Integrated scheduling and RTO of RGP with MPC and PI controllers

N. Yusoff, M. Ramasamy

Research output: Contribution to journalArticle

Abstract

This study proposes an integrated framework of scheduling and Real-Time Optimization (RTO) of a Refrigerated Gas Plant (RGP). At the top layer, a high fidelity dynamic model of RGP is subjected to scheduling of plant operating mode from natural gas liquids to sales gas and vice-versa. Set points from mode scheduling are passed down to the steady-state RTO layer. Modeling mismatch is minimized by rigorously exchanging values of key variables between dynamic and steady-state models. Optimal trajectories of set points are obtained using sequential quadratic programming algorithm with constraints. These trajectories are disjointedly implemented by Model Predictive Control (MPC) scheme and Proportional-Integral (PI) controllers for comparison. Four case studies for each mode scheduling are performed to illustrate efficacy of the proposed approach.

Original languageEnglish
Pages (from-to)3027-3033
Number of pages7
JournalJournal of Applied Sciences
Volume9
Issue number17
DOIs
Publication statusPublished - 2009

Fingerprint

Gas plants
Model predictive control
Scheduling
Controllers
Trajectories
Quadratic programming
Dynamic models
Natural gas
Sales
Liquids
Gases

Keywords

  • Gas plant
  • Model predictive control
  • Real-time optimization
  • Scheduling

ASJC Scopus subject areas

  • General

Cite this

@article{c5914398aac14afaac7f06e1187b3b9d,
title = "Integrated scheduling and RTO of RGP with MPC and PI controllers",
abstract = "This study proposes an integrated framework of scheduling and Real-Time Optimization (RTO) of a Refrigerated Gas Plant (RGP). At the top layer, a high fidelity dynamic model of RGP is subjected to scheduling of plant operating mode from natural gas liquids to sales gas and vice-versa. Set points from mode scheduling are passed down to the steady-state RTO layer. Modeling mismatch is minimized by rigorously exchanging values of key variables between dynamic and steady-state models. Optimal trajectories of set points are obtained using sequential quadratic programming algorithm with constraints. These trajectories are disjointedly implemented by Model Predictive Control (MPC) scheme and Proportional-Integral (PI) controllers for comparison. Four case studies for each mode scheduling are performed to illustrate efficacy of the proposed approach.",
keywords = "Gas plant, Model predictive control, Real-time optimization, Scheduling",
author = "N. Yusoff and M. Ramasamy",
year = "2009",
doi = "10.3923/jas.2009.3027.3033",
language = "English",
volume = "9",
pages = "3027--3033",
journal = "Journal of Applied Sciences",
issn = "1812-5654",
publisher = "Asian Network for Scientific Information",
number = "17",

}

Integrated scheduling and RTO of RGP with MPC and PI controllers. / Yusoff, N.; Ramasamy, M.

In: Journal of Applied Sciences, Vol. 9, No. 17, 2009, p. 3027-3033.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Integrated scheduling and RTO of RGP with MPC and PI controllers

AU - Yusoff, N.

AU - Ramasamy, M.

PY - 2009

Y1 - 2009

N2 - This study proposes an integrated framework of scheduling and Real-Time Optimization (RTO) of a Refrigerated Gas Plant (RGP). At the top layer, a high fidelity dynamic model of RGP is subjected to scheduling of plant operating mode from natural gas liquids to sales gas and vice-versa. Set points from mode scheduling are passed down to the steady-state RTO layer. Modeling mismatch is minimized by rigorously exchanging values of key variables between dynamic and steady-state models. Optimal trajectories of set points are obtained using sequential quadratic programming algorithm with constraints. These trajectories are disjointedly implemented by Model Predictive Control (MPC) scheme and Proportional-Integral (PI) controllers for comparison. Four case studies for each mode scheduling are performed to illustrate efficacy of the proposed approach.

AB - This study proposes an integrated framework of scheduling and Real-Time Optimization (RTO) of a Refrigerated Gas Plant (RGP). At the top layer, a high fidelity dynamic model of RGP is subjected to scheduling of plant operating mode from natural gas liquids to sales gas and vice-versa. Set points from mode scheduling are passed down to the steady-state RTO layer. Modeling mismatch is minimized by rigorously exchanging values of key variables between dynamic and steady-state models. Optimal trajectories of set points are obtained using sequential quadratic programming algorithm with constraints. These trajectories are disjointedly implemented by Model Predictive Control (MPC) scheme and Proportional-Integral (PI) controllers for comparison. Four case studies for each mode scheduling are performed to illustrate efficacy of the proposed approach.

KW - Gas plant

KW - Model predictive control

KW - Real-time optimization

KW - Scheduling

UR - http://www.scopus.com/inward/record.url?scp=68949216989&partnerID=8YFLogxK

U2 - 10.3923/jas.2009.3027.3033

DO - 10.3923/jas.2009.3027.3033

M3 - Article

AN - SCOPUS:68949216989

VL - 9

SP - 3027

EP - 3033

JO - Journal of Applied Sciences

JF - Journal of Applied Sciences

SN - 1812-5654

IS - 17

ER -