Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

High Efficient Blowing Technique for Large and Medium Converters

The high efficient blowing technique includes increasing oxygen supply intensity and optimizing slag forming. The oxygen supply intensity on 300 t converters of No. 1 steelmaking shop at Baosteel reaches 3.83 m^3/（t · min）, and at Taiyuan Steel, Lianyuan Steel, Pingxiang Steel and other steel plants, the oxygen supply intensity on medium converters is in the range of 4.0--4.4m^3/（t · min）. The productivity of converter can be increased by 8% -- 15% with adopting this technique. The whole technique, including design and manufacture of lance nozzle with reasonable pacnolontenue of outlets, technique of oxygen supply and slag forming, has been developed by CISRI to meet the need of technique transfer.

Treatment of Aniline-Contaminated Wastewater by Sequencing Biofilm Batch Reactor System Under Micro-Aerobic Condition

Sequencing biofilm batch reactor(SBBR) under micro-aerobic condition was applied to the treatment of aniline-contaminated wastewater in this study.Hydraulic retention time(HRT) of 12—36 h and dissolved oxygen(DO) concentration of 0.1—0.5 mg/L were selected as the operating variables to model,analyze and optimize the process.Five dependent parameters,aniline(AN),chemical oxygen demand(COD),ammonium,total nitrogen(TN) and total phosphorus(TP) removal efficiencies as the process responses,were studied.From the results,increase in DO concentration could promote the AN,COD and ammonium removal;increase in HRT could also lead to increase of the AN and ammonium removal,but might decrease COD removal due to endogenous respiration and soluble microbial products.In the SBBR system,24 h for HRT and 0.5 mg/L for DO concentration were chosen as the optimum operating condition.The actual removal efficiencies of COD,AN and ammonium under the optimum operating condition were 98.37%,100%and 89.29%,respectively.The experimental findings were in close agreement with the model prediction.The presence of glucose could promote bacterial growth and has positive influence on AN degradation and ammonium removal.