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Research Papers

Energy-Oriented Modeling and Optimization of a Heat Treating Furnace

[+] Author and Article Information
Vincent R. Heng

McKetta Department of Chemical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: vincent.heng@utexas.edu

Hari S. Ganesh

McKetta Department of Chemical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: hariganesh@utexas.edu

Austin R. Dulaney

McKetta Department of Chemical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: austindulaney@utexas.edu

Andrew Kurzawski

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: andrew.kurzawski@utexas.edu

Michael Baldea

McKetta Department of Chemical Engineering,
Institute for Computational Engineering
and Sciences,
The University of Texas at Austin,
Austin, TX 78712
e-mail: mbaldea@che.utexas.edu

Ofodike A. Ezekoye

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: dezekoye@mail.utexas.edu

Thomas F. Edgar

McKetta Department of Chemical Engineering,
Energy Institute,
The University of Texas at Austin,
Austin, TX 78712
e-mail: tfedgar@austin.utexas.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 11, 2016; final manuscript received December 9, 2016; published online April 13, 2017. Assoc. Editor: Yang Shi.

J. Dyn. Sys., Meas., Control 139(6), 061014 (Apr 13, 2017) (13 pages) Paper No: DS-16-1023; doi: 10.1115/1.4035460 History: Received January 11, 2016; Revised December 09, 2016

In this paper, we develop an energy-focused model of an industrial roller hearth heat treating furnace. The model represents radiation heat transfer with nonparticipating gas and convective heat transfer. The model computes the exit temperature profile of the treated steel parts and the energy consumption and efficiency of the furnace. We propose a dual iterative numerical scheme to solve the conservation equations and validate its efficacy by simulating the dynamics of the furnace during startup, as well as for steady-state operation. A case study investigates energy consumption within the furnace under temperature control. We first consider a heuristic control strategy using simple linear controllers. A response surface approach is then used to find the optimal set points that minimize energy consumption while ensuring desired part temperature properties are met when processing is complete. With optimized set points, 4.8% less energy per part is required versus the heuristic set points.

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References

Figures

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Fig. 1

Prototype furnace schematic for roller hearth furnace. Metal parts are heated by combustion of natural gas in radiant tube burners. After exiting the furnace, the parts are placed into an oil quench bath to induce the crystal structure change and give the parts properties such as hardness, shear strength, and tensile strength. Based on schematic by AFC-Holcroft [6].

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Fig. 3

Angles between a subsurface of A1 and the visible areas of surfaces A2 and A3

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Fig. 4

Dual iterative solution algorithm for combined radiosity and temperature profile furnace model

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Fig. 5

Zone temperatures (controlled variable) and total fuel flow to zones (manipulated input) for the heuristic temperature set points 1000 K, 1150 K, 1200 K, and 1250 K

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Fig. 6

Heat map of the furnace during steady-state (constant input–output) operation. The temperature range is 800–1300 K to highlight the temperature differences at the upper end of the spectrum. To restrict the temperature range, we use “clipping,” and all temperatures lower than 800 K are shown in the hue corresponding to 800 K. The temperature of the leftmost steel part is below the 800 K threshold. Burners are highlighted with a bold line, and parts are highlighted with a thin line. The granularity of the part temperature profile is increased for this explanatory figure, but these results do not vary significantly (i.e., are within 10 K) from those obtained using the detailed model described in the rest of the paper.

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Fig. 7

Part exit conditions for 40 parts in the heuristic operation. The lines without markers show the standard deviation around the average part temperature. The parts here are heated to a minimum temperature of 1126 K.

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Fig. 8

Heat input to the part and temperatures for part number 20 with time in the furnace (and thus position)

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Fig. 9

Response surfaces for the total energy input to the system per part. The model has four inputs, i.e., the temperature set points in each of four zones. Two plots are shown, holding two temperatures constant: (a) response surface 1 and (b) response surface 2.

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Fig. 10

Zone temperatures (controlled variable) and total fuel flow to zones (manipulated input) for the optimal temperature set points 1000 K, 1030 K, 1191 K, and 1221 K

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Fig. 11

Part exit conditions for 40 parts in optimal operation. The lines without markers show the standard deviation around the average part temperature.

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Fig. 12

Heat input to the part and temperatures for part 20 with time (and inherently position) in the furnace for offline optimized inputs

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