0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

United States Energy Information Administration, 2014, “ Monthly Energy Review,” EIA, Washington, DC, accessed Dec. 15, 2014, http://www.eia.gov/totalenergy/data/monthly/
Stones, E. , Ferland, K. , and Noack, M. , 2007, “ Industrial Efficiency,” NPC Global Oil and Gas Study, accessed Dec. 25, 2016, http://www.npc.org/study_topic_papers/5-dtg-industrial-efficiency.pdf
Viswanathan, V. , Davies, R. , and Holbery, J. , 2005, “ Opportunity Analysis for Recovering Energy From Industrial Waste Heat and Emissions,” Pacific Northwest National Laboratory, Richland, WA.
Pellegrino, J. , Margolis, N. , Justiniano, M. , Miller, M. , and Thekdi, A. , 2004, “ Energy Use, Loss and Opportunities Analysis,” Energetics, Incorporated and E3M, Incorporated, Columbia, MD, accessed Dec. 25, 2016, https://www1.eere.energy.gov/manufacturing/intensiveprocesses/pdfs/energy_use_loss_opportunities_analysis.pdf
Thekdi, A. , 2010, “ Energy Efficiency Improvement Opportunities in Process Heating for the Forging Industry,” E3M, Canton, NY.
Holcroft, A. , 2014, “ Conveyor Furnaces: Continuous Conveyor Thermal Treatment System,” AFC-HOLCROFT, Wixom, MI, accessed Apr. 9, 2015, http://www.afc-holcroft.com/userfiles/file/pdf/ConveyorFurnace_Brochure.pdf
Ramamurthy, H. , Ramadhyani, S. , and Viskanta, R. , 1995, “ A Thermal System Model for a Radiant-Tube Continuous Reheating Furnace,” J. Mater. Eng. Perform., 4(5), pp. 519–531. [CrossRef]
Liao, Y. , Wu, M. , and She, J. , 2006, “ Modeling of Reheating-Furnace Dynamics Using Neural Network Based on Improved Sequential-Learning Algorithm,” IEEE Computer Aided Control System Design, IEEE International Conference on Control Applications, IEEE International Symposium on Intelligent Control (CACSD-CCA-ISIC), Munich, Germany, Oct. 4–6, pp. 3175–3181.
Xuegang, S. , Chao, Y. , and Yihui, C. , 2009, “ Dynamic Modeling of Reheat-Furnace Using Neural Network Based on PSO Algorithm,” 2009 International Conference on Mechatronics and Automation (ICMA 2009), Changchun, China, Aug. 9–12, pp. 3097–3101.
Kim, Y. , Moon, K. , Kang, B. , Han, C. , and Chang, K. , 1998, “ Application of Neural Network to the Supervisory Control of a Reheating Furnace in the Steel Industry,” Control Eng. Pract., 6(8), pp. 1009–1014. [CrossRef]
Laurinen, P. , and Röning, J. , 2005, “ An Adaptive Neural Network Model for Predicting the Post Roughing Mill Temperature of Steel Slabs in the Reheating Furnace,” J. Mater. Process. Technol., 168(3), pp. 423–430. [CrossRef]
Pan, J. , Li, Y. , and Li, D. , 2002, “ The Application of Computer Simulation in the Heat-Treatment Process of a Large-Scale Bearing Roller,” J. Mater. Process. Technol., 122(2), pp. 241–248. [CrossRef]
Tang, Y. , Laine, J. , Fabritus, T. , and Harkki, J. , 2010, “ Different Methods Obtained by PHOENICS Simulation to Improve the Performance of Pusher-Type Steel Slab Reheating Furnace,” Oulu University, Oulu, Finland.
Triebl, D. , Spijker, C. , Raupenstrauch, H. , Jarosik, A. , and Angeli, G. , 2014, “ CFD-Simulation Eines Direkt Befeuerten Ofens zur Vorbehandlung Feuerverzinkter Stahlbänder,” BHM Berg Hüttenmännische Monatsh., 159(7), pp. 310–311. [CrossRef]
Balbis, L. , Balderud, J. , and Grimble, M. , 2008, “ Nonlinear Predictive Control of Steel Slab Reheating Furnace,” American Control Conference (ACC), Seattle, WA, June 11–13, pp. 1679–1684.
Yang, Y. , and Lu, Y. , 1986, “ Development of a Computer Control Model for Slab Reheating Furnaces,” Comput. Ind., 7(2), pp. 145–154. [CrossRef]
Steinboeck, A. , Wild, D. , Kiefer, T. , and Kugi, A. , 2010, “ A Mathematical Model of a Slab Reheating Furnace With Radiative Heat Transfer and Non-Participating Gaseous Media,” Int. J. Heat Mass Transfer, 53(25), pp. 5933–5946. [CrossRef]
Yoshitani, N. , Ueyama, T. , and Usui, M. , 1994, “ Optimal Slab Heating Control With Temperature Trajectory Optimization,” 20th International Conference on Industrial Electronics, Control and Instrumentation (IECON’94), Bologna, Italy, Sept. 5–9, Vol. 3, pp. 1567–1572.
Mochida, A. , Kudo, K. , Mizutani, Y. , Hattori, M. , and Nakamura, Y. , 1997, “ Transient Heat Transfer Analysis in Vacuum Furnaces Heated by Radiant Tube Burners,” Energy Convers. Manage., 38(10), pp. 1169–1176. [CrossRef]
