This paper presents a mixed-integer
linear programming (MILP) model
with a continuous time representation to address the Tank Farm Operation
Problem (TFOP) of finished products in refineries. Real scenarios
are considered, which were obtained from the planning of refineries
and the external pipeline network scheduling, proposed by Ind. Eng. Chem. Res.2010495661.
The developed MILP model determines the scheduling of loading and
unloading operations in the tank farm of finished products at each
refinery, but is subjected to time-window constraints. A decomposition
approach has been applied, and multiproduct scenarios proposed by Ind. Eng. Chem. Res.2010495661 were
broken in single product scenarios. Each one of these scenarios is
related with the tank farm in a specific refinery. Therefore, they
are presenting volumes and values of stored inventories, maximum capacity
tanks, and start and end times to product movements at the refinery
interfaces (production, demand, and pipelines). The proposed MILP
model searches a scheduling that minimizes the movements within the
refinery tank farm in order to respect the imposed operational and
structural constraints. Further, for making feasible the scheduling
in a smaller computational time, an iterative algorithm is developed
and a new model approach, named MILP-IA, is added within the solution
process. The results allow us to analyze the model computational time,
the temporal and structural violations, and the number of product
movements for each scenario. For the studied cases, we can also check
for attending to time and monthly volume constraints for each interface.
Finally, the results also indicate that the proposed MILP-IA approach
finds solutions in computational times on the order of minutes. The
obtained solutions contribute to improve the transfer and storage
activities (TS) on two main points: (i) they minimize
the number of movements, facilitating the plant operational tasks
(searching for routes); and (ii) they provide feedback
to the pipeline scheduling, creating a collaborative integration between
refinery subsystems, linking all information about internal and external
product movements at refineries.
The current work contributes to the research in the area of pipelines non-destructive testing by presenting new methodologies for the automatic analysis of welds radiographs. Object recognition techniques based on genetic algorithms were used for the automatic weld bead detection. In addiction, we developed an image digital filter for the detection of defects in the weld bead zone. These methodologies were tested for 120 digital radiographs from carbon steel pipeline welded joints. These images were acquired by a storage phosphor system using double-wall radiographic exposing technique with single-wall radiographic viewing, according to the ASME V code. As a result, even defects that are hard to be detected by human vision are automatically highlighted and extracted from the whole image to be classified in the further stages of the weld inspection process.
This paper introduces new techniques to support industrial radiographic inspection, aiming at automatic corrosion monitoring in pipeline systems. Using the methodologies we proposed, pipeline components to be inspected are automatically detected in the radiographic image, their wall thicknesses are measured and parameters for corrosion detection are computed. For the automatic detection and recognition of pipeline components, we developed a new method that uses image matching techniques in conjunction with genetic algorithms. For measuring the pipe wall thicknesses we used image segmentation techniques based on the analysis of image line profiles. As a result, analysis of pipeline radiographs for corrosion monitoring can be automatically performed, improving the reliability and speed of the inspection process.
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