Data-driven robust optimization for integrated refinery planning and blending scheduling under multi-mode uncertainty

Yu Wenhao; Dai Xin; Yue Yuanhang; Lin Xinwei; Yang Minglei

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Data-driven robust optimization for integrated refinery planning and blending scheduling under multi-mode uncertainty

Updated：2026-09-17

- Data-driven robust optimization for integrated refinery planning and blending scheduling under multi-mode uncertainty
- Chinese Journal of Chemical Engineering Vol. 95, Issue 7, Pages: 175-193(2026)
- Affiliations：
  
  Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  Guangdong Petrochemical Co., Ltd, Jieyang 522031, China
- Author bio：
  
  *Corresponding authors. E-mail addresses:xindai@ecust.edu.cn(X. Dai)
  *Corresponding authors. E-mail addresses:mlyang@ecust.edu.cn(M. Yang).
- Funds：
- DOI：
  CLC：
- Received：18 June 2025，
  
  Revised：2026-02-07，
  
  Accepted：25 February 2026，
  
  Online First：16 April 2026，
  
  Published：2026-07
- Accepted：
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Yu Wenhao, Dai Xin, Yue Yuanhang, et al. Data-driven robust optimization for integrated refinery planning and blending scheduling under multi-mode uncertainty[J]. Chinese Journal of Chemical Engineering, 2026, 95(7): 175-193.
DOI：

Yu Wenhao, Dai Xin, Yue Yuanhang, et al. Data-driven robust optimization for integrated refinery planning and blending scheduling under multi-mode uncertainty[J]. Chinese Journal of Chemical Engineering, 2026, 95(7): 175-193. DOI：

摘要

Abstract

Integrating refinery planning and product blending scheduling under uncertainty is challenging due to multi-mode operating conditions and mismatched temporal scales. This work proposes a bilevel optimization framework that explicitly coordinates long-term refinery planning with short-term blending scheduling. To represent mode-dependent uncertainty in blending behavior

a data-driven robust optimization (DDRO) strategy is developed

in which mode-associated uncertainty sets are constructed from historical data using a multi-stage clustering (MSC) algorithm combined with weighted support vector machines (SVM). This enables uncertainty modeling that is adaptive to operating modes and less conservative than conventional holistic robust approaches. To solve the resulting bilevel robust model efficiently

a robust-buffer-based decomposition (RBD) algorithm is proposed

which iteratively introduces a specification buffer at the planning level to preserve scheduling feasibility under uncertainty. In the case study

the proposed multi-mode robust framework improves the net profit by approximately 1.0% compared to holistic robust model

while maintaining 100% feasibility in quality constraints and reducing computational time by more than 50% relative to the single-level formulation. These results demonstrate that the proposed framework effectively balances economic performance

robustness

and computational tractability in integrated refinery planning and scheduling.

关键词

Keywords

references

B. Brunaud, I.E. Grossmann, Perspectives in multilevel decision-making in the process industry, Front. Eng 4 (3) (2017) 256.

D. van de Berg, N. Shah, E.A. del Rio-Chanona, Hierarchical planningscheduling-control: optimality surrogates and derivative-free optimization, Comput. Chem. Eng. 188 (2024) 108726.

J. Morlet-Espinosa, A. Flores-Tlacuahuac, A Bayesian optimization approach for data-driven mixed-integer nonlinear programming problems, AIChE J. 70 (9) (2024) e18448.

L.S. Dias, M.G. Ierapetritou, Integration of planning, scheduling and control problems using data-driven feasibility analysis and surrogate models, Comput. Chem. Eng. 134 (2020) 106714.

J.H. Lee, J. Shin, M.J. Realff, Machine learning: overview of the recent progresses and implications for the process systems engineering field, Comput. Chem. Eng. 114 (2018) 111—121.

I. Harjunkoski, T. Ikonen, H. Mostafaei, T. Deneke, K. Heljanko, Synergistic and intelligent process optimization: first results and open challenges, Ind. Eng. Chem. Res. 59 (38) (2020) 16684—16694.

