ON-LINE FOULING MONITORING TECHNIQUE FOR SHELL-TUBE HEAT EXCHANGERS

Author(s):  
Shanrang Yang ◽  
N. Li ◽  
H. Zhao ◽  
C. J. Zhang
Author(s):  
Samuel W. Glass ◽  
Morris Good ◽  
Ericka Forsi ◽  
Robert Montgomery

Abstract On-line structural health corrosion monitoring in advanced molten salt reactor heat exchangers is desirable for detecting tube degradation prior to leaks that either would cause mixing of heat exchanger fluids or release of radiologically contaminated fluids beyond the design containment boundary. This program seeks to demonstrate feasibility for a torsional wave mode sensor to attach to the outside of a long (30-m) heat exchanger tube in the stagnant flow area where the tube joins the heat exchanger plenum and where it is possible to protect a sensor cable from high-force flow connecting through a heat exchanger shell to a monitoring instrument. The envisioned sensor and cable management approach will be impractical to implement on existing heat exchangers; rather sensors must be installed in conjunction with the heat exchanger fabrication. Initially, flaw surrogates of interest (50% notch and 50% flat bottom hole) have been detected in a 3-m tube using low-temperature PZT piezoelectric crystals. The transducer consisted of multiple shear elements placed circumferentially around a tube. The program will continue to investigate higher temperature piezoelectric ceramics, long-term performance of high temperature adhesives, and flaw sensitivity on long (30-m +) tubes.


2000 ◽  
Author(s):  
Gerardo Díaz ◽  
Mihir Sen ◽  
Rodney L. McClain

Abstract It has been shown that artificial neural networks (ANNs) can be used to simulate and control thermal systems such as heat exchangers. It is known that the characteristics of thermal components such as heat exchangers vary with respect to time mainly due to fouling effects. There is a need of a model that can adapt to the new characteristics of the thermal system. In this work adaptive artificial neural networks are used to control the outlet air temperature of a heat exchanger test facility. The neurocontrollers are adapted on-line on the basis of different criteria. The parameters of the ANNs are modified considering target error and stability conditions of the closed loop system analyzed as a nonlinear iterative map. We also implement a minimization of a performance index that quantifies the energy consumption. It is shown numerically and experimentally that the neural network is able to control the thermal facility, and is also able to adapt to different disturbances applied to the system, while minimizing the amount of energy used.


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