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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]

To improve the accuracy, rapidity, and versatility of pneumatic control valves, mechanism modeling, and optimization analysis on pneumatic actuators were carried out, and then a new positioning strategy based on parameter tuning and optimal control technique was proposed consequently. Firstly, a dynamic model of valve position based on optimal control under constraint conditions was established, and the characteristic parameters of valve position motion were determined. Then, the adaptive processing of switching points was introduced into the optimal control strategy to improve the versatility of the control. Finally, according to different control strategy partitions, the interactive control of the piezoelectric air switch by fully open control and PWM improves the control accuracy of the algorithm. The experimental analysis of the control effect with other brand positioners shows that the proposed control strategy shortens the system adjustment time, improves the accuracy of valve position, and avoids the overshoot. [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 CuCl2/Al2O3 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. [ABSTRACT FROM AUTHOR]

The understanding of the water effect on olefin selectivity in Fischer–Tropsch synthesis (FTS) is limited by the complexity of the reaction network. Herein, we employ propene hydrogenation as a model reaction to isolate the water effect on olefin adsorption and hydrogenation from the complex reaction of FTS. It is clearly observed that the added water inhibits the activity of propene hydrogenation on two cobalt catalysts supported on high-surface-area alumina (HAS Al2O3) and low-surface-area alumina (LSA Al2O3), respectively. The inhibiting effect is much stronger for Co/HSA Al2O3. DFT investigation demonstrates that the in situ generated OH, rather than H2O and O, impedes the adsorption of propene and thus decreases the activity of propene hydrogenation. The suppressive effect of OH on propene adsorption is attributed to the downshift of the d-band center and the Bader charge of the catalyst surface. The DFT-based kinetic analysis finds that the higher site coverage of OH results in the more pronounced negative effect on propene hydrogenation. Furthermore, the theory of OH-induced weak olefin adsorption and low olefin hydrogenation activity could rationalize the enhancement effect of water on the olefin selectivity and the particle size dependence of the water effect in FTS. The insights obtained here may inspire researchers to optimize olefin selectivity by manipulating the electronic properties of catalysts with hydroxyl species. [ABSTRACT FROM AUTHOR]

Understanding the scaling relations of adsorption energies and activation energies greatly facilitates the computational catalyst design. To reduce the computational cost and guarantee efficiency, improved scaling relations were advocated in this study to rapidly acquire the energetics for transition metal surface reactions and further to rapidly and effectively map the activity of transition-metal catalysts. The overall catalytic activity for the surface reactions between C-, H- and O-containing species could be related to their adsorption energies using C, H and O binding energies as descriptors via improved scaling relations. The UBI-QEP (unity bond index-quadratic exponential potential) method, one of the scaling relations used to estimate the adsorption energies from descriptors, was significantly improved by taking into account the changes in the A–B bond indexes during adsorption and the molecular structure of adsorbed species using density functional theory (DFT) data as a benchmark. The improved UBI-QEP approach could satisfactorily predict the DFT (BEEF-vdW) and experimental adsorption energies. DFT calculations with the BEEF-vdW functional were also employed for establishing the BEP (Brønsted–Evans–Polanyi) relationships as scaling relations to correlate the reaction heats with activation energies for C–H, C–O, C–C, and O–H bond cleavages and recombination. The capability of the improved UBI-QEP+BEP approach was tested as a generic framework to map the activity trend for steam methane reforming (a probe reaction) through microkinetic modeling. The results demonstrated that our approach reduces the computational cost by six orders of magnitude while maintaining a reasonable degree of accuracy as compared to the DFT (BEEF-vdW) and experimental approaches. [ABSTRACT FROM AUTHOR]

