Transactions of Nonferrous Metals Society of China The Chinese Journal of Nonferrous Metals

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中国有色金属学报

ZHONGGUO YOUSEJINSHU XUEBAO

第14卷    第z1期    总第100期    2004年5月

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文章编号:1004-0609(2004)S1-0106-06
人工神经网络在过程工业中的应用
陈丙珍

(清华大学 化学工程系, 北京 100084)

摘 要: 当前, 集过程实时监测、 故障诊断、 模拟、 优化、 控制以及调度等各层次功能于一体的过程工业生产过程综合自动化成了过程工业界和学术界共同关注的热点之一。与离散产品的制造业相比, 由于流程型工业过程具有强非线性的特点, 给实现流程工业综合自动化造成很大的困难, 因此必须引入新的思路, 开发新的方法。 人工神经网络是一种模拟人类思维活动的并行分布式的信息处理系统,可用于映射任何连续函数及进行模式识别, 同时还具有自学习功能, 实现知识的自动获取, 自20世纪90年代以来已在过程系统工程领域内受到广泛的瞩目。重点讨论了人工神经网络在过程系统建模、 故障诊断以及在线优化等方面的应用, 以展示这种方法在流程工业综合自动化中的良好应用前景。 

 

关键字: 人工神经网络; 过程系统建模; 过程系统故障诊断;遗传算法

Applications of artificial neural networks in process industry
CHEN Bing-zhen

Department of Chemical Engineering, 
Tsinghua University, Beijing 100084, China

Abstract:Recently, the computer integrated process systems which integrate the real-time detection, fault diagnosis, modeling, optimization, control, planning and other information techniques at different levels have become a topic of common interests in both industry and academy. Compared with the manufacturing industry for discrete products, the continuous process industry possesses the characteristics of strong non-linearity, which makes the computer integrated process systems very difficult to be realized. Therefore, there is an urgent need to introduce new ideas and new methodologies. Artificial neural network is a kind of parallel information handling system for emulating human’s thought. It could be used for mapping any continuous functions and carrying out pattern recognition, meanwhile, it could realize the acquaintance of knowledge automatically. Since 1990s ANN has attracted wide attention in the field of process systems engineering. The emphasis will be put on illustration of the applications of ANN in the process modeling, process fault diagnosis, on-line optimization and other aspects so that to fully demonstrate the good potential of applying ANN in developing the computer integrated process systems.

 

Key words: artificial neural network; process modeling; process fault diagnosis; genetic algorithm

ISSN 1004-0609
CN 43-1238/TG
CODEN: ZYJXFK

ISSN 1003-6326
CN 43-1239/TG
CODEN: TNMCEW

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