Ever increasing energy cost has led to the pursuit of heat integration approaches
in process industries in order to recover heat from the product streams as much
as possible into the process streams and hence improve the energy efficiency
of the plant. Generally multi-pass shell and tube heat exchangers are used as
the heat recovery units. Due to the complex nature of crude oils the heat exchangers
in the crude preheat train in refineries are prone to fouling. The total financial
penalties associated with fouling may include the loss of thermal efficiency
of heat transfer equipment, high fluid pressure drops, costs for anti foulant
additives, physical and/or chemical cleaning and loss of production due to unscheduled
plant shutdowns. Muller-Steinhagen (1995) estimated
the total cost of all heat exchanger fouling in the UK is of the order of USD
2.5 billion and the equivalent cost in the USA is USD 14 billion. The typical
annual cost of cleaning a fouled industrial heat exchanger is estimated at between
USD 40000 and 50000 (Panchal and Huangfu, 2000). Crude
oil fouling is generally believed to be caused by impurities in the crude oil
such as corrosion products, water and salt, precipitation of insoluble asphaltene,
thermal decomposition or auto-oxidation of reactive constituents in the oil.
There is a renewed interest in understanding fouling mechanisms, modeling the fouling processes and identifying appropriate fouling mitigation strategies in refineries, especially in crude preheat trains. There are only a few research groups pursuing intensive research on crude oil fouling besides our research centre are University British Columbia, Heat Transfer Research Inc., Argonne National Labs., a consortium of universities comprising of Imperial College, Bath University and Cambridge University in association with Engineering Sciences Data Unit (ESDU), UK. Independent studies are also being carried out by the refineries, the results of which are not available in public domains.
In this study, an attempt is made to review the development of models for crude oil fouling in the refinery preheat trains. It also identifies the limitations of the models and provides recommendation for future research in this area.
FOULING MECHANISMS AND FACTORS INFLUENCING FOULING
Better understanding of fouling mechanisms and the various factors influencing
fouling is essential for the development of appropriate fouling models. In general,
fouling mechanisms are classified into five categories as: chemical reaction
fouling, particulate fouling, corrosion fouling, crystallization fouling and
biological fouling. In the case of crude oil fouling, crystallization of inorganics,
corrosion, chemical reaction of organics, deposition of particulates or a combination
of these play an important role (Bott, 1995). Fouling
can be divided into these mechanisms in theory, but in practice, it is often
the interaction between two or more types. The above fouling mechanisms generally
occur in a series of steps as outlined below (Epstein, 1983):
Initiation or delay period: When a new or clean heat exchanger is commissioned,
initially the heat transfer coefficient remains unchanged for a certain period
of time before it starts to decline due to fouling. This initiation or delay
period may last anytime from few seconds to several days. The duration of this
phase depends upon factors such as type of fouling, surface temperature and
surface conditioning. For instance, no initiation period occurs for particulate
fouling while for chemical reaction fouling there is an initiation period for
fouling to begin.
Transport: The precursors that are responsible for the deposit formation on the surface are originally either suspended or dissolved in the bulk fluid and they are transported from bulk fluid to heat transfer surface through diffusion. The driving force for the transport is the difference between the concentrations of precursor in the bulk fluid and at the surface. The rate of transportation of these species can be described by:
where, Cb and Cs are the concentrations of precursor in the bulk and at the surface, respectively and kt is the mass transfer coefficient.
Deposition: When the fouling precursors reach the heat transfer surface, they either stick to the surface, leave the surface or react to form substances that finally stick to the surface. Deposition can either be controlled by chemical reaction, diffusion or adhesion.
Removal: As the deposit layer starts building on the heat transfer surface, some part of it may be removed by the action of fluid shear and mass transfer. The amount of the deposit removed depends upon the strength of the deposit layer. The removal can also be by mass transfer where the fouling precursors are removed from the surface or the thermal boundary layer to the bulk fluid.
Aging: Every deposit layer on the surface is subjected to aging with
time. Aging may increase the strength of the deposit by polymerization, re-crystallization,
etc. Aging is the least investigated and understood step and is usually ignored
in modeling attempts.
Factors influencing fouling: The most important factors influencing fouling are: (1) surface temperature, (2) bulk velocity, (3) bulk temperature, (4) crude type and (5) crude blending.
Surface temperature: The rate of fouling increases exponentially with
increasing surface temperature for almost all fouling mechanisms (Scarborough
et al., 1979; Eaton and Lux, 1984; Crittenden
et al., 1992; Asomaning, 1997; Saleh
et al., 2005a; Srinivasan and Watkinson 2005).
