Impact of crude distillation unit model accuracy on refinery production planning

Gang FU, Pedro A. Castillo CASTILLO, Vladimir MAHALEC

PDF(575 KB)
PDF(575 KB)
Front. Eng ›› 2018, Vol. 5 ›› Issue (2) : 195-201. DOI: 10.15302/J-FEM-2017052
RESEARCH ARTICLE
RESEARCH ARTICLE

Impact of crude distillation unit model accuracy on refinery production planning

Author information +
History +

Abstract

In this work, we examine the impact of crude distillation unit (CDU) model errors on the results of refinery-wide optimization for production planning or feedstock selection. We compare the swing cut+ bias CDU model with a recently developed hybrid CDU model (Fu et al., 2016). The hybrid CDU model computes material and energy balances, as well as product true boiling point (TBP) curves and bulk properties (e.g., sulfur % and cetane index, and other properties). Product TBP curves are predicted with an average error of 0.5% against rigorous simulation curves. Case studies of optimal operation computed using a planning model that is based on the swing cut+ bias CDU model and using a planning model that incorporates the hybrid CDU model are presented. Our results show that significant economic benefits can be obtained using accurate CDU models in refinery production planning.

Keywords

impact of model accuracy on production planning / swing cut+ bias CDU model / hybrid CDU model / refinery feedstock selection optimization / optimization of refinery operation

Cite this article

Download citation ▾
Gang FU, Pedro A. Castillo CASTILLO, Vladimir MAHALEC. Impact of crude distillation unit model accuracy on refinery production planning. Front. Eng, 2018, 5(2): 195‒201 https://doi.org/10.15302/J-FEM-2017052

References

[1]
Alattas A M, Grossmann I E, Palou-Rivera I (2011). Integration of non-linear crude distillation models in refinery planning optimization. Industrial & Engineering Chemistry Research, 50(11): 6860–6870
CrossRef Google scholar
[2]
Brooks R W, van Walsem F D, Drury J (1999). Choosing cut-points to optimize product yields. Hydrocarbon Processing, 78(11): 53–60
[3]
Castillo P A, Mahalec V (2014). Inventory pinch based multi-scale model for refinery production planning. Computer Aided Chemical Engineering, 33: 283–288
[4]
Fu G, Mahalec V (2015). Comparison of methods for computing crude distillation product properties in production planning and scheduling. Industrial & Engineering Chemistry Research, 54(45): 11371–11382
CrossRef Google scholar
[5]
Fu G, Sanchez Y, Mahalec V (2016). Hybrid model for optimization of crude oil distillation units. AIChE Journal, 62(4): 1065–1078
CrossRef Google scholar
[6]
Guerra O J, Le Roux A C (2011). Improvements in petroleum refinery planning: 1. formulation of process models. Industrial & Engineering Chemistry Research, 50: 13404–13418
[7]
Li W, Hui C W, Li A (2005). Integrating CDU, FCC and product blending models into refinery planning. Computers & Chemical Engineering, 29(9): 2010–2028
CrossRef Google scholar
[8]
Menezes B C, Kelly J D, Grossmann I E (2013). Improved swing-cut modeling for planning and scheduling of oil-refinery distillation units. Industrial & Engineering Chemistry Research, 52(51): 18324–18333
CrossRef Google scholar
[9]
Zhang J, Zhu X, Towler G (2001). A level-by-level debottlenecking approach in refinery operation. Industrial & Engineering Chemistry Research, 40(6): 1528–1540
CrossRef Google scholar

Acknowledgment

This work has been supported by the Ontario Research Foundation, McMaster Advanced Control Consortium, and Imperial Oil.

RIGHTS & PERMISSIONS

2017 The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
AI Summary AI Mindmap
PDF(575 KB)

Accesses

Citations

Detail

Sections
Recommended

/