Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

Growth, Metabolism and Yield of Rice Cultivated in Soils Amended with Fly Ash and Cyanobacteria and Metal Loads in Plant Parts

Soil amendment with fly ash（FA） and combined supplementation with N_2-fixing cyanobacteria masses as biofertilizer were done in field experiments with rice. Amendments with FA levels, 0, 0.5, 1.0, 2.0, 4.0, 8.0 and 10.0 kg/m2, caused increase in growth and yield of rice up to 8.0 kg/m2, monitored with several parameters. Pigment contents and enzyme activities of leaves were enhanced by FA, with the maximum level of FA at 10.0 kg/m2. Protein content of rice seeds was the highest in plants grown at FA level 4.0 kg/m2. Basic soil properties, p H value, percentage of silt, percentage of clay, water-holding capacity, electrical conductivity, cation exchange capacity, and organic carbon content increased due to the FA amendment. Parallel supplementation of FA amended plots with 1.0 kg/m2 N_2-fixing cyanobacteria mass caused further significant increments of the most soil properties, and rice growth and yield parameters. 1000-grain weight of rice plants grown at FA level 4.0 kg/m2 along with cyanobacteria supplementation was the maximum. Cyanobacteria supplementation caused increase of important basic properties of soil including the total N-content. Estimations of elemental content in soils and plant parts（root and seed） were done by the atomic absorption spectrophotometry. Accumulations of K, P, Fe and several plant micronutrients（Mn, Ni, Co, Zn and Cu） and toxic elements（Pb, Cr and Cd） increased in soils and plant parts as a function of the FA gradation, but Na content remained almost unchanged in soils and seeds. Supplementation of cyanobacteria had ameliorating effect on toxic metal contents of soils and plant parts. The FA level 4.0 kg/m2, with 1.0 kg/m2 cyanobacteria mass supplementation, could be taken ideal, since there would be recharging of the soil with essential micronutrients as well as toxic chemicals in comparative lesser proportions, and cyanobacteria mass would cause lessening toxic metal loads with usual N_2-fixation.