In the literature, optimization models deal with planning and scheduling of several subsystems of the petroleum supply chain such as oilfield infrastructure, crude oil supply, refinery operations and product transportation. The focus of the present work is to propose a general framework for modeling petroleum supply chains. As a starting point, processing units are modeled based on the framework developed by Pinto et al. [Computers and Chemical Engineering 24 (2000) 2259]. Particular frameworks are then proposed to storage tanks and pipelines. Nodes of the chain are considered as grouped elementary entities that are interconnected by intermediate streams. The complex topology is then built by connecting the nodes representing refineries, terminals and pipeline networks. Decision variables include stream flow rates, properties, operational variables, inventory and facilities assignment. The resulting multiperiod model is a large-scale MINLP. The proposed model is applied to a real-world corporation and results show model performance by analyzing different scenarios.

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This work focuses on the scheduling of an in-line diesel blending and distribution subsystem of an oil refinery. The formulation is based on a hybrid time representation in which time points are equally distributed along the time horizon, within which time slots of variable length are postulated. The hybrid time representation takes advantage of the flexibility of the continuous time representation and enables handling of intermediate due dates with the use of fixed time points. Time variables are defined in terms of resources instead of transfer operations leading to a smaller size formulation. Results from a real-world case are used to validate the proposed formulation that takes into consideration capacities and operating rules while minimizing costs. Results of the multiperiod scheduling are also compared to a day-ahead production planning policy. Computational efficiency is achieved after adding valid inequalities and symmetry breaking constraints.

The fertilizer industry is becoming increasingly essential for the future of mankind as the world population continues to grow. In order for the food production rate to meet the increasing food demand, the available tillable areas need to be more productive, which can be achieved through the use of fertilizers. In this work, we present three MILP production scheduling approaches for addressing a typical phosphate fertilizer problem, comprising continuous tasks as well as short and lengthy batch tasks. The scheduling problem is also featured as presenting mixed storage policies and sequencedependent changeover times. All proposed formulations are based on a continuous singletime grid. In the first approach, batch tasks are allowed to freely take place over multiple time slots. In the second approach, batch tasks span a pre-defined number of time slots, and in the third hybrid approach, short batch tasks are allowed to freely take place over multiple time slots, while lengthy batch tasks span a pre-defined number of time slots. In all approaches, continuous tasks are assigned to a single time slot. A discrete time-based formulation is also used as a baseline comparison. It is verified that the first approach quickly becomes intractable as the number of time slots is increased. The second approach requires trial-and-error regarding the fixed number of time slots used for short batch tasks despite the excellent computational performance. Ultimately, the hybrid approach enables finding the optimal production schedule for a month scheduling horizon in approximately 4 CPU minutes without having to run multiple problem instances.

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.

A aplicação de estratégia de controle avançado em moinhos de bolas tipicamente usados em usinas de beneficiamento de minério é um tema cuja importância vem crescendo sistematicamente. Tal fato é justificado pelo aumento de produção, melhoria da qualidade do produto final, economia de energia e menor desgaste do revestimento interno do moinho. O sistema de controle avançado apresentado neste trabalho contempla o emprego de controlador multivariável acoplado a sistema de controle regulatório, que permite ao moinho alcançar condições ótimas de operação de acordo com os critérios técnicos estabelecidos como premissa de projeto. A implantação do sistema de controle avançado foi realizada de forma simples e rápida, permitindo comunicação direta com o sistema de controle regulatório (SDCD/CLP). Os resultados obtidos permitem afirmar que houve significativa redução da variabilidade na granulometria do material após a moagem, redução do consumo de energia e aumento de produção.