Implementation of a condition monitoring system on an electric arc furnace through a risk-based methodology

Abstract

This article presents the deployment of a condition monitoring system on an electric arc furnace in a steel-making company, ranging from the development of the system until its implementation and the results achieved by its use in the plant. A step-wise risk-based methodology is introduced and it is adopted to deploy the condition monitoring system. The electric arc furnace is a relevant asset for safety issues; due to the characteristics of the furnace-running continuously at high temperatures and in harsh environmental conditions-many components cannot be visually inspected; thus, a maintenance system, with real-time monitoring capabilities, represents a proper solution to keep under control the asset health state. Besides the monitoring activity, appropriate risk information must also be shown to maintenance personnel to effectively improve maintenance activity. In this concern, the condition monitoring system, herein presented, can be considered an E-maintenance tool, integrated within an existing information and communication technology infrastructure, and representing one practical application of E-maintenance concept within industry.

Data-driven CBM tool for risk-informed decision-making in an electric arc furnace

“…Since we assume that physical models and mathematical equations cannot be easily defined due to the complexity of the manufacturing asset under study, prior knowledge is extracted through a Process Hazard Analysis (PHA). Relying on experts' knowledge, PHA initially aims at identifying the asset critical scenarios according to risk management principles, and then at mapping the manufacturing asset behavior in terms of healthy, abnormal, faulty states, and related failure mechanisms determiningas physical and/or chemical transformation processes the asset degradation [15]. Among the different PHA techniques to study asset failures (FMEA, HAZOP, Fault Trees, Event Trees, Reliability Diagrams, and so on), the work described in this paper relies on Fault Tree Analysis (FTA), judged as convenient technique for the specific risk-based analysis of the faults of the safety-critical component under study.…”

“…In Tenaris Dalmine there are two EAFs, with the following characteristics: capacity 105 t, diameter 6.1 m, weight of the structure 230 t. Each furnace allows to obtain liquid steel, being charged by selected scrap and varying percentages of pig iron [15], and processing with an average power request of 67 MW per cast, and a maximum power peak of 89 MW. The casting cycle is around 38.5 min and this corresponds to a productivity above 150 t/h.…”

“…The test-bed implementation of this data-driven approach is put in the context of the steel making industry, and finally leads to the development of a smart maintenance tool acting as watchdog agent of risky scenarios due to a critical component of an Electric Arc Furnace (EAF). The paper grounds on some first information provided by [14,15] and on an extension of [16].…”