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The misalignment and lead crown relief make the tooth partial contact along the tooth width direction, which greatly affects the mesh characteristics. In this paper, a mesh stiffness model of spur gear pairs with misalignment and lead crown relief is proposed based on the slice theory, in which the slice coupling effect is presented. The slice coupling is simulated as a spring and the corresponding model is developed. Then, the solving procedure of the mesh stiffness model is studied. The deformation transfer model is derived between the springs in contact state and non-contact state. An iterative computation method is designed to calculate the mesh deformation of tooth pairs. Finally, the mesh stiffness model is validated by the finite element method for a spur gear pair with ideal tooth profile, misalignment and lead crown relief. Effects of the misalignment and lead crown relief on mesh stiffness of gear pairs are also studied. The results reveal that the proposed model is accurate, effective and efficient for spur gear pairs with ideal tooth profile, misalignment and lead crown relief. It is suggested that the slice coupling should be considered in mesh stiffness calculation of gear pairs with misalignment and lead crown relief.

Feature extraction is a major challenge in operational reliability assessment, which requires techniques and prior knowledge. Deep auto-encoder (DAE) is a popular deep learning method and is widely used in feature extraction. However, low generalization ability and structure parameters design are still the major problems of DAE for operational reliability assessment. To overcome the two problems, an ensemble DAE is proposed for operational reliability assessment. Firstly, different structure parameters are employed to design a series of DAEs for feature learning from the measured data. Secondly, a feature ensemble strategy is designed to enhance the generalization ability of the DAE model, in which the features learned by different DAEs are clustered to remove the irrelevant DAEs and select the more general feature subset. Finally, the operational reliability indicator is defined by the Euclidean distance of the selected features and the operational reliability model is developed. The proposed method is utilized to analyze the experimental bearings and the results indicate that the proposed method is effective for operational reliability assessment.

Real-time monitoring and rapid evaluation of bearing operating conditions, especially for the reliability evaluation and remaining useful life (RUL) prediction, are major challenges in the rotating machinery field. A two-dimensional (2-D) deep convolution neural network (CNN) is proposed for rapidly evaluating reliability and predicting RUL, in which a signal conversion method is proposed for converting the one-dimensional signal into the 2-D image to satisfy the input requirements of the 2-D CNN. Different activation functions are employed to implement the conversion of the input data to the output data for each layer of the network, and dropout is only adopted in the hidden layer to change the network structure to prevent overfitting. The maximum correlation entropy with regular terms is employed as the loss function of the model to obtain better training performance compared with the mean square error (MSE). Then, the rolling bearing degradation vibration data is applied to the proposed model to verify the accuracy and rapidity. The results show that the proposed method has good accuracy and fast calculation ability in bearing reliability evaluation and RUL prediction, especially, its time consumption is shorter than that by other deep learning networks.

Mathematical models based on instant environmental inputs are increasingly applied to optimize the operation of wastewater treatment plants (WWTPs) for improving treatment efficiency. This study established a numerical model consisting of the activated sludge module ASM3 and EAWAG bio-P module, and calibrated the model using data from a full-scale experiment conducted in a WWTP in Nanjing, China. The calibrated model was combined with online sensors for water temperature, chemical oxygen demand, -N and -P to optimize and dynamically adjust the operation of the WWTP. The results showed that, compared to the original default operation mode, the effluent water quality was significantly improved after optimization even without supplementation of external carbon or alkalinity, and the required aeration rate in spring, summer, autumn, and winter was reduced by 15, 41, 33 and 11%, respectively. The study indicated that there was the potential for application of closed-loop automatic control to regulate operating parameters to improve wastewater treatment processes through the integration of data on influent characteristics and environmental conditions from sensors, and results from simulation models.

An improved time-varying mesh stiffness (TVMS) model of a helical gear pair is proposed, in which the total mesh stiffness contains not only the common transverse tooth bending stiffness, transverse tooth shear stiffness, transverse tooth radial compressive stiffness, transverse gear foundation stiffness and Hertzian contact stiffness, but also the axial tooth bending stiffness, axial tooth torsional stiffness and axial gear foundation stiffness proposed in this paper. In addition, a rapid TVMS calculation method is proposed. Considering each stiffness component, the TVMS can be calculated by the integration along the tooth width direction. Then, three cases are applied to validate the developed model. The results demonstrate that the proposed analytical method is accurate, effective and efficient for helical gear pairs and the axial mesh stiffness should be taken into consideration in the TVMS of a helical gear pair. Finally, influences of the helix angle on TVMS are studied. The results show that the improved TVMS model is effective for any helix angle and the traditional TVMS model is only effective under a small helix angle.