L. Yang, J.Z. Ren, M. Eden, C.L. Chang, W.F. Shen, A penalty-free hybrid algorithm framework based on feasible stream matching principle for large-scale heat exchanger networks synthesis, Chem. Eng. Sci. 298 (2024) 120419.

S. Terrazas-Moreno, P.A. Trotter, I.E. Grossmann, Temporal and spatial Lagrangean decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers, Comput. Chem. Eng. 35 (12) (2011) 2913—2928.

P. Castillo Castillo, P.M. Castro, V. Mahalec, Global optimization algorithm for large-scale refinery planning models with bilinear terms, Ind. Eng. Chem. Res. 56 (2) (2017) 530—548.

J.A. Persson, M. Göthe-Lundgren, Shipment planning at oil refineries using column generation and valid inequalities, Eur. J. Oper. Res. 163 (3) (2005) 631—652.

A. Uribe-Rodríguez, P.M. Castro, G. Guillén-Gosálbez, B. Chachuat, Assessment of lagrangean decomposition for short-term planning of integrated refinery-petrochemical operations, Comput. Chem. Eng. 174 (2023) 108229.

R.E. Franzoi, B.C. Menezes, J.D. Kelly, J.A.W. Gut, I.E. Grossmann, Large-scale optimization of nonconvex MINLP refinery scheduling, Comput. Chem. Eng. 186 (2024) 108678.

H.K. Yang, D.E. Bernal, R.E. Franzoi, F.G. Engineer, K. Kwon, S. Lee, I.E. Grossmann, Integration of crude-oil scheduling and refinery planning by Lagrangean decomposition, Comput. Chem. Eng. 138 (2020) 106812.

L.J. Su, D.E. Bernal, I.E. Grossmann, L.X. Tang, Modeling for integrated refinery planning with crude-oil scheduling, Chem. Eng. Res. Des. 192 (2023) 141—157.

B. Colson, P. Marcotte, G. Savard, An overview of bilevel optimization, Ann. Oper. Res. 153 (1) (2007) 235—256.

Y.F. Chu, F.Q. You, J.M. Wassick, A. Agarwal, Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method, Comput. Chem. Eng. 72 (2015) 255—272.

M.A. Munõz, S. Pineda, J.M. Morales, A bilevel framework for decision-making under uncertainty with contextual information, Omega 108 (2022) 102575.

B. Beykal, S. Avraamidou, I.P.E. Pistikopoulos, M. Onel, E.N. Pistikopoulos, DOMINO: data-driven optimization of bi-level mixed-integer nonlinear problems, J. Glob. Optim. 78 (1) (2020) 1—36.

Z.K. Li, M.G. Ierapetritou, Integrated production planning and scheduling using a decomposition framework, Chem. Eng. Sci. 64 (16) (2009) 3585—3597.

Z.K. Li, M.G. Ierapetritou, Production planning and scheduling integration through augmented Lagrangian optimization, Comput. Chem. Eng. 34 (6) (2010) 996—1006.

F.P. Li, M.L. Yang, W.L. Du, X. Dai, Development and challenges of planning and scheduling for petroleum and petrochemical production, Front. Eng. Manag. 7 (3) (2020) 373—383.

P.M. Verderame, J.A. Elia, J. Li, C.A. Floudas, Planning and scheduling under uncertainty: a review across multiple sectors, Ind. Eng. Chem. Res. 49 (9) (2010) 3993—4017.

D.J. Yue, F.Q. You, Planning and scheduling of flexible process networks under uncertainty with stochastic inventory: MINLP models and algorithm, AIChE J. 59 (5) (2013) 1511—1532.

F.P. Li, F. Qian, W.L. Du, M.L. Yang, J. Long, V. Mahalec, Refinery production planning optimization under crude oil quality uncertainty, Comput. Chem. Eng. 151 (2021) 107361.

