In steel industry, without consideration of blast furnaces, reheating furnaces are classified the biggest energy consumers. The research of efficient control algorithm permitting less energy consumption has become an important issue for those furnaces. In this paper, a nonlinear Model Predictive Controller (MPC) is designed for a steel slab walking-beam reheating furnace. A numerical nonlinear model is utilized in predictions of furnace thermal behavior and in optimization of furnace zone temperature set points. The MPC control strategy uses this numerical model to solve at each sampling instant a constrained dynamic optimization problem in order to obtain the best zone temperature set points. This optimization problem is solved using Nelder-Mead simplex method that allows fast decline of objective function. The controller is able to deal with non-steady-state operating situations of the furnace. Simulation based on industrial data shows an energy reduction of 5%, and a significant improvement of heating performances.
In most of industrial processes, the measurement are central to the process control and quality management. This become even truer when measurement data are used to develop and support PHM strategies. In this context, many software are installed in order to collect data for providing quality assessment at each step of the manufacturing process. However, measurement error or drift are not considered leading to downgrading / rejected products / suboptimal running conditions that comes from measurement drift not detected on time. In concrete, these lead to bigger penalty than losses of production due to stopping time for repairing sensors. Indeed, generally speaking, process data is the “raw material” for many business processes, starting from process control strategy, PHM strategies to Business Intelligence. Thus being able to ensure data quality and reliability is of first importance. Towards this end, methods and tools are required to support online measurement monitoring, predictive diagnosis and reliability enhancement.In this paper, a dedicated approach developed in collaboration with ArcelorMittal Research is presented. It consists in the development of intelligent software that would enable sensor measurement validation taking into account process parameters and operational conditions. An illustrative case study is extracted from an ongoing application developed for the finishing line in ArcelorMittal plant at Florange in France. Results regarding measurement reliability assessment as well as sensor failure anticipation will be described.
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