It is significant to develop economically cheaper, more environmentally friendly bifunctional electrocatalysts for both water reduction reaction (WRR) and water oxidation reaction (WOR). In this paper, a NiO/Ni3S2 core–shell hybrid directly grown on nickel foam is constructed using a simple two-step hydrothermal and sulfuration method. When the NiO/Ni3S2/NF electrode is first tested as a catalyst, it reveals a high catalytic activity and durability for WOR. The smaller overpotential of only 270 mV obtained at 30 mA cm−2 is better than the benchmark of precious metal RuO2 materials (410 mV @ 30 mA cm−2), benefiting from the unique hierarchical structure of NiO/Ni3S2/NF with more active sites and fast electron transfer. Moreover, the NiO/Ni3S2/NF electrode also exhibits a fairly good WRR performance compared with the individual NiO/NF and NF electrodes, showing a smaller overpotential of only 130 mV at 10 mA cm−2. In addition to this, a very low cell voltage of 1.59 V is obtained at 10 mA cm−2 in 1.0 M KOH, when NiO/Ni3S2/NF is also tested as a bifunctional water splitting electrode. The experimental results indicate that the oxide/sulfide-based catalysts could serve as an ideal electrode candidate and bring the superiority of a synergistic strategy into full play, which offers a new idea toward the development of efficient and environmentally friendly water splitting electrochemical electrode materials.

A “slice method” is adopted to calculate the time-varying mesh stiffness (TVMS) of helical gear pairs, where the tooth is divided into many individual sliced spur gears along the tooth width, and the TVMS of helical gear pairs is obtained by accumulating the TVMS of the sliced spur gear pairs. Based on the slice method, an improved analytical method (IAM) is proposed to calculate TVMS of the helical gear pairs. The proposed method is also verified by comparing TVMS obtained from IAM with that obtained from references and the finite element method. Based on the proposed IAM, the effects of the helix angle, gear width, modification coefficient and friction coefficient on mesh characteristics are also discussed. The results show that the total contact ratio of helical gear changes under different helix angles and gear tooth widths, and the fluctuation of mesh stiffness will reduce when the overlap contact ratio approaches an integer. Meanwhile, addendum modifications can not only change the total contact ratio but also affect the mean mesh stiffness of gear pairs. Furthermore, not only will the friction coefficients affect the mean mesh stiffness, and it can lead to an abrupt change of TVMS in double-teeth engagement region.

Direct organic recovery is an alternative option for enhanced energy recovery to achieve energy self-sufficiency or even carbon neutrality for wastewater treatment plants. Previous research reported that hybrid coagulation/adsorption microfiltration is efficient in recovering sewage organics. However, the fouling mechanism of adsorbent cake layer, which was formed during hybrid organic recovery process, needs to be evaluated to develop an appropriate fouling control strategy. In this study, the effects of adsorbent cake layer on the irreversible fouling during actual sewage concentration were analyzed quantitatively. Filtration resistance analysis based on dead-end filtration experiments provided evidences that the existence of adsorbent cake layer prevented severe irreversible fouling. After three filtration cycles, the reduced irreversible fouling by cake layer was as high as 20.84 × 1010 m−1, which was nearly three times of the resistance 7.04 × 1010 m−1 when no cake layer existed. This confirmed that the adsorbent cake layer controlled fouling, demonstrating that hybrid coagulation/adsorption microfiltration as a promising process for sewage organic recovery.

Time-varying mesh stiffness (TVMS) and the dynamic coupling between the helical gears have a great influence on the vibration characteristics of a helical gear rotor system. Considering the effects of TVMS and adopting two coupling models (lateral-torsional coupling model and lateral-torsional-axial-swing coupling model), the dynamic behavior of a helical gear system was studied. First, an analytical model was used to analyze TVMS of a helical gear pair where the helical tooth is simulated by many spur tooth slices along the direction of the tooth width and the mesh stiffness of each slice is calculated using the energy method. Then, considering the effects of the TVMS excitation, the finite element model of a helical gear rotor system was established. Gear mesh was simulated by the above-mentioned two coupling models to investigate the effects of coupling forms on the system vibration characteristics. The strain energy was used to distinguish the dominant mode and dominant shaft of a gear system in natural characteristics analysis. The results show that the full coupling model can analyze accurately the vibration characteristics of the system and the axial and swing motions cannot be ignored in vibration analysis. Finally, the effects of helix angle on TVMS and vibration responses of a helical gear system were also studied.

