In this paper the development of a heat exchanger as a
pilot plant for educational purpose is discussed and the use of neural
network for controlling the process is being presented. The aim of the
study is to highlight the need of a specific Pseudo Random Binary
Sequence (PRBS) to excite a process under control. As the neural
network is a data driven technique, the method for data generation
plays an important role. In light of this a careful experimentation
procedure for data generation was crucial task. Heat exchange is a
complex process, which has a capacity and a time lag as process
elements. The proposed system is a typical pipe-in- pipe type heat
exchanger. The complexity of the system demands careful selection,
proper installation and commissioning. The temperature, flow, and
pressure sensors play a vital role in the control performance. The
final control element used is a pneumatically operated control valve.
While carrying out the experimentation on heat exchanger a welldrafted
procedure is followed giving utmost attention towards safety
of the system. The results obtained are encouraging and revealing
the fact that if the process details are known completely as far as
process parameters are concerned and utilities are well stabilized then
feedback systems are suitable, whereas neural network control
paradigm is useful for the processes with nonlinearity and less
knowledge about process. The implementation of NN control
reinforces the concepts of process control and NN control paradigm.
The result also underlined the importance of excitation signal
typically for that process. Data acquisition, processing, and
presentation in a typical format are the most important parameters
while validating the results.
[1] R.W.Miller and R.Roy, "Nonlinear Process Identification Using
Decision Theory," IEEE transactions on Automatic Control October
1964, pp538-540.
[2] Cesare Alippi and Vincenzo Piuri, " Experimental Neural Networks for
Prediction and Identification," IEEE transactions on Instrumentation
and Measurement, Vol. 45, No.2, April 1996, pp 670-676.
[3] E. H. Magali R.G. Meireles Paulo, E.M. Almeida and Marcelo Godoy, "
A Comprehensive Review for Industrial Applicability of Artificial
Neural Networks," IEEE Transactions on Industrial Electronics, Vol.
50. No. 3, June 2003, pp585-601.
[4] J. Wang, "O Dubois, J Nicolas, A Billat, " Adaptive Neural Network
control of the Temperature in Oven," LAM. UFR Sciences Exactes et
Naturelles, BP 347 Reims FRANCE, pp 8/1-8/3.
[5] F.G.Shinskey, "Process Control Systems, Application, Design, and
Tuning," Third Edition, McGraw-HILL 1988, pp 3-72.
[6] M.A. Abdelghani-Idrissi, F. Bagui, "Countercurrent Double-Pipe Heat
Exchanger subjected to Flow-Rate Step Change, Part-I: New steady state
Formulation," Journal of Heat Transfer Engineering, vol.23, No.5,
December 2002, pp 4-11.
[1] R.W.Miller and R.Roy, "Nonlinear Process Identification Using
Decision Theory," IEEE transactions on Automatic Control October
1964, pp538-540.
[2] Cesare Alippi and Vincenzo Piuri, " Experimental Neural Networks for
Prediction and Identification," IEEE transactions on Instrumentation
and Measurement, Vol. 45, No.2, April 1996, pp 670-676.
[3] E. H. Magali R.G. Meireles Paulo, E.M. Almeida and Marcelo Godoy, "
A Comprehensive Review for Industrial Applicability of Artificial
Neural Networks," IEEE Transactions on Industrial Electronics, Vol.
50. No. 3, June 2003, pp585-601.
[4] J. Wang, "O Dubois, J Nicolas, A Billat, " Adaptive Neural Network
control of the Temperature in Oven," LAM. UFR Sciences Exactes et
Naturelles, BP 347 Reims FRANCE, pp 8/1-8/3.
[5] F.G.Shinskey, "Process Control Systems, Application, Design, and
Tuning," Third Edition, McGraw-HILL 1988, pp 3-72.
[6] M.A. Abdelghani-Idrissi, F. Bagui, "Countercurrent Double-Pipe Heat
Exchanger subjected to Flow-Rate Step Change, Part-I: New steady state
Formulation," Journal of Heat Transfer Engineering, vol.23, No.5,
December 2002, pp 4-11.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52822", author = "Sudhir Agashe and Ashok Ghatol and Sujata Agashe", title = "Automation of Heat Exchanger using Neural Network", abstract = "In this paper the development of a heat exchanger as a
pilot plant for educational purpose is discussed and the use of neural
network for controlling the process is being presented. The aim of the
study is to highlight the need of a specific Pseudo Random Binary
Sequence (PRBS) to excite a process under control. As the neural
network is a data driven technique, the method for data generation
plays an important role. In light of this a careful experimentation
procedure for data generation was crucial task. Heat exchange is a
complex process, which has a capacity and a time lag as process
elements. The proposed system is a typical pipe-in- pipe type heat
exchanger. The complexity of the system demands careful selection,
proper installation and commissioning. The temperature, flow, and
pressure sensors play a vital role in the control performance. The
final control element used is a pneumatically operated control valve.
While carrying out the experimentation on heat exchanger a welldrafted
procedure is followed giving utmost attention towards safety
of the system. The results obtained are encouraging and revealing
the fact that if the process details are known completely as far as
process parameters are concerned and utilities are well stabilized then
feedback systems are suitable, whereas neural network control
paradigm is useful for the processes with nonlinearity and less
knowledge about process. The implementation of NN control
reinforces the concepts of process control and NN control paradigm.
The result also underlined the importance of excitation signal
typically for that process. Data acquisition, processing, and
presentation in a typical format are the most important parameters
while validating the results.", keywords = "Process identification, neural network, heat
exchanger.", volume = "2", number = "3", pages = "267-5", }