Refinery Reactor Modeling and Analysis

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
  • Fractionation
  • 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.

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