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.

Reducing the emissions of greenhouse gas is a worldwide problem that needs to be solved urgently for sustainable development in the future. The solubility of CO₂ in ionic liquids is one of the important basic data for capturing CO₂. Considering the disadvantages of experimental measurements, e.g., time-consuming and expensive, the complex parameters of mechanism modeling and the poor stability of single data-driven modeling, a multi-model fusion modeling method is proposed in order to predict the solubility of CO₂ in ionic liquids. The multiple sub-models are built by the training set. The sub-models with better performance are selected through the validation set. Then, linear fusion models are established by minimizing the sum of squares of the error and information entropy method respectively. Finally, the performance of the fusion model is verified by the test set. The results showed that the prediction effect of the linear fusion models is better than that of the other three optimal sub-models. The prediction effect of the linear fusion model based on information entropy method is better than that of the least square error method. Through the research work, an effective and feasible modeling method is provided for accurately predicting the solubility of CO₂ in ionic liquids. It can provide important basic conditions for evaluating and screening higher selective ionic liquids.

Process design and control are closely related to each other in chemical engineering activities. Traditionally, process design and control system design are carried out in sequence. However, the integration of process design and control (IPDC) can bring greater economic benefits and process dynamic performance than traditional sequential design methods. This is true, particularly for modern chemical processes, in which various process units become more interacting and compact owing to the widespread use of heat integration and recycled streams, and the resulted impacts between process design and control begin to significantly influence both the capital and operational costs. Recently, considerable studies about the IPDC for chemical processes have been reported in published literature. The purpose of the paper is to survey the applications of optimization-based integrating process design and control for chemical processes. Firstly, attention has been focused on the applications of IPDC to different process units, for example, chemical reactors and separation columns. Then, the survey is extended to the applications of IPDC to plant-wide chemical processes. Finally, the future research challenges in the application of IPDC to chemical processes have been briefly discussed.

Solubility data is one of the essential basic data for CO₂ capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO₂ in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO₂ solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO₂ in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO₂ capture.

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.

The productivity of batch processes is related to the time required to complete each batch. A self-adjustment optimal method for the vinyl chloride (VC) suspension polymerization is proposed for minimizing polymerization time. By optimizing the concentration of the composite initiators, the polymerization reaction proceeds smoothly so that the cooling capacity of the reactor is fully utilized and the polymerization time is minimized accordingly. In this work, a thermodynamic model is presented to estimate both online reaction advancement and the heat transfer characteristic of the reactor; a kinetic model of composite initiators is referenced to predict the polymerization rate; and a parameter adjustment model is applied to adjust the initiator partition coefficient of the kinetic model for ensuring the prediction performance and compute the maximum limiting polymerization rate for updating the constraints of optimization model. Results show that by choosing the optimal concentration of composite initiators under the constraints of reaction kinetics, maximum limiting polymerization rate, etc., a significant reduction in the total processing time is achieved.