3.000
2.500
Severe
2.000
1.500
1.000
High
0.500
Moderate
0.000
Low
Lifespan Concern Index, LSCI
exchanger simulators, and real crude unit fouling factor data, a robust correlation between the stability parameters determined by the field ASIT and the increase in fouling rate was determined. A proprietary index for fouling based on ASIT measurements, called LSCI, was developed from thermodynamics combined with laboratory and field data (see Figure 4 ). Fouling factors and NFIT calculations Preheat exchanger performance rating and the fouling impact on each exchanger heat transfer capability are eval - uated by calculating fouling factors using a well-known recognised process simulator. Once the fouling factor for all the hot heat exchangers is calculated for the hot preheat train, it is possible to determine the normalised furnace inlet temperatures (NFIT). NFIT is calculated at a standard set of process conditions for the flow and inlet temperatures for both the cold and hot sides of the heat exchanger, allowing the outlet tem- perature to vary based on the calculated fouling factor. Therefore, with the ‘re-scaling’ of the furnace inlet temper - ature trend obtained in this way, it is determined uniquely from the overall fouling on the preheat exchangers. It can provide a clear evaluation of the energy loss in heating the crude oil to the design inlet furnace temperature. In fact, from this normalised parameter, it can calculate the extra fuel gas necessary to compensate for the impact of fouling. The accurate calculation of fouling factors required inten - sive work on thermocouples reading optimisation, which Figure 4 LSCI correlates the decrease of stability vs the rel - ative increase fouling rate
was possible thanks to RAM technology and operations support. Field monitoring of fouling tendency While the blends are planned by RAM, taking into consid - eration the evaluated risks of fouling by the LSCI index pro - vided by Baker Hughes before arrival of WTI, the stability of the crudes after desalting is continuously monitored by a field ASIT unit at the refinery lab (see Figure 5 ). Machine learning models LSCI yields relative fouling rate increases from ASI data and can evaluate an ‘average’ decay of NFIT (fouling rates) on an average crude unit preheat exchanger train, such as in Figure 5. The impact of LSCI on crude unit fouling factor increase rates depends on aspects like velocity and operating tem - peratures. The impact of these operating aspects on fouling at a given LSCI is a complex phenomenon and difficult to model with traditional chemical engineering methods. Machine learning-based methods extend the capabil - ities of traditional multiple linear regression analysis with the capability to handle complex non-linear aspects. We applied these models for modelling and predicting the RAM fouling rates from LSCI and process data. Some proprietary models, like Perceptron, are ideal for dealing with complex non-linear effects and give accurate fitting and predictions. Using the LSCI data from the field, it was possible to predict the fouling rates when processing WTI without the Baker Hughes antifoulant treatment. A decay of -0.060°C/ Figure 5 ASI data allows evaluation of fouling potential by LSCI
Extra fuel gas due to fouling is reduced by 3,777 t/yr and CO₂ emissions are reduced by 11,726 t/yr
RAM data, T3 fouling, fuel gas as basic/€
Untreated
Treated
Benefit of Lifespan
NFIT
1.82°C/month
0.40°C/month
Extra fuel gas due to fouling, t/yr
4,841
1,064 3,303
-3,777 -11,726
CO₂ emessa, t/yr
15,029
Estimated extra fuel gas costs, €/yr CO₂ trading oil estimated costs, €/yr
2,686,075.34 1,140,663.87
590,346.23 250,695.36 276,060.00 841,041.59
-2,095,729.11 -889,968.51
Chemical program cost, €/yr
–
276,060.00
Estimated, fouling, €/yr
3,826,739.21
2,709,637.63 -2,985,697.63
Total savings of Lifespan chemical and monitoring program for T3, €/yr
Table 2
38
PTQ Q4 2024
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