Optimization and Scheduling of Byproduct Gas System in Steel Plant

A mathematical model was proposed to optimize byproduct gas system and reduce