SCI和EI收录∣中国化工学会会刊

›› 2014, Vol. 22 ›› Issue (7): 788-794.DOI: 10.1016/j.cjche.2014.05.014

• PROCESS CONTROL • 上一篇    下一篇

A Composite Model Predictive Control Strategy for Furnaces

Hao Zang, Hongguang Li, Jingwen Huang, Jia Wang   

  1. Automation Department, Beijing University of Chemical Technology, Beijing 100029, China
  • 收稿日期:2013-06-15 修回日期:2013-12-29 出版日期:2014-07-28 发布日期:2014-08-23
  • 通讯作者: Hongguang Li

A Composite Model Predictive Control Strategy for Furnaces

Hao Zang, Hongguang Li, Jingwen Huang, Jia Wang   

  1. Automation Department, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2013-06-15 Revised:2013-12-29 Online:2014-07-28 Published:2014-08-23

摘要: Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

关键词: Furnace, Tracking nonlinear model predictive control, Economic nonlinear model predictive control, Distributed model predictive control

Abstract: Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

Key words: Furnace, Tracking nonlinear model predictive control, Economic nonlinear model predictive control, Distributed model predictive control