Potassium (K), an important impurity in syngas from biomass, can have a large influence on the activity and selectivity of cobalt-based Fischer–Tropsch synthesis (FTS) catalysts in Biomass to Liquids (BTL) processes. In this work, the potassium adsorption behavior on hcp cobalt was systematically studied using density functional theory. The surface energy calculations and Wulff construction of the equilibrium shape of hcp cobalt showed it is dominated by 10 facets. The interaction of K with these facets has been investigated. The results showed that the stepped facet (10−12) has the highest K adsorption energy of −2.40 eV. The facets (0001), (10−10), (10−11), (10−15), and (21−30) also showed relatively high K adsorption energies in the range of −2.28 to −2.34 eV. The corrugated facets exhibited comparatively lower K adsorption energies (−2.04 to −2.18 eV), and would be less favorable for K adsorption. It was also found that the adsorption properties depend on coverage, where the K adsorption energy decreased with increasing coverage. Diffusion energy barrier calculations indicated that K was mobile on typical facets (0001) and (10−11) with very low diffusion barriers (＜0.15 eV). On stepped facets, although K could move freely along the same step (diffusion barrier ＜0.01 eV), diffusion from one step to another had a significantly higher barrier of 0.56 eV. This suggested that K atoms would be mobile to some extent during FTS reaction conditions, and tend to occupy the most favorable sites independent of their initial position. The results obtained in this work provide valuable information on the interaction of K with cobalt surfaces, relevant for practical cobalt catalysts and their application in BTL processes. [ABSTRACT FROM AUTHOR]

• The reaction mechanisms of each step evolve with the surface Cl/Cu ratios. • The origin of the K effect was explored by Van-der-Waals corrected DFT calculations. • Free energy analysis is consistent with the experimental observation. • KCl modifies the electronic structure of the surface. Potassium promoters are widely used in industrial catalysts of ethylene oxychlorination to achieve optimum performance. However, the origin of the promoter effect is not fully understood. Herein, we investigated the potassium promotion effect on CuCl 2 /γ-Al 2 O 3 catalyzed ethylene oxychlorination by kinetic experiments and DFT calculations. Kinetic experiments observed the effect of KCl on individual steps, and the evolution of the reaction mechanisms with Cl/Cu ratios. Further exploration via DFT found that KCl increases the formation energy of Cl vacancy (Δ E v) and thus inhibits the reduction activity, which is attributed to the shift-down of Bader charge of Cl. Δ E v boosts with the decline of Cl/Cu ratios; thus, ethylene can extract Cl atoms from the surface with a Cl/Cu ratio of 2, while it anchors to metal atoms at lower Cl/Cu ratios. The distinct adsorption modes reflect the evolution of reaction mechanisms with Cl/Cu ratios. KCl facilitates the adsorption of oxygen owing to the shift-up of the Bader charge of Cu atom. Potassium reduces the Gibbs free energy barrier of the oxidation step, which agrees with the experimental observation. It is concluded that the potassium promoter effect on the catalytic performance mainly results from the modification of the charge of surface atoms. [ABSTRACT FROM AUTHOR]

Understanding the chain growth mechanism is of vital importance for the development of catalysts with enhanced selectivity towards long-chain products in cobalt-catalyzed Fischer-Tropsch synthesis. Herein, we discriminate various C1 + C1 coupling reactions by theoretical calculations and kinetic isotope experiments. CHx(x=0−3), CO, HCO, COH, and HCOH are considered as the chain growth monomer respectively, and 24 possible coupling reactions are first investigated by theoretical calculations. Eight possible C1 + C1 coupling reactions are suggested to be energetically favorable because of the relative low reaction barriers. Moreover, five pathways are excluded where the C1 monomers show low thermodynamic stability. Effective chain propagation rates are calculated by deconvoluting from reaction rates of products, and an inverse kinetic isotope effect of the C1 + C1 coupling reaction is observed. The theoretical kinetic isotope effect of CO + CH2 is inverse, which is consistent with the experimental observation. Thus, the CO + CH2 pathway, owing to the relatively lower barrier, the high thermodynamic stability, and the inverse kinetic isotope effect, is suggested to be a favorable pathway. [ABSTRACT FROM AUTHOR]