The removal of total nitrogen in wastewater treatment plants (WWTPs) is often unsatisfactory for a variety of reasons. One possible measure to improve nitrogen removal is the addition of external carbon. However, the amount of carbon addition is directly related to WWTP operation costs, highlighting the importance of accurately determining the amount of external carbon required. The objective of this study was to obtain a low nitrate concentration in the anoxic zone of WWTPs efficiently and economically by optimizing the external carbon source dosage. Experiments were conducted using a pilot-scale pre-denitrification reactor at a Nanjing WWTP in China. External carbon source addition based on online monitoring of influent wastewater quality and a developed nitrification–denitrification numerical model was investigated. Results showed that carbon addition was reduced by 47.7% and aeration costs were reduced by 8.0% compared with those using a fixed-dose addition mode in the pilot reactor. The obtained technology was applied to the full-scale Jiangxinzhou WWTP in Nanjing with promising results.

RUL prediction of bearings plays an essential role in avoiding unwanted downtime and improving machines' reliability. A linear reliability indicator approach for RUL prediction is proposed. Multiple deep auto-encoder models are established to extract deep features, and a clustering method is employed to select the original feature set. Monotonicity is set as a criterion for evaluating and selecting original optimal features from the original feature set and a linear reliability indicator is established through it. Based on linear reliability indicator and feature interpolation, the original feature set is transformed into a mapping feature set. Finally, the mapping feature set is trained by a reliability evaluation model and the particle filter is employed to predict RUL. To demonstrate the excellent capability of the proposed approach, the vibration signal from the public PRONOSTIA bearing datasets is used. Experiment results show that the proposed approach can achieve high accuracy in RUL prediction.

A model is introduced for analyzing the influence of tooth shape deviations and assembly errors on the helical gear mesh stiffness, loaded transmission error, tooth contact stress and tooth root stress. The helical gear is approximated as a series of independent spur gear slices along axial direction whose face-width is relatively small. The relative position relationships among those sliced teeth in mesh are developed with tooth profile errors and the stiffness of the sliced tooth is calculated by the potential energy method. From the equilibriums of the forces, gear mesh stiffness, loaded transmission error, tooth contact stress and tooth root stress are calculated. Then two cases are presented for validation of the model. It is demonstrated that the model is effective for calculating the stiffness of helical gear pairs. Finally, the effects of the tooth tip reliefs, lead crown reliefs and misalignments on the gear mesh stiffness, transmission error, tooth contact stress and tooth root stress are analyzed. The results show that mesh stiffness decreases, loaded transmission error, the maximum tooth contact stress and the maximum tooth root stress grow with the increasing tooth tip relief, lead crown relief and misalignment. And tooth edge has concentrated tooth contact stresses with a gear misalignment.

Remaining useful life (RUL) prediction has been a hotspot topic, which is useful to avoid unexpected breakdowns and improve reliability. Different bearing failure behaviors caused by multiple failure modes may lead to inconsistent feature distribution, which affects the prediction model performance. To accurately predict the RUL of bearing under different failure behaviors, a transferable convolutional neural network (TCNN) is proposed to learn domain invariant features. In the proposed method, a convolutional neural network is employed to extract the degradation features. Then multiple-kernel maximum mean discrepancies are integrated into optimization objective to reduce distribution discrepancy. The trained TCNN can be used to predict RUL by feeding data. Its effectiveness is verified by a run-to-failure bearing dataset. The comparison results reveal that the proposed method avoids the influence of kernel selection, improves the performance of domain adaptation effectively, and achieves a better RUL prediction performance.

