Tons of dry compressed CO captured per year
Culpability values
100%
14%
25-K-102 19-K-101
12%
60-M-K-101 65-M-P-201 59-M-K-100 64-M-K-200 62-M-K-103 29-K-103 61-M-K-102 66-M-P-300 63-M-P-100
80%
10%
60%
8%
6%
40%
4%
20%
2%
47-P-201 55-P-300 37-P-200 33-P-100
0%
0%
Annualised
Culpability Pumps P-100, P-200, P-300 , and P-201 are the major contributors to system performance losses 0% 1% 2% 3% 4% 5% 6% 7% 8% 9%
Probability to exceed Occurences The Base Case system will capture at most 408.2 t/y, which is below target (410 kt/y)
100%
16%
57-E-301 45-E-200 13-HX-100 51-HX-300 27-E-101 31-E-102 7-K-501 19-K-101 15-K-100 60-M-K-101 25-K-102 62-M-K-103 61-M-K-102 59-M-K-100 29-K-103
14%
80%
12%
10%
60%
8%
40%
6%
4%
20%
2%
0%
0%
Probability to exceed Occurences Annualised
0% 1% 2% 3% 4% 5% 6% 7%
Culpability The focus now should be shifted to compressor K-103, K-102 , K-100, and K-101 and corresponding motors
The system will capture at least 410 kt/y with a probability of 88% (meets the target)
Figure 3 Debottlenecking a carbon capture projects through Aspen Fidelis
Roadmap to manage long-term emissions In Qatar, the Ras Laffan Olefins Company (RLOC) and Qatar Chemical Company (QChem) complex (the largest ethylene crackers, polyethylene, and aromatics producer in Qatar) have used Aspen Fidelis to review current performance and to create a roadmap to incrementally reduce emissions. After creating a detailed model of the three complexes and their interconnectivity, the study provided marginal abatement cost curves along with total required investments. Variables such as furnace combustion optimisation, use of solar panels, steam metering and distribution network, and
detailed dataset comprising 8,760 hours of renewable power data. This level of granularity allowed for a more accurate and comprehensive analysis of how renewable power fluctuations impact project dynamics. This approach avoided system bottlenecks on the one hand and over- design on the other (see Table 1 ). Fidelis model input and output variables were: Input: Power input profile, model configuration (such as flow, unit, and storage), initial material balance, objective functions, and constraints. Output: Production profile (daily, weekly, monthly, and annual), average capacity factor, and storage level profile.
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