Adaptive model predictive control using neural networks

Technical Report · Thu Sep 01 00:00:00 EDT 1994

DOI:https://doi.org/10.2172/10178912· OSTI ID:10178912

Baum, C C; Buescher, K L; Hanagandi, V; Jones, R; Lee, K

The authors work in controlling chemical processes included the following: (1) to develop neural networks and training procedures that are well suited to: small amounts of off-line training data for on-line control of systems with substantial time lags; (2) to develop generic Model Predictive Control (MPC) software; and (3) to control the following simulated systems using MPC: continuously stirred tank reactor with jacket dynamics; plasma etching model for semiconductor manufacture; and distillation columns. Details descriptions are given of the three points.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-36
• OSTI ID: 10178912
• Report Number(s): LA-UR-94-2725; ON: DE94018086; TRN: AHC29420%%50
• Resource Relation: Other Information: PBD: [1994]
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
CHEMICAL REACTORS
ON-LINE CONTROL SYSTEMS
DISTILLATION EQUIPMENT
NEURAL NETWORKS
TRAINING
INDUSTRIAL PLANTS
SEMICONDUCTOR DEVICES
320303
990200
EQUIPMENT AND PROCESSES
MATHEMATICS AND COMPUTERS