Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning

Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning

Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles models in real-time optimization (RTO) and model predictive...

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Bibliographic Details
Main Authors: Zhihao Zhang, Zhe Wu, David Rincon, Panagiotis D. Christofides
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Mathematics
Subjects:
real-time optimization
nonlinear processes
process control
model predictive control
chemical reactor control
distillation column control
Online Access:https://www.mdpi.com/2227-7390/7/10/890

Internet

https://www.mdpi.com/2227-7390/7/10/890

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