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 consum...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.展开更多
Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distri...Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distributed-nonlinear autoregressive with exogenous inputs correlation model(STD-NARXCM)to spatial temporal distributed-autoregressive with exogenous inputs correlation model(STD-ARXCM)in working point is established.Secondly,a new coordinated time-sharing control architecture in different time periods is proposed,which is along the length of the SRRF to improve the control performance.Thirdly,a hybrid control algorithm of expert-fuzzy is proposed to improve the dynamic of the temperature and the heating rate during time period 0 to t_(1).A hybrid control algorithm of expert-fuzzy-PID is proposed to enhance the control accuracy and the heating rate during time period t_(1) to t_(2).A hybrid control algorithm of expert-active disturbance rejection control(ADRC)is proposed to boost the anti-interference and the heating rate during time period t_(2) to t_(3).Finally,the experimental results show that the coordinated time-sharing algorithm can meet the process requirements,the maximum deviation of temperature value is 8-13.5℃.展开更多
文摘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.
基金This work was supported by the National Natural Science Foundation of China(Nos.62173032 and 62003038).
文摘Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distributed-nonlinear autoregressive with exogenous inputs correlation model(STD-NARXCM)to spatial temporal distributed-autoregressive with exogenous inputs correlation model(STD-ARXCM)in working point is established.Secondly,a new coordinated time-sharing control architecture in different time periods is proposed,which is along the length of the SRRF to improve the control performance.Thirdly,a hybrid control algorithm of expert-fuzzy is proposed to improve the dynamic of the temperature and the heating rate during time period 0 to t_(1).A hybrid control algorithm of expert-fuzzy-PID is proposed to enhance the control accuracy and the heating rate during time period t_(1) to t_(2).A hybrid control algorithm of expert-active disturbance rejection control(ADRC)is proposed to boost the anti-interference and the heating rate during time period t_(2) to t_(3).Finally,the experimental results show that the coordinated time-sharing algorithm can meet the process requirements,the maximum deviation of temperature value is 8-13.5℃.
文摘为了准确反映高炉冶炼过程的复杂性,实现高炉过程的准确控制,基于模式识别的理论设计了一种高炉冶炼专家系统,利用该系统可以实现对关键控制参数如风量、风压、风温等进行模式识别,在此基础上能够实现高炉过程的总体评估,同时利用模式识别技术可以对影响高炉过程的重要现象,如炉顶煤气流分布、炉型变化、布料等进行有效的分类管理,实现对高炉过程的准确控制,从而提高了高炉运行的稳定性,高炉的技术经济指标有明显改善,2010年8月高炉专家系统在武钢5号高炉投运后,降低焦比8.42 kg/t,节约焦炭7732.3 t;增产34142 t.
