Document Type
Article
Publication Date
1-1-1999
Abstract
As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identification. However, only in recent years, with the upsurge in the research on nonlinear control, has its use in process control been widespread. This paper intend to provide an extensive review of the various applications utilizing neural networks for chemical process control, both in simulation and online implementation. We have categorized the review under three major control schemes; predictive control, inverse-model-based control, and adaptive control methods, respectively. In each of these categories, we summarize the major applications as well as the objectives and results of the work. The review reveals the tremendous prospect of using neural networks in process control. It also shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time.
Keywords
Adaptive control systems, Computer simulation, Data reduction, Neural networks, Nonlinear control systems, Online systems, Predictive control systems, Chemical process control, Process control.
Divisions
fac_eng
Publication Title
Artificial Intelligence in Engineering
Volume
13
Issue
1
Publisher
Artificial Intelligence in Engineering
Additional Information
Cited By (since 1996): 179 Export Date: 5 March 2013 Source: Scopus CODEN: AIENE Language of Original Document: English Correspondence Address: Hussain, Mohamed Azlan; Univ Malaya, Kuala Lumpur, Malaysia