Y. Chen, M. Lin, H. Jiang, Z.H. Yuan, B.Z. Chen, Optimal design and operation of refinery hydrogen systems under multi-scale uncertainties, Comput. Chem. Eng. 138 (2020) 106822.

S. Amaran, T. Zhang, N.V. Sahinidis, B. Sharda, S.J. Bury, Medium-term maintenance turnaround planning under uncertainty for integrated chemical sites, Comput. Chem. Eng. 84 (2016) 422—433.

D.J. Yue, F.Q. You, Optimal supply chain design and operations under multiscale uncertainties: nested stochastic robust optimization modeling f ramework and solution algorithm, AIChE J. 62 (9) (2016) 3041—3055.

P. Mohajerin Esfahani, D. Kuhn, Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations, Math. Program. 171 (1) (2018) 115—166.

E. Delage, Y.Y. Ye, Distributionally robust optimization under moment uncertainty with application to data-driven problems, Oper. Res. 58 (3) (2010) 595—612.

J. Long, J.W. Zhu, N. Wang, J.Z. Zhai, T.T. Xu, C. Liang, L. Zhao, Data-driven robust operating optimization of energy-material coupled system in refineries under uncertainty, Expert Syst. Appl. 267 (2025) 126184.

S. Razm, N. Brahimi, R. Hammami, A. Dolgui, Row and column-wise robust optimization model for biorefineries storing perishable biomass under weather uncertainty: boosted by machine learning, Comput. Ind. Eng. 200 (2025) 110823.

A. Baghban, P.M. Castro, F. Oliveira, Data-driven robust optimization for pipeline scheduling under flow rate uncertainty, Comput. Chem. Eng. 193 (2025) 108924.

W.L. He, J.M. Zhao, L. Zhao, Z. Li, M.L. Yang, T.B. Liu, Data-driven two-stage distributionally robust optimization for refinery planning under uncertainty, Chem. Eng. Sci. 269 (2023) 118466.

C. Wang, X. Peng, C. Shang, C. Fan, L. Zhao, W.M. Zhong, A deep learningbased robust optimization approach for refinery planning under uncertainty, Comput. Chem. Eng. 155 (2021) 107495.

X. Dai, X.Q. Wang, R.C. He, W.L. Du, W.M. Zhong, L. Zhao, F. Qian, Data-driven robust optimization for crude oil blending under uncertainty, Comput. Chem. Eng. 136 (2020) 106595.

M.R. Ghiasvand, D. Rahmani, A novel weighted data-driven robust optimization approach for creating adjustable uncertainty sets, Comput. Chem. Eng. 178 (2023) 108390.

C. Ning, F.Q. You, Data-driven adaptive nested robust optimization: general modeling framework and efficient computational algorithm for decision making under uncertainty, AIChE J. 63 (9) (2017) 3790—3817.

C. Ning, F.Q. You, Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods, Comput. Chem. Eng. 112 (2018) 190—210.

W.H. Yu, F.P. Li, X. Dai, W.L. Du, M.L. Yang, Continuous-time formulation for integrated refinery planning and diesel blending scheduling problems, Ind. Eng. Chem. Res. 64 (7) (2025) 3918—3936.

Y. Beck, I. Ljubić, M. Schmidt, A survey on bilevel optimization under uncertainty, Eur. J. Oper. Res. 311 (2) (2023) 401—426.

F.P. Li, F. Qian, M.L. Yang, W.L. Du, V. Mahalec, Product tri-section based crude distillation unit model for refinery production planning and refinery optimization, AIChE J. 67 (2) (2021) e17115.

M. Bicego, M.A.T. Figueiredo, Soft clustering using weighted one-class support vect or machines, Pattern Recognit 42 (1) (2009) 27—32.

B. Han, C. Shang, D.X. Huang, Multiple kernel learning-aided robust optimization: learning algorithm, computational tractability, and usage in multi-stage decision-making, Eur. J. Oper. Res. 292 (3) (2021) 1004—1018.

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