Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy.

Supercapacitors became more and more important recently in the area of energy storage and conversion. Their large power deliveries abilities, high stability and environmental friendliness characteristics draw tremendous attention in high-power applications such as public transit networks. Carbonaceous materials with unique surface and electrochemical properties were widely used in supercapacitors as electrode materials. This review focuses on the developments in supercapacitor electrodes made from carbonaceous materials recently, their working principle and evaluation parameters were summarized briefly. The preparation methods and electrochemical properties of different carbonaceous materials were compared and classified. It was found that the surface situation (e.g., porous structure, hydrophilic) of carbonaceous materials strongly affect the electrochemical performances of supercapacitor. So far, active carbons would be the most applicable carbonaceous electrode materials owing to their good chemical stability and conductivity, extensive accessibility inexpensiveness. But their energy densities still fall behind practical demands. Both theoretical calculations and experimental studies show that surface modification and doping of carbonaceous materials can not only optimize their pore size, structure, conductivity and surface properties, but also can introduce extra pseudocapacitance into these materials. Considering global environmental pollution and energy shortage problems nowadays, we sincerely suggested that future work should focus on domestic, medical and industrial wastes residues derived carbonaceous materials and scaled production process such as reactors and exhaust gas treatment.

Over the past two decades, the alkaline persulfate oxidation (PO) with thermal and/or ultraviolet (UV) assisted digestion method has been widely used for digestion of nitrogen containing compounds (N-compounds) in water quality routine analysis in laboratory or on-line analysis, due to its simple principle, high conversion rate, high percent recovery, low-cost. However, this digestion method still has some inevitable problems such as complex operations, high contamination potential, batch N blanks, higher reaction temperature (120–124 °C) and time-consuming (30–60 min). In this study, ozone (O₃) was selected as the oxidant for digestion of N-compounds through analysis and comparison firstly. Secondly, we proposed and compared the UV and/or ultrasound (US) combined with ozone (UV/O₃, US/O₃ and UV/US/O₃) synergistic digestion methods based on O₃ with sole O₃ oxidation method on digestion efficiency (digestion time and conversion rate) of standard N-compounds. Simultaneously, the influence of reaction temperature, pH of water sample, concentration of O₃ and mass flow rate, UV intensity, US frequency and power on digestion efficiency were investigated, and then the optimum parameters for digestion system were obtained. Experimental results indicated that UV radiation can effectively induce and promote the decomposition and photolysis of O₃ in water to generate hydroxyl radicals (•OH), while US can promote the diffusion and dissolution of O₃ in water and intensify the gas-liquid mass transfer process for the reaction system. Meanwhile, results showed that the UV/US/O₃ synergistic digestion method had the best digestion efficiency under the optimum conditions: water sample volume, 10 mL; pH of water sample, 11; O₃ mass flow rate, 3200 mg/h; reaction temperature, 30 °C; digestion time, 25 min; UV lamp power, 18 W; distance between UV lamp and reactor, 2 cm; US frequency, 20 kHz; US power, 75 W. The conversion rate (CR) of synthetic wastewater samples varied from 99.6% to 101.4% for total dissolved nitrogen (TDN) in the range of 1.0~4.0 mg/L. The UV/US/O₃ synergistic digestion method would be an effective and potential alternative for digestion of N-compounds in water quality routine analysis in laboratory or on-line analysis.

Pipeline transportation is widely used in industrial production and daily life. In order to reduce the waste of resources and economic losses caused by pipeline leakage, effective pipeline leakage detection and localization technology is particularly important. Among the many leakage detection methods, the model-based method for pipeline leakage detection and localization is widely used. However, the key to the method is how to obtain an accurate and reliable pipeline model to ensure and improve the detection accuracy. This paper proposes a novel method to obtain a reliable pipeline-mechanism model that fused data and mechanism models based on Bayesian theory. Moreover, in the process of Bayesian fusion, the complexity and calculations in the mechanism models were greatly reduced by establishing a surrogate model. After that, the multidimensional posterior distribution was sampled by the Markov chain Monte Carlo-differential evolution adaptive metropolis (ZS) (MCMC-DREAM (ZS)) algorithm, and the uncertainty in the model was updated to obtain a reliable pipeline-mechanism model. Subsequently, the pipeline resistance coefficient, which could be calculated based on the reliable pipeline-mechanism model, was proposed as an indicator for detecting whether the pipeline leaked or not. Finally, the pipeline leak model was used to determine the location of the leak. The reliable pipeline-mechanism model was applied in an experimental device to validate its performance. The results showed that the proposed method improved the accuracy and reliability of the mechanism model, and, in addition, the leakage could be accurately located.

Aiming at the drawbacks of conventional methods for the digestion of total dissolved phosphorus (TDP) in water, a novel ozone-based photoactivated synergistic oxidative digestion device based on a high-voltage discharge ozone generator with an oxygen source and a jacketed bubbling reactor was presented. The effects of reaction temperature, reaction time, ozone flow, initial pH of solution, common inorganic anions (Cl^–, \({\text{CO}}_{3}^{{2 - }}\), \({\text{HCO}}_{3}^{ - }\)), and total nitrogen on the digestion efficiency were investigated. Combining the improved ammonium molybdate spectrophotometric method, proposed the new determination method for TDP and verified its performance within the range of 0.1–2.0 mg/L. Results showed that this method has good linearity (R² = 0.9997), precision (0.40%), accuracy (–0.50%), and a low limit of detection (0.0042 mg/L) under the optimized digestion process conditions (reaction temperature of 40°C, reaction time of 20 min, ozone flow of 0.6 L/min, initial pH of 5). This method provides a promising approach for routine analysis or online monitoring of TDP in water.

A novel determination method based on UV/O ₃ synergistic oxidation-malachite green-dispersive liquid–liquid microextraction (DLLME)-spectrophotometry can achieve the enrichment of trace amount of antimony in water and high-precision detection.