It is imperative to develop bifunctional electrocatalysts with good activity and stability for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). In this paper, a hierarchical NiCo2O4/Ni3S2 hybrid is synthesized by direct growth on nickel foam (NF) using co-precipitation and sulfuration as a general two-step method. When the NiCo2O4/Ni3S2/NF material is used as an electrode, it displays high activity and perfect stability for OER. A low overpotential of only 360 mV is obtained at 40 mA cm-2, comparable to the benchmark of IrO2 electrodes (330 mV overpotential at 40 mA cm-2), benefiting from the particular hybrid structure of NiCo2O4/Ni3S2/NF with large surface area and fast electron transfer. In addition, the NiCo2O4/Ni3S2/NF sample also reveals a superior elevated HER activity compared to NiCo2O4/NF and NF catalysts individually, for which a low overpotential of only 143 mV is obtained at 10 mA cm-2. Beyond that, NiCo2O4/Ni3S2/NF is also used as a bifunctional water splitting catalyst, for which a very low cell voltage of 1.58 V is acquired at 10 mA cm-2 in 1.0 M KOH. The results reveal that oxide/sulfide-based materials can be used as a perfect electrode candidate and afford the advantage of the synergy strategy, which opens a new route toward desired water splitting electrochemical devices of high activity and environmentally friendly electrode materials.

Sequence batch reactors (SBR) with simultaneous feeding/draw mode and low-strength wastewater were used for the cultivation of aerobic granules, and analysis of bacterial community diversity were conducted. Results revealed that the ratio of chemical oxygen demand/total nitrogen removal amount for R1 with real wastewater and R2 with synthetic wastewater decreased from 9.9 to 8.7 and, 29.9 to 21.1, respectively, when volumetric exchange ratio (VER) decreased from 90% (stage I) to 50% (stage II), indicating that organic matter in real and low-strength wastewater was fully utilized with lower VER by denitrifying bacteria. Relative abundances of the genus Dechloromonas, Pseudomonas, Bacillus in R1, which are responsible for denitrifying phosphorus removal, were much higher than that in R2, accounting for the high efficiency of nitrogen and phosphorus removal from real wastewater with low influent C/N ratio of 3.6 on average. These results provide useful information for improving wastewater treatment efficiency in the future.

Tungsten trioxide/reduced graphene oxide (WO3/RGO) nanocomposite film was fabricated by the sol–gel spin-coating technique, using a mixed colloidal dispersion of WO3 precursor and GO. The structure, surface morphology and optical properties of WO3 and WO3/RGO films were investigated by means of X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy, high-resolution transmission electron microscopy and UV-visible spectrophotometry. The electrochemical and electrochromic performances of the WO3 and WO3/RGO films were studied by chronoamperometry, in situ optical transmittance and cyclic voltammogram analysis. In the WO3/RGO nanocomposite film, monoclinic WO3 nanoparticles were grafted onto the surface of RGO sheets via C–W and C–O–W chemical bonds. The results showed that the WO3/RGO nanocomposite film exhibited shorter coloration–bleaching times (tc = 9.5 s and tb = 7.6 s), higher coloration efficiency (75.3 cm2 C−1 at 633 nm), larger optical modulatory range (59.6% at 633 nm) and better cyclic stability compared with WO3 films, which are attributed to faster Li+ ion diffusion and electron transfer rate. The methodology provides a facile and industrially scalable approach to obtain fast switching electrochromic materials.

Performance of a full-scale anoxic-oxic activated sludge treatment plant (4.0×10(5) m(3)/day for the first-stage project) was followed during a year. The plant performed well for the removal of carbon, nitrogen and phosphorus in the process of treating domestic wastewater within a temperature range of 10.8°C to 30.5°C. Mass balance calculations indicated that COD utilization mainly occurred in the anoxic phase, accounting for 88.2% of total COD removal. Ammonia nitrogen removal occurred 13.71% in the anoxic zones and 78.77% in the aerobic zones. The contribution of anoxic zones to total nitrogen (TN) removal was 57.41%. Results indicated that nitrogen elimination in the oxic tanks was mainly contributed by simultaneous nitrification and denitrification (SND). The reduction of phosphorus mainly took place in the oxic zones, 61.46% of the total removal. Denitrifying phosphorus removal was achieved biologically by 11.29%. Practical experience proved that adaptability to gradually changing temperature of the microbial populations was important to maintain the plant overall stability. Sudden changes in temperature did not cause paralysis of the system just lower removal efficiency, which could be explained by functional redundancy of microorganisms that may compensate the adverse effects of temperature changes to a certain degree. Anoxic-oxic process without internal recycling has great potential to treat low strength wastewater (i.e., TN