This paper investigates the use of adaptive artificial neural networks (ANNs) to control the exit air temperature of a compact heat exchanger. The controllers, based on an internal model control scheme, can be adapted on-line on the basis of different performance criteria. By numerical simulation a methodology by which the weights and biases of the neural network are modified according to these criteria was developed. An ANN controller for an air-water compact heat exchanger in an experimental facility is then implemented. The parameters of the neural net are modified using three criteria: minimization of target error, stabilization of the closed-loop performance of the controller, and minimization of a performance index that we have taken to be the energy consumption. It is shown that the neural network is able to control the air exit temperature in the heat exchanger. The neurocontroller is able to adapt to major structural changes in the system as well as to simultaneously minimize the amount of energy used.

Issue Section:
Porous Media, Particles, and Droplets
Keywords:
heat exchangers, temperature control, model reference adaptive control systems, neurocontrollers, Artificial Intelligence, Control, Heat Transfer, Heat Exchangers, Systems
Topics:
Artificial neural networks, Control equipment, Energy consumption, Errors, Heat exchangers, Temperature, Water, Adaptive control
1.
Sen, M., and Yang, K. T., 2000, “Applications of Artificial Neural Networks and Genetic Algorithms in Thermal Engineering,” CRC Handbook of Thermal Engineering, section 4.24, F. Kreith, ed., pp. 620–661.
2.
Dı´az
,
G.
,
Sen
,
M.
,
Yang
,
K. T.
, and
McClain
,
R. L.
,
1999
, “
Simulation of Heat Exchanger Performance by Artificial Neural Networks
,”
HVAC&R Research Journal
,
5
, No.
3
, pp.
195
–
208
.
3.
Kays, W. M., and London, A. L., 1984, Compact Heat Exchangers, 3rd ed., McGraw-Hill, New York.
4.
Sunde´n, B., and Faghri, M., (eds.), 1998, Computer Simulations in Compact Heat Exchangers, Computational Mechanics Publications, Boston, MA.
5.
Dı´az
,
G.
,
Sen
,
M.
,
Yang
,
K. T.
, and
McClain
,
R. L.
,
2001
, “
Dynamic Prediction and Control of Heat Exchangers Using Artificial Neural Networks
,”
International Journal of Heat and Mass Transfer
,
44
, pp.
1671
–
1679
.
6.
Marwah
,
M.
,
Li
,
Y.
, and
Mahajan
,
R. L.
,
1996
, “
Integrated Neural Network Modeling for Electronic Manufacturing
,”
J. Electron. Manuf.
,
6
, No.
2
, pp.
79
–
91
.
7.
Blazina, A., and Bolf, N., 1997, “Neural Network-Based Feedforward Control of Two-Stage Heat Exchange Process,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1, pp. 25–29.
8.
Ayoubi. M. 1997, “Dynamic Multi-Layer Perceptron Networks: Application to the Nonlinear Identification and Predictive Control of a Heat Exchanger,”Applications of Neural Adaptive Control Technology, World Scientific Series in Robotics and Intelligent Systems, 17, pp. 205–230.
9.
Nahas
,
E. P.
,
Henson
,
M. A.
, and
Seborg
,
D. E.
,
1992
, “
Nonlinear Internal Model Control Strategy for Neural Network Models
,”
Comput. Chem. Eng.
,
16
, No.
12
, pp.
1039
–
1057
.
10.
Chen
,
C. T.
,
Hwu
,
J.
, and
Chang
,
W. D.
,
1999
, “
Nonlinear Process Control Based on Using an Adaptive Single Neuron
,”
J. Chin. Inst. Chem. Eng.
,
30
, No.
2
, pp.
141
–
149
.
11.
Haykin, S., 1994, Neural Networks, A Comprehensive Foundation, Macmillan College Publ. Co., New York.
12.
Dı´az, G., Sen, M., Yang, K. T., and McClain, R. L., 2001, “Stabilization of Thermal Neurocontrollers,” International Journal of Heat and Mass Transfer, in press.
13.
Hunt
,
K. J.
,
Sbarbaro
,
D.
,
Zbikowski
,
R.
, and
Gawthrop
,
P. J.
,
1992
, “
Neural Networks for Control Systems—A Survey
,”
Automatica
,
28
, No.
6
, pp.
1083
–
1112
.
14.
Landau, I. D., Lozano, R., and M’Saad, M., 1998, Adaptive Control, Springer-Verlag, London.
15.
Zhao, X., 1995, “Performance of a Single-Row Heat Exchanger at Low In-Tube Flow Rates,” M.S. thesis, Department of Aerospace and Mechanical Engineering, University of Notre Dame, IN.
Copyright © 2001
 by ASME
You do not currently have access to this content.