Dealing with Multiple Tank Outflows and In-Line Blending in Continuous-Time Crude Oil Scheduling Problems

Abstract

The crude oil scheduling problem has been focus of many studies in the past, which is justified by its importance in the oil industry. Optimizing crude oil blending is of paramount importance given that it can substantially impact the economic performance of a refinery. In this work, operational features of a real-world existing refinery are addressed. Only operations limited to the refinery battery are in scope such as splitting of parcels unloaded from a supplying pipeline segment, tank heels and capacities, brine settling time, multiquality tracking, crude distillation unit (CDU) straight-run products profile, multiple tank outputs, and multiple CDU inputs. Moreover, different policies as to the handling of the refinery tank farm are evaluated. The presented formulation is taking from the multioperation sequencing (MOS) proposed by Mouret, Grossmann, and Pestiaux (Comput. Chem. Eng.20113510381063). The novelty of the present work relies on how to accurately impose lower and upper bounds on the flow rate of multiple tank outputs given that the formulation is based on a unit-specific time grid. Two approaches are proposed and evaluated. The resulting MINLP models are exhaustively tested with six distinguishing real-world scenarios in which blending can include up to 36 crude oil grades, different tank availability, initial inventories, and scheduled parcels to arrive at the refinery. Two solution algorithms that avoid solving the full-scale MINLP problem are used and compared. The computational experiments show that the proposed formulations are able to handle a wide range of problem instances in reasonable computational time, despite of the size dimension.

Integrated scheduling of a multi-site mining supply chain with blending, alternative routings and co-production

“…Numerous studies have been conducted in the literature to address combined problems of integrated scheduling and blending operations. Scheduling with blending operations have dealt with either a fixed number of recipes (Joly et al 2002;Jia and Ierapetritou 2003;Li et al 2009Li et al , 2016Bengtsson et al 2013) or a recipe optimisation followed by an order scheduling (Glismann and Gruhn 2001;Jia and Ierapetritou 2004;Mouret et al 2009;Castillo and Mahalec 2015;Neiro et al 2019). The multi-period problem has been solved with several solution techniques such as mixed-integer non-linear programming (Joly et al 2002;Kolodziej et al 2013;Li et al 2016;Neiro et al 2019) and mixed-integer linear programming (Jia and Ierapetritou 2003;Méndez et al 2006;Li et al 2009).…”

“…Scheduling with blending operations have dealt with either a fixed number of recipes (Joly et al 2002;Jia and Ierapetritou 2003;Li et al 2009Li et al , 2016Bengtsson et al 2013) or a recipe optimisation followed by an order scheduling (Glismann and Gruhn 2001;Jia and Ierapetritou 2004;Mouret et al 2009;Castillo and Mahalec 2015;Neiro et al 2019). The multi-period problem has been solved with several solution techniques such as mixed-integer non-linear programming (Joly et al 2002;Kolodziej et al 2013;Li et al 2016;Neiro et al 2019) and mixed-integer linear programming (Jia and Ierapetritou 2003;Méndez et al 2006;Li et al 2009). Most of the analysed articles refer to an economic optimisation criterion, including i) minimisation of the operating cost (Joly et al 2002;Jia and Ierapetritou 2004;Li et al 2009Li et al ,2016Cerda et al 2016 b), and ii) maximisation of profit (Glismann and Gruhn 2001;Jia and Ierapetritou 2004;Méndez et al 2006;Bengtsson et al 2013;Kolodziej et al 2013;Lotero et al 2016)…”