Biomass estimation and optimal control of the baker's yeast fermentation process

Beluhan, Damir; Gosak, Darko; Pavlović, Nediljko; Vampola, Milan

doi:10.1016/0098-1354(95)87067-9

Cited by 10 publications

(5 citation statements)

References 1 publication

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“…Development of control profiles by using various methodologies such as Tabu search and genetic algorithms were explored in bio and chemical processes due to their enormous advantages. (Beluham et al 1995; Chen et al 1998; Lin and Miller 2004). The main objective of this study is to build a mathematical model that relies on its potential parameters to estimate the quantity of the Rhamnolipid production by P. aeruginosa.…”

Section: Introductionmentioning

confidence: 99%

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Eswari

kavya

2016

AMB Expr

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Rhamnolipids are bio surfactants which are extra-cellular glycolipids composed of l-rhamnose and 3-hydroxyalkanoics. Rhamnolipids are produced through fermentation process by using Pseudomonas sp. as the species. An alteration to the traditional procedures in order to achieve increase in the production of biosurfactants, a numerous process technologies have been adopted in fed batch mode. Fed batch mode facilitates the high production of product by avoiding the substrate or product inhibition line of attack. To overcome the controlling parameters which reduce the product yield, optimal control profiles are designed. In order to develop viable control methods for fed-batch fermentation of Rhamnolipid production, multiple substrate feeding strategies were employed and their efficiencies were compared with different substrates concentration of glucose, nitrogen and phosphorous. The product formation depends upon the substrate feeding strategy and so, the fed-batch fermentation was carried out by using P. aeruginosa providing substrates at manifold rates. With the obtained experimental data, using the kinetic models (logistic equation and by Luedeking Piret), the kinetic parameters were estimated. These kinetic parameters were implemented in tabu search algorithm and this programme was executed in Dev-C++, optimal control profiles were generated as a result. These obtained optimal control profiles have shown an increase in productivity of rhamnolipid with a decline in computational time. Through this procedure, the optimal control profiles of substrate feeding strategies of glucose, nitrogen and phosphorous were estimated. In comparison with other algorithms like genetic algorithm, Tabu Search algorithm was able to generate an accurate optimal control profiles with a reduction in their intricacy.

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Section: Introductionmentioning

confidence: 99%

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Eswari

kavya

2016

AMB Expr

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“…Several kinds of state observers have been proposed since the 1960s and many of them have been applied in the field of biotechnology [1,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A comparison of three sets of DSP algorithms for monitoring the production of ethanol in a fed-batch baker’s yeast fermenter

Mangesh

Jana

2008

Measurement

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“…Optimising control deals with the problem of changing the operating conditions of a dynamic process on-line to achieve economic optimum. In recent years, various optimising control techniques have been reported for different applications including bioreactors (Beluham et al, 1995;Chen et al, 1998;Kim et al, 1991;Noda et al, 2000;Zhou et al,1999). In bioprocesses, several factors such as complex nature of microorganism growth, disturbance dynamics, parameter uncertainties and noisy process variables severely affect the operating performance.…”

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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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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Biomass estimation and optimal control of the baker's yeast fermentation process

Cited by 10 publications

References 1 publication

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

A comparison of three sets of DSP algorithms for monitoring the production of ethanol in a fed-batch baker’s yeast fermenter

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

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