Model Predictive Control for Hybrid Annealing Furnace at Tata Steel IJmuiden

Formulation and validation of model and controller

Master thesis (2017)

Authors

D.R. Nusman Mechanical Engineering

Contributors

Hai Wu (supervisor 1)
B.H.K. De Schutter (supervisor 1)
A.J.J. van den Boom (supervisor 2)
Riccardo M.G. Ferrari (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2017 Diederick Nusman
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Published Date

27-09-2017

Language

English

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Faculty

Mechanical Engineering

Abstract

The demand from the automotive industry for thinner, stronger and more ductile steels led to development of the so called Advanced High Strength Steels (AHSS). These new steel grades are produced at one of the hot dip galvanizing lines, “Dompel Verzinklijn 3” (DVL3), at Tata Steel IJmuiden. The annealing furnace in DVL3 consists out of a number of consecutive sections. One of these sections is the Slow-Cooling Section.

The Slow-Cooling Section, a so-called hybrid furnace, is installed with both heating and cooling capabilities to effectively adjust the temperature required by the metallurgical prescription of a specific steel strip product. The required temperature and associated cooling or heating demand can vary from strip to strip, depending on the desired mechanical properties and strip dimensions, which complicates the control of the Slow-Cooling Section. This is further complicated by process variations in production speed and system delays in control instrumentation. It becomes apparent that in order to produce these high quality steel grades, an improved solution to achieve precise temperature control in the slow cooling process has to be found.

In this research a Model Predictive Controller (MPC) is developed for the control of the Slow-Cooling Section of DVL3. This advanced control system is based on a mathematical model, developed in the first part of this research, incorporating the heat transfer from and to the steel strips inside the Slow-Cooling Section. The nonlinear furnace model is validated with historical furnace data, showing good agreement for various process conditions. MPC is applied using two different methods. The first method is based on one linear internal model. The second method approximates the nonlinear mathematical model by the use of piecewise affine models. Case studies are performed to test the performance of the new controllers. The performance of the new controllers are compared with the current temperature control of DVL3.

A critical discussion is presented based on the results of both Model Predictive Controllers. It is shown that the predictive ability, especially in transient conditions is improved with the newly developed MPC and more accurate temperature control is achieved. Recommendations are given to further improve the control performance.