In the oil industry, any improvement in the planning and execution of the associated operations (e.g., production, storage, distribution) can generate considerable profits. To achieve this, the related activities need to be optimized. Within these activities, planning and scheduling occur at the different levels of the oil supply chain, from the strategic to the operational levels looking from global networks to sets of individual resources. This work looks into the planning, namely the assignment/ sequencing of activities that occur in a multiproduct, multipipeline system. The aim is to contribute to the definition of generic models that can help the decision-making process characterized by a high level of complexity. An approach formed by two mixed integer linear programming (MILP) formulations that act in sequence is proposed. The first generic MILP planning model calculates volumes for attending the necessary requirements on inventory management of the producer and consumer areas. As a result, this model defines the products and the total volumes to be transported in order to attain storage goals, while respecting operational constraints, demands of consumers, and pipeline capacity. Then, the planning model results are used by an MILP assignment and sequencing model, which splits the total volume into operational batches and determines the sequence of pumping for the batches during the available horizon. The developed approach is applied to a real-world pipeline network that includes 30 bidirectional multiproduct pipelines associated with 14 node areas: four refineries, two harbors, six depots/parks of pumps and valves, and two final clients.

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Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).