Model-based Predictive Control of a Solar Reactor

被引：0

作者：

Karout, Youssef ^{[1
,2
]}

Curcio, Axel ^{[1
,3
]}

Eynard, Julien ^{[1
,2
]}

Thil, Stephane ^{[1
,2
]}

Rodat, Sylvain ^{[3
]}

Abanades, Stephane ^{[3
]}

Grieu, Stephane ^{[1
,2
]}

机构：

[1] Tecnosud, PROMES CNRS UPR 8521, Rambla Thermodynam, F-66100 Perpignan, France

[2] Univ Perpignan Via Domitia UPVD, 52 Ave Paul Alduy, F-66860 Perpignan, France

[3] PROMES CNRS UPR 8521, 7 Rue 4 Solaire, F-66120 Font Romeu Odeillo Via, France

来源：

SOLARPACES 2022, 28TH INTERNATIONAL CONFERENCE ON CONCENTRATING SOLAR POWER AND CHEMICAL ENERGY SYSTEMS, VOL 1 | 2023年

关键词：

Model-Based Predictive Control; Solar Reactor; Syngas Production; BIOMASS GASIFICATION;

D O I：

10.52825/solarpaces.v1i.647

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The present paper deals with the modelling and control of a solar reactor designed to produce syngas, by exploiting concentrated solar power. A model of the reactor based on the thermodynamic equilibrium is developed. Two model-based predictive control strategies are proposed: the first strategy (MPC strategy 1) aims to maintain the reactor's temperature at its nominal value whereas the second strategy (MPC strategy 2) aims to maintain the reactor's temperature at its nominal value, while maximizing the use of solar energy. Finally, these strategies are compared to a reference strategy, which is based on a combination of a rule-based controller and an adaptive PID controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasting models.

引用

页数：7

共 50 条

[41] Fuzzy model-based predictive control of a heat exchanger
Fischer, M
Nelles, O
Isermann, R
ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL 1997, 1998, : 409 - 414
[42] Nonlinear model-based predictive control using a generalised hammerstein model and its application to a semi-batch reactor
M'sahli F.
Abdennour R.B.
Ksouri M.
International Journal of Advanced Manufacturing Technology, 2002, 20 (11): : 844 - 852
[43] Cruise control on a combine harvester using model-based predictive control
Coen, T.
Saeys, W.
Missotten, B.
De Baerdemaeker, J.
BIOSYSTEMS ENGINEERING, 2008, 99 (01) : 47 - 55
[44] Lifted Bilinear Model-based Linear Model Predictive Control with Scalability
Kanai, Masaki
Yamakita, Masaki
IFAC PAPERSONLINE, 2023, 56 (02): : 9405 - 9410
[45] Constraint Optimal Model-Based Disturbance Predictive and Rejection Control Method of a Parabolic Trough Solar Field
Wei, Shangshang
Gao, Xianhua
Li, Yiguo
ENERGIES, 2024, 17 (22)
[46] Model-Based Reactor Design to Control Hyperbranched Polymer Architecture
Tobita, Hidetaka
MACROMOLECULAR REACTION ENGINEERING, 2018, 12 (04)
[47] On the Unrestricted Horizon Predictive Control - a fully stochastic model-based predictive approach
Trentini, Rodrigo
Silveira, Antonio
Kutzner, Ruediger
Hofmann, Lutz
2016 EUROPEAN CONTROL CONFERENCE (ECC), 2016, : 1322 - 1327
[48] Model-based Irrigation Control using Model Predictive Control and DSSAT Crop Simulator
Jang, Jisung
Tian, Di
He, Q. Peter
2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 5314 - 5319
[49] Wind turbine control: A model for the future Advantages of model-based predictive control algorithms
Dickler, Sebastian
Winterrneyer-Kallen, Thorben
Konrad, Thomas
Abel, Dirk
ATP MAGAZINE, 2021, (05): : 84 - 93
[50] An Implementation Framework of Model Predictive Control for HVAC Systems: A Case Study of EnergyPlus Model-Based Predictive Control
Zhang, Zhiang
Lam, Khee Poh
2017 ASHRAE ANNUAL CONFERENCE PAPERS, 2017,

← 1 2 3 4 5 →