Kang, D. , Kim, Y. , Kim, Y. , Kim, W. , and Kim, K. , 2007, Experimental and Numerical Studies on the Thermal Analysis of the Plate in Indirectly-Fired Continuous Heat Treatment Furnace, Vol. 1988, VDI BERICHTE, Dusseldorf, Germany, p. 533.
Pike, H., Jr. , and Citron, S. , 1970, “ Optimization Studies of a Slab Reheating Furnace,” Automatica, 6(1), pp. 41–50. [CrossRef]
Yang, Y. , and Lu, Y. , 1988, “ Dynamic Model Based Optimization Control for Reheating Furnaces,” Comput. Ind., 10(1), pp. 11–20. [CrossRef]
Steinboeck, A. , Graichen, K. , and Kugi, A. , 2011, “ Dynamic Optimization of a Slab Reheating Furnace With Consistent Approximation of Control Variables,” IEEE Trans. Control Syst. Technol., 19(6), pp. 1444–1456. [CrossRef]
Chen, W. , Chung, Y. , and Liu, J. , 2005, “ Analysis on Energy Consumption and Performance of Reheating Furnaces in a Hot Strip Mill,” Int. Commun. Heat Mass Transfer, 32(5), pp. 695–706. [CrossRef]
Han, S. , Chang, D. , and Huh, C. , 2011, “ Efficiency Analysis of Radiative Slab Heating in a Walking-Beam-Type Reheating Furnace,” Energy, 36(2), pp. 1265–1272. [CrossRef]
Panjkovic, V. , and Gloss, R. , 2012, “ Fast Dynamic Heat and Mass Balance Model of Walking Beam Reheat Furnace With Two-Dimensional Slab Temperature Profile,” Ironmaking Steelmaking, 39(3), pp. 190–209. [CrossRef]
Incropera, F. , 2011, Fundamentals of Heat and Mass Transfer, Wiley, Hoboken, NJ.
Hottel, H. C. , and Sarofim, A. F. , 1967, Radiative Transfer, McGraw-Hill, New York, pp. 31–37.
Niederer, M. , Strommer, S. , Steinboeck, A. , and Kugi, A. , 2014, “ A Simple Control-Oriented Model of an Indirect-Fired Strip Annealing Furnace,” Int. J. Heat Mass Transfer, 78, pp. 557–570. [CrossRef]
Tiwari, M. , Mukhopadhyay, A. , and Sanyal, D. , 2005, “ Process Modeling for Control of a Batch Heat Treatment Furnace With Low NOx Radiant Tube Burner,” Energy Convers. Manage., 46(13), pp. 2093–2113. [CrossRef]
Chapra, S. C. , and Canale, R. P. , 2012, Numerical Methods for Engineers, Vol. 2, McGraw-Hill, New York.
Zhang, B. , Chen, Z. , Xu, L. , Wang, J. , Zhang, J. , and Shao, H. , 2002, “ The Modeling and Control of a Reheating Furnace,” 2002 American Control Conference, Anchorage, AK, May 8–10, Vol. 5, pp. 3823–3828.
Seborg, D. , Edgar, T. , and Mellichamp, D. , 2004, Process Dynamics and Control, Wiley, Hoboken, NJ.
Badgwell, T. , Breedijk, T. , Bushman, S. , Butler, S. , Chatterjee, S. , Edgar, T. , Toprac, A. , and Trachtenberg, I. , 1995, “ Modeling and Control of Microelectronics Materials Processing,” Comput. Chem. Eng., 19(1), pp. 1–41. [CrossRef]
Myers, R. , Montgomery, D. , and Anderson-Cook, C. , 2009, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Vol. 705, Wiley, Hoboken, NJ.
Cozad, A. , Sahinidis, N. V. , and Miller, D. C. , 2014, “ Learning Surrogate Models for Simulation-Based Optimization,” AIChE J., 60(6), pp. 2211–2227. [CrossRef]
Tibshirani, R. , 1996, “ Regression Shrinkage and Selection Via the Lasso,” J. R. Stat. Soc. Ser. B, 58(1), pp. 267–288.
Wächter, A. , and Biegler, L. , 2006, “ On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming,” Math. Program., 106(1), pp. 25–57. [CrossRef]
Abbaschian, R. , and Reed-Hill, R. , 2008, Physical Metallurgy Principles, Cengage Learning, Stamford, CT.

Figures

Grahic Jump Location
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].

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
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

Grahic Jump Location
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.

Grahic Jump Location
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.

Grahic Jump Location
Fig. 8

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

Grahic Jump Location
Fig. 11

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

Grahic Jump Location
Fig. 12

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

Grahic Jump Location
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.

Grahic Jump Location
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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In