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??