Development and challenges of planning and scheduling for petroleum and petrochemical production

Li, Fupei; Yang, Minglei; Du, Wenli; Dai, Xin

doi:10.1007/s42524-020-0123-3

Cited by 10 publications

(1 citation statement)

References 81 publications

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“…However, scheduling optimization for crude oil is challenging with the interaction of discrete events and continuous processes. Currently, crude scheduling still relies on the experience of schedulers due to the lack of effective techniques or software tools [2]. Therefore, short-term scheduling optimization for crude oil has attracted extensive attention from both academics and industries.…”

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confidence: 99%

Large-scale crude oil scheduling: A framework of hybrid optimization based on plan decomposition

Zhang

et al. 2022

2022 IEEE Congress on Evolutionary Computation (CEC)

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With the scaling up of crude oil scheduling in modern refineries, large-scale crude oil scheduling problems (LSCOSPs) emerge with thousands of binary variables and nonlinear constraints, which are challenging to be optimized by traditional optimization methods. To solve LSCOSPs, we take the practical crude oil scheduling from a marine-access refinery as an example and start with modeling LSCOSPs from crude unloading, transportation, crude distillation unit processing, and inventory management of intermediate products. On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement. In the global search stage, we devise several heuristic rules based on the empirical operating knowledge to generate a well-performing initial population and accelerate convergence in the mixed variables space. In the local refinement stage, a repair strategy is proposed to move the infeasible solutions towards feasible regions by further optimizing the local continuous variables. During the whole evolutionary process, the proposed dual-stage framework plays a crucial role in balancing exploration and exploitation. Experimental results have shown that DSEA/HR outperforms the state-of-the-art and widely-used mathematical programming methods and metaheuristic algorithms on LSCOSP instances within a reasonable time.

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confidence: 99%