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Industrial soft measurement method based on bionic intelligent ant colony algorithm

A technology of ant colony algorithm and ant colony optimization algorithm, which is applied in the field of soft sensor based on bionic intelligent ant colony algorithm, can solve problems such as poor versatility, difficulty in determining model control parameters, and low precision

Inactive Publication Date: 2010-08-11
ZHEJIANG UNIV
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Problems solved by technology

However, the existing industrial soft-sensing methods are complicated, the control parameters of the model are difficult to determine, the measurement error is large, the accuracy is not high, and the versatility is poor

Method used

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  • Industrial soft measurement method based on bionic intelligent ant colony algorithm
  • Industrial soft measurement method based on bionic intelligent ant colony algorithm
  • Industrial soft measurement method based on bionic intelligent ant colony algorithm

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Embodiment Construction

[0076] The industrial soft-sensing method based on the bionic intelligent ant colony algorithm of the present invention includes on-site intelligent instruments connected with on-site industrial objects, on-site data acquisition and preprocessing modules, RBF neural network modeling modules, bionic intelligent ant colony algorithm optimization modules, data Storage and update module, soft measurement module, high-speed industrial processor, etc. The main functions of the on-site data acquisition and preprocessing module are:

[0077] On-site data collection is to collect data from the database when the system is normal according to the interval of each sampling time set, and the collected data are corresponding auxiliary variables in the soft measurement of the continuous stirring reactor. These variables mainly include: reaction conversion rate, reaction temperature , feed flow and coolant flow, etc. After collecting these auxiliary variables, normalize the data, so that the ...

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Abstract

The invention discloses an industrial soft measurement method based on a bionic intelligent ant colony algorithm, comprising the following steps of: (1) determining key variables used in the process of soft measurement and collecting variable data from the historical database of the key variables as training samples when a system is normal; (2) normalizing the variable data of the training samples in a soft measurement intelligent processor to enable the mean value of the processed key variables to be 0 and the variance to be 1; and (3) establishing the function of a RBF (Radial Basis Function) neural network to obtain a soft measurement model, training the RBF neural network by using the normalized variable data, and then determining the hidden layer node number and the primary function center of the RBF neural network by using the bionic intelligent ant colony algorithm. The method of the invention has the advantages of convenient determination of control parameters and wide range of application and can be generalized in various industrial processes; and besides, the invention has good effect of adopting the RBF neural network to model, high data fitting precision and simple and convenient operation and avoids complicated mechanism modeling.

Description

technical field [0001] The invention relates to the field of process variable measurement of a continuous stirring reactor, in particular to a soft sensor method based on a bionic intelligent ant colony algorithm. Background technique [0002] In industrial process control, in order to make the production device in the best operating condition, realize card edge control, and produce more high-value products, so as to improve the economic efficiency of the device, it is necessary to pay attention to the product quality or the important factors closely related to the product quality. Process variables are strictly controlled, and product quality is the key to the survival and development of enterprises in the fierce competition. The real-time monitoring of variables generally adopts indirect quality index control methods and direct measurement methods, but there are deficiencies such as low precision, expensive analyzers, difficult maintenance, and difficulty in providing real...

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Application Information

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IPC IPC(8): G06N3/00G06N3/08
Inventor 刘波王宁
Owner ZHEJIANG UNIV
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