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The influence of fouling dynamics on
distillation preheat train equipment
cleaning planning Bruna Carla Gonçalves de Assis
Fábio dos Santos Liporace
Lívia Pereira Lemos Costa
Sérgio Gregório de Oliveira
R&D on Integrity, Sustainability and Optimization
Logistics & Optimization
CENPES / PDISO / LO
Goals:
1. Estimate the fouling factor for each heat exchanger
2. Calculate the thermal efficiency
a. Preheat train
b. Preheat train branch
c. Heat exchanger
3. Estimate the heat exchanger cleaning benefits R$/year
cl
op
Q
Q
clopf
UUR
11
How it works
1. PI connection to get process data on real time;
2. SS detection;
3. Preheat train simulations on Petrox/HTRI:
a. Heat and Mass simulation (Uop)
b. HX rigorous simulation (Ucl)
c. Cleaning simulation
4. Rf dynamic fouling model;
5. HX cleaning optimization.
Rf dynamic models within FoulingTR
1. Semi-empirical approach Ebert-Panchal and similars
2. Empirical approach linear and asymptotic
FoulingTR chooses the best model for the data)
w
f
af
RT
E
dt
dR
expRe 4.0ReexpRe
f
af
RT
E
dt
dR
ctRR ff 0,))/exp(1( tRR ff
Heat exchanger Rf model Parameters
M-501B Polley et al. alfa = 2.64895e+052; Ea = 417.507;
gama = 3.95786e-012
M-501A Linear with estimation of Rf0 Rf0 = 0.00310002, c = 1.02443e-011
M-538 Linear with estimation of Rf0 Rf0 = 0.000102756, c = 1.20769e-012
M-541 Linear with estimation of Rf0 Rf0 = 0.000826369, c = 1.24803e-010
M-536 Linear with estimation of Rf0 Rf0 = 0.000538786, c = 4.14103e-010
M-546 Ebert&Panchal alfa = 13.8642, beta = 0.941904, Ea =67.2667,
gama = 2.6176e-013
M-537 Linear with estimation of Rf0 Rf0 = 0.00201365, c = 2.29293e-010
M-548 Linear c = 0
M-542 Linear with estimation of Rf0 Rf0 = -0.00176492, c = 1.6617e-009
M-507A/B Linear with estimation of Rf0 Rf0 = 0.00289926, c = 9.8346e-011
M-517C Linear with estimation of Rf0 Rf0 = 0.00180379, c = 1.38171e-010
M-517A/B Linear c = 0
M-505B Linear with estimation of Rf0 Rf0 = 0.00474607, c = 7.4933e-012
M-505A Linear with estimation of Rf0 Rf0 = 0.0116408, c = 8.94552e-011
Considerations to take into account the Rf dynamics
1. only one (1) HX, or PHT branch, per time;
2. the cleaning occurs at time 0
3. it is assumed that the HX will maintain its Rf dynamics after cleaning
4. future horizon of 4 months during 4 months, it is assumed that no
other cleaning occurs
REMAN U-2111
REFAP U-01
U-50
U-650
REPAR U-2100
REPLAN U-200A
U-200
RLAM U-4
U-9
U-32
U-35
REGAP U-01
U-101
U-52
REDUC U-1510
U-1710
BC P-08
P-18
P-19
P-20
P-37
P-47
RIO P-50
P-53
P-56
REVAP U-210
RPBC UV
RECAP U-500
RPCC U-260
U-270
BS P-66
Conclusions
It was shown that the Rf dynamics of each HX play a great role in the
determination of which HX should be removed for cleaning and,
therefore, should be taken into account;
This is just one of many answers you can get by using FoulingTR. It
also performs HEN cleaning optimization using Rf models.
Thanks for your attention
Questions??