The effect of surface temperature on the fouling rate is generally expressed by an Arrhenius-type equation.
Activation energy, E and the proportionality factor, A, in Eq.
2 are determined from experimental data at varying surface temperatures
and constant velocity, fluid composition and geometry. There are also attempts
to use the film temperature, Tf, instead of surface temperature,
Ts (Ebert and Panchal, 1995; Saleh
et al., 2005a; Srinivasan. and Watkinson, 2005).
Ebert and Panchal (1995) used the film temperature in
their analysis of fouling rates and activation energy calculation for crude
oil-slip-stream coking. Saleh et al. (2005a)
calculated the activation energy values for a light Australian crude oil at
film temperature and also at surface temperature. Activation energy determined
based on film temperature will be higher than that based on the surface temperature.
The use of film temperature or surface temperature depends upon the location
of occurrence of the chemical reaction. If the chemical reaction occurs in the
thermal boundary layer, film temperature is used. On the other hand, surface
temperature is used if the chemical reaction occurs on the heat transfer surface
(Asomaning et al., 2000).
Flow velocity: In crude preheat trains, fouling rate decreases or increases
with an increase in velocity. For a given bulk temperature and heat flux, the
fouling rate decreases with an increase in flow velocity when the fouling is
reaction controlled (Asomaning, 1997). In this case, an
increase in velocity increases the heat transfer coefficient and thus reduces
the wall and film temperatures. On the other hand, if the fouling rate is controlled
by mass transfer of the fouling species from the bulk fluid to the surface,
then the mass transfer coefficient from the bulk fluid to the surface will increase
with increase in velocity leading to an increase in fouling rate.
For a case where the fouling rate decreases with increase in velocity, the
effect of velocity can be summarized in two ways as:
||If the deposit layer is weak, the shear stress at the wall
which is directly proportional to the fluid velocity may give rise to erosion
of the fouling layer which offsets the deposition of the foulant
||If the foulant material is formed in the thermal boundary layer adjacent
to the hot surface where the deposition rate is the highest, then the formed
deposit diffuse back into the bulk fluid. The rate of mass transfer of foulant
increases with increase in velocity and thus reduces the fouling rate (ESDU,
Watkinson and Epstein (1969) developed a model for
gas oil fouling and found that the initial fouling rate is inversely proportional
to mass flow rate and a similar dependence of initial fouling rate on flow velocity
was observed by other researchers (Scarborough et al.,
1979; Paterson and Fryer, 1988; Asomaning,
1997; Saleh et al., 2005a; Crittenden
et al., 2009).
Bulk temperature: The effect of bulk temperature on deposit formation
was studied by a few researchers and contradicting conclusions were reported.
Some researchers have observed that the fouling rate increased with a decrease
in bulk temperature (Lambourn and Durrieu, 1983; Eaton
and Lux, 1984; Fuhr et al., 1991; Storm
et al., 1996; Asomaning, 1997). The decrease
in bulk temperature at a constant velocity and surface temperature results in
an increase in the thermal driving force and hence an increase in the fouling
rate. An increase in fouling rate with an increase in bulk temperature has also
been reported in the literature (Saleh et al., 2005a;
Srinivasan and Watkinson, 2005).
Asphaltenes are large, complex ring structure molecules that are found in the
crude oil and insoluble asphaltenes are considered to be the major cause of
fouling in crude oil systems (Dickakian and Seay, 1988).
The solubility of asphaltene plays an important role in crude oil fouling. Generally,
the solubility of asphaltene in crude oil increases with increase in temperature
(Fuhr et al., 1991). A complex relationship between
asphaltene solubility and temperature has been reported by Lambourn
and Durrieu (1983) in which the solubility of asphaltene increased to a
maximum at 140°C and then decreased at higher temperatures. At high bulk
temperatures, the asphaltene is in the form of solution in crude oil and the
fouling rate is low whereas at low bulk temperatures, asphaltene precipitates
out from crude oil and the fouling rate is high.
The bulk temperature effects are also strongly interrelated with the Reynolds
number (Asomaning, 1997). Increase in bulk temperature
decreases the viscosity and hence increases the Reynolds number. At high Reynolds
numbers, the thickness of the thermal boundary layer becomes smaller and the
rate of formation of fouling precursors decreases due to the reduction in the
volume for the chemical reaction.
