Experimental investigation on backscattering interference cancellation for full-duplex UOWC based on time-reversal preprocessing.

A co-frequency and full-duplex (FD) underwater optical wireless communication (UOWC) system has the potential to significantly enhance spectral efficiency, reduce complexity, and further facilitate UOWC networking. However, the inevitable performance degradation due to self-interference introduced by backscattering presents a significant challenge. In this paper, we first experimentally explore the inherent characteristics of the underwater backscattering channel. Subsequently, we propose a digital-domain backscattering interference cancellation (BIC) algorithm for FD-UOWC systems, incorporating a time-reversal preprocessing. We then experimentally investigate the communication performance of an FD-UOWC system to verify the feasibility of the proposed BIC algorithm under different channel conditions. The experimental results validate the effectiveness of the proposed method, yielding substantial enhancements in bit error rate performance across diverse scenarios.

Selectively retaining nutrients in biochar in magnesium added two-zone staged copyrolysis of blue algae and corn gluten wastes.

The disposal of blue algae (BA) and corn gluten (CG) wastes and the simultaneous recovery of abundant phosphorus (P) and nitrogen (N) by pyrolysis to obtain biochars with high fertility is a promising strategy. However, pyrolysis of BA or CG alone by a conventional reactor cannot reach the target. Herein, we propose a novel MgO-enhanced N and P recovery method by designing a two-zone staged pyrolysis reactor to highly efficiently recover N and P with easily available plant forms in BA and CG. The results show that a 94.58% total phosphorus (TP) retention rate was achieved by means of the special two-zone staged pyrolysis method, in which the effective P (Mg2PO4(OH) and R-NH-P) accounted for 52.9% of TP, while the total nitrogen (TN) reached 4.1 wt%. In this process, stable P was formed first at 400 °C to avoid rapid volatilization and then to form hydroxyl P at 800 °C. Meanwhile, Mg-BA char in the lower zone can efficiently absorb N-containing gas generated by the upper CG, forming dispersible N. This work is of great significance for improving the green utilization value of P and N in BA and CG.

A back propagation neural network model for accurately predicting the removal efficiency of ammonia nitrogen in wastewater treatment plants using different biological processes.

Accurately predicting the water quality of treated water from a water treatment plant (WWTP) based on the obtained operating database is of great significance. However, it is difficult for common mechanistic models to work well. In this study, a back propagation artificial neural network (BPANN) model with high accuracy was developed to predict the denitrification efficiency based on a 1-year operating database. Standardized principal component analysis (PCA) methods were used to address the data, and the PCA processed data exhibited the best accuracy. In three WWTPs adopting the anaerobic/anoxic/oxic (A2O) process, the ammonia nitrogen removal efficiency of WWTPs was successfully predicted by using five variables: inlet flow rate, pH value, original ammonia nitrogen concentration, Chemical oxygen demand (COD) concentration, and total phosphorus concentration. Importantly, the obtained BPANN model can be effectively used for other widely used treatment processes, such as oxidation ditch (OD), sequencing batch reactor activated sludge process (SBR), membrane bioreactor (MBR), and cyclic activated sludge technology (CAST), by simply optimizing the training data ratios between 50/50 and 90/10. This is the first trial to set up a universal model for predicting the denitrification efficiency of WWTPs adopting common biological processes. The model could be used to choose the optimum treatment process in the new WWTP design or take action in advance to avoid the risk of excessive emissions when the already built WWTPs are subjected to sudden shocks.

Effect of Laser Scanning Speed on the Microstructure and Mechanical Properties of Laser-Powder-Bed-Fused K418 Nickel-Based Alloy.

Laser powder bed fusion (LPBF) is a powder-bed-based metal additive manufacturing process with multiple influencing parameters as well as multi-physics interaction. The laser scanning speed, which is one of the essential process parameters of the LPBF process, determines the microstructure and properties of the components by adjusting the instantaneous energy input of the molten pool. This work presents a comprehensive investigation of the effects of the laser scanning speed on the densification behavior, phase evolution, microstructure development, microhardness, and tensile properties of K418 alloy prepared by laser powder bed fusion. When the scanning speed is 800 mm/s, the microstructure of the material is dominated by cellular dendrite crystals, with coarse grains and some cracks in the melting tracks. When the scanning speed is increased to 1200 mm/s, a portion of the material undergoes a cellular dendrite-columnar crystal transition, the preferred orientation of the grains is primarily (001), and internal defects are significantly reduced. When the scanning speed is further increased to 1600 mm/s, columnar crystals become the main constituent grains, and the content of high-angle grain boundaries (HAGBs) within the microstructure increases, refining the grain size. However, the scanning speed is too fast, resulting in defects such as unmelted powder, and lowering the relative density. The experimental results show that by optimizing the laser scanning speed, the microhardness of the LPBF-ed K418 parts can be improved to 362.89 ± 5.01 HV, the tensile strength can be elevated to 1244.35 ± 99.12 MPa, and the elongation can be enhanced to 12.53 ± 1.79%. These findings could help determine the best scanning speed for producing K418 components with satisfactory microstructure and tensile properties via LPBF. In addition, since the LPBF process is largely not constrained and limited by the complexity of the geometric shape of the part, it is expected to manufacture sophisticated and complex structures with hollow, porous, mesh, thin-walled, special-shaped inner flow channels and other structures through the topology optimization design. However, due to the relatively narrow LPBF process window, this study will benefit from LPBF in producing a lightweight, complex, and low-cost K418 product, greatly improving its performance, and promoting the use of LPBF technology in the preparation of nickel-based superalloys.

Effect of Laser Energy Density on the Microstructure and Texture Evolution of Hastelloy-X Alloy Fabricated by Laser Powder Bed Fusion.

It is of great importance to study the microstructure and textural evolution of laser powder bed fusion (LPBF) formed Hastelloy-X alloys, in order to establish a close relationship between the process, microstructure, and properties through the regulation of the Hastelloy-X formation process parameters. In this paper, components of a Hastelloy-X alloy were formed with different laser energy densities (also known as the volume energy density VED). The densification mechanism of Hastelloy-X was studied, and the causes of defects, such as pores and cracks, were analyzed. The influence of different energy densities on grain size, texture, and orientation was investigated using an electron backscatter diffraction technique. The results show that the average grain size, primary dendrite arm spacing, and number of low angle grain boundaries increased with the increase of energy density. At the same time, the VED can strengthen the texture. The textural intensity increases with the increase of energy density. The best mechanical properties were obtained at the VED of 96 J·mm-3.