(427a) Optimal Operation of an Austenitization Furnace

Optimal Operation of an Austenitization Furnace

Vincent Heng and Michael Baldea

McKetta Department of Chemical Engineering

The University of Texas at Austin, 1 University Station C0400, Austin, TX 78712

Email: mbaldea@che.utexas.edu

The production and primary processing of metals are energy-intensive operations, accounting for about 2 quadrillion BTU (quads) of energy consumption in the United States every year. Of this, a significant percentage (up to 30% in some cases) is lost as waste heat^[1], owing to both inherent inefficiencies and ineffective control strategies.

In this presentation, we examine the operation of such a metal processing system, and propose a novel approach for improving its energy efficiency. In particular, we focus on the real time optimization of an austenitization furnace currently in operation at an industrial partner. Austenitization is a heat treating operation that consists of heating metal parts, followed by rapid quenching in an oil bath, with the goal of inducing specific metal properties (e.g., tensile strength, hardness). Heating occurs in high-temperature furnaces that are operated in a continuous manner. The metal parts travel on rollers through the furnace, which is heated by ceiling and floor radiant tube burners spanning the length of the furnace. In the system under consideration, the overall heat input is on the order of 10 million BTU per hour to achieve part temperatures of 1400F, with a part residence time of four hours.

The operational objective of the process is to heat the metal to a minimum temperature threshold prior to quenching, while maintaining a relatively uniform temperature profile within the parts. During operation, part temperatures are not measured and thus cannot be explicitly controlled. Instead, furnace temperature is used as an implicit metric for the temperature of the steel parts, and is controlled by means of manipulating the flow rate of fuel (natural gas) to the burners. Operator experience and heuristics are used to determine the proper furnace temperature set points (defined for individual zones along the furnace) to achieve the desired metal exit temperatures. This empirical operation typically leads to excess energy consumption due to an “overcompensation” effect, where the furnace temperature is adjusted such that the part exit temperatures exceed (often by a fair margin) the desired temperature threshold ^[2].

In this work, we develop a model-based approach for improving energy efficiency in austenitization furnaces. We first describe a first-principles model of the austenitization furnace. We then develop simplified response-surface models that capture the evolution of the part temperature and energy consumption of the process as a function of the temperature set points, and utilize these models to formulate an optimization problem aimed at finding the temperature set points that minimize energy usage while obtaining the desired part exit conditions. Our results show an optimal longitudinal temperature profile with high-temperature ends and a cooler middle region. This represents a paradigm shift from the current operating prescription, which is based on increasing the temperature lengthwise from the inlet to the outlet of the furnace. We validate our findings by simulating the detailed furnace model; the simulation results confirm that significant energy savings can indeed be derived from the proposed optimal strategy. We also show that the optimal temperature profile can be easily recomputed for different operating conditions (e.g., changes in part shapes or production rate), and our strategy therefore lends itself very well to real-time implementation. We finalize our presentation with a discussion of future work concerning the implementation of the proposed strategy in the plant.

^[1]V Viswanathan, R Davies, and J Holbery. Opportunity analysis for recovering energy from industrial waste heat and emissions. Pacific Northwest National Laboratory, 2005.

^[2]A Thekdi. Energy efficiency improvement opportunities in process heating for the forging industry. E3M, 2010.