Chaotic Identification and Prediction of Silicon Content in Hot Metal

The time series data of silicon content in hot metal were identified to have the chaotic feature because of the positive maximum Lyapunov exponent, and then the time scales to predict future were estimated. Finally a chaotic local-region model was constructed to predict silicon content in hot metal with good performance due to high hitting rate.

Pulsed Nd:YAG laser cladding of high silicon content coating on low silicon steel

A pulsed Nd：YAG （yttrium aluminum garnet） laser-based technique was employed to clad low silicon steel with preplaced Si and Fe mixed powders for high Si content. The surface morphology, microstructural evolution, phase composition, and Si distribution, within the obtained cladding coatings, were characterized by optical microscopy （OM）, field emission scanning electron microscopy （FE-SEM）, with associated energy dispersive spectroscopy （EDS）, transmission electron microscopy （TEM）, and X-ray diffraction （XRD）. The microhardness was also measured along the depth direction of the specimens, A crack- and pore-free cladding coating through excellent metallurgical bonding with the substrate was successfully prepared on low silicon steel by means of optimized single-track and multi-track laser cladding. The phases of the coating are a-Fe, T-Fe, and FeSi. The high microhardness of the lasercladding zone is considered as an increase in Si content and as the refined microstructure produced by the laser treatment. The Si contents of the cladding coatings were about 5.8wt% in the single-track cladding and 6.5wt% in the multi-track cladding, respectively.

Rapid Determination of Silicon Content in Rice

A method for rapid determination of silicon content in rice was introduced. The reliability of this method was verified by using a recombinant inbred line （RIL） population of rice cross Zhenshan 97B / Milyang 46. Two hundred and forty-nine RILs were transplanted in two replications. Simple correlation coefficients on the silicon content in the hull, flag leaf and stern in rice between duplicate samples of 498 rice materials were 0.97954, 0.97026 and 0.98848, respectively. Ten representative samples were selected for measurement using the high-temperature alkaline fusion method. Simple correlation coefficient between the silicon contents determined by the high-temperature alkaline fusion method and by the present method is 0.9993.

Fuzzy Prediction of Silicon Content for BF Hot Metal

Some key operation variables influencing hot metal silicon content were selected, and time lag of each of them was obtained. A standardized fuzzy system model was developed to approach the random nonlinear dynamic system of the change of silicon content, forecast the change of silicon content and calculate silicon content. The prediction of hot metal silicon content is very successful with the data collected online from BF No. 1 at Laiwu Iron and Steel Group Co.

A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal

A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the population, the NICGA has the advantages of decreasingthe population size, enhancing the local search ability, and improving the computational efficiencyand optimization precision. In a multi4ayer feed forward neural network model for predicting thesilicon content in hot metal, the NICGA was used to optimize the connection weights and thresholdvalues of the neural network to improve the prediction precision. The application results show thatthe precision of predicting the silicon content has been increased.

An Improved Artificial Neural Network Model for Predicting Silicon Content of Blast Furnace Hot Metal

Based on the skills of initializing weight distribution, adding an impulse in a neural network and expanding the ideal of plural weights, an artificial neural network model with three connection weights between one and another neural unit was established to predict silicon content of blast furnace hot metal. After the neural network was trained in the off-line state on the basis of a large number of practical data of a commercial blast furnace and making many learning patterns, satisfactory testing and simulating results of the model were obtained.

New insights into the effects of silicon content on the oxidation process in silicon-containing steels

Simultaneous thermal analysis（STA） was used to investigate the effects of silicon content on the oxidation kinetics of silicon-containing steels under an atmosphere and heating procedures similar to those used in industrial reheating furnaces for the production of hot-rolled strips. Our results show that when the heating temperature was greater than the melting point of Fe2SiO4, the oxidation rates of steels with different silicon contents were the same; the total mass gain decreased with increasing silicon content, whereas it increased with increasing oxygen content. The oxidation rates for steels with different silicon contents were constant with respect to time under isothermal conditions. In addition, the starting oxidation temperature, the intense oxidation temperature, and the finishing oxidation temperature increased with increasing silicon content; the intense oxidation temperature had no correlation with the melting of Fe2SiO4. Moreover, the silicon distributed in two forms： as Fe2SiO4 at the interface between the innermost layer of oxide scale and the iron matrix, and as particles containing silicon in grains and grain boundaries in the iron matrix.

Dissection of QTLs for Hull Silicon Content on the Short Arm of Rice Chromosome 6

The QTL qHUS6 for hull silicon content in rice was previously located on the short arm of rice chromosome 6. By using an F2：3 population segregating in the RM587-RM19784 region harboring qHUS6 in an isogenic background, two QTLs for hull silicon content were detected, of which qHUS6-1 was located in the distal region and qHUS6.2 in the region proximal to the centromere. Three rice plants carrying small heterozygous segments in the target region were selected, of which two covered the qHUS6-1 region and the other covered the qHUS6-2 region. Three F2：3 populations were derived from the selfed seeds of the three plants, respectively. QTL mapping was performed using the two populations segregating in the qHUS6-1 region, and qHUS6-1 was delimited to a 147.0-kb region flanked by the markers RM510 and RM19417. Five groups of F3 lines with different genotypic compositions in the qHUS6-2 region were selected from the other F2-3 population. Two QTLs were separated with two-way ANOVA, of which qHUS6-2a was located in the interval defined by RM19706-RM19795 and qHUS6-2b in the interval RM314-RM19665.

