Monitoring of fed-batch E. coli fermentations with software sensors

A soft sensor approach is described for controlling metabolic overflow from mixed acid fermentation and glucose overflow metabolism in a fed-batch cultivation for production of recombinant green fluorescence protein (GFP) in Escherichia coli. The hardware part of the sensor consisted of a near-infrared in situ probe that monitored the E. coli biomass and an HPLC analyzer equipped with a filtration unit that measured the overflow metabolites. The computational part of the soft sensor used basic kinetic equations and summations for estimation of specific rates and total metabolite concentrations. Two control strategies for media feeding of the fed-batch cultivation were evaluated: (1) controlling the specific rates of overflow metabolism and mixed acid fermentation metabolites at a fixed pre-set target values, and (2) controlling the concentration of the sum of these metabolites at a set level. The results indicate that the latter strategy was more efficient for maintaining a high titer and low variability of the produced recombinant GFP protein.

Section: Introductionmentioning

confidence: 99%

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Gustavsson

Mandenius

2012

“…Table 1 presents descriptions of model parameters with the values used in this study. For biological wastewater treatment processes, some of the process states of interest are often not directly measurable [8,9] or their determination through analytical methods is not suited to online monitoring; in such cases they can be obtained through a recursive estimation algorithm using the measurable process variables. It is well known that the growth rate of biomass is slow compared to the variation of the substrate.…”

Section: Sequencing Batch Reactormentioning

confidence: 99%

“…However, some state variables in wastewater treatment processes cannot be directly measured online (e.g. biomass and substrate concentration) due to a lack of reliable and inexpensive online measurement systems [8,9]. Hence, many studies have focused on developing a recursive online estimation algorithm to reconstruct the unmeasurable states from available measurements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Lee

Hong

Suh

et al. 2011

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called 'particle'. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.

“…Some of these methods are based on the Kalman or extended Kalman filter [20,29,38,39,41]. However, they usually result in complex algorithms that in general do not guarantee convergence [10].…”

mentioning

confidence: 99%

Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms

Battista

Picó

Garelli

et al. 2012

Navarro Herrero, JL. (2012). Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms. Bioprocess and Biosystems Engineering. 35(9):1-11. doi:10.1007/s00449-012-0752-y. Abstract This paper deals with the estimation of unknown signals in bioreactors using sliding observers. Particular attention is drawn to estimate the specific growth rate of microorganisms from measurement of biomass concentration. In a recent article, notions of high-order sliding modes have been used to derive a growth rate observer for batch processes. In this paper we generalize and refine these preliminary results. We develop a new observer with a different error structure to cope with other types of processes. Also, we show that these observers are equivalent, under coordinate transformations and time scaling, to the classical super-twisting differentiator algorithm, thus inheriting all its distinctive features. The new observers family achieves convergence to time-varying unknown signals in finite time, and presents the best attainable estimation error order in the presence of noise. Also, the observers are robust to modeling and parameter uncertainties since they are based on minimal assumptions on bioprocess dynamics. In addition, they have interesting applications in fault detection and monitoring. The observers performance in batch, fed-batch and continuous bioreactors is assessed by experimental data obtained from the fermentation of Saccharomyces Cerevisiae on glucose.