Applications of neural networks in chemical engineering: Hybrid systems
- Oak Ridge National Lab., TN (USA)
- Southwestern Louisiana Inst., Lafayette, LA (USA)
Expert systems are known to be useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process advisory. However, expert system applications are traditionally limited to knowledge domains that are heuristic and involve only simple mathematics. Neural networks, on the other hand, represent an emerging technology capable of rapid recognition of patterned behavior without regard to mathematical complexity. Although useful in problem identification, neural networks are not very efficient in providing in-depth solutions and typically do not promote full understanding of the problem or the reasoning behind its solutions. Hence, applications of neural networks have certain limitations. This paper explores the potential for expanding the scope of chemical engineering areas where neural networks might be utilized by incorporating expert systems and neural networks into the same application, a process called hybridization. In addition, hybrid applications are compared with those using more traditional approaches, the results of the different applications are analyzed, and the feasibility of converting the preliminary prototypes described herein into useful final products is evaluated. 12 refs., 8 figs.
- Research Organization:
- Oak Ridge National Lab., TN (USA)
- Sponsoring Organization:
- DOE/ER
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6476361
- Report Number(s):
- CONF-901155-2; ON: DE91002868
- Resource Relation:
- Conference: American Institute of Chemical Engineers fall annual meeting, Chicago, IL (USA), 11-16 Nov 1990
- Country of Publication:
- United States
- Language:
- English
Similar Records
Hybrid system for fault diagnosis using scanned input: A tutorial
XROUTE: A knowledge-based routing system using neural networks and genetic algorithms