Investigation on Steelmaking with Hot Metal Containing Low Silicon Content in Large Converter

There are some problems in steelmaking with hot metal containing low silicon content such as difficulty in slag formation, less slag for dephosphorization and slag adhesion on oxygen lance and hood. To overcome these problems, experiments wcrc conducted and some improvements were obtained, such as adding appropriate flux, increasing the lance position slightly during steelmaking and using effective multi-outlet nozzle. Moreover, to keep normal heating rate, the ore and scrap charge should be reduced due to less chemical heat input in steelmaking.

Effect of Silicon Content and Shake-Out Time on Hardness and Grain Size Properties of GL 250 Cast Iron

The properties of cast iron grade GL 250 are dependent on the microstructures developed during casting. These microstructures are in turn dependent on the composition of the alloy, type of mould and other numerous casting practice variables such as shake-out time, pouring temperature, mould ambient conditions and inoculating technique. In this work, the effect of silicon content and shake-out time on the grain size (GS) and hardness properties of GL 250 cast iron was studied using a pouring temperature of 1400℃ and sand mould casting. Using charge materials consisting of pig iron and other additives, GL 250 castings containing silicon contents of 1.7, 2.1 and 2.7% were casted using a constant pouring temperature of 1400℃, molding sand of specified properties and ambient mould temperature of 32℃. Results showed that type A flake type was obtained at 30mins shakeout time for all samples for the C.I composition under study. Increasing shake-out time decreased hardness and increased carbide grain size. Increasing silicon content was observed to increase grain size and reduce free graphite but with resultant decrease in hardness. Two mathematical relationships were derived. One related grain-size to silicon content and shakeout time while the second related Brinnel Hardness to Silicon content and shake-out time. They are: Grain Size=0.40 Si+0.17Shake-out Time-0.15 and BHN=-60.53Si-7.15Shake-out Time+329.35 at 1400℃ pouring temperature in a molding sand of specified properties and sand mould ambient temperature of 32℃.

Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes

With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application.It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output.In the model construction process,the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content,the normalization method was used to eliminate the influence of dimensionality of sample information,and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables,control variables,and silicon content of molten iron in the blast furnace smelting process.The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron,and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction.The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm.The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%;the hit rate of continuous forecasting one step ahead reaches 91.16%,and the hit rate of continuous forecasting five steps ahead is 87.41%.This model,which can realize the nodal dynamics of state variables,has better promotion value.

Model of Hot Metal Silicon Content in Blast Furnace Based on Principal Component Analysis Application and Partial Least Square

In blast furnace （BF） iron-making process, the hot metal silicon content was usually used to measure the quality of hot metal and to reflect the thermal state of BF. Principal component analysis （PCA） and partial least- square （PLS） regression methods were used to predict the hot metal silicon content. Under the conditions of BF rela- tively stable situation, PCA and PLS regression models of hot metal silicon content utilizing data from Baotou Steel No. 6 BF were established, which provided the accuracy of 88.4% and 89.2%. PLS model used less variables and time than principal component analysis model, and it was simple to calculate. It is shown that the model gives good results and is helpful for practical production.

Influence of Mold Preheating and Silicon Content on Microstructure and Casting Properties of Ductile Iron in Permanent Mold

The effects of the mold preheating and the silicon content of ductile iron on the percentage of carbides,graphite nodule counts and shrinkage volume were investigated.The results showed that the percentage of carbides and the shrinkage volume decreased when the mold preheating increased.The ductile iron with the carbon equivalent of 4.45 % and the silicon content of 2.5% without any porosity defects was achieved when the mold preheating was 450 ℃.Increasing the silicon content in the range of 2.1%-3.3% led to the increase in graphite nodule count and graphite size and the decrease in percentage of carbides.It is due to the increase in induced expansion pressure during the graphite formation with the increasing of silicon content.The suitable condition for casting a sound product of ductile iron without the riser at the mold preheating temperature of 300 ℃ is the silicon content of 3.3% and carbon equivalent of 4.7%.

Fine mapping of qHUS6.1,a quantitative trait locus for silicon content in rice(Oryza sativa L.)

