Data-driven robust optimization for crude oil blending under uncertainty

Abstract Optimization of crude oil blending helps improve the operating efficiency of refineries. However, widespread uncertainties, such as oil properties, bring difficulty in realizing this task. A data-driven robust optimization (DDRO) approach is proposed to optimize crude oil blending under uncertainty. First, the blending effect model is used to extract uncertainties of oil components from production data by recursive least squares method. Second, the uncertainty set is constructed by combining principle component analysis and robust kernel density estimation based on the historical data of blending effects. A novel data-driven robust model for recipe optimization of crude oil blending is developed by utilizing the obtained uncertainty set. The dual transformation is applied to derive the linear counterpart of the DDRO model. A case study is adopted to illustrate the effectiveness of the proposed method.

[1]  Baoding Liu,et al.  Dependent-chance programming in fuzzy environments , 2000, Fuzzy Sets Syst..

[2]  B. Murty,et al.  Global optimization for prediction of blend composition of gasolines of desired octane number and properties , 2004 .

[3]  Haibo He,et al.  Data-Adaptive Robust Optimization Method for the Economic Dispatch of Active Distribution Networks , 2019, IEEE Transactions on Smart Grid.

[4]  Xingsheng Gu,et al.  Chance constrained programming models for refinery short-term crude oil scheduling problem , 2009 .

[5]  Feng Qian,et al.  An Application of the Particle Swarm Optimization on the Gasoline Blending Process , 2011, ICAIC.

[6]  Chao Shang,et al.  Data-driven robust optimization based on kernel learning , 2017, Comput. Chem. Eng..

[7]  Gang Rong,et al.  Data-driven robust optimization under correlated uncertainty: A case study of production scheduling in ethylene plant , 2018, Comput. Chem. Eng..

[8]  Jinliang Ding,et al.  Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network , 2018 .

[9]  Fengqi You,et al.  Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty , 2017 .

[10]  Clayton D. Scott,et al.  Robust kernel density estimation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  José de Jesús Rubio,et al.  Least square neural network model of the crude oil blending process , 2016, Neural Networks.

[12]  Silvio Hamacher,et al.  A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty , 2016, Eur. J. Oper. Res..

[13]  Allen L. Soyster,et al.  Technical Note - Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming , 1973, Oper. Res..

[14]  Liang Zhao,et al.  Operational optimization of industrial steam systems under uncertainty using data‐ D riven adaptive robust optimization , 2018, AIChE Journal.

[15]  Yiping Feng,et al.  Optimizing Crude Oil Operations under Uncertainty , 2009 .

[16]  J. M. Pinto,et al.  Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management , 1996 .

[17]  Zhiwu Li,et al.  Energy efficiency optimization in scheduling crude oil operations of refinery based on linear programming , 2017 .

[18]  Fengqi You,et al.  Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods , 2018, Comput. Chem. Eng..

[19]  George B. Dantzig,et al.  Linear Programming Under Uncertainty , 2004, Manag. Sci..

[20]  Hamid Shahriari,et al.  Robust optimization under correlated polyhedral uncertainty set , 2016, Comput. Ind. Eng..

[21]  Qi Zhang,et al.  An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load , 2016, Comput. Chem. Eng..

[22]  RubioJosé de Jesús.  Least square neural network model of the crude oil blending process , 2016 .

[23]  Fengqi You,et al.  Optimal processing network design under uncertainty for producing fuels and value‐added bioproducts from microalgae: Two‐stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm , 2017 .

[24]  Maryam Kamgarpour,et al.  Exploiting structure of chance constrained programs via submodularity , 2018, Autom..

[25]  Paul I. Barton,et al.  Chance-constrained optimization for refinery blend planning under uncertainty , 2017 .

[26]  Jaime Cerdá,et al.  Scheduling multipipeline blending systems supplying feedstocks to crude oil distillation columns , 2017 .

[27]  Christodoulos A. Floudas,et al.  Scheduling of crude oil operations under demand uncertainty: A robust optimization framework coupled with global optimization , 2012 .

[28]  Anja De Waegenaere,et al.  Robust Solutions of Optimization Problems Affected by Uncertain Probabilities , 2011, Manag. Sci..

[29]  Fengqi You,et al.  Resilient design and operations of process systems: Nonlinear adaptive robust optimization model and algorithm for resilience analysis and enhancement , 2017, Comput. Chem. Eng..

[30]  Chrysanthos E. Gounaris,et al.  Multi‐stage adjustable robust optimization for process scheduling under uncertainty , 2016 .