Crude type: The crude oil is a mixture of a large number of hydrocarbons. The most commonly found molecules are paraffins, naphthenes, aromatic hydrocarbons and asphaltenes. The crude oils can be classified as light, medium or heavy according to its measured API gravity. Heavy oils contain much higher proportions of asphaltenes and sulfur than medium or light oils and they tend to foul at a faster rate.
Crude blending: Another important factor which influences the fouling
is crude blending. Blending of crudes can cause unstable mixes which precipitate
species such as asphaltene and result in rapid fouling (Wilson
and Polley, 2001). The crude oil incompatibility and the precipitation of
asphaltene on blending of crude oils can cause significant fouling and coking
in crude preheat train. For this reason, the crude oil compatibility model and
tests were developed to predict proportions and order of blending of oils that
would avoid incompatibility (Wiehe et al., 2001).
Saleh et al. (2005b) studied the effect of mixing
and blending crude oils at certain operating conditions with the intention of
using the results to guide a fouling mitigation strategy.
EXPERIMENTAL STUDIES ON FOULING
Crude oil fouling in preheat exchangers is a complex phenomenon that depends
on a number of variables such as crude type and composition, design parameters
of the heat exchangers and the operating conditions. Research using actual plant
data is slow, subject to a variety of logistical and operational requirements
which do not lend themselves well to fundamental scientific studies (Crittenden
et al., 1992) and can create difficulties in the interpretation of
the thermal data (Takemoto et al., 1999). Generally,
the fouling characteristics of crude oils are established through experiments
in laboratory experimental units which are designed and operated under controlled
operating conditions to achieve accelerated fouling rates. Several types of
laboratory units have been reported to be used in the study of crude oil fouling
characteristics. Stirred batch cells (Eaton and Lux, 1984;
Young et al., 2009), recycle flow loop with a
tubular cross section (Crittenden et al., 2009)
and recycle flow loop with annular cross section (Wilson
and Watkinson, 1995; Bennett et al., 2009)
have been used to characterize crude oil fouling. Once-through continuous flow
fouling units have been reported to be used in the refineries and are known
as field fouling units (Kuru et al., 1997). The
disadvantage of field fouling units is that the crude oil properties do not
remain constant as the crude to the refinery changes very frequently. Recycle
flow loop with annular flow geometry has been predominantly used due to their
advantages such as visual observation of the fouling deposits, easier to collect
foulant samples and clean the surface for reuse, etc.
Whilst laboratory studies can eliminate the principal practical disadvantages
of studying fouling on refinery exchangers they, in turn, introduce their own
disadvantages, the principal one being that the crude oil is not exposed to
the time-temperature-flow history of the crude in the oil refinery (Young
et al., 2009). One of the major drawbacks of the experimental results
reported in literature is that the operating conditions are chosen arbitrarily.
Guidelines or systematic procedures to choose the operating conditions have
not been discussed by the researchers.
High surface temperatures or heat fluxes and low velocities are generally used
to accelerate the fouling rates. Knudsen et al. (1999)
described the experimental procedure for the determination of threshold fouling
curve for a desalted crude oil. Tests were performed in a circulation system
in which the crude oil is circulated through an annular test section at velocities
ranging from 0.91 to 3.1 m sec-1 and at two bulk temperatures of
149 and 204°C. The experiments have been carried out at surface temperatures
ranging from 177 to 329°C and the experimental data were reported.
CRUDE OIL FOULING MODELS
Mathematical models to represent fouling are necessary to predict the fouling
rates as a function of key design and operational parameters. A large number
of semi-empirical models for crude oil fouling have been reported in literature
(Kern and Seaton, 1959; Crittenden
and Kolczkowski, 1979; Crittenden et al., 1987;
Epstein, 1994; Ebert and Panchal,
1995; Panchal et al., 1997; Polley
et al., 2002; Nasr and Givi, 2006). These
models were developed based on the experimental data from laboratory test rigs.
The models describing fouling usually are based on the well-known concept of
Kern and Seaton (1959) approach where the net fouling
rate is the difference between the rates of deposition and removal.
Fouling rate = Rate of deposition-Rate of removal
The basic differences between various models reported in literature are in
the description of the deposition and removal terms. The rate of deposition
is described by either a transport-reaction model or reaction alone model while
the rate of removal is described either by shear-related or mass-transfer related
expressions. In general, transport-reaction models are more rigorous than the
reaction alone models.
