Energy-Oriented Modeling and Optimization of a Heat Treating Furnace

被引:13
|
作者
Heng, Vincent R. [1 ]
Ganesh, Hari S. [1 ]
Dulaney, Austin R. [1 ]
Kurzawski, Andrew [2 ]
Baldea, Michael [3 ]
Ezekoye, Ofodike A. [2 ]
Edgar, Thomas F. [4 ]
机构
[1] Univ Texas Austin, McKetta Dept Chem Engn, Austin, TX 78712 USA
[2] Univ Texas Austin, Dept Mech Engn, Austin, TX 78712 USA
[3] Univ Texas Austin, Inst Computat Engn & Sci, McKetta Dept Chem Engn, Austin, TX 78712 USA
[4] Univ Texas Austin, Energy Inst, McKetta Dept Chem Engn, Austin, TX 78712 USA
基金
美国国家科学基金会;
关键词
NEURAL-NETWORK; TEMPERATURE; SIMULATION;
D O I
10.1115/1.4035460
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:13
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