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Title: Applications of neural networks in chemical engineering: Hybrid systems

Abstract

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.

Authors:
;  [1];  [2]
  1. (Oak Ridge National Lab., TN (USA))
  2. (Southwestern Louisiana Inst., Lafayette, LA (USA))
Publication Date:
Research Org.:
Oak Ridge National Lab., TN (USA)
Sponsoring Org.:
DOE/ER
OSTI Identifier:
6476361
Report Number(s):
CONF-901155-2
ON: DE91002868
DOE Contract Number:  
AC05-84OR21400
Resource Type:
Conference
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
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; CHEMICAL ENGINEERING; NEURAL NETWORKS; EXPERT SYSTEMS; HYBRID SYSTEMS; ENGINEERING; 990200* - Mathematics & Computers

Citation Formats

Ferrada, J.J., Osborne-Lee, I.W., and Grizzaffi, P.A. Applications of neural networks in chemical engineering: Hybrid systems. United States: N. p., 1990. Web.
Ferrada, J.J., Osborne-Lee, I.W., & Grizzaffi, P.A. Applications of neural networks in chemical engineering: Hybrid systems. United States.
Ferrada, J.J., Osborne-Lee, I.W., and Grizzaffi, P.A. Mon . "Applications of neural networks in chemical engineering: Hybrid systems". United States. https://www.osti.gov/servlets/purl/6476361.
@article{osti_6476361,
title = {Applications of neural networks in chemical engineering: Hybrid systems},
author = {Ferrada, J.J. and Osborne-Lee, I.W. and Grizzaffi, P.A.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1990},
month = {1}
}

Conference:
Other availability
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