A transport-reaction model was developed by Crittenden
and Kolaczkowski (1979) considering chemical reaction as well as the transport
of fouling precursor to and from the heated surface. They also proposed a modified
model that includes a back-diffusion term (Crittenden et
al., 1987). Epstein (1994) observed that at
time zero, it is fundamentally difficult to justify the finite concentration
of foulant at the surface which would be required for back diffusion to occur.
Epstein developed a model for the initial chemical reaction fouling rates at
the surface in which the surface attachment is proportional to residence time
of the fluid at the surface. The greater the residence time, the greater would
be the opportunity for the chemical reaction to occur. The relationship between
the initial fouling rate and the mass flux is given as:
where, m is the stoichiometric factor, ρf the foulant density,
kf the thermal conductivity of foulant and φ is the deposition
mass flux. The driving force for the mass transfer from the bulk fluid to the
heater surface of foulant precursor was expressed as the difference between
its bulk and surface concentrations, Cb and Cs, respectively
(Epstein, 1994). The deposition mass flux is given by:
where, k and k= are constants, Sc
is Schmidt number, f is the friction factor, ρ is the fluid density, μ
is fluid viscosity and n is the order of the reaction plus attachment process.
The first term in the denominator represents the mass transfer of foulant or
precursor to the heated surface and the second term represents the reaction
and attachment aspects. Epsteins model showed an excellent fit to Crittendens
data for initial fouling rates of polymerization of styrene (Crittenden
et al., 2009). It was also able to explain the effects of temperature
and velocity. This model could not be used for describing the crude oil fouling
due to the reasons such as the order of the reaction + attachment term, n and
Sc are unknown for the crude oil fouling and it is also difficult
to isolate the key precursors of fouling as the crude oil has complex compositions
and this creates difficulty in finding out the concentration of exact precursor
and its role in fouling.
Yeap et al. (2005) reduced the Epsteins
model to a function of groups of dimensional parameters A, B, C and E for turbulent
flow conditions with mean velocity, u, with a mass transfer related removal
They estimated the parameters of the above model using plant data from a UK refinery that processes mainly light to medium North Sea crudes.
Considerable interest has been expressed in the concept of threshold fouling
conditions for crude oils using less rigorous semi-empirical models. Ebert
and Panchal (1995) proposed a semi-empirical model for predicting the linear
rate of fouling as a function of film temperature and fluid velocity Eq.
where α, β, E and γ are constants to be determined from the
experimental data. This model was originally developed using the Exxon crude-oil-slip
stream coking data obtained by Scarborough et al.
(1979) in a joint research project with US Department of Energy. This model
assumes that foulant forming reactions occur in the thermal boundary layer at
a mean film temperature, Tf, foulant is transported by diffusion
and turbulence eddies from the boundary layer to the bulk flow and the net rate
of deposition is the difference between the rate of formation and rate of removal.
This model allowed users to estimate operating conditions where the fouling
rate would be close to zero which is termed as the threshold fouling conditions.
The threshold fouling curve was determined by setting Eq. 6
to zero and calculating film temperatures for a wide range of wall shear stresses.
Ebert and Panchal model ignored the effect of crude oil thermal conductivity
and specific heat and only considered the effect of crude oil density and viscosity
through Reynolds number. Panchal et al. (1997)
modified the Ebert and Panchal model by incorporating the Prandtl number. The
revised model is given as:
The value of β was assumed to be -0.66 and the film temperature Tf, was determined as:
Experimental data from a high pressure autoclave fouling unit under various operating conditions were used in their study.
Polley et al. (2002) observed that (1) for turbulent
flow through circular tubes, the exponent of the Reynolds number of -0.8 is
more appropriate than -0.66; (2) the use of wall temperature in the Arrhenius
term is more appropriate than the film temperature and (3) the removal mechanism
is by mass transfer prior to the formation of a deposit; a simplistic approach
to introduce mass transfer dependence is to use Reynolds number to a power of
0.8, in the same way that the convective mass transfer coefficient varies with
velocity. Based on these observations Polley et al.
(2002) made simple modifications to the Ebert and Panchal model as:
Polley et al. (2002) verified their model using
Knudsens experimental data. Nasr and Givi (2006)
proposed a threshold fouling model which is independent of Prandtl number as:
The model was verified with the experimental data by Saleh
et al. (2005a) for Australian crude oil. The activation energy was
determined through the Arrhenius plot. In this model, the value of β was
determined together with the other model parameters α and γ. A value
of -1.547 was reported for the Australian light crude oil. The authors have
claimed that their model describes the data better than the earlier models.
