An integrated Asian Upstream- Reﬁning- PetChem Major required a high ﬁdelity ﬁrst fundamental reﬁnery reactor simulation model of its proprietary in- RFCC technology, to achieve the business objective of enhanced performance from its assets
As the demand for asset utilization coupled with energy optimization increases, a rigorous process simulation model becomes a critical aid.
A right process model can enable achieving multiple objectives like:
- Process Optimization
- Economic Optimization
- Reducing the gap between Plan Actual
- Improved Process understanding & analysis etc
To build a high ﬁdelity oﬀ-line Custom Model for a Resid Fluid Catalytic Cracking (RFCC) unit built on “company’s in- proprietary technology”.
The unit started-up in 1998 had various technology upgrades until 2008 & covered:
- Reaction – Regeneration section
- Absorption – Stabilization section
- Compressors units
- The main blower – ﬂue gas turbine units
- Standby Main Blower CO Waste Heat Boiler Products Reﬁning
- Gas fractionation
The model deﬁnition, model development & model deployment philosophy of the project was to form the core foundation of replicating similar models across multiple units at other sites of the company.
The project challenges included data deﬁnition, model convergence & model robustness for handling the proprietary new-generation process technology conﬁguration on a Commercial Process Simulation Software Platform.
Develop, Calibrate, and Validate a rigorous ﬁrst fundamental steady-state model of Residual Fluid Catalytic Cracker (RFCC).
The developed RFCC model covered the following sections:
- Wet Gas Compressor
- FCC Reactor
- Absorber – De-sorber Stabilization Section De-propanizer
- Propane – Propylene Splitter
The model was matched to get the KPI’s within ± 2%. The model case studies included:
The model was calibrated with “operating data” from the customer. The model was validated with multiple sets of operating data & customer engineering staﬀ was trained on model use, model maintenance through multiple case studies.
- Change in feed rate
- Change in feed composition
- Change in reactor temperatures
- Yield Optimization by changing reactor temperatures
Proﬁt Optimization based on the cost of feed slate and product.