Dong Lin

Ambient temperature changes can significantly affect the measurement accuracy of non-dispersive infrared (NDIR) carbon dioxide (CO₂) sensors. This study evaluates a temperature compensation method that uses the Whale Optimization Algorithm (WOA) to optimize the initial weights and biases of a backpropagation neural network (BPNN). The resulting WOA-BPNN model is compared with a conventional BPNN and a particle swarm optimization-optimized backpropagation neural network (PSO-BPNN) under the same experimental protocol. Using the complete 30-point dataset for model fitting, the WOA-BPNN achieved a maximum absolute error of 27.55 ppm and a mean absolute percentage error (MAPE) of 3.87% on the eight measured 45 °C conditions, outperforming BPNN and PSO-BPNN within the tested conditions. These results demonstrate the ability of WOA-BPNN to fit the measured temperature-concentration data and provide a promising basis for temperature compensation.

Due to climate change, corrosive conditions, and hydrogen-rich environments, steel energy pipelines inevitably develop a variety of defects. These deficiencies compromise pipeline safety and reliability, and neglecting them may result in pipeline leaks, fractures, and even potentially catastrophic explosions. Although a considerable body of literature reviews the effects of metal-loss defects like corrosion and cracks on pipeline safety and reliability, the impact of geometric deformation, like dents, lacks a comprehensive review. This work employs a hybrid systematic literature review (SLR) and bibliometric analysis (BA) to investigate the current research status of pipeline dent assessment. Four questions are answered: (1) What are the publication distribution characteristics, active journals, production organizations, and production authors related to research on pipeline dents? (2) What criteria have been employed for evaluating the pipeline dent? (3) From what perspective has the integrity of dented pipelines been assessed, and what research approaches have been used? (4) What are the future challenges and prospects of pipeline dent studies? The findings demonstrate that depth-, strain-, and damage-based evaluation criteria are widely employed to assess pipeline dents, each with merits and limitations. Despite the simplicity and ease of use of depth- and strain-based criteria, they are prone to underestimation flaws. In contrast, damage-based criteria, which consider multiple factors, are limited by their complexity and high computational resource requirements. The reliability of dented pipelines is predicted with remaining strength, fatigue life, and failure pressure using theoretical modeling, experimental testing, numerical simulation, or a combination of these methods. Future dent studies should involve refining numerical models, full-scale testing under varied loading conditions, and integrating advanced sensing techniques for real-time inspection.

Dents are a common geometric deformation defect in pipelines where the dented section becomes susceptible to local buckling, significantly threatening the integrity and reliability of the pipeline. This paper developed a novel finite element (FE) machine learning (ML)-based approach to analyze and predict the critical buckling moment (CBM) of dented pipelines under combined internal pressure and bending moment (BM) loading. By quantifying the parametric effects on CBM and developing a dataset, an Extreme Learning Machine (ELM) framework through hybrid algorithm integration, combining Bald Eagle Search (BES), Lévy flight, and Simulated Annealing (SA), was proposed to achieve highly accurate CBM predictions. This study offers valuable insights into evaluating the buckling resistance of dented pipelines subjected to complex loading conditions.

Existing CO₂ leakage risk assessment frameworks for CO₂ capture, geological storage and utilization (CCUS) projects face limitations due to subjective biases and poor adaptability to long-term scale sequestration. This study proposed a dynamic risk assessment method for CO₂ leakage based on a timeliness analysis of different leakage paths and accurate time-dependent numerical simulations, and it was applied to the first CO₂ enhanced gas recovery (CCUS-EGR) pilot project of China in the Maokou carbonate gas reservoir in the Wolonghe gas field. A 3D geological model of the Maokou gas reservoir was first developed and validated. The CO₂ leakage risk under different scenarios including wellbore failure, caprock fracturing, and new fracture activation were evaluated. The dynamic CO₂ leakage risk of the CCUS-EGR project was then quantified using the developed method and numerical simulations. The results revealed that the CO₂ leakage risk was observed to be the most pronounced when the caprock integrity was damaged by faults or geologic activities. This was followed by leakage caused by wellbore failures. However, fracture activation in the reservoir plays a neglected role in CO₂ leakage. The CO₂ leakage risk and critical risk factors dynamically change with time. In the short term (at 5 years), the project has a low risk of CO₂ leakage, and well stability and existing faults are the major risk factors. In the long term (at 30 years), special attention should be paid to the high permeable area due to its high CO₂ leakage risk. Factors affecting the spatial distribution of CO₂, such as the reservoir permeability and porosity, alternately dominate the leakage risk. This study established a method bridging gaps in the ability to accurately predict long-term CO₂ leakage risks and provides a valuable reference for the security implementation of other similar CCUS-EGR projects.

In the process of shale gas extraction, fracturing technology is often used, and the extracted gas contains large amounts of sand, resulting in the risk of blockage and erosion of surface gathering equipment, which seriously affects safe production....

The oxidation-absorption technology of tail gas is perfect for natural gas purification plants to ensure the up-to-standard discharge of sulfur dioxide emissions, but it can produce a large amount of wastewater. In this paper, a facile and full-scale reuse treatment strategy based on the sequential combination of disc tube reverse osmosis and low-temperature and low-pressure evaporation desalination was proposed and studied. The produced light yellow wastewater was acid sulfate-rich organic wastewater, in which sulfate ions (SO₄²⁻) existed up to 6479 mg/L, and the chemical oxygen demand (COD), 5-day biochemical oxygen demand (BOD₅), total organic carbon (TOC), ammonia nitrogen (ammonia-N), total nitrogen (TN) and suspended solid (SS) were 207, 71.9, 67.6, 1.28, 70.5 and 20.9 mg/L, respectively. After the reuse treatment, there was COD (6 mg/L), BOD₅ (1.4 mg/L), TOC (0.9 mg/L), TN (2.07 mg/L), SS (6 mg/L) and SO₄²⁻ (90 mg/L) in permeated water, and condensate water with COD (11 mg/L), BOD₅ (2.3 mg/L), TOC (4.3 mg/L), SS (2 mg/L) and SO₄²⁻ (1.2 mg/L) was obtained. Thereby, pollution indexes were reduced after the reuse treatment so as to meet the water quality standard (GB/T18920-2022) in China, and the total water recovery rate reached 98.2 vol%. Ultimately, the priority pollutant migration mechanism during the reuse treatment process was determined.