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Abstract−Through proper monitoring, problems can be identified and isolated well before the economics of the process
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are threatened. In contrast to most conventional methods, fouling can be detected when the heat exchanger operates
in transient states. Statistical analysis is used to develop a fouling growth model of a heat exchanger subjected to fouling.
The statistical analysis was considered for four different types of distributions out of which the lognormal distribution
was found to be most suitable. Experiments were conducted with a single pass shell and tube heat exchanger with water
both as the hot and cold fluids. The results show that the proposed tool is very effective in detecting critical fouling
in a heat exchanger, which can be utilized for predicting the optimal maintenance schedule. Hence, the results of this
work can find application in predicting the reduction in heat transfer efficiency due to fouling in heat exchangers that
are in operation and assist the exchanger operators to plan cleaning schedules.5636
To whom correspondence should be addressed.
INTRODUCTION
The accumulation of scale, organic matter, corrosion products,
coke, particulates or other deposits on a heat transfer surface is a
phenomenon called fouling that costs the process industries heavily.
These deposits degrade heat exchanger performance over time com-
pared with “clean” conditions at start up. The fouling layer is a con-
ductive resistance to heat transfer that must be accounted for in the
design heat transfer coefficient. Fouling thickness and thermal con-
ductivity both contribute to the resistance. Simultaneously reduced
cross sectional flow area also increases pressure drop in the fouled
region.
Several researchers have worked on theoretical modeling of the
fouling behavior in different kind heat exchangers under different
operating conditions. Polley et al. presented a logical framework for
analyzing chronic fouling problems in refinery pre-heat trains [1,2]
and introduced the concept of threshold fouling. Muller-Steinhagen
[3,4] give a complete review of the state of the art in the area of foul-
ing mitigation by various techniques. Watkinson and Wilson [5] do
a comprehensive review of the chemical reaction fouling due to
organic fluids. A new model has been proposed by Nasr and Givi [6]
that includes terms for fouling formation and removal. This model
has also reported the fouling behavior and drawn threshold curves
to identify fouling and no fouling formation zones. Negrao et al. [7]
have shown prediction of heat exchanger effectiveness from classi-
cal literature relations as a function of NTU and heat capacity ratio.
To enforce compliance with critical pressure and operational crite-
ria, heat exchangers must be cleaned often according to a regular
maintenance schedule [8]. The scheduling of cleaning interventions
can be based on the prior knowledge of the time behavior of the
thermal resistance deposits in the inpidual exchanger [9,10]. As
fouling is usually not visible from outside the industrial processing
equipment, a direct method of measurement of the fouling developed
on the heat transfer surfaces of a heat exchange device is almost im-
possible. This can only be ascertained and quantified from its effects
on various performance parameters of a heat exchanger [11,12].
The classical detection methods are based on study of the heat
transfer coefficient or the effectiveness, temperature measurements,
ultrasonic or electrical measurements and weighing of heat exchanger
pipes [13,14]. But to get accurate results, these methods require the
system to present successive steady states, which is far too restric-
tive or costly.
The drawback of these techniques is mainly due to limited num-