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

A penicillin fermentation and neural network technology, applied in the field of soft measurement optimization modeling, can solve the problems of measurement result error, one-sidedness, incompleteness, etc., and achieve the effect of reducing complexity, improving model stability, and good modeling accuracy

Inactive Publication Date: 2010-12-29
JIANGSU UNIV
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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 rule

Method used

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

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Embodiment Construction

[0019] Such as figure 1 Shown, the specific implementation steps of the present invention are as follows:

[0020] 1. Determine the on-line measurable variables, process input variables and indirect measurable variables that need to be tested offline in the penicillin fermentation process.

[0021] According to the kinetic model of the penicillin fed-batch fermentation process shown in the following formula (1), combined with the actual penicillin fermentation process, the direct online measurable variables of the penicillin fermentation process are dissolved oxygen, pH value and fermentation broth volume; process input variables is the flow acceleration rate of glucose, corn steep liquor, gluten powder, potassium dihydrogen phosphate and ammonia water; the indirect measurable variables to be tested offline are the mycelial concentration X, the substrate concentration S, and the product concentration P, so as to determine the on-line reliability of the penicillin fermentation ...

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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...

Claims

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Application Information

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IPC IPC(8): G01N33/00G06N3/00
Inventor 黄永红孙玉坤夏成林王博朱湘临
Owner JIANGSU UNIV
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