Pipelines have been proved to be an efficient and economic way to transport oil products. However, the determination of the scheduling of operational activities in pipeline networks is a difficult task, and efficient methods to solve such complex problem are required. In this contribution, a real-world pipeline network is studied, and an optimization model is proposed in order to address the network scheduling activities. A hierarchical approach is proposed on the basis of the integration of a mixed integer linear programming (MILP) model and a set of heuristic modules. This article exploits the MILP model, the main goal of which is to determine the exact time instants that products should be pumped into the pipelines and received in the operational areas. These time instants must satisfy the pipeline network management and operational constraints for a predefined planning period. Such operational constraints include pipeline stoppages, movement of batches through many areas/pipelines, use of preferential routes to avoid contamination losses, on-peak demand hours of pumping, local constraints, reversions of flow direction, and surge tank operations, while satisfying a series of production/consumption requirements. The developed continuous-time model is applied to a large real-world pipeline system, where more than 14 oil derivatives and ethanol are transported and distributed between supply and demand nodes.

These results provide evidence that Bt allergens are distinct and have relatively low to moderate cross-reactivity with Dermatophagoides spp. allergens. Bt allergens should therefore be included in the diagnostic panel for the evaluation of allergic disorders in the tropics, and the development of new diagnostic and therapeutic strategies should include allergens of Bt.

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).

This work shows prospects of long-period fibre grating applications as transducers for fuel conformity analysis. The proposed long-period grating transducer was employed to assess the gasoline conformity in commercial gas stations. Grating responses were used to train and validate a radial base function topology of an artificial neural network. The obtained results show that fibre optic sensors supervised by artificial neural networks can integrate systems for smart sensing with high applicability in the petrochemical field. The radial base function had reached a correct classification probability of approximately 94%. The device applicability in the analysis of hydrated ethanol fuel was also investigated by measuring the concentration of ethanol in ethanolwater mixtures. The results showed that the developed transducer can be used to infer the ethanol-water concentration with a resolution of up to 0.23%.

This work presents an approach for scheduling of operational activities in a large real-world pipeline network, where oil derivatives and ethanol are transported and distributed among refineries, terminals, depots, and final clients. The hierarchical decomposition approaches to solve the pipeline-scheduling problem presented by Boschetto et al. [Ind. Eng. Chem. Res. 2010, 49, 5661] and Magataõ et al. [Ind. Eng. Chem. Res. 2012, 51, 4591], which are based on the integration of mixed integer linear programming (MILP) models and a set of heuristic modules, are merged and compounding blocks are also improved. Thus, a novel decomposition approach for scheduling product distribution through a pipeline network is proposed. In addition, this work presents a new MILP approach for the last hierarchical level: the timing block (timing model). This paper expands and improves the former MILP model, which was the timing block core. A series of operational constraints were considered within a continuous time representation in order to determine the exact time instants that products should be pumped into the pipelines and received in the operational areas during a scheduling horizon of, typically, 1 month. Within the new MILP timing model, turn shift constraints, local constraints, and surge tank constraints are improved; immediate pumping constraints are proposed. In addition, a decomposition approach for the new MILP model is also proposed within this article. This decomposition is based on a relax-and-fix heuristic implemented by a sequential run of two MILP models: MLC (Model with Local Constraints) and MST (Model with Seasonal costs and Turn shift constraints). The MILP decomposition goal is to reduce the computational load, if seasonal costs and turn shift constraints are active, without quality solution losses. The proposed approach is applied to the solution of real case studies of a pipeline network that includes 30 bidirectional multiproduct pipelines associated with 14 nodes (four refineries, two harbors, six depots, and two final clients). Computational results have been attained in a reasonable computational time (from seconds to a few minutes) for the addressed pipeline network.