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As energy conversion systems continue to grow in complexity, pneumatic control valves may exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system reliability. Consequently, the analysis of time-domain signals and the utilization of artificial intelligence, including deep learning methods, have emerged as pivotal approaches for addressing these challenges. Although deep learning is widely used for pneumatic valve fault diagnosis, the success of most deep learning methods depends on a large amount of labeled training data, which is often difficult to obtain. To address this problem, a novel fault diagnosis method based on the attention-weighted relation network (AWRN) is proposed to achieve fault detection and classification with small sample data. In the proposed method, fault diagnosis is performed through the relation network in few-shot learning, and in order to enhance the representativeness of feature extraction, the attention-weighted mechanism is introduced into the relation network. Finally, in order to verify the effectiveness of the method, a DA valve fault dataset is constructed, and experimental validation is performed on this dataset and another benchmark PU rolling bearing fault dataset. The results show that the accuracy of the network on DA is 99.15%, and the average accuracy on PU is 98.37%. Compared with the state-of-the-art diagnosis methods, the proposed method achieves higher accuracy while significantly reducing the amount of training data. [ABSTRACT FROM AUTHOR]

In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance. [ABSTRACT FROM AUTHOR]

The defect plays a significant role in tuning the electronic structure of metal compounds such as oxides, chlorides, sulfides and nitrides, which is frequently used to explain the enhanced catalytic activity. However, the dynamic change of defect relating to the oxidation state change during the reaction is rarely addressed, and it is still not able to be appropriately predicted since the kinetic modeling of the redox reaction involving the in‐situ formation of defects is challenged by the dynamic evolution of the active sites and the complex kinetic phenomena. Here, for the first time, we present a general method to build a new kinetic model of the redox reaction cycle by combined kinetics of reduction‐ and oxidation steps to predict the time and spatially resolved formation of defects of solid catalysts and the reaction products in ethylene oxychlorination on CuCl2/Al2O3 catalyst. The defect concentration can be tuned by manipulating the relative rates of the reduction‐ and oxidation steps. [ABSTRACT FROM AUTHOR]

A bifunctional catalyst comprising CuCl 2 /Al 2 O 3 and nitrogen-doped carbon was developed for an efficient one-pot ethylene oxychlorination process to produce vinyl chloride monomer (VCM) up to 76 % yield at 250 °C and under ambient pressure, which is higher than the conventional industrial two-step process (≈50 %) in a single pass. In the second bed, active sites containing N-functional groups on the metal-free N-doped carbon catalyzed both ethylene oxychlorination and ethylene dichloride (EDC) dehydrochlorination under the mild conditions. Benefitting from the bifunctionality of the N-doped carbon, VCM formation was intensified by the surface Cl*-looping of EDC dehydrochlorination and ethylene oxychlorination. Both reactions were enhanced by in situ consumption of surface Cl* by oxychlorination, in which Cl* was generated by EDC dehydrochlorination. This work offers a promising alternative pathway to VCM production via ethylene oxychlorination at mild conditions through a single pass reactor., (© 2020 The Authors. Published by Wiley-VCH GmbH.)