Feedstock
Products
CO 2 emissions
5,312,500 crude oil 165,671 green H 2
Total products Gasoline product Diesel product
4,887,500 1,075,250 1,857,250 1,466,250 488,750
993,333
Complete refinery
5,352
Naphtha and gasoline complex Diesel, kero and conversion units
393,000 594,982
Kerosene product Other products - units
Other units
Optional case: 100,000 BBL processing refinery, high conversion. Carbon capture for blue hydrogen and synthetic fuels
Table 2 Hybrid refinery or fuels production centre feedstock and products
Capacity (A, TPA)
Cost (CE, Euros)
Capacity (A, TPA)
Cost (CE, Euros)
CCUS
A1 A2 A3
900,000 39,000 165,671
102,000,000 1,557,500,000 625,000,000 Total cost: 2,284,500,000
CCUS Synth
A1 A2 A3
884,230 20,075 100,000
102,806,197 1,031,658,184 461,670,496 Total cost: 1,596,134,877
Synth fuels
Green H 2
Green H 2
Table 4 Capital cost optimised using optimisation LP evolutionary algorithms
The analysis is based on the methanol to SAF process, comprising methanol synthesis, dimethyl ether (DME) production, and olefins oligomerisation. The synthetic fuels unit will produce 10% of the middle distillate products, enabling a 10% reduction in the load on the conventional middle distillates hydrotreatment units, as well as a reduction in the crude oil feed to the refinery, improving the reduction in the carbon footprint (see Table 2 ): • Synthetic fuels unit (e-SAF & e-diesel): 332,350 TPA • CO 2 : 1,258,902 TPA • H 2 : 176,246 TPA Table 3 Preliminary Class 5 estimate for the cost of new configuration
The CO 2 emissions in the two analysis cases present the conditions shown in Figure 5 . Optimisation of hybrid solution alternatives by means of linear programming concept The configuration analysed in the previous sections complies with the following criteria: • Maximum reduction of CO 2 emissions. • Use 100% of the CO 2 captured inside the refinery as feedstock (no vent, no transportation out of refinery). Table 3 give a preliminary Class 5 estimate for the cost of the new configuration. The size of each unit was optimised using linear programming (LP) with the following variables: • Total cost must be set to a calculated minimum. • A positive impact of carbon credit is included for an equivalent period of five years as a cost reduction factor. • Restrictions according to:
2 , 500 , 000
2 , 125 , 000
2 , 000 , 000
CCUS A1 <900,000 CCUS A1 >600,000
1 , 500 , 000
993 , 333
1 , 000 , 000
Synthetic fuels unit A2 <39,000 Synthetic fuels unit A2 >20,000
500 , 000
Hydrogen electrolysis unit A3 <166,000 Hydrogen electrolysis unit A3 >100,000. These variables were used to conduct an LP optimisation using the evolutionary optimisation methodology. Evolutionary optimisation
0
CO base case re nery
CO optional case re nery
Figure 5 CO 2 emission comparison base and optional cases (values in TPA)
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