Qingqing Wang

As the main means of energy transportation, pipelines have the characteristics of 24 h uninterrupted transportation, low cost, strong transmission capacity, and low risk, and they have become an important device for natural gas transportation. However, due to defects caused by the pipeline itself and external forces, it is easy for natural gas leakage to cause major accidents, serious environmental pollution, and huge losses of life and property. Traditional pipeline defect detection technology can be interfered with by a large number of noise signals when collecting defect signals, and the noise reduction method is simple but has poor accuracy, so it cannot directly obtain effective pipeline defect information from the signal and cannot effectively improve the emergency intensity and emergency level of pipeline operation and maintenance management. Therefore, a new noise reduction method, ICEEMDAN-LMS, for natural gas pipelines is proposed in this paper, and the method is calculated and tested in detail. The effectiveness of the new method is verified through the analysis of a pipeline defect signal and normal signal. Moreover, the proposed method can reduce noise more effectively than conventional methods, which has clear value in engineering applications. Importantly, this noise reduction method not only provides a reliable basis for the intelligent diagnosis of pipeline defect signals, but can also be an important reference for helping management departments to make decisions and emergency plans and formulate on-site treatment programs.

Some natural gases are toxic because they contain hydrogen sulfide (H₂S). The solubility pattern of elemental sulfur (S) in toxic natural gas needs to be studied for environmental protection and life safety. Some methods (e.g., experiments) may pose safety risks. Measuring sulfur solubility using a machine learning (ML) method is fast and accurate. Considering the limited experimental data on sulfur solubility, this study used consensus nested cross-validation (cnCV) to obtain more information. The global search capability and learning efficiency of random forest (RF) and weighted least squares support vector machine (WLSSVM) models were enhanced via a whale optimization–genetic algorithm (WOA-GA). Hence, the WOA-GA-RF and WOA-GA-WLSSVM models were developed to accurately predict the solubility of sulfur and reveal its variation pattern. WOA-GA-RF outperformed six other similar models (e.g., RF model) and six other published studies (e.g., the model designed by Roberts et al.). Using the generic positional oligomer importance matrix (gPOIM), this study visualized the contribution of variables affecting sulfur solubility. The results show that temperature, pressure, and H₂S content all have positive effects on sulfur solubility. Sulfur solubility significantly increases when the H₂S content exceeds 10%, and other conditions (temperature, pressure) remain the same.