After around 50 years of development, the key substance known as polyethylene has been extremely influential in a variety of industries. This paper investigates how polyethylene materials have been used in the domains of water, packaging, and medicine to advance contemporary society in order to comprehend the physical and chemical alterations that polyethylene undergoes after being subjected to long-term environmental variables (e.g., temperature, light, pressure, microbiological factors, etc.). For the safe operation of polyethylene materials, it has always been of the utmost importance to evaluate polyethylene’s service life effectively. This paper reviews some of the most common literature journals on the influence of environmental factors on the degradation process of polyethylene materials and describes methods for predicting the lifetime of degradable polyethylene materials using accelerated aging tests. The Arrhenius equation, the Ozawa–Flynn–Wall (OFW) method, the Friedman method, the Coats–Redfern method, the Kissinger method and Kissinger–Akahira–Sunose (KAS) method, Augis and Bennett’s method, and Advanced Isoconversional methods are all discussed, as well as the future development of polyethylene.

Metal pipes are often interfered with by currents of irregular frequency and wavelength. This is called stray current interference. The corrosion of metal pipes caused by stray current interference is one of the many factors contributing to their deterioration. Pipeline corrosion and leakage caused by stray currents can significantly impact the safety, environment, and economy of the surrounding areas. Over the past few years, stray current interference has become one of the research hotspots in the petrochemical and natural gas transportation industries. This review article investigates accident cases caused by stray currents and compares the corrosion mechanisms of DC stray currents and AC stray currents, analyzes the influence of pipeline material, environment, voltage and current, stress, and other factors on corrosion, and puts forward corrosion protection measures, such as cathodic protection, coating technology, internal corrosion control, and means of detecting stray current corrosion. Finally, it provides an outlook on future research directions on corrosion protection against stray currents.

For the problem of classification and identification of defects in polyethylene (PE) gas pipelines, this paper firstly performs preliminary screening of the acquired images and acquisition efficiency of defective image acquisition was improved. Images of defective PE gas pipelines were pre-processed. Then, edge detection of the defective images was performed using the improved Sobel algorithm and an adaptive threshold segmentation method was applied to segment the defects in the pipeline images. Finally, the defect images were morphologically processed to obtain binary images. The obtained binary images were applied with VGG16 to complete the training of the defect classifier. The experimental findings show that in the TensorFlow API environment, the test set’s highest accuracy reached 97%, which can achieve the identification of defect types of underground PE gas transmission pipelines.

In recent years, polyethylene (PE) pipes have been widely utilized for urban natural gas transmission. However, as the use of high-density polyethylene (HDPE) pipes increases, their service life and long-term performance assessment have become one of the most significant issues to be addressed. There has been a ton of studies on PE pipe life prediction techniques both domestically and internationally, but very little has been carried out on PE pipe life prediction in actual gas working environments with varying acid and alkaline levels. This experiment accelerates the aging of PE pipes using acid and alkaline corrosion immersion experiments to determine the lifespan of PE pipes. This study aims to investigate the performance changes of HDPE under strong, weak, and neutral corrosion conditions using corrosion solutions with PH values of 1, 5, and 8, to propose the impact of corrosion caused by various acids and alkalies on the HDPE aging life for natural gas, and to develop a mathematical model between the aging life of polyethylene and the PH values of acid and alkali corrosion solutions. The studies involved soaking and corroding HDPE pipes with various acidity and alkalinity chemicals to speed up the aging process, and then the tensile test was used to determine the mechanical characteristics of the aged PE pipes. Based on our findings, the empirical equation between acidity and service life of PE pipes is obtained by the mathematical fitting method, and a life prediction model of buried city gas HDPE pipes is proposed. The actual life of the aged pipes is determined by the relationship between strain-hardening (SH) modulus and aging time. The findings demonstrate that the service life of PE pipes changes with different levels of acidity and alkalinity: 1.872 days, 1060.507 days, and 1128.58 days following corrosive solution-accelerated aging with solution acidities of PH1, PH5, and PH8, respectively. The life prediction method applies to various plastic pipes in comparable environments as well as HDPE city gas pipes that are subject to acid and alkali corrosion forces.

Polyethylene (PE) pipes are widely used in urban gas transportation due to their good toughness and corrosion resistance. Currently, the designed service life of a PE pipeline is 50 years, and some urban gas PE pipelines are approaching their service life. However, research on the aging assessment of PE pipelines is not complete, and it is impossible to effectively predict their aging status in service. Once urban gas PE pipelines are damaged, serious accidents may be caused. PE80 and PE100 pipelines are commonly used for urban gas, and improved accelerated aging tests were conducted considering different conditions of pressure, pipe diameter, and temperature. According to the experimental results, an aging life prediction method for PE pipes was constructed based on the Arrhenius formula considering multiple effect factors.

Gas polyethylene (PE) pipes have an become essential component of the urban gas pipeline network due to their long service life and corrosion resistance. To prevent safety incidents, regular monitoring of gas pipelines is crucial. Traditional inspection methods face significant challenges, including low efficiency, high costs, and limited applicability. Machine vision-based inspection methods have emerged as a key solution to these issues. Despite this, the method also encounters the problem of scarcity of defect samples and uneven data distribution in gas pipeline defect detection. For this reason, an improved Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. By integrating the Minibatch Discrimination (MD), Spectral Normalization (SN), Self-Attention Mechanism (SAM) and Two-Timescale Update Rule (TTUR), the proposed approach overcomes the original DCGAN’s limitations, including mode collapse, low resolution of generated images, and unstable training, the data augmentation of defective images inside the pipeline is realized. Experimental results demonstrate the superiority of the improved algorithm in terms of image generation quality and diversity, while the ablation study validates the positive impact of the improvement in each part. Additionally, the relationship between the number of augmented images and classification accuracy, showing that classifier performance improved across all scenarios when generated defect images were included. The findings indicate that the images produced by the improved model significantly enhance defect detection accuracy and hold considerable potential for practical application.