A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Abstract

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream. To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional (3-D) space, is a strategic problem due to its large-scale, complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

Dynamic Monitoring System of Oil Pipeline Leakage for Oil and Gas Safety

“…According to the theory of accidental release of energy caused by accidents, accidents are the accidental release of energy or dangerous substances. Excessive energy acting on the human body or dangerous substances interfering with the energy exchange between the human body and the outside world are the direct causes of personal injury [6] . The process of hazard identification mainly includes data collection, uncertainty analysis, hazard identification, hazard classification, hazard classification, preparation of hazard identification report, etc., as shown in Figure 2: It can be seen from Figure 2 that the risk is caused by incomplete data or information.…”

“…In formula (6), R represents the outlet temperature vector of the heating furnace in the oil transfer station, L represents the total number of heating furnaces in the oil transfer station, φ represents the total number of internal and external transmission pumps in the oil transfer station, M represents the flow of internal and external transmission pumps in the oil transfer station, N represents the fuel cost required by the oil transfer station to heat the exported oil, and E represents the power cost required by the oil transfer station to pressurize the exported oil. When solving, first determine the oil transportation temperature of the pipeline, then calculate the oil transportation pressure under the given oil transportation temperature, and then judge whether the scheme is optimal.…”

Water Saving Control of Turfgrass Irrigation Robot Using Genetic Simulated Annealing Algorithm

“…e simulated annealing algorithm has strong local searchability. It can jump out of the local minimum and tend to the global optimum [36,37]. In addition, it has good asymptotic convergence, simple description, flexible application, suitable for parallel calculation, and overcomes initial value dependence.…”

Efficient unmanned aerial vehicle paths design for post‐disaster damage assessment of overhead transmission lines

“…In ref. [24], the authors used mixed‐integer non‐linear programming model to minimise the inspection path length of UAVs for oil and gas pipeline networks. An energy efficient path planning algorithm for UAVs was proposed in ref.…”