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Lysine fermentation process feeding prediction control system and method based on fuzzy neural network

A technology of fuzzy neural network and lysine fermentation, applied in general control system, control/regulation system, adaptive control, etc., can solve problems such as poor loop coupling and inability to adapt to the dynamic characteristics of the fermentation process, etc. The effect of strong generalization ability, enhanced adaptive ability, and strong reasoning ability

Inactive Publication Date: 2010-06-30
JIANGSU UNIV
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Problems solved by technology

[0006] In order to overcome the deficiencies of the existing feeding control schemes that cannot adapt to the dynamic characteristics of the fermentation process, strong nonlinearity, coupling between loops, and inability to obtain good control effects, the present invention provides a method that can solve the dynamics of the fermentation process. characteristics, strong nonlinearity, coupling between loops, and a fuzzy neural network-based feed predictive control method and system for lysine fermentation process with good control effect

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  • Lysine fermentation process feeding prediction control system and method based on fuzzy neural network
  • Lysine fermentation process feeding prediction control system and method based on fuzzy neural network
  • Lysine fermentation process feeding prediction control system and method based on fuzzy neural network

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Embodiment Construction

[0028] follow below figure 1 The basic framework shown is described in detail.

[0029] 1. Establish a nonlinear prediction model based on fuzzy neural network

[0030] Model Neural Network (FNN) is a five-layer adaptive neural network that introduces fuzzy operations, and combines the advantages of fuzzy logic and neural networks. The input and output relationship of fuzzy neural network is as follows: figure 2 shown.

[0031] Wherein the first layer is the input layer, and each node of this layer is directly connected with each component of the input vector, which plays a role of input value=[x 1, x 2 ,...,x n ] to the next layer, the number of nodes in this layer is N 1 = n; the second layer is the membership function layer, and each node in this layer completes the function of a Gaussian membership function, for the jth node of the i variable in this layer:

[0032] μ ij ( x i ...

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Abstract

The invention relates to a lysine fermentation process feeding prediction control system and method based on a fuzzy neural network. The control method comprises the following steps: firstly using batch data to train the fuzzy neural network and establish a nonlinear prediction model of lysine fermentation process, secondly measuring the input / output information of model utilization history and future batch data to predict the future output of the lysine fermentation process, using the model to output error and perform feedback compensation so as to obtain closed loop output, finally comparing closed loop output and reference input trajectory, utilizing quadratic form performance index to perform rolling optimization, and calculating to obtain feeding control quantity which is needed to add in the system currently. The system comprises a site intelligent detecting instrument and peristaltic pump which are directly connected with a fermenter and an intelligent controller, wherein feeding prediction control algorithm is embedded in the system intelligently. The system and method of the invention can adapt to the dynamic characteristic of the lysine fermentation process and the coupling between strong open loops, thus obtaining good control effect.

Description

technical field [0001] The invention relates to the field of advanced control of microbial fermentation process, in particular to a fuzzy neural network-based feeding prediction control system and method for lysine fermentation process. technical background [0002] The role of bioengineering in human life is increasingly showing its importance and urgency, and a large part of its contribution to human beings is realized through the development of biochemical engineering technology. For example, antibiotics, drugs, enzyme preparations, monosodium glutamate, yeast, beer, etc. used by people are inseparable from the fermentation industry. Therefore, microbial fermentation process is the core of biochemical engineering. In the fermentation industry, most of the raw materials used are grains. Therefore, the use of modern control technology for this traditional fermentation industry to conduct research on automation technology is extremely important for promoting the development...

Claims

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Application Information

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IPC IPC(8): G05B13/02
Inventor 孙玉坤嵇小辅王博
Owner JIANGSU UNIV
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