-type systems, applied to integrated pest control

et al. 2013

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Linear verticum-type control and observation systems have been introduced for modelling certain industrial systems, consisting of subsystems, vertically connected by certain state variables. Recently the concept of verticum-type observation systems and the corresponding observability condition have been extended by the authors to the nonlinear case. In the present paper the general concept of a nonlinear verticum-type control system is introduced, and a sufficient condition for local controllability to equilibrium is obtained. In addition to a usual linearization, the basic idea is a decomposition of the control of the whole system into the control of the subsystems. Starting from the integrated pest control model of Rafikov and E.H. Limeira (2012) a nonlinear verticum-type model has been set up an equilibrium control is obtained. Furthermore, a corresponding bioeconomical problem is solved minimizing the total cost of integrated pest control (combining chemical control with a biological one).

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“…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%

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

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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.

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Dynamic optimization of bioprocesses: Efficient and robust numerical strategies

Cited by 183 publications

References 38 publications

Sequential Activation of Metabolic Pathways: a Dynamic Optimization Approach

Sequential Activation of Metabolic Pathways: a Dynamic Optimization Approach

Equilibrium control of nonlinear verticum-type systems, applied to integrated pest control

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

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