Analysis and Modeling of Steel Industry Reheating Furnace Billets Temperature

In the present work, topics related to the production processes of the steel industry are addressed; the analysis has been focused on the rolling mill process of semi-finished products of an Italian steel plant, which follows the reheating process of the billets, carried out inside a reheating furnace. For this purpose, an in-depth knowledge of the industrial process and a preventive data analysis work, capable of informing on the behavior of the process itself, have been conducted. Thanks to the obtained results, it will then be possible to create models and control systems capable of ensuring energy saving while guaranteeing high product quality and limiting pollution emissions. The first problem addressed in the present work deals with an in-depth analysis of the values recorded by the temperature sensors (thermal imaging camera and pyrometers) and the absorption values recorded in the rolling mills. The position of the thermal imaging camera, initially located at the furnace exit, was verified to be not optimal due to the presence of scale that altered the temperature measurements. The thermal imaging camera was subsequently repositioned downstream of the descaling device and a new analysis was carried out on the new data available. In the second part of the work a mathematical model of the temperature decay of the billets have been developed which models the billets’ temperature behavior from the exit of the furnace through the descaling device and up to the last rolling mill stand. The initial analysis phase was functional in order to have at disposal a subset of data for the modelling validation. Satisfactory results were obtained in both the addressed problems.

I. Introduction

Energy is a fundamental element for the secondary sector; in fact, at the basis of activities of individual industries, whether it is metallurgical, petrochemical such as even textile, food or manufacturing, there is a need to have energy for it carrying out the individual processes. However, energy consumption is to be counted among main causes of current environmental risks. It is precisely in order to limit negative consequences that industries have long been adopting a policy aimed at contain waste, in line with the so-called Green Economy energy policy [1]. Steel industry involves many energy-intensive processes: these processes contribute to environmental risks, which could be limited by adopting policies aimed at reducing wastefulness according to Green Economy. Thanks to the implementation of Advanced Process Control (APC) systems [2] (e.g. based on Model Predictive Control (MPC) techniques [3], [4], [5]), which support operators in the different processes, it is possible to optimize industrial processes ensuring a significant energy saving. In this context, data analysis is assuming a key role within the Industry 4.0 framework.