Identification for purity estimation using neural network

被引：0

作者：

Okamoto, M ^{[1
]}

Hayden, NA ^{[1
]}

Wada, T ^{[1
]}

Shirakawa, Y ^{[1
]}

机构：

[1] Japan Energy Corp, Kurashiki, Okayama 712, Japan

来源：

(SYSID'97): SYSTEM IDENTIFICATION, VOLS 1-3 | 1998年

关键词：

neural networks; distillation columns; prediction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

When the quality of the chemical product is controlled, real-time measurements are needed. At present day, on-line hardware sensors are installed. However, the cycle of measurement is every 60 minutes. When the product quality is out of specification, one cannot know that information in real time because of long measurement cycle. The real time sensor was developed using neural networks to predict purity of the chemical product. As a result, the regressive neural network model indicated the most finest prediction result in on line application.

引用

页码：777 / 781

页数：5

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