Title

Optimization of temporal processes: A model predictive control approach

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2009

Journal/Book/Conference Title

IEEE Transactions on Evolutionary Computation

Volume

13

Abstract

A dynamic predictive-control model of a nonlinear and temporal process is considered. Evolutionary computation and data mining algorithms are integrated for solving the model. Data-mining algorithms learn dynamic equations from process data. Evolutionary algorithms are then applied to solve the optimization problem guided by the knowledge extracted by data-mining algorithms. Several properties of the optimization model are shown in detail, in particular, a selection of regressors, time delays, prediction and control horizons, and weights. The concepts proposed in this paper are illustrated with an industrial case study in combustion process. 2008 IEEE.

Keywords

Sustainability, A dynamic predictive-control model of a nonlinear and temporal process is considered. Evolutionary computation and data mining algorithms are integrated for solving the model. Data-mining algorithms learn dynamic equations from process data. Evolutionary algorithms are then applied to solve the optimization problem guided by the knowledge extracted by data-mining algorithms. Several properties of the optimization model are shown in detail, in particular, a selection of regressors, time delays, prediction and control horizons, and weights. The concepts proposed in this paper are illustrated with an industrial case study in combustion process. 2008 IEEE.

Published Article/Book Citation

IEEE Transactions on Evolutionary Computation, 13:1 (2009) pp.169-179.



This document is currently not available here.

Share

COinS
 

URL

http://ir.uiowa.edu/cee_pubs/503