Artificial Neural Network And Genetic Algorithm Optimization Of Operating Conditions Of The Xylitol Fermentation Process

In this thesis, the optimization of operating conditions in the batch fermentation process for producing xylitol from Dylose by Gandida mogii is investigated. The artificial neural network (ANN) is allurpose and robust. It is very suitable to describe the biochemical reaction process that is high nonlinear. The genetic algorithm (GA) is an optimizing method that is simple and very effective. It can also be used in the optimization of operating conditions of the biochemical reaction process. First, we analyze the fermentation system and choose the most important affects that effect the xylitol fermentation process, and we design the experiments using uniform design. Then the process is simulated by artificial neural networks, and we get an effective model 10?0. Using this model, we can know in advance the tendency of fermentation under given conditions. This is very useful in practice. Furthermore, after the study of fundamental theories and methods of the simple genetic algorithms (SGA), We changed it in three aspects: Use uniform design in the generation of original strings, add fitness change and change the stop condition. Taking the output of the model 9? that describes the relation between operating conditions and target as fitness, genetic algorithms find a new operating condition. And the results of experiment show that under this condition, the target value is improved in a great degree. This thesis is one part of Fijian province nature science fund item.