Olympia Roeva

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.

InterCriteria Analysis of ACO and GA Hybrid Algorithms

In this paper, the recently proposed approach for multicriteria decision making—InterCriteria Analysis (ICA)—is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed.

Parameter Estimation of a Monod-Type Model Based on Genetic Algorithms and Sensitivity Analysis

Mathematical models and their parameters used to describe cell behavior constitute the key problem of bioprocess modelling, in practical, in parameter estimation. The model building leads to an information deficiency and to non unique parameter identification. While searching for new, more adequate modeling concepts, methods which draw their initial inspiration from nature have received the early attention. One of the most common direct methods for global search is genetic algorithm. A system of six ordinary differential equations is proposed to model the variables of the regarded cultivation process. Parameter estimation is carried out using real experimental data set from an E. coli MC4110fed-batch cultivation process. In order to study and evaluate the links and magnitudes existing between the model parameters and variables sensitivity analysis is carried out. A procedure for consecutive estimation of four definite groups of model parameters based on sensitivity analysis is proposed. The application of that procedure and genetic algorithms leads to a successful parameter identification.

InterCriteria Analysis Approach to Parameter Identification of a Fermentation Process Model

In this investigation recently developed InterCriteria Analysis (ICA) is applied aiming at examination of the influence of a genetic algorithm (GA) parameter in the procedure of a parameter identification of a fermentation process model. Proven as the most sensitive GA parameter, generation gap is in the focus of this investigation. The apparatuses of index matrices and intuitionistic fuzzy sets, laid in the ICA core, are implemented to establish the relations between investigated here generation gap, from one side, and model parameters of fed-batch fermentation process of Saccharomyces cerevisiae, from the other side. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about observed interactions are derived.

InterCriteria Analysis of crossover and mutation rates relations in simple genetic algorithm

In this investigation recently developed InterCriteria Analysis (ICA) is applied to examine the influences of two main genetic algorithms parameters - crossover and mutation rates during the model parameter identification of S. cerevisiae and E. coli fermentation processes. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between investigated genetic algorithms parameters, from one hand, and fermentation process model parameters, from the other hand. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about derived interactions are reported.

InterCriteria Analysis of Generation Gap Influence on Genetic Algorithms Performance

In this investigation InterCriteria Analysis (ICA) is applied to examine the influences of one of the genetic algorithms parameters—the generation gap (ggap). The investigation is carried out during the model parameter identification of E. coli MC4110 cultivation process. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between ggap and GAs outcomes (computational time and decision accuracy), on one hand, and cultivation process model parameters on the other hand. The obtained results after ICA application are analyzed in terms of convergence time and model accuracy and some conclusions about derived interactions are reported.

InterCriteria Analysis of a model parameters identification using genetic algorithm

In this paper we apply an approach based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets - namely InterCriteria Analysis. The main idea is to use the InterCriteria Analysis to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch cultivation process. Moreover, based on results of series of identification procedures we observe the mutual relations between model parameters and considered optimization techniques outcomes, such as execution time and objective function value. Based on InterCriteria Analysis we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined, in terms of InterCriteria Analysis, criteria.

Fed-batch cultivation control based on genetic algorithm PID controller tuning

In this paper a universal discrete PID controller for the control of E. coli fed-batch cultivation processes is designed. The controller is used to control feed rate and to maintain glucose concentration at the desired set point. Tuning the PID controller, to achieve good closed-loop system performance, using genetic algorithms is proposed. As a result the optimal PID controller settings are obtained. For a short time the controller sets the control variable and maintains it at the desired set point during the process. Application of the designed controller provides maintaining of the accuracy and efficiency of the system performance.

Generalized Net model of asymptomatic osteoporosis diagnosing

Osteoporosis is a growing major public health problem with impact that crosses medical, social, and economic lines. It is now recognized that it is extremely important to diagnose osteoporosis before a fragility fracture occurs. The purpose of the present study is to give an example of application of Generalized Nets in orthopedics and traumatology and to propose a novel approach for diagnosing the asymptomatic osteoporosis in elderly patients.

Hybrid GA-ACO Algorithm for a model parameters identification problem

In this paper, a hybrid scheme, to solve optimization problems, using a Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) is introduced. In the hybrid GA-ACO approach, the GA is used to find a feasible solutions to the considered optimization problem. Next, the ACO exploits the information gathered by the GA. This process obtains a solution, which is at least as good as-but usually better than-the best solution devised by the GA. To demonstrate the usefulness of the presented approach, the hybrid scheme is applied to the parameter identification problem in the E. coli MC4110 fed-batch fermentation process model. Moreover, a comparison with both the conventional GA and the stand-alone ACO is presented. The results show that the hybrid GA-ACO takes the advantages of both the GA and the ACO, thus enhancing the overall search ability and computational efficiency of the solution method.