Silicon is essential for optimal growth of rice(Oryza sativa L.).This study was conducted to fine map qHUS6.1,a quantitative trait locus(QTL) for rice hull silicon content previously located in the interval RM510-RM19417 on the short arm of chromosome 6,and to analyze the effect of this QTL on the silicon content in different organs of rice.Selfed progenies of a residual heterozygous line of rice were detected using 13 microsatellite markers in the vicinity of qHUS6.1.Three plants with overlapping heterozygous segments were selected.Three sets of near isogenic lines(NILs) were developed from the selfed progenies of the 3 plants.They were grown in a paddy field and the silicon contents of the hull,flag leaf,and stem were measured at maturity.Based on analyses of the phenotypic distribution and variance among different genotypic groups in the same NIL set,a significant genotypic effect was shown in the NIL set that was heterogenous in the interval RM19410-RM5815,whereas a significant effect was not found in the remaining 2 NIL sets that were heterogenous in either of the intervals RM4923-RM19410 or RM19417-RM204.On comparison among the physical positions of the 3 heterogenous segments,qHUS6.1 was delimited to a 64.2-kb region flanked by RM19410 and RM19417 that contains nine annotated genes according to the genome sequence of Nipponbare.This QTL showed strong effects on all of the three traits tested,and the enhancing alleles were always derived from the paternal line Milyang 46.The present study will facilitate the cloning of qHUS6.1 and the exploration of new genetic resources for QTL fine mapping.

Factors Influencing Droplet Size of Silicone Oil Emulsion with High Solid Content

A silicone oil emulsion with 60% of solid content was prepared with methyl silicone oil used as the main material by means of the emulsifier-in-oil method.The influence of emulsification conditions on the droplet diameter of silicone oil emulsion was discussed.The experimental results showed that the emulsification method,including the quantity of the emulsifier,the time and temperature of emulsification,the emulsifying water dosage,and the stirring speed,had significant impact on the droplet size.The optimal conditions were identified to achieve a smallest droplet diameter of the emulsion at an emulsifier dosage of 6%,an emulsification temperature of 70 ℃,an emulsification time of 30 min,and a stirring speed of 1100 r/min,with water added in two portions at a ratio of 1:1.The high-solid content silicone oil emulsion with a mean droplet diameter of 2.731 μm was prepared under these conditions that could ensure absence of stratification and floating oil under centrifuging at a speed of 3000 r/min for 30 min.

Chemical Analysis Method for Magnesia and Magnesia-Alumina Refractories——Gravimetric-Molybdenum Blue Photometric Method for Determination of Silicon Dioxide Content

This standard specifies the gravimetric-molybdenum blue photometric method for determination of silicon dioxide content.

Hydrogen Content and Porosity Behavior of Hypereutectic Aluminum-silicon Alloy with Phosphorus

By making castings that pick up gas from moisture in red sand molds,the porosity generated at different cooling rates was discussed during solidification of hypereutectic Al-25%Si alloy without and with phosphorus additions. The effect of phosphorus addition on hydrogen content in the melt was also studied. It was observed that the phosphorus addition made hydrogen content in alloy melts present a “see-saw' tendency.In addition to primary silicon refinement,the phosphorus promoted gas porosity formed not only in slowly cooled sections, but also in rapidly cooled sections. There was a small difference in density of full dense sample between P-refined and unrefined castings, with a larger density associated with phosphorous addition. The change of the surface tension seemed more reasonable to explain the mechanism of porosity behavior.

Effect of carbon, silicon, and manganese content on mean flow stress at elevated temperature

Ten studied steels including different carbon content, silicon content, and manganese content were deformed in compression over a temperature range of 600 ?C to 1 000 ?C at the strain rate of 1 s-1. The curves of the mean flow stressdeformation temperature were drawn up. The mean flow stresses of higher carbon content steels decreased continuously as the applied deformation temperature increased in the whole temperature range, while the mean flow stress of lowest carbon steel displayed an abrupt drop near the two phases region. The reason for the abrupt drop phenomena was explained as the result of phase transformation. The mean flow stresses of steels with high silicon content and low manganese content also have this phenomena.

Synthesis and Characterization of Mesoporous Silicon Oxynitride MCM-41 with High Nitrogen Content

Mesoporous silicon oxynitrides MCM-41 were synthesized successfully. The resulting materials not only have high nitrogen contents and good structural characteristics of MCM-41 (high surface area, narrow pore size distribution and good order), but also are amorphous. The composition and structure of the materials were investigated by CNH element analysis, XPS, Si MAS NMR, XRD, HRTEM and N-2 sorption, respectively. Mesoporous silicon oxynitrides MCM-41 with a high nitrogen content are still non-crystal (amorphous).

Effects of Phenyl Content and Molecular Structure on the Refractive Index of Organic Silicon Polymer

The effects of phenyl content and group distribution on the refractive index of phenyl silicone oil were investigated by synthesis and characterization of silicone oils of different molecular structures.A group contribution function model was established to predict the refractive index. The results showed that refractive index of phenyl silicone oil increased as its phenyl content increased. A linear equation had been established to describe the quantitative relationship between the refractive index and phenyl content.Refractive index values from the group contribution function model showed good consistency with experimental results.