Soft measuring method of key parameters in edible mushroom fermenting process based on improved CS-BPNN

A fermentation process and soft measurement technology, applied in the field of online estimation, can solve the problems of difficult real-time online measurement, nonlinearity, key biochemical parameters of susceptible bacteria, etc.

Inactive Publication Date: 2018-03-09
JIANGSU UNIV
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Problems solved by technology

In the present invention, aiming at the problems of non-linearity, large hysteresis, susceptibility to bacterial contamination and difficulty in real-time online measurement of key biochemical parameters in the fermentation process of edible fungi, a soft sensor modeling method for improving CS-BP neural network is proposed

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  • Soft measuring method of key parameters in edible mushroom fermenting process based on improved CS-BPNN
  • Soft measuring method of key parameters in edible mushroom fermenting process based on improved CS-BPNN
  • Soft measuring method of key parameters in edible mushroom fermenting process based on improved CS-BPNN

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Embodiment Construction

[0043] The following implementation examples combined with the prediction of key state variables in the fermentation process of edible fungi and figure 2 The shown implementation flow chart describes the implementation example of the present invention in detail.

[0044] Select auxiliary vectors: select auxiliary variables, select external variables that can be directly measured and are closely related to the process, use the auxiliary variable selection algorithm based on discrete PSO to select the most suitable external variables as auxiliary variables of the soft sensor model;

[0045] 1. Acquisition of fermentation process data

[0046] Edible fungus fermentation adopts SF-500L fermentation monitoring system for fermentation and cultivation. Such as figure 1 The monitoring system shown consists of a fermenter 1, a steam generator 2, an air compressor 3, an air filter, and 4 a water supply system. During the fermentation process of edible fungi, the air flow q is collec...

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Abstract

The invention discloses a soft measuring method of key parameters in an edible mushroom fermenting process based on an improved CS-BPNN. The method includes steps of analyzing a technical mechanism ofthe edible mushroom fermenting process, and selecting a proper external variable to apply an assistant variable selection algorithm based on discrete PSO to select the most suitable external variableas the assistant variable of the soft measuring model; building up a training sample database according to history tank batch data, and performing normalization processing on it; then designing a BPneutral network model, and optimizing weight value and threshold value of the neutral network by means of an improved cuckoo algorithm; training the optimized neutral network, and forecasting the hypha biomass by the optimized model. By adopting the improved cuckoo algorithm, the BP neutral network is optimized; compared with the standard CS-BP neutral network method, the rate of convergence and local search ability are greatly promoted, the real-time online accurate forecasting of the key variables in the edible mushroom fermenting process is realized; the soft measuring method is significantto the parameter detection and optimal control of the mushroom fermenting process.

Description

technical field [0001] The invention is an online estimation problem of the key biochemical variable, the mycelium biomass, which is difficult to be measured online and in real time by physical sensors during the fermentation process of edible fungi, and belongs to the technical field of soft measurement and soft instrument construction. Background technique [0002] Fermentation is an important type of industrial production engineering, and its typical characteristics are complex internal mechanism, poor repeatability, poor production fluctuation, and highly nonlinear and time-varying characteristics; online measurement of it is the process of feeding, oxygen supply, etc. An important basis for dynamic control, but also the premise and basis for optimal scheduling. Generally, important process variables are measured through periodic laboratory sampling, off-line operation, and analysis. The sampling period has a great influence on the measured variable values, and the time...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/02G06N3/08G06N3/00
CPCG01N33/02G06N3/006G06N3/084
Inventor 朱湘临宋彦丁煜函王博郝建华华天争
Owner JIANGSU UNIV
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