Vinyl chloride rectification temperature control method based on fuzzy neural network

A technology of fuzzy neural network and temperature control method, which is applied in the direction of temperature control, self-adaptive control and general control system using electric mode, can solve the problems of control level staying, difficult to achieve satisfactory purification effect, etc., and achieves the goal of improving control accuracy. Effect

Pending Publication Date: 2020-01-03
北京和隆优化科技股份有限公司
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Problems solved by technology

[0004] In the past, single-loop PID control or decoupling control with feed-forward was usually used in the control of vinyl chloride rectification towers, and even some places still rely on the experi...

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  • Vinyl chloride rectification temperature control method based on fuzzy neural network
  • Vinyl chloride rectification temperature control method based on fuzzy neural network
  • Vinyl chloride rectification temperature control method based on fuzzy neural network

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Embodiment Construction

[0013] The present invention will be further described below with reference to the accompanying drawings.

[0014] refer to figure 1 , a method for temperature control of vinyl chloride rectification based on fuzzy neural network, comprising the following steps: (1) signal acquisition; (2) fuzzy neural network control; (3) learning algorithm.

[0015] The signal acquisition in the step (1) is to discretely sample the feed flow, column top temperature, middle temperature, column kettle temperature, and column top reflux flow through the signal collector at intervals of 10 seconds (one sampling period), and analyze the The current sampled value and the past 5 consecutive sampled values ​​are subjected to moving average filtering, the maximum and minimum values ​​are removed from the 5 consecutively sampled values, and the remaining 3 values ​​are summed and averaged to obtain the filter value.

[0016] The fuzzy neural network control in the step (2) has a total of 5 layers of ...

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Abstract

The invention discloses a vinyl chloride rectification temperature control method based on a fuzzy neural network, and relates to the field of vinyl chloride rectification production control. The vinyl chloride rectification temperature control method comprises the steps that firstly, sensor signals are discretely collected through a signal collecting module, and are subjected to filtering processing; secondly, fuzzy neural network control is conducted according to signal collecting module output, fuzzification, fuzzy reasoning, fuzzy decision-making and defuzzification output are conducted insequence to obtain required control parameter values, and nonlinear decoupling control is directly conducted on an actuator; and finally, through a BP learning algorithm, the link weight, the gaussian function center value and the width value in a controller are learned and corrected, thus the temperature error of a rectifying tower is converged after output of the controller, and the stability of the system is improved on the basis of improving controlling precision.

Description

technical field [0001] The invention relates to a temperature control method for vinyl chloride rectification based on a fuzzy neural network, in particular to a temperature control method for a high boiling tower of vinyl chloride rectification. Background technique [0002] Vinyl chloride monomer (VCM) is one of the most important raw materials in the chemical industry. Currently, the VCM monomer used to produce polyvinyl chloride (PVC) accounts for more than 96% of the world's total output. The purity of vinyl chloride monomer is directly related to the conversion rate of vinyl chloride produced by the final PVC polymerization, and has an important impact on the quality of PVC polymerization products. The purification process of vinyl chloride monomer after the formation reaction is mainly through rectification. The low-boiling tower heats the crude vinyl chloride raw material liquid to below the boiling point of vinyl chloride, so that the low-boiling impurities are sepa...

Claims

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Application Information

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IPC IPC(8): G05D23/19G05B13/04
CPCG05D23/19G05B13/042
Inventor 于现军吕伟军陆晟波
Owner 北京和隆优化科技股份有限公司
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