Optimal Operation And Scheduling Of Batch Fermentation Process

In chemical, food, metallurgical and other production manufacturing, there exist not only continuous variables, but also a large number of batch ones. In a batch process, with a large number of continuous variables and discrete events available, they mutually influence each other intimately, which demands advanced control strategies for batch industrial processes.Aiming to a problem specific, this thesis focuses on the investigation of the penicillin fermentation process which is considered a typical batch production process. The genetic algorithm was employed as the tool to deal with optimization and scheduling for batch process. Genetic algorithm plays an important role on intelligent optimization in terms of accuracy and speed, which simplifies the optimization of multi-objective parameters of penicillin fermentation process.In this context, the penicillin fermentation Pensim simulation platform was utilized for research. This penicillin simulation platform mechanism is characterized as Birol non-structural models, which offer a more comprehensive insight of penicillin fermentation. A VB-based application platform for completely processing the penicillin fermentation process parameters was developed, achieving automatic data processing of the fermentation process, providing variable profiles and parameter query functions, which enjoy a convenient data processing.A beer fermentation is introduced in the latter part of the article. With TPN models of the beer fermentation process available, GA algorithm is invoked to achieve the optimal scheduling strategy of the beer fermentation process. Simulink/stateflow tool was utilized to demonstrate the feasibility and effectiveness of the propused algorithems.