[Display omitted] • Using UV–vis-NIR to analyze the kinetics of ethylene oxychlorination. • Co-promoter effect of alkali and alkali earth metals on ethylene oxychlorination. • Methods to design and predict the reaction rate and Cu2+ concentration for the steady-state. The comprehensive kinetic study for CuCl 2 /γ-Al 2 O 3 -based catalysts was performed to elucidate the co-promoter effect in the ethylene oxychlorination, one of the most significant industrial processes to produce vinyl chloride monomer. Kinetic analysis was performed separately by transient kinetic studies, taking account of the reduction and oxidation steps in the catalytic cycle. The promoter effects on ethylene oxychlorination were ascribed to their influence on the Cu2+ reduction or Cu+ oxidation in the catalytic cycle. The reaction rate-diagram was gained and used to predict the steady-state reaction rate and Cu oxidation state by an operando setup combing MS and UV–vis-NIR spectroscopy. The results indicated that the K-doped catalyst could greatly increase the reaction rate of the oxidation step, which gave rise to higher Cu2+ concentration on the catalyst. Mg-doped catalyst had a great effect on enhancing the reaction rate for the reduction step. K and Mg co-doped catalyst had the dual effect, both the reaction rate and Cu oxidation state were located between K and Mg mono-doped catalyst. The results of steady-state reactions indicated that the reaction rates were quite close with that predicated by the rate-diagram. Byproduct analysis during the steady-state was also performed, the results demonstrated that the co-promoted catalysts can also reduce the byproduct formation. The current study is expected to provide one way for exploring the potential benefits of co-promotion on CuCl 2 /γ-Al 2 O 3 -based industrial oxychlorination catalysts to improve the catalytic performance and understand the reaction further. [ABSTRACT FROM AUTHOR]

• Cofed C 2 H 4 hydrogenated faster than CO, and mainly produced ethane and higher olefins. • Cofed C 2 H 4 followed a similar reaction mechanism as Fischer-Tropsch synthesis. • Cofed C 2 H 4 enhanced chain growth probability and increased C 3+ O/P ratio. • Hydrocracking of cofed C 2 H 4 formed C 1 monomer and the C 1 monomer got further incorporated into the growing chain. Understanding the secondary reactions of olefins in Fischer Tropsch Synthesis (FTS) is crucial for enhancing olefin selectivity in Fischer Tropsch to Olefin (FTO) reaction. Equimolar ethene towards CO was used as a probe to be cofed into the syngas (H 2 : CO = 2) to study the effect on the product distribution over cobalt-based catalysts supported on γ-alumina, α-alumina and carbon nanotube (CNT), respectively. The experiments were performed at T = 210 °C; P tot = 1.85 bar, P CO = 0.11 bar; P H2 = 0.22 bar; P C2H4 = 0 or 0.11 bar with Ar as the balance. On all three catalysts, ethene co-feeding at this concentration inhibited CO conversion and the products were mainly from ethene. The products showed similar Anderson-Schulz-Flory (ASF) distribution as typical for FTS products. Ethene co-feeding can both enhance the chain growth probability and significantly increase the O/P ratio of C 3 -C 6 hydrocarbons. An isotopic switch from 12CO/H 2 /Ar→13CO/H 2 /C 2 H 4 /Kr proved that ethene can hydrocrack to C 1 intermediates and be incorporated into the growing chain. On the other hand, the hydrogenation activity can be significantly suppressed due to the H 2 scavenging effect and possibly competitive adsorption with H 2. [ABSTRACT FROM AUTHOR]

• Small Ni particles exhibit higher steam methane reforming activity than large ones. • The size-dependent activity of Ni catalysts is related to the surface-dependent activity. • Ni(2 1 1) is the most active surface for steam methane reforming. • Decreased Ni(2 1 1) surface fraction results in reduced Ni activity as size increases. Fundamental understanding of the size-dependent activity is essential to harness powers of the nanocatalysts. Here we report an experimental and theoretical study of the Ni particle size effect on activity of steam methane reforming (SMR) to achieve a better understanding of the size dependence of kinetic behavior at an atomic level. A kinetic study illustrated the higher forward methane turnover frequency on the smaller sized Ni particles. The size-dependent activity was well reproduced by microkinetic modeling on a truncated octahedron model with the kinetic parameters estimated by the improved unity bond index-quadratic exponential potential (UBI-QEP) and the Brønsted–Evans–Polanyi (BEP) relationship. Microkinetic modeling suggested that the size-dependent activity of Ni catalysts is associated with the surface-dependent activity. Much higher activity of Ni(2 1 1) than Ni(1 1 1) and Ni(1 0 0) accompanied by decreased Ni(2 1 1) surface fraction results in reduced Ni activity as particle size increases. The activity of Ni(1 1 1) is limited by high free energy barriers, while that of Ni(1 0 0) is limited by site blockage by C* and CH*. This work offers a feasible approach to gain insight into size-dependent activity and to aid rational catalyst design for SMR in which preparing extremely small Ni particles (≤6 nm) might be a good strategy. [ABSTRACT FROM AUTHOR]

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.