Management of Fouling in Crude Oil Preheat Trains
Figure 1: Average Fouling Rate in Preheat Train Shell-and-tube Units Against Estimated Wall Shear Stress – Related to Tube-side Velocity
0.001 Figure 3: Example of Fouling Management-based Fouling Mitigation A a 0.0001 TOP PA b
0.00001
0.000001 B 0.0000001 0 4 8 12 16 The solid line shows the curve-fit reported by Joshi et al., 2009;4 20 24 28 Average wall shear stress (Pa) solid symbols are the fouling rates obtained
from five refineries; open triangles and open squares are pilot plant fouling data; black circles in dashed ellipse indicate heat exchangers where shell-side fouling was thought to be significant. Reproduced with permission from Ishiyama et al., 2010.5
Figure 2: Comparison of Simulated Coil Inlet Temperature and Pressure Drop Across a Preheat Train
300 280 260 240 B 220 200 PT 180 0 6 12 Time (months)
Dashed lines show coil inlet temperature, solid lines show pressure drop. B = base case, FM = modified using fouling management methods; PT = modified using pinch technology approach. Reproduced with permission from Yeap et al., 2005.2
which will vary from exchanger to exchanger. Using those values to simulate the performance of the same unit reduces the uncertainty introduced by using data taken from another PHT. Data reconciliation for fouling monitoring is straightforward in principle, as refineries regularly collect and archive operating data electronically. Software tools such as Persimmon™ (KBC Energy Services) and smartPM™ (IHS) are available to analyse this data and audit the fouling within the PHT. Engineers can be advised of chronic fouling events, the effectiveness of chemical dosing and when individual exchangers should be cleaned (with associated costs and penalties for not cleaning). An example of fouling auditing is the data set marked by the ellipse in Figure 1. These fouling rates were obtained for a set of units with a crude vacuum residue on the shell-side and the trend clearly lies outside the expected behaviour. This prompted an investigation of shell-side fouling and desalter performance, which helped to resolve the problem.
18 18 24 VR Heavy kerosene PA
Figure 3A: original network; Figure 3B: coil inlet temperature (CIT) evolution for different scenarios; Figure 3C: revised network to counter high fouling rates in units a and b. Simulations performed with smartPM™. Reproduced with permission from Ishiyama et al., 2011.6
Data reconciliation requires reliable sensors and adequate coverage of the network. If insufficient sensors (thermopiles, flow meters and pressure tappings) are installed on a PHT, the uncertainty can prevent calculations from reaching reliable solutions. Sensors also have to be maintained and calibrated, which represents a small cost to be compared with the energy losses involved.
Treatment
Performing fouling monitoring over time allows a ‘fouling map’ of the PHT to be constructed and critical exchangers identified. Their performance can be improved by regular cleaning, redesign of the unit or by use of antifouling chemicals. The fouling data can be used to construct a refinery-specific fouling model, which can be used to predict the impact of changes such as cleaning, or unit redesign on the performance of the PHT. This approach is demonstrated for the hot end
HYDROCARBON WORLD – VOLUME 6 ISSUE 1 FM
FM B
PT
10 15 20 25 30 35
5 200 190 0 C 200 400 TOP PA a b 600 Time (days) 800 1,000
Retrofit a and b Optimised cleaning
Occasional cleaning Base case
220 210 230 240
VR
Heavy kerosene PA
Coil inlet temperature (ºC)
Average rate of fouling (m2
K W-1
day-1
)
Pressure drop Coil inlet temperature (ºC)
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