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With the vigorous development of network technology, optical networks are developing in the direction of ultra-highspeed and large-capacity. Since the structure of the optical network is becoming more complex.the optical signal will inevitably suffer various impairments in the optical network. In optical communication equipment, the most important parameter of the physical layer is the Optical Signal-to-Noise Ratio (OSNR). Its value directly determines whether the service can operate normally. Once the requirements are not met, it will cause transmission errors or failures, reducing service quality, and transmission consumption. This paper proposes an accurate and efficient OSNR prediction method for optical communication nodes. By combining analytic prior knowledge methods and deep learning-based posterior knowledge methods, an optical communication node OSNR based on hybrid machine learning algorithms are proposed. The prediction method uses prior knowledge to reduce the training cost of neural networks and provides high-accuracy OSNR predictions. The result shows that the method proposed in this paper can provide high-accuracy machine learning models under more demanding conditions.

FeTiO3 and FeTiO3-Fe2O3 solid solution systems were synthesized based on the ceramic method. The synthetic FeTiO3-Fe2O3 solid solutions were subjected to isothermal carbothermal reduction experiments at 1150℃ combining with thermodynamic anal-ysis. The synthetic products were systematically studied by X-ray diffraction and scanning electron microscopy-energy dispersive spec-troscopy. The results show that the synthetic products are all ideal with homogeneous components. From the view of reduction, the smaller the value of x, the molar fraction of FeTiO3, the earlier the carbothermal reduction of xFeTiO3-(1-x)Fe2O3 solid solutions start, and the higher the maximum reduction rate is. As an intermediate phase, pseudobrookite (FeTi2O5-Fe2TiO5 (Fe3Ti3O10)) does not disappear during the whole initial reduction stage until the formation of metal iron (Fe) and ulv-spinel (Fe2TiO4).

The basic characteristics of ironsand were systematically studied by chemical analysis, scanning electron microscopy and X-ray diffraction. A series of experiments were performed to investigate reduction-magnetic separation for Fe and Ti from iron-sand. The effects of reduction temperature, reduction time, carbon ratio, magnetic density, and grinding fineness on the reduction-magnetic separation process were investigated. It could be found that the ironsand mainly consisted of titanomagnetite and titanohematite. The optimal process parameters were obtained as follows:reduction temperature, 1300℃; reduction time, 30 min; C/O molar ratio, 1.1; magnetic density, 50 mT; and the grinding fineness of -0. 074 μm, 86. 34%. Under such conditions, the metallization rate, the iron grade of magnetic substance and the iron recovery reached to 94.23%, 97.19% and 90. 28%, respectively, and the titanium grade of non-magnetic substance and the titanium recovery were 57. 94% and 87.22%, respectively. The effective separation of iron and titanium could be achieved in this reduction-magnetic separation process.

A laboratory study is carried out to characterize the effect of basicity (varying 1.5–3.5) on magnetite iron ore pellets containing TiO2 (content 5%). Green pellets are made by the laboratory scale balling disc and roasted at a horizontal tube furnace with 1220 °C. Thermogravimetric analysis ('TG') is used to analyze the thermal decomposition of green pellets. X-ray diffraction ('XRD'), scanning electron microscope and energy dispersive spectroscopy ('SEM-EDS') are utilized to systematically analyze the phase composition, microstructure and elemental distribution of the pellets. The findings show that as the basicity increases the porosity increases first and then decreases while the compressive strength is opposite to porosity to decrease first and then increases. With the increases of basicity, CaO responded with Fe2O3 and TiO2 to form CaFe2O4, Ca2Fe2O5 and CaTiO3. 'TG' find basicity has a negative effect on pellet oxidation. Factsage7.1 software shows that with the increase of basicity, the liquid content increases. The 'SEM-EDS' result present that CaTiO3 is formed around hematite, impeding the hematite connection, increasing porosity and reducing compressive strength. Being depend on the experimental results, a schematic of the oxidation mechanism is presented, providing the theoretical foundations for the study of high basicity pellets. [ABSTRACT FROM AUTHOR]

The permeability index is an important variable to reflect the operating state of a blast furnace quickly, intuitively and comprehensively. Given the multi-scale, nonlinear and massive characteristics of the blast furnace smelting process data, a prediction model of permeability index based on the WD-NL-transformer (wavelet de-noising-nonlinear-transformer) method was proposed. The key variables affecting permeability index were selected from multiple perspectives by Pearson correlation coefficient and copula entropy. The extreme outliers were optimised using box plots and Lagrange linear interpolation. A data set was constructed after wavelet de-noising to build the prediction model with nonlinear processing capability, time-series prediction capability, and early stopping method. The results show that the MSE of WD-NL-transformer is 0.0093, with the RMSE of 0.0086. Meanwhile, the R2 is 0.9859, and the running time is 19.59 s, which means the model has great prediction accuracy and inference speed. The proposed model could provide theoretical support for advanced control of permeability index, which is of practical significance to ensure the stability of blast furnace smelting and accelerate the metallurgical process of intelligent automation. [ABSTRACT FROM AUTHOR]

Reduced activation ferrite/martensitic steels containing yttrium (Y), titanium (Ti), and zirconia (Zr) are melted using vacuum induction melting–electroslag remelting (ESR) to study the effects of second phases on the microstructure, tensile properties, and impact toughness of the alloys. In addition, two heat treatment processes are used to obtain the dispersed distribution of fine M23C6 carbides in reduced activation steel. The results show that ESR not only refined micron inclusions but also increase the number of submicron inclusions in the alloys. The number of submicron inclusions per unit volume of the ESR ingots is 1.11–1.43 × 1019 m−3. These inclusions not only reduced the austenite grain size through pinning but also effectively pinned dislocations to improve the mechanical properties of the steel. MX carbonitrides rather than M23C6 precipitated first in the steel with A–A–T heat treatment at 920 °C, decreasing the mean size of M23C6. The refined grain size, martensitic laths, and M23C6 are beneficial to the mechanical properties of the alloys. The yield strengths of the E–Y–Ti and E–Y–Zr alloys are 659 and 657 MPa, and their ductile–brittle transition temperatures are −93 and −86 °C, respectively. [ABSTRACT FROM AUTHOR]