Steel production is a critical index to measure the infrastructural growth and development of a nation. The steel production capacity of a nation has a significant impact on its GDP. Even though steel rolling has been in the industry since the early 1700s, there have not been significant advancements in its technology space. The safety hazards to personnel and failure of process components have persistently posed daunting challenges for the steel industry. The advent of automation in the late 1900s helped the industry manage some of these challenges to a certain extent. Despite these technological improvements, the realm of steel rolling is still not explored thoroughly because steel rolling is a highly integrated and complex system with numerous process parameters impacting the quality of the finished product. As a result, the study of the dynamics of steel rolling is still under active research targeted toward improving the complex processes involved in the industry.

External factors play a significant role in the complexity of the steel rolling process. The scope of the work herein attempts to identify and model some of these significant external factors, also known as "disturbances" in control terminology. A Finite-Element Method (FEM) based simulator for the rolling process simulation incorporating the external disturbances is explored. The outcomes from this simulation will enable establishing regression models that facilitate quantifying external disturbances’ effects on the technological/process parameters in steel rolling.

The thesis is focused on studying the external disturbance of speed mismatch and proposing a quantitative model for evaluating the effect of speed mismatch on the process parameters of rolling. Subsequent to the development of the proposed disturbance quantitative model, three controller systems - Proportional Integral Derivative (PID) controller, Linear Quadratic Regulator (LQR) controller, and Model Predictive Control (MPC) controller will be implemented to evaluate comparisons between the controllers for disturbance rejection and reference tracking.

The scope of the work presented in this thesis is significant as it focuses on developing a quantitative model for the process disturbances that prevail in the steel rolling industry.