Volume 1
Editorial
From Editor-in-Chief
Worldwide Scientists
New Books
Forthcoming Events
International Symposium and Young Scientist's School "Bioprocess Systems - BioPS'04"
In Memoriam
Modelling and Optimization of Biotechnological Systems
Functional State Modelling of Saccharomyces cerevisiae Cultivations1-15
Tania Pencheva, Iasen Hristozov, Stoyan Tzonkov, Bernd Hitzmann
[ +/- abstract ][ full text ]
The implementation of functional state approach for modelling of yeast cultivation is considered in this paper. This concept helps in monitoring and control of complex processes such as bioprocesses. Using of functional state modelling approach for fermentation processes aims to overcome the main disadvantage of using global process model, namely complex model structure and big number of model parameters. The main advantage of functional state modelling is that the parameters of each local model can be separately estimated from other local models parameters. The results achieved from batch, as well as from fed-batch, cultivations are presented.
Ultrasonic Measurements and its Evaluation for the Monitoring of Saccharomyces cerevisiae Cultivation16-29
Young-Lok Cha, Bernd Hitzmann
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The monitoring and supervision of batch Saccharomyces cerevisiae cultivations are presented by ultrasonic velocity measurements. The measurements are performed in a by-pass to reduce the influence of bubbles. Using these signals the typical phases of such cultivations can be identified. Applying a multi-linear regression model the ultrasonic velocity can be estimated by the biomass, the glucose and the ethanol concentration with a mean estimation error of 1.6 m/s. The multi-linear regression model has also been used to predict one of the three process variables by the other two and the ultrasonic velocity. Here the mean error of prediction is 0.6 g/L, 2.3 g/L and 1.5 g/L for biomass, glucose and ethanol concentration respectively. Using a Kalman filter theses variables have been estimated with mean errors of 0.6 g/L, 1.8 g/L and 1.6 g/L.
A Genetic Algorithms Based Approach for Identification of Escherichia coli Fed-batch Fermentation30-41
Olympia Roeva, Tania Pencheva, Bernd Hitzmann, Stoyan Tzonkov
[ +/- abstract ][ full text ]
This paper presents the use of genetic algorithms for identification of Escherichia coli fed-batch fermentation process. Genetic algorithms are a directed random search technique, based on the mechanics of natural selection and natural genetics, which can find the global optimal solution in complex multidimensional search space. The dynamic behavior of considered process has known nonlinear structure, described with a system of deterministic nonlinear differential equations according to the mass balance. The parameters of the model are estimated using genetic algorithms. Simulation examples for demonstration of the effectiveness and robustness of the proposed identification scheme are included. As a result, the model accurately predicts the process of cultivation of E. coli.
Equivalent Models and Exact Linearization by the Optimal Control of Monod Kinetics Models42-56
Yuri Pavlov, Krassimira Ljakova
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The well-known global biotechnological models are non-linear and nonstationary. In addition the process variables are difficult to measure, the model parameters are time varying, the measurement noise and measurement delay increase the control problems, etc. One possible way to solve some of these problems is to determine the most simple and easy for use equivalent models. The differential geometric approach [DGA] and especially the exact linearization permit an easy application of the optimal control. The approach and its application in the control of the biotechnological process are discussed in the paper. The optimization technique is used for fed-batch and continuos biotechnological processes when the specific growth rate is described by the Monod kinetics.
Control of Biotechnological Systems
Optimal Control of a Fed-batch Fermentation Process by Neuro-Dynamic Programming57-66
Tatiana Ilkova, Stoyan Tzonkov
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In this paper the method for optimal control of a fermentation process is presented, that is based on an approach for optimal control - Neuro-Dynamic programming. For this aim the approximation neural network is developed and the decision of the optimization problem is improved by an iteration mode founded on the Bellman equation. With this approach computing time and procedure are decreased and quality of the biomass at the end of the process is increased.
Modelling, Optimization and Optimal Control of Small Scale Stirred Tank Bioreactors67-82
Mitko Petrov, Tatjana Ilkova, Stoyan Tzonkov, Uldis Viesturs
[ +/- abstract ][ full text ]
Models of the mass-transfer in a stirred tank bioreactor depending on general indexes of the processes of aeration and mixing in concrete simplifications of the hydrodynamic structure of the flows are developed. The offered combined model after parameters identification is used for optimization of the parameters of the apparatus construction. The optimization problem is solved by using of the fuzzy sets theory and in this way the unspecified as a result of the model simplification are read. In conclusion an optimal control of a fed-batch fermentation process of E. coli is completed by using Neuro-Dynamic programming. The received results after optimization show a considerable improvement of the mass-transfer indexes and the quantity indexes at the end of the process.
Bioprocess Systems
Utilisation of Food and Woodworking Production By-products by Composting83-98
Uldis Viesturs, Dzidra Zarina, Silvija Strikauska, Andrejs Berzins
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The purpose of the study was to develop laboratory-scale technologies for composting milk/cheese whey, spent liquor, brewery yeast, fish processing by-products, etc., adding these by-products and special microorganism associations to the basic material - sawdust, bark, etc., also arranging different experimental composting sites.
Two Trichoderma strains (Tr. lignorum, Tr. viride) and a nitrification association for regulating the circulation of nitrogen-ammonification and nitrification processes were applied. Monitoring of the composting quality was realised by microbiological and chemical analyses, and biotests for compost quality (toxicity) assessment. For purifying the polluted air from the composting facilities, the biofiltration technique was realised in a modified SSF system. Biodegradation of ammonia was investigated in a two-stage system with the inert packing material - dolomite broken bricks, and hemoautotrophic microorganisms: DN-1 (Pseudomonas sp.), DN-2 (Nitrosomonas sp.), DN-3 (Nitrobacter sp.) and DN-13 (Sarcina sp.). For hydrogen sulphide biodegradation, Thiobacillus thioparus-3 was immobilised on glass bricks as the carrier material. Biodegradation efficiency of hydrogen sulphide was 87%.
Biodegradation of ammonia in the first step in the two-stage system reached 77%, degradation of the gas remaining in the second step was 75%. Compostís quality was similar to black soil - brown-coloured, with good soil odour and without toxic compounds.
Biosensors
Gated Detection Measurements of Phosphorescence Lifetimes99-112
Yordan Kostov, Govind Rao
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A low-cost, gated system for measurements of phosphorescence lifetimes is presented. An extensive description of the system operating principles and metrological characteristics is given. Remarkably, the system operates without optical filtering of the LED excitation source. A description of a practical system is also given and its performance is discussed. Because the device effectively suppresses high-level background fluorescence and scattered light, it is expected to find wide-spread application in bioprocess, environmental and biomedical fields.

© 2004, BAS, Institute of Biophysics and Biomedical Engineering