Stoyan Tzonkov

Membranes for optical pH sensors

Abstract A method for producing membranes for optical pH sensors is described. The pH indicators (Neutral Red and Congo Red) are covalently bonded to a transparent acetylcellulose film. The membranes have good durability ( > 9 months) and a short response time (ca. 30 s). The method is easy to perform and uses acetylcellulose as a carrier. The reagents for activating the cellulose support are inexpensive, non-toxic and widely available.

Multiple model approach to modelling of Escherichia coli fed-batch cultivation extracellular production of bacterial phytase

The paper presents the implementation of multiple model approach to modelling of Escherichia coli BL21(DE3)pPhyt109 fed-batch cultivation processes for an extracellular production of bacterial phytase. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. Multiple model approach is an alternative concept which helps in modelling and control of complex processes. The main idea is the development of a model based on simple submodels for the purposes of further high quality process control. The presented simulations of E. coli fed-batch cultivation show how the process could be divided into different functional states and how the model parameters could be obtained easily using genetic algorithms. The obtained results and model verification demonstrate the effectiveness of the applied concept of multiple model approach and of the proposed identification scheme.

Dynamic model of an optical absorption-based pH sensor

A mathematical model of an optical absorption-based pH sensor is described. Emphasis is placed on the influence of the parameters of the sensor and the pH on the dynamic characteristics of the sensor. It is shown that the response is ‘non-symmetrical’, i.e., the response time when pHinitial– pHfinal > 0 is not equal to the response time when pHinitial– pHfinal < 0. This theoretical result was confirmed by experiment. The influence of different parameters, viz., pKind, Iµ diffusion coefficient and thickness of the membrane, was also investigated.

IMPLEMENTATION OF FUNCTIONAL STATE APPROACH FOR MODELLING OF ESCHERICHIA COLI FED-BATCH CULTIVATION

ABSTRACT This paper presents the implementation of functional state approach to modelling of Escherichia coli fed-batch cultivation. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. The functional state approach of a process is an alternative concept which helps in modelling and control of complex processes. The approach main idea is developing of models based on multiple submodels for each functional states (operating regime). In each functional state the process is described by a conventional type of model, called the local model, which is valid in this state. For parameter identification of the model the genetic algorithms are used. Genetic algorithms are directed random search techniques, applying the mechanics of natural selection and natural genetics, which can find the global optimal solution in complex multidimensional search spaces. Based on the available experimental data and simulations of E. coli fed-batch cultivation it is shown how this process can be divided into functional states and how the model parameters can be obtained on the basis of genetic algorithms. By simulation and comparison between the results and experimental data, can be seen how the concept of functional state approach works and how effective is the proposed identification scheme.

MULTIMODEL APPROACH FOR MODELLING OF BIOTECHNOLOGICAL PROCESSES

ABSTRACT The implementation of functional state approach for modelling of biotechnological processes 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 biotechnological processes aims to overcome the main disadvantage of using global process model, namely complex model structure and big number of model parameters, which complicate the model simulation and parameter estimation. The main advantage of functional state modelling is that the parameters of each local model can be separately estimated from other local models parameters.

Optimal control of biotechnological processes described by fuzzy sets

Abstract Fuzzy set theory is applied to extend the optimal control problem for complex objects such as biotechnological processes. It allows inclusion of all possible and admissible process states in the problem formulation. A new approach for model representation with convenient fuzzy sets taking into account expert experience is proposed. Bellman-Zadehs approach is applied to transform a fuzzy optimal control problem ot a deterministic one which is solved numerically. Optimal control of a real-life fermentation process is determined and a better solution is reached due to more realistic and flexible problem formulation.

AN APPROACH FOR OPTIMAL CONTROL OF FED- BATCH FERMENTATION PROCESS WITH MULTI CONTROL VARIABLES

ABSTRACT In this paper a fermentation process of E. Coli production is presented by using real laboratory experimental data. The adequate model of the process is proved. An effective algorithm for process optimization in the presence of three optimal control variables: rotation speed, gas flow rate and substratum floating rate is developed by using a modified approach of method of the dynamic programming. The analysis of results shows that the complex investigation with more control variables vastly raises efficiency of the process.

An Approach for Modeling of Aerobic Fed-Batch Fermentation Process

ABSTRACT In this paper is development an model for small scale E. Coli fermentation and identifying the model parameters for an effective and reliable of the reactor dynamic with laboratory data from DFG project (5). Model parameters were identified by non-linear regression technique assisted by computer program. Parametric sensitivity analysis indicated specific grown rate of biomass to the most sensitive model parameter. Statistical validity of the model indicated confidence on the prediction of the model.

Neural Network Models of S. Carlsbergensis Batch Cultivation

ABSTRACTAll modern control theories require a sufficiently accurate model of the plant to be controlled. In the case of complex nonlinear models an alternative to identify the dynamics of the system is to use neural network methodology because there is no need to know in advance functional form of the model—it is determined simultaneously with model parameters. In the present paper two neutral network models of S. carsbergensis batch cultivation are proposed. From the obtained results it can be concluded that neural networks allow to reach higher accuracy of non-linear models. The possibility of adding internal memory in the model without raising the cost of identification makes neural networks very appropriate for modelling such complex non-linear systems as biotechnological processes.

ОПТИМАЛНО УПРАВЛЕНИЕ НА НЕПРЕКЪСНАТ ФЕРМЕНТАЦИОНЕН ПРОЦЕС ЗА ПРОИЗВОДСТВО НА ЕТАНОЛ

РЕЗЮМЕРассматривается задача управления ферментационным процессом при производстве этанола. Задача оптимал-ьного управления равбивается на три подвадачи: а) статическая оптимизация процесса; б) оптимальное стартирование процесса (start-up); в) динамическая оптимизация процесса. Первая из них решается посредством различных критериев оптимальности. Задача оптимального стартирования процесса решается путем динамической оптимизации, которая осуществляется с использованием алгоритма, реализующего дискретный принцип максимума. Третья подзадача решается на базе линеаризованной модели. Исследована минимальная реализация линеаризованной модели в близости различных рабочих точек. Синтезирован оптимальный регулятор по состоянию. Для иллюстрации полученных результатов приведены данные стимулирования с помощью компьютера.