A novel MINLP model of front-end crude scheduling for refinery with consideration of inherent upset minimization

In this paper, a new methodology has been developed to deal with the front-end crude scheduling (FECS) problems with the consideration of inherent upset minimization (IUM). Specifically, the primary and secondary inherent upsets (IUs) have been defined and modeled to, respectively, address flowrate fluctuations of feeding crude distillation units and the long-distance pipeline (LDPL). Based on such IU characterizations, a new MINLP model with the unit-based continuous time representation is developed to determine the optimal FECS solution by minimizing the overall operating cost and instability along the entire scheduling time horizon. In addition to the IUM, another two merits are also included in this study: (i) the trans-mixing (TM) issue along with crude transportation inside the LDPL has been modeled; (ii) multiple types of crudes with multiple key properties have been simultaneously considered. The developed FECS MINLP model is solved by the ANTIGONE solver to obtain the global optima. The efficacy of the proposed methodology and the FECS model, and the effect of IUM have been investigated through various case studies.