Dynamic fuzzy neural network based penicillin fermentation process soft measuring modeling method

Abstract

The invention discloses a dynamic fuzzy neural network based penicillin fermentation process soft measuring modeling method. The method comprises the following steps of: determining an on-line measurable variable, a process input variable and an indirect measurable variable requiring an off-line assay of a penicillin fermentation process; analyzing the relevancy of the process input variable and the on-line measurable variable with a dominant variable with a coincident relevance algorithm by using the indirect measurable variable as the dominant variable; carrying out secondary variable selection to determine an auxiliary variable; and finally, establishing a soft measuring model by using a dynamic fuzzy neural network and optimizing the parameters of the model, wherein the determined auxiliary variable is used as an input variable of the soft measuring model and the dominant variable is used as an output variable. The invention overcomes the defect of serious dependence on experiential selection of the traditional fuzzy neural network in the aspects of establishing an initial model, determining rule numbers, and the like, reduces the complexity of the soft measuring model, further improves the model stability and has good modeling precision.

Description

technical field [0001] The invention relates to the technical field of soft sensor optimization modeling in the biological fermentation process, in particular to a soft sensor modeling method for estimating key biochemical variables in the fermentation process by using a dynamic fuzzy neural network model in the penicillin fermentation process. Background technique [0002] Microbial fermentation engineering is widely used in the production of antibiotics, amino acids and fine chemical products. Microbial fermentation is involved in many fields such as pharmaceutical industry, chemical industry, light industry food and environmental protection. basis of industrialization. However, in the modern microbial fermentation industry, some key state variables in the biological fermentation process, such as mycelia concentration, substrate concentration, and product concentration, lack online direct measurement methods. The accurate and real-time measurement of these key biochemical...

AI Technical Summary

Problems solved by technology

However, the traditional fuzzy neural network relies heavily on empirical selection in the establishment of the initial model and the determination of the number of rules. In the process of microbial fermentation, the experience of experts is often incomplete or even one-sided, which leads to errors in measurement results. and imprecise

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