Optimal Control of a Fed-batch Fermentation Process by Neuro-Dynamic Programming

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

Using of Dynamic and Rollout Neuro - Dynamic Programming for Static and Dynamic Optimization of a Fed-batch Fermentation Process

A fed-batch fermentation process is examined in this paper for experimental and further dynamic optimization. The static optimization is developed for to be found out the optimal initial concentrations of the basic biochemical variables - biomass, substrate and substrate in the feeding solution. For the static optimization of the process the method of Dynamic programming is used. After that these initial values are used for the dynamic optimization carried out by a submethod of Neuro-dynamic programming-rollout. The general advantage of this method is that the number of the iterations in the cost approximation part is decreased

Comparative Analysis of Two Models of the Strouma River Ecosystem

A modified method of regression analysis for modelling of the water quality of river ecosystems is offered. The method is distinguished from the conventional regression analysis of that the factors included in the regression dependence are time functions. Two type functions are tested: polynomial and periodical. The investigations show better results the periodical functions give. In addition, a model for analysis of river quality has been developed, which is a modified method of the time series analysis. The model has been applied for an assessment of water pollution of the Strouma river. An assessment for adequately of the obtained model of the statistical criteria - correlation coefficient, Fisher function and relative error is developed and it shows that the models are adequate and they can be used for modelling of the water pollution on these indexes of the Strouma river. The analysis of the river pollution shows that there is not a materially increase of the anthropogenic impact of the Strouma river in the Bulgarian part for the period from 2001 to 2004

Modelling of a Batch Whey Cultivation of Kluyveromyces marxianus var. lactis MC 5 with Investigation of Mass Transfer Processes in the Bioreactor

This study presents a mathematical model of a batch fermentation of lactose oxidation from a natural substratum in a cultivation by the strain Kluyweromyces marxianus var. lactis MC 5. In the model of the process, the mass transfer in the bioreactor for oxygen concentration in the gas phase (GP) and in the liquid phase (LP) is based on the dispersion model of the GP. In addition, perfect mixing in LP is included. Nine models were investigated for specific growth rate and specific oxygen consumptions rate: Monod, Mink, Tessier, Aiba, Andrews, Haldane, Luong, Edward and Han-Levenspiel. In regard to the parameter estimation, the worst observed error was used for all experiments as an objective function. This approach is a special case of multi objective parameter estimation problems allowing the parameter estimation problem to become a min-max problem. The results obtained (values of criteria, relative error and statistics λ) for the specific growth rate showed that the best fit to experimental data is achieved when applying the Mink model. In a combination a Mink, and Monod, Mink, Luong, Haldane, and Han-Levenspiel are used for specific oxygen consumptions rate. Based on the investigation, it was discovered that the best fit belonged to the models of Mink and Haldane, Mink and Luong and Mink and Han-Levenspiel. Therefore, these particular models are used for modeling the batch processes

Application of a Fuzzy Neural Network for Modeling of the Mass-Transfer Coefficient in a Stirred Tank Bioreactor

A type of a fuzzy neural network for mathematical modeling of the volumetric mass-transfer coefficient is presented in the paper. Performed investigations show that the presented fuzzy neural network can be successfully used for modeling of such a complex process, like mass-transfer

Application of Different Mixing Systems for the Batch Cultivation of the Saccharomyces cerevisiae. Part I: Experimental Investigations and Modelling

Experimental investigations in different mixing conditions (impulse and vibromixing) in a Saccharomyces cerevisiae batch cultivation are presented in this paper. The investigation is carried out in a 5 l laboratory bioreactor (working volume 3 l). Mathematical models of the process for the two mixing systems are developed. The obtained results have shown that the models are adequate and will be used for process optimisation for the two mixing systems