(515s) A Rigorous Refinery Dynamic Model for Advanced Process Control and Operational Optimization | AIChE

(515s) A Rigorous Refinery Dynamic Model for Advanced Process Control and Operational Optimization

Authors 

Santander, O. S. - Presenter, The University of Texas at Austin
Baldea, M., The University of Texas at Austin
Today’s environmental regulations, stringent competition, and oil market uncertainty call for a tighter integration of the decision making process in oil refining operations. This, in turn, requires strengthening the communication between production planning, short-term production scheduling and advanced process control layers of the decision-making hierarchy. To this end, the development of accurate and computationally tractable dynamic mathematical models of refining facilities becomes critical. Taking advantage of modern simulation, control and optimization tools requires equation-oriented modeling and open model architectures, that allow for flexibility in modifying the representation of unit operations, adding new units and investigating new control structures. To the knowledge of the authors, most of the models available in the open literature are structurally incomplete (i.e., they do not cover all the key units of a refinery) and/or may be outdated implementation-wise [1,2,3,4,5,6].

In this paper, we address the aforementioned issues by developing an open-source model of a key section of an oil refinery, the fluidized-bed catalytic cracker (FCC). Our model represents a significant extension of the FCC model originally introduced by McFarlane et al [1]. It includes the cracker unit, as well as the fractionator column and wet gas compressor, and by considers a large number of (pseudo) components such that the streams (“cuts”) drawn from the fractionation column are representative of those obtained in industry [7].

Against this backdrop, we develop a model predictive control structure based on the dynamic matrix control paradigm. Our model is freely available for download and can be used for simulation as well as a basis for further studies on, e.g., big data analysis, fault detection.

References

  1. R.C. McFarlane et al., Dynamic simulator for a Model IV fluid catalytic cracking unit, Computers and Chemical Engineering 17 (1993) 275-300
  2. H. Huang, Simulation and control of complex distillation columns, PhD dissertation, Texas Tech University, Lubbock, Texas (2000)
  3. L.F.L Moro, D. Odloak, Constrained multivariable control of fluid catalytic cracking converters, Journal of Process Control, 5 (1995) 29-39
  4. C.B. Chung, J.B. Riggs, Dynamic simulation and nonlinear-model-based product quality control of a crude tower, AIChE 41 (1995) 122-134
  5. J. M. Pinto et al., Planning and scheduling models for refinery operations, Computers and Chemical Engineering 24 (2000) 2259-2276
  6. O. Santander, M. Baldea, On the interaction and integration of production planning and (advanced) process control , Computers and Chemical Engineering 133 (2020) 1-17
  7. M. Heydari et al., Study of seven-lump kinetic model in the fluid catalytic cracking unit, American Journal of Applied Sciences 7 (2010) 71-76