WSSe Nanocomposites for Enhanced Photocatalytic Hydrogen Evolution and Methylene Blue Removal under Visible-Light Irradiation.

In this study, a novel tungsten disulfide diselenide (WSSe) nanocomposite by a facile hydrothermal process with great capable photocatalytic efficiency for hydrogen evolution from water and organic compound removal was discussed. The WSSe nanocomposites form heterojunctions in order to inhibit the quick recombination rate of photo-induced electrons and holes. This is considered to be a useful method in order to enhance the capability of photocatalytic hydrogen production. The hydrogen production rate of the WSSe nanocomposites approaches 3647.4 µmol/g/h, which is 12 and 11 folds the rates of the bare WS2 and WSe2, respectively. Moreover, the excellent photocatalytic performance for Methylene blue (MB) removal (88%) was 2.5 and 1.8 times higher than those of the bare WS2 and WSe2, respectively. The great photocatalytic efficiency was owing to the capable electrons and holes separation of WSSe and the construction of the heterostructure, which possessed vigorous photocatalytic oxidation and reduction potentials. The novel one-dimensional structure of the WSSe heterojunction shortens the transport pathway of the photo-induced electrons and holes. It possesses the great capable photocatalytic efficiency of the hydrogen production and organic dye removal. This study offers an insight into the route of interfacial migration and separation for induced charge carriers in order to generate clean hydrogen energy and to solve the issue of environmental pollution.

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning.

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller's health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller's health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning.

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird's eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Survey on nitrogen removal and membrane filtration characteristics of Chlorella vulgaris Beij. on treating domestic type wastewaters.

The main objective of this study is to evaluate the nitrogen assimilation and filtration characteristics of Chlorella vulgaris Beij. when treating domestic wastewaters. Chlorella could assimilate organic nitrogen, ammonia and nitrate in wastewater, and the mean cell residence time (MCRT) to achieve the maximum biomass content in a bioreactor was different for each individual nitrogen source used. The experimental results showed that using nitrate as the only nitrogen source was the most favorable for biomass growth. With ammonia and nitrate coexisting in the aquatic phase, Chlorella possibly utilized ammonia first, and this was unfavorable to subsequent biomass growth. Nitrifying bacteria in wastewaters significantly affected Chlorella growth as they possibly competed with Chlorella in assimilating ammonia and nitrate in domestic wastewater. In a submerged ultrafiltration (UF) membrane module, with an initial concentration of 850 mg/L of Chlorella, the optimized flux was 0.02 m(3)/(m(2)·h), and the filtration cycle was 30 min. A 'dual membrane bioreactor (MBR)' configuration using UF membranes for Chlorella incubation was proposed. MBR1 provides an environment with long MCRT for efficient nitrification. The converted nitrate is assimilated by Chlorella in MBR2 to sustain its growth. UF permeate from MBR1 is bacteria-free and does not affect the growth of Chlorella in MBR2. MCRT of Chlorella growth is controlled by the UF membrane of MBR2, providing the flexibility to adjust variations of nitrogen composition in the wastewater.

Survey on production quality of electrodialysis reversal and reverse osmosis on municipal wastewater desalination.

Water shortage has become an emerging environmental issue. Reclamation of the effluent from municipal wastewater treatment plant (WWTP) is feasible for meeting the growth of water requirement from industries. In this study, the results of a pilot-plant setting in Futian wastewater treatment plant (Taichung, Taiwan) were presented. Two processes, sand filter - ultrafiltration - reverse osmosis (SF-UF-RO) and sand filter - electrodialysis reversal (SF-EDR), were operated in parallel to evaluate their stability and filtrate quality. It has been noticed that EDR could accept inflow with worse quality and thus required less pretreatment compared with RO. During the operation, EDR required more frequent chemical cleaning (every 3 weeks) than RO did (every 3 months). For the filtrate quality, the desalination efficiency of SF-EDR ranged from 75 to 80% in continuous operation mode, while the conductivity ranged from 100 to 120 µS/cm, with turbidity at 0.8 NTU and total organic carbon at 1.3 mg/L. SF-EDR was less efficient in desalinating the multivalent ions than SF-UF-RO was. However for the monovalent ions, the performances of the two processes were similar to each other. Noticeably, total trihalomethanes in SF-EDR filtrate was lower than that of SF-UF-RO, probably because the polarization effects formed on the concentrated side of the EDR membrane were not significant. At the end of this study, cost analysis was also conducted to compare the capital requirement of building a full-scale wastewater reclamation plant using the two processes. The results showed that using SF-EDR may cost less than using SF-UF-RO, if the users were to accept the filtrate quality of SF-EDR.