It may be noted that Nasr and Givi model has become more empirical than the
earlier models since a value of -1.547 for β has no physical significance
as compared to the other models. The disadvantage with this model may be that
it cannot be used for extrapolation at other operating conditions.
Neural Networks (NN) based models have recently become the focus of much attention
largely because of their capability to handle complex and non-linear systems.
A neural network fouling model has been developed successfully for preheat exchangers
of crude distillation unit based on the plant historical data (Radhakrishnan
et al., 2007). NN models were also developed based on experimental data
from a lab-scale test rig (Aminian and Shahhosseini, 2008).
The major advantage with NN modeling is that it is independent of fouling mechanism
whereas the semi-empirical models cannot describe fouling mechanisms other than
the reaction fouling.
ASSESSMENT AND FUTURE DIRECTIONS
Extrapolating the laboratory data to field fouling situations has several shortcomings.
Asomaning et al. (2000) has identified issues
of significance, when assessing the appropriateness of extrapolating laboratory
fouling data to the field include: (1) effect of fluid composition, (2) effect
of fluid recirculation in the laboratory unit on fouling data, (3) the nature
of fouling mechanisms in the field and in the laboratory, (4) the fluid dynamics
of heat exchangers in the field and fouling units in the laboratory, (5) pressure
effects and the predominance of sub-cooled boiling conditions in laboratory
units and (6) the fact that laboratory experiments are done under carefully
controlled conditions while field processes are subjected to vagaries of the
process. These factors should be addressed whenever data obtained in the laboratory
are to be extrapolated to the field. If the mechanisms in the field and in the
laboratory are not identical, the data from the two situations will not be comparable.
Recirculation of test fluid, which results in long periods of heating, may alter
its composition and result in differences between the fouling results obtained
in the laboratory and the field. If the crude oil is heated for a long time
with recirculation, the state of aggregation and the solubility behavior of
asphaltene can change and the fouling data obtained will differ from that obtained
with once-through flow conditions. Laboratory fouling tests are usually performed
under severe and accelerated conditions such as high surface temperatures and
low velocities, resulting in asymptotic thermal fouling resistance versus time
plots. On the other hand, conditions in the field may give rise to linear curves
with the same fluid. Given the accelerated nature of laboratory tests, fouling
rates, induction periods and fouling resistances may not be comparable to those
in the field. Accelerated fouling conditions, which are usually found in the
laboratory, may give rise to rapid aging of deposits and this aging could result
in weakening of the deposit strength due to rapid thermal degradation. This
will facilitate removal and thereby result in asymptotic fouling curves. Aging
could also result in strengthening of the deposit due to further polymerization.
This could favor linear fouling curves. Whether both of these processes occur
in the laboratory and field experiments to the same degree is not known. The
predictive ability of laboratory data may improve if experiments are planned
to minimize the effects of factors identified and listed.
In most of the reported studies on hydrocarbon fouling, reference to boiling
was rarely made although the operating conditions used suggest that it was indeed
often present (Oufer, 1990). The fouling characteristics
determined at these operating conditions will be influenced by boiling and are
not applicable to crude preheat exchangers. Generally, the crude preheat exchangers
operate in the forced convective heat transfer regime and it is only appropriate
that the heat transfer in the laboratory experimental units is also in the same
heat transfer regime to study the fouling in the preheat exchangers. The effect
of bulk temperature is not studied extensively as compared to the surface temperature.
Usually the activation energy is determined at different surface temperatures
by keeping the bulk temperature constant. In crude preheat trains, the crude
oil is subjected to different bulk temperatures as it passes through a series
of heat exchangers. The effect of salvation of precursors varies with bulk temperature
and it differs from crude to crude. Activation energy determined at a constant
bulk temperature may not be applicable for the same crude at other bulk temperatures
and therefore, the activation energy shall be determined as a function of bulk
temperature. Determining the solubility of precursors at different temperatures
is therefore a step necessary in determining true activation energy. More research
is required in investigating the effect of bulk temperature on fouling.
Fouling is a complex phenomenon which follows different mechanisms. Several factors such as surface and bulk temperatures, fluid velocity, crude type/composition and crude blending affect the fouling rate. Threshold fouling models are gaining considerable interests in recent years for mitigating fouling by estimating threshold operating conditions. More research is required to understand the fouling mechanisms better and also to study the effect of bulk temperature on fouling.
Authors of the study gratefully acknowledge the support and facilities provided by Universiti Teknologi Petronas.