Adaptive steady‐state optimization of biomass productivity in continuous fermentors

Harmon, Jeffrey L.; Svoronos, Spyros A.; Lyberatos, G.

doi:10.1002/bit.260300302

Cited by 26 publications

(10 citation statements)

References 16 publications

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“…A dynamic model of the chemostat is described by the following differential equations (Harmon et al, 1987): where c is the biomass concentration, s the is substrate concentration, w is the weighted average of previous substrate concentrations, s f is the substrate feed concentration, D is the dilution rate, μ m is the maximum specific growth rate, k s is the monad constant and Y is the yield. The parameter a is the delay term which is a measure of the organisms'ability to adjust their growth rate when a change in the condition of the chemostat occurs.…”

Section: Application Systemmentioning

confidence: 99%

“…It is usual to view nonlinear systems as linear systems and compensate for nonlinearities through adaptation of linear model parameters. On-line optimisation involving adaptive process models has been widely employed to biochemical processes (Jang et al, 1987;Hamer and Richenberg, 1988;Harmon et al, 1987;Rolf and Lim, 1985;Ryhiner et al,1992). In most of those applications, a linear process model with a simple structure is employed and convergence in parameters and efficiency in operation is achieved.…”

Section: Introductionmentioning

confidence: 99%

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Optimal State Estimation and on-Line Optimisation of a Biochemical Reactor

Jujjavarapu

Anand

Venkateswarlu³

2013

Chemical and Process Engineering

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An on-line optimising control strategy involving a two level extended Kalman filter (EKF) for dynamic model identification and a functional conjugate gradient method for determining optimal operating condition is proposed and applied to a biochemical reactor. The optimiser incorporates the identified model and determines the optimal operating condition while maximising the process performance. This strategy is computationally advantageous as it involves separate estimation of states and process parameters in reduced dimensions. In addition to assisting on-line dynamic optimisation, the estimated time varying uncertain process parameter information can also be useful for continuous monitoring of the process. This strategy ensures that the biochemical reactor is operated at the optimal operation while taking care of the disturbances that are encountered during operation. The simulation results demonstrate the usefulness of the two level EKF assisted dynamic optimizer for on-line optimising control of uncertain nonlinear biochemical systems.

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Section: Application Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal State Estimation and on-Line Optimisation of a Biochemical Reactor

Jujjavarapu

Anand

Venkateswarlu³

2013

Chemical and Process Engineering

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“…Rolf and Lim [10,11] applied this method to optimize the volumetric productivity of baker's yeast fermentation in a chemostat through selection of optimal dilution rate. Harmon and coworkers [12] also illustrated these features by optimizing the production of biomass in a continuous fermenter. Hamer and Richenberg [13] employed this algorithm for on-line optimizing control of a packed bed immobilized cell bioreactor.…”

Section: Optimizing Control Approachesmentioning

confidence: 99%

Control of fermenters – a review

Rani¹,

Rao²

1999

Bioprocess Engineering

135

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Fermenter control has been an active area of research and has attracted more attention in recent years. This is due to the new developments in other related areas which can be exploited to overcome the inherent dif®-culties in fermenter control. Beginning with conventional regulatory control of operating variables such as temperature, pH and dissolved oxygen concentration, research in fermenter control has undergone signi®cant changes including the recent neural network based approaches. The objective of the paper is to focus the attention of the researchers to the developments in the control of batch, fedbatch and continuous fermenters over the past few years.

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“…This strategy of optimization is useful for adaptive on-line control (e.g., refs. 19,34,47), and it can be customized to include specific strategies like periodic optimization.' It can also simplify the mathematical analysis, because originally nonlinear processes can be viewed as linear processes with time-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimization in integrated biochemical systems

Voit

1992

Biotech & Bioengineering

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As of yet, steady-state optimization in biochemical systems has been limited to a few studies in which networks of fluxes were optimized. These networks of fluxes are represented by linear (stoichiometric) equations that are used as constraints in a linear program, and a flux or a sum of weighted fluxes is used as the objective function. In contrast to networks of fluxes, systems of metabolic processes have not been optimized in a comparable manner. The primary reason is that these types of integrated biochemical systems are full of synergisms, antagonisms, and regulatory mechanisms that can only be captured appropriately with nonlinear models. These models are mathematically complex and difficult to analyze. In most cases it is not even possible to compute, let alone optimize, steady-state solutions analytically. Rare exceptions are S-system representations. These are nonlinear and able to represent virtually all types of dynamic behaviors, but their steady states are characterized by linear equations that can be evaluated both analytically and numerically. The steady-state equations are expressed in terms of the logarithms of the original variables, and because a function and its logarithms of the original variables, and because a function and its logarithm assume their maxima for the same argument, yields or fluxes can be optimized with linear programs expressed in terms of the logarithms of the original variables.

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Adaptive steady‐state optimization of biomass productivity in continuous fermentors

Cited by 26 publications

References 16 publications

Optimal State Estimation and on-Line Optimisation of a Biochemical Reactor

Optimal State Estimation and on-Line Optimisation of a Biochemical Reactor

Control of fermenters – a review

Optimization in integrated biochemical systems

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