Neural Networks in product design

Poly Vinyl Alcohol (PVA) Production Process Control Based on Neural Network PID
Fuzzy neural networks , Nonhomogeneous media , Product design , Product

The Mid-Temperature Control of Vinyl acetate (VAC) Compound Reaction (MTCVCR) is an important part of the Poly Vinyl Alcohol (PVA) production process (PPP), and now the PPP is mainly used normal PID control technology. It is difficult for the traditional PID control method to reach an optimum result in certain cases because the MTCVCR is rather a closed loop system, which has the character of non-linear, time varying and big hysteretic. Intelligent control independent of model of a plant and based on knowledge offers a new idea for improving the process control quality, of which neural network, as one of modern information process technologies, have some advantages in many applications. Neural network control became aregarded research direction. And nerual PID control is one of the main techniques in research and application due to the dominant place of PID types control in industrial process control recently. If the controller ehich has better performance and is wieldy like PID can be found, there will be significance in both theory and practice.This article takes Jiangxi Chemical Fibre and Chemical Engineering Limited Liability Company PVA extension and reconstruction project as the background. After analyzing technical characteristics of VAC production process and being combined with the problems which exist in practice, the neural network control method has been improved with PID control and makes use of their advantages in controlling, and applies it to the MTCVCR. In this way, we can get a good effect, and solve big time lag, strong jamming and so on, thich traditional control methods can’t solve. By the simulation in computer and analyzing the theory, the PID control method based on neural network has been proved to be effective. At last, a mid-temperature control of VAC Compound Reaction control system is designed according to the theory of neural network PID controller, and it proves that the neural network PID control methords are feasible in this project.

Source: Engineering Science Paper

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