IEICE TRANSACTIONS on Information
CCP-Based Plant-Wide Optimization and Application to the Walking-Beam-Type Reheating Furnace
Summary :
In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.9 pp.2239-2247
- Publication Date
- 2016/09/01
- Publicized
- 2016/06/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7171
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Yan ZHANG
SMU
Hongyan MAO
ECNU
Keyword
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Yan ZHANG, Hongyan MAO, "CCP-Based Plant-Wide Optimization and Application to the Walking-Beam-Type Reheating Furnace" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 9, pp. 2239-2247, September 2016, doi: 10.1587/transinf.2016EDP7171.
Abstract: In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7171/_p
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@ARTICLE{e99-d_9_2239,
author={Yan ZHANG, Hongyan MAO, },
journal={IEICE TRANSACTIONS on Information},
title={CCP-Based Plant-Wide Optimization and Application to the Walking-Beam-Type Reheating Furnace},
year={2016},
volume={E99-D},
number={9},
pages={2239-2247},
abstract={In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.},
keywords={},
doi={10.1587/transinf.2016EDP7171},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - CCP-Based Plant-Wide Optimization and Application to the Walking-Beam-Type Reheating Furnace
T2 - IEICE TRANSACTIONS on Information
SP - 2239
EP - 2247
AU - Yan ZHANG
AU - Hongyan MAO
PY - 2016
DO - 10.1587/transinf.2016EDP7171
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2016
AB - In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.
ER -
English
