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The germ concentration is an important biology parameter, which affects the yield of glutamic acid fermentation process. It is difficult to realize the on-line and real-time detection. By analyzing the producing process of glutamic acid, the process parameters, which influence the germ concentration, have been found, and samples have been selected from field history data, then the soft-sensing model...
With massive data of a fermentation process, a single data-based soft-sensor modeling method suffers from heavy features and bad accuracy. A novel soft sensor using multi-model neural network (MNN) based on modified kernel fuzzy clustering is proposed. Firstly, features of sample data are extracted using principal component analysis (PCA) and the secondary variables are determined by PCA. Secondly,...
The proposed method is adopted by genetic algorithm and BP neural network combine together, so GA-BP network is structured and used to the study of fault model and fault diagnose. It combines the ability of genetic algorithm with global optimizing and neural network with learning and memory, and resolves to problems that are slowly study speed and local minimum solution to produce easily for the BP...
Aiming at the complexity of the glutamic acid fermentation process, a neural network dynamic model of the fermentation process was established. The improved differential evolutionary algorithm (DEA) was used to the multi-variables optimal control of the fermentation process and the optimal control trajectories of operating variables were found out. Some improvements of the primitive DEA were made...
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