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Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof

A technology of penicillin fermentation and fuzzy nerves, applied in biological neural network models, electrical program control, comprehensive factory control, etc., can solve problems such as omission of important information of industrial objects, inability to improve soft sensor accuracy, and complex models

Inactive Publication Date: 2009-11-25
JIANGSU UNIV
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Problems solved by technology

The selection of traditional auxiliary variables is mainly based on the process mechanism of industrial objects and expert experience. The number of auxiliary variables determined in this way is considerable, and the degree of correlation varies greatly. If they are all used as auxiliary variables of soft measurement, the model is bound to be very complicated. Not only can not improve the accuracy of soft measurement, but also important information of industrial objects may be missed

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  • Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof
  • Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof
  • Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof

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[0046] Specific implementation plan

[0047] The embodiment of the present invention: First, according to the mechanism model of the penicillin fermentation process, the input volume of each feed, the online directly measurable output volume and the non-direct measurable volume that need offline testing are selected and determined according to the mechanism model of the penicillin fermentation process. Then choose to determine the input and output of the soft sensor in the penicillin fermentation process, and establish the soft sensor model and the soft sensor inverse model, and then use the static fuzzy neural network plus 7 differentiators and determine through the training of each static fuzzy neural network Each weight parameter constitutes a fuzzy neural inverse, which realizes the function of soft sensor inverse. Finally, the obtained fuzzy nerves are inversely connected in the penicillin fermentation process to realize the indirect measurability (hypha concentration x 1 , T...

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Abstract

Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof is a method for resloving the problem that the crucial biochemical quantity in penicillin fermentation process is difficult to be measured by physical sensor on-line and real-time. Fuzzy neural inverse soft-sensing method establishes a soft-sensor (11) model based on a kinetic equation in penicillin fermentation process (1), on this basis eatablishes an inverse model of the soft-sensor according to inverse system method, and then uses static fuzzy neural network (41) and a differentor to establish fuzzy neural inverse (4) through a free parameters determined by training the static fuzzy neural network, then the soft-sensor inverse is implemented, finally links the fuzzy neural inverse after the penicillin fermentation process to implement on-line and real-time soft-sensing of fungi concentration x[1], substrate concentration x[2] and products concentration x[3]. Specific implementation of the fuzzy neural inverse is the constructed fuzzy neural inverse system applies embedded microprocessor ARM processor.

Description

Technical field [0001] The invention is used to solve the problem of online estimation of the three key biochemical quantities of product concentration, total sugar concentration, and mycelium concentration that are difficult to be measured on-line and real-time with physical sensors during penicillin fermentation, and belongs to the technical field of soft measurement and system construction. . Background technique [0002] In many industrial control occasions, there are a large class of such variables: they are closely related to product quality and need to be strictly controlled, but due to technical or economic reasons, it is currently difficult or impossible to directly detect through physical sensors. Typical examples are the concentration of the product components of the rectification tower, the concentration of reactants and product distribution in the chemical reactor, and the biochemical quantity in the biological fermentation tank, and so on. In order to solve the meas...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418G06N3/06
CPCY02P90/02
Inventor 孙玉坤黄永红王博嵇小辅刘国海
Owner JIANGSU UNIV
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