Abstract
Quench hardening is the process of strengthening and hardening ferrous metals and alloys by heating the material to a specific temperature to form austenite (austenitization), followed by rapid cooling (quenching) in water, brine or oil to introduce a hardened phase called martensite. The material is then often tempered to increase toughness, as it may decrease from the quench hardening process. The austenitization process is highly energy-intensive and many of the industrial austenitization furnaces were built and equipped prior to the advent of advanced control strategies and thus use large, sub-optimal amounts of energy. The model computes the energy usage of the furnace and the part temperature profile as a function of time and position within the furnace under temperature feedback control. In this paper, the aforementioned model is used to simulate the furnace for a batch of forty parts under heuristic temperature set points suggested by the operators of the plant. A model predictive control (MPC) system is then developed and deployed to control the the part temperature at the furnace exit thereby preventing the parts from overheating. An energy efficiency gain of 5.3 % was obtained under model predictive control compared to operation under heuristic temperature set points tracked by a regulatory control layer.
Highlights
Countries around the world are aiming for economic growth that is inclusive, smart and sustainable
We describe the development and implementation of an model predictive control (MPC) system for controlling the temperatures of the parts exiting an industrial austenitization furnace using a model-based case study
We rely on the radiation-based nonlinear model of the furnace developed in Heng et al [24] to develop a hierarchical, multi-rate control structure, whereby the setpoints of regulatory controllers are set by a multiple input, single output MPC that is computed at a much lower frequency than the regulatory control moves
Summary
Countries around the world are aiming for economic growth that is inclusive, smart and sustainable. In this manner, the energy usage of the system is reduced considerably compared to the current regulatory control scheme (which is effectively open-loop with respect to product temperatures). To this end, we rely on the radiation-based nonlinear model of the furnace developed in Heng et al [24] to develop a hierarchical, multi-rate control structure, whereby the setpoints of regulatory controllers are set by a multiple input, single output MPC that is computed at a much lower frequency than the regulatory control moves
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