ABSTRACT
As a natural gas pipeline approaches the end of its service life, the integrity of the pipeline starts failing because of corrosion or cracks. These and other defects affect the normal production and operation of the pipeline. Therefore, the identification of pipeline defects is critical to ensure the normal, safe, and efficient operation of these pipelines. In this study, a combination of adaptive adjustment based on conversion probability and Gaussian mutation strategy was used to improve the flower pollination algorithm (FPA) and enhance the search ability of traditional flower pollination. The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. A Gaussian mutation strategy was integrated with IFPA to optimise the initial input weights and thresholds of the extreme learning machine (ELM), improve the balance and exploration ability of the algorithm, and increase the efficiency and accuracy for identifying pipeline defects. The proposed IFPA-ELM model for pipeline defect identification effectively overcomes the tendency of FPA to converge to local optima and that of ELM to engage in overfitting, which cause poor recognition accuracy. The identification rates of various pipeline defects by the IFPA-ELM algorithm are 97% and 96%, which are 34% and 13% higher, respectively, than those of FPA and FPA-ELM. The IFPA-ELM model may be used in the intelligent diagnosis of pipeline defects to solve practical engineering problems. Additionally, IFPA could be further optimised with respect to the time dimension, parameter settings, and general adaptation for application to complex engineering optimisation problems in various fields.
Subject(s)
Natural Gas , Pollination , Algorithms , FlowersABSTRACT
This study focuses on a safety evaluation method for underground gas storage. Gas storage is usually constructed underground in complex environments, and the service life of such facilities is limited. To ensure the secure and long-term operation of gas storage facilities, safety evaluation has become the focus of management. The present paper provides an effective method for safety evaluation. An index system was established as the foundation of the analysis for this evaluation, and the matter-element extension method was applied to obtain a quantitative evaluation result. For the weight values of each index in the matter-element extension method, this paper presents a comprehensive weight computation method based on vague sets and entropy. By application of this method, the safety level of a gas storage facility in the Jintan salt mines (in Jiangsu, China) was calculated, and the evaluation result was 4.6433, which meant the safety level was V and the underground gas storage was slightly at risk. It indicated that the influence on the overall safety and tightness of this gas storage could be ignored in the operation process, but the frequency of regular monitoring should be increased. The defective indexes were also obtained, such as salt rock cohesion, the roof thickness, the volume contraction ratio, the interlayer content, the height of the casing shoe and the adjacent cavity pressure difference, which need to be monitored and modified. This paper evaluated the safety of the underground gas storage from a unique perspective. It is expected that the results of this research will contribute to the maintenance and operational decisions, and provide a reference for management in the energy industry.
