(724h) Implications of Heat Integration in Energy Savings during Heat Treating of Steel

Ganesh, H. S., McKetta Department of Chemical Engineering, The University of Texas at Austin
Edgar, T. F., McKetta Department of Chemical Engineering, The University of Texas at Austin
Baldea, M., The University of Texas at Austin
In the United States, metal processing is the fourth largest contributor to energy consumption in the industrial sector with annual demands of around 2 quadrillion BTU (quads) [1]. For a particular steel processing plant, 65% to 85% of the energy is spent as fuel required to operate furnaces.

Steel slabs are typically forged into the desired shape and the resulting workpieces are heat treated for hardness and strength. During heat treating, the finished or pre-finished parts are indirectly heated in a furnace in an inert atmosphere to prevent surface oxidation and then quenched in water, oil or brine. Heat treating is an energy-intensive process and its economics can be improved by optimizing furnace design and furnace operations, in particular fuel use.

In this talk we focus on a currently operating heat treating furnace at an industrial partner, with the goal of increasing the energy efficiency by model-based system reconfiguration and optimization. The two-scale model [2] developed in our previous work is used to simulate the furnace operation. The model predicts the macro-scale furnace energy usage and the part temperature distribution and micro-scale grain size distribution as a function of processing time. We observe that about 40% of the energy input to the system is lost via burner exhaust gases. Hence, we propose to achieve energy savings by installing a recuperator, i.e., a feed-effluent heat exchanger that captures heat from the burner exhaust gases to preheat burner inlet air-fuel mix. We study the steady-state and open-loop dynamic implications of implementing the recuperator. Further savings can be achieved by identifying the optimal operating conditions of the furnace that both reduce the energy consumption and satisfy the product quality. To this end, we proceed by identifying surrogate models of the (steady-state) system behavior, which we then use to solve a pseudo-steady state energy optimization problem. The furnace temperature profile obtained from the optimization is tracked using a set of distributed temperature controllers. The proposed scheme results in considerable energy savings, and we demonstrate that such savings are available both in the nominal (steady-state) case and in the presence of transient disturbances.


[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] HS Ganesh, TF Edgar, and M Baldea. Model based control of product toughness and energy minimization of an austenitization furnace. Journal of Process Control (submitted), 2017.