類神經網路在動態化工製程失誤與診斷上的應用

• Main Author: 陳昭秀
• Other Authors: Mr.CHANHG
• Format: Others
• Language: zh-TW
• Published: 1993
• Online Access: http://ndltd.ncl.edu.tw/handle/54509795074895282165

碩士 === 國立成功大學 === 化學工程研究所 === 81 === For the purpose of achieving an economical production scale, the newly-designed chemical processes tend to be much larger and more complex. Also, to meet the need for optimizing performance, the demand for tighter control has become a trend in modern plants. As a result, probability of faults and/or operational problems in chemical industries have increased significantly in recent years. Therefore, there is a real incentive for the development of automatic fault detection and diagnosis techniques to be used as an aid in plant operation. of our research is to assess the feasibility of adopting, The objective artificial neural networks (ANNs) in fault detection and diagnosis for dynamic systems}. Although there is a large volume of related publications avail- able, most of them used steady-state data to train ANNs and, as such, the task of fault diagnosis can only be carried out after reaching a new steady state. To avoid this drawback,the two-stage ramework proposed by Isermann (1982) and Panossian (1988) was utilized to incorporate two ANNs in series in our study. On the of the main advantages of using an ANN is its ability to perform complex functional mapping without formulating accurate mathematical models. Once trained, ANNs can be implemented accord -ing to on-line data. Therefore, in order to verify the feasibil- ity of the proposed approach, a pilot plant, which simlulates the operation of pipeline networks, has been assembled in our laboratory.