A modular simulation package for fed-batch fermentation: penicillin production

Bírol, Gülnur; Ündey, Cenk; Çınar, Ali

doi:10.1016/s0098-1354(02)00127-8

Cited by 528 publications

(374 citation statements)

References 15 publications

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“…During this stage biomass growth rate must be kept constant to maintain high penicillin production. So a readily metabolisable sugar such as glucose is supplied continuously into the system instead of being added all at once at the beginning [4,3]. We obtain the batch data using a modular simulator (PenSim v2.0) for fed-batch fermentation developed by the monitoring and control group of the Illinois Institute of Technology in the year of 2002 [10].…”

Section: Process Descriptionmentioning

confidence: 99%

Multiway kernel independent component analysis based on feature samples for batch process monitoring

2009

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a b s t r a c tMost batch processes generally exhibit the characteristics of nonlinear variation. In this paper, a nonlinear monitoring technique is proposed using a multiway kernel independent component analysis based on feature samples (FS-MKICA). This approach first unfolds the three-way dataset of a batch process into the two-way one and then chooses representative feature samples from the large two-way input training dataset. The nonlinear feature space abstracted from the unfolded two-way data space is then transformed into a high-dimensional linear space via kernel function and an independent component analysis (ICA) model is established in the mapped linear space. The proposed FS-MKICA method can significantly reduce the computational cost in extracting the kernel ICA model since it is based on the small subset of feature samples rather than on the entire input sample set. We supply two statistics, the I 2 statistic of process variation and the squared prediction error statistic of residual, for on-line monitoring of batch processes. The proposed method is applied to detecting faults in the fed-batch penicillin fermentation process. The standard linear ICA method for batch process monitoring, known as the multiway independent component analysis (MICA), is also applied to the same benchmark batch process. The simulation results obtained in this nonlinear batch process application clearly demonstrate the power and superiority of the new nonlinear monitoring method over the linear one. The FS-MKICA approach can extract the nonlinear features of the batch process while the MICA method cannot.

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Section: Process Descriptionmentioning

confidence: 99%

Multiway kernel independent component analysis based on feature samples for batch process monitoring

2009

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“…The unstructured model for penicillin production in a fed-batch fermentor given by Birol et al [22] has been used for simulation studies. The model consists of nine differential equations relating bioreactor states (biomass, substrate, product, dissolved oxygen, CO 2 parameters are given in Birol et al [22].…”

Section: Fed-batch Bioreactormentioning

confidence: 99%

“…The model consists of nine differential equations relating bioreactor states (biomass, substrate, product, dissolved oxygen, CO 2 parameters are given in Birol et al [22]. While carrying out the simulations, we assume that the bioreactor operates isothermally and the fermentor temperature is maintained at the optimum value (25°C) by a perfect temperature controller.…”

Section: Fed-batch Bioreactormentioning

confidence: 99%

“…Thus, the manipulated inputs have to be changed continuously to keep pH and DO at desired setpoints. As the input-output dynamics changes significantly with time, isolating faults such as pH controller failure or sudden decrease in agitation power are challenging problems from the viewpoint of fault diagnosis [22]. Simulations are carried out for a batch time of 400 h, with a sampling time of 1.2 min.…”

Section: Fed-batch Bioreactormentioning

confidence: 99%

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Intelligent state estimation for fault tolerant nonlinear predictive control

Deshpande

Patwardhan

Narasimhan

2009

Journal of Process Control

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“…A sample of this includes the works of Birol et al (2002), where the model of the penicillin production process has been extended by including additional input variables such as agitation power and aeration rates. Nielsen et al (2003) proposed an empirical correlation to represent the oxygen transfer coefficient for a bioreactor.…”

Section: Introductionmentioning

confidence: 99%

Including dissolved oxygen dynamics into the Bt δ-endotoxins production process model and its application to process control

Amicarelli

Sciascio

Toibero

et al. 2010

Braz. J. Chem. Eng.

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-This paper proposes a model to characterize the Dissolved Oxygen Dynamics (DO) for the Bacillus thuringiensis (Bt) δ-endotoxins production process. The objective of this work is to include this dynamics into a phenomenological model of the process in order to facilitate the biomass estimation from the knowledge of oxygen consumption; and for control purposes, by allowing the addition of a new control variable in order to favorably influence the bioprocess evolution. The mentioned DO model is based on first principles and parameter estimation and model verification are supported by real experimental data. Finally, a control strategy is designed based on this model with its corresponding asymptotic stability and robustness analysis.

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A modular simulation package for fed-batch fermentation: penicillin production

Cited by 528 publications

References 15 publications

Multiway kernel independent component analysis based on feature samples for batch process monitoring

Multiway kernel independent component analysis based on feature samples for batch process monitoring

Intelligent state estimation for fault tolerant nonlinear predictive control

Including dissolved oxygen dynamics into the Bt δ-endotoxins production process model and its application to process control

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