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Soft-sensing modeling method and soft meter of multi-model neural network in biological fermentation process

A neural network and biological fermentation technology, applied in the field of optimization modeling of soft measuring instruments, can solve the problems of inaccurate measurement results and low measurement accuracy, and achieve the effect of strong anti-interference ability, high prediction accuracy and simple model

Inactive Publication Date: 2010-01-20
JIANGSU UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the defects of low measurement accuracy and inaccurate measurement results in the biological fermentation growth process in the prior art, and provide a soft sensor modeling method and its soft instrument for biological fermentation process based on multi-model neural network Structure, online measurement, fast calculation speed, strong anti-interference ability and high precision

Method used

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  • Soft-sensing modeling method and soft meter of multi-model neural network in biological fermentation process
  • Soft-sensing modeling method and soft meter of multi-model neural network in biological fermentation process
  • Soft-sensing modeling method and soft meter of multi-model neural network in biological fermentation process

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Embodiment 1

[0072] refer to figure 1 , mainly including biological fermentation equipment, on-site intelligent instruments for measuring easily measurable variables, controllers for measuring operating variables, DCS database modules for storing data, biomass concentration soft measurement value display instruments, the on-site intelligent instruments, control The device is connected with biological fermentation equipment and DCS database module.

[0073] Such as figure 2 , the specific implementation steps of kernel fuzzy C-means clustering are:

[0074] Step 1. Given the number of clusters C, allowable error ε, t=1;

[0075] Step 2. Set group size N, inertia weight w, learning factor c 1 , c 2 , the index weight m;

[0076] Step 3. Initialize particle swarm l 1 , l 2 ,...,l C , where l j is a set of arbitrarily generated cluster centers, from the sample set X={x 1 , x 2 ,...,x N}, take any C vectors to initialize l j ;

[0077] Step 4. Calculate the kernel matrix K(x i ,...

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Abstract

The invention discloses a soft-sensing modeling method and a soft meter of a multi-model neural network in a biological fermentation process. The method comprises the following steps: a data preprocessing module preprocesses input variable data by a normalization and principle component analysis method; and then the data preprocessing module carries out cluster division on a preprocessed principle component variable set; through and then a BP neural network model module respectively establishes sub neural networks according to different clusters and finally establishes a soft-sensing model of the multi-model neural network. The soft-sensing model of the multi-model neural network is used for measuring biomass concentration in a fermentation process on line, and a measurement value is displayed through a biomass concentration soft-sensing value displayer. The invention introduces a core fuzzy C mean clustering algorithm based on a particle swarm algorithm and combines the mean clustering algorithm with the modeling method of the multi-model neural network, and the established model is simple, realizes the on-line measurement of the biomass concentration and has timely control, high measurement accuracy and strong capacity of resisting disturbance.

Description

technical field [0001] The invention relates to an optimized modeling method for soft measuring instruments, which is applied to the technical field of soft measuring and soft instrument construction for biological fermentation, specifically introduces a multi-model neural network modeling method in the biological fermentation process, and online measurement is difficult in the actual fermentation process. The key variables measured in . 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. Due to the complexity of the mechanism of the microbial fermentation process and the complexity of the continuous fed-batch fermentation process, the control of the microbial fermentation process has become diffi...

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Application Information

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IPC IPC(8): G06N3/02G05B19/418C12M1/34
CPCY02P80/20Y02P90/02
Inventor 刘国海徐海霞梅从立周大为
Owner JIANGSU UNIV
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