Page 265 - Distributed model predictive control for plant-wide systems

11

Hot-Rolled Strip Laminar Cooling

Process with Distributed Predictive

Control

11.1 Introduction
Recently, customers require increasingly better quality for hot-rolled strip products, such as
automotive companies expect to gain an advantage from thinner but still very strong types of
steel sheeting that make their vehicles more efficient and more environmentally compatible.
In addition to the alloying elements, the cooling section is crucial for the quality of products
[116]. Hot-rolled strip laminar cooling (HSLC) process is used to cool a strip from an initial
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temperature of roughly 820–920 C down to a coiling temperature of roughly 400–680 C,
according to the steel grade and geometry. The mechanical properties of the corresponding
strip are determined by the time–temperature course (or cooling curve) when the strip is cooled
down on the runout table [116–117]. The precise and highly flexible control of the cooling
curve in the cooling section is therefore extremely important.
Most of the control methods (e.g., Smith predictor control [118], element tracking control
[119], self-learning strategy [120], and adaptive control [121]) pursue the precision of coil-
ing temperature and care less about the evolution of strip temperature. In these methods, the
control problem is simplified so greatly that only the coiling temperature is controlled by the
closed-loop part of the controller. However, it is necessary to regulate the whole evolution
procedure of strip temperature if better properties of strip are required. This is a large-scale,
MIMO, parameter-distributed complicated system. Therefore, the problem is how to control
the whole HSLC process online precisely with the size of HSLC process and the computational
efforts required.
Model predictive control (MPC) is widely recognized as a practical control technology with
high performance, where a control action sequence is obtained by solving, at each sampling
instant, a finite horizon open-loop receding optimization problem and the first control action is
applied to the process [26]. An attractive attribute of MPC technology is its ability to system-
atically account for process constraints. It has been successfully applied to various linear and
nonlinear systems in the process industries and is becoming widespread. For large-scale and

Distributed Model Predictive Control for Plant-Wide Systems, First Edition. Shaoyuan Li and Yi Zheng.
© 2015 John Wiley & Sons (Asia) Pte Ltd. Published 2015 by John Wiley & Sons (Asia) Pte Ltd.