P tro-SIM
KBC Explorer
Process Digital Twin
KPIs, DQPs, MPIs
Raw data
Dashboard
Data Archive
Historian and LIMS
Advanced Analytics
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P tro-SIM™
Process model
Monitoring service
LP Submodel
KPI estimation
ERP
Figure 1 Digital twin architecture
improve plant performance. These applications include vis- ualising KPIs for performance tracking, reconciling data in production accounting, updating LP models in the supply chain, optimising processes to improve yield and energy, conducting real-time optimisation through quick gain calcu- lations, and managing corrosion to monitor equipment and system degradation. These applications underscore the value of digitalisation in the refining process and are addressed in the remainder of this study. Digital twin architecture The digital twins are process models connected through OPCs with historians such as IP.21, Exa Quantum, OSI PI, or any other real-time data gateways, as shown in Figure 1 . The models are calibrated using test data to ensure energy and mass balance accuracy. After calibrating the model, it is scheduled to run, and the results appear on dashboards. Other applications use these to generate advanced analytics. 4 The success achieved from this system depends on whether the model is accurate and current. An outdated model limits the operation’s potential, resulting in value leak- age, lost opportunities, and substantial financial costs. Visualisation: Enhancing KPI management In the refinery, KPIs act as a compass, guiding performance tracking of key metrics such as temperature, pressure, equipment status, and more. Digital twins, adept at tracking and measuring KPIs, calculate intrinsic parameters such as yields, energy consumption, and column performance such as flooding, heat exchanger fouling, furnace efficiencies,
coking tendencies, and emissions along with the benchmark parameters. Closing these gaps between the actual meas- urements and the benchmarks adds value. 7 KPI management uses a strategic approach that aligns with the company’s goals to optimise plant and equipment performance. This approach ensures measurable progress. Derived from plant measurements, KPIs offer real-time insights into critical parameters such as unit throughput, feed, and product quality. Furthermore, calculations address yields and fractionation efficiency to identify process improvement opportunities. The intrinsic layer, estimated via a process digital twin, dives into issues such as column flooding, exchanger UA and fouling factors, and coking inside heater tubes. Using these intrinsic KPIs, operators can maximise asset utilisation and proactively improve the plant’s efficiency. Essentially, this system not only evaluates performance holistically but also provides insight to continuously improve individual assets or the entire complex. 7 Figures 2 and 3 illustrate trends in product yields and intrinsic parameters, respectively. Figure 2 indicates the product yields vs timeline such as day/month. Figure 3 shows the intrinsic parameter limits and trends for jet flooding and downcomer backup, which are regularly calculated. Production accounting: Single version of the truth The typical production accounting digital twin serves as the facility’s single version of the truth, laying the founda- tion for the hydrocarbon balance and loss control initiatives as shown in Figure 4 . The system not only generates the hydrocarbon balance accurately, but it also detects losses.
Fuel gas LPG Naphtha
Distillate 1 Distillate 2 Bottoms
Day /Month
Figure 2 KPI product yield (wt%) trends
60
PTQ Q1 2024
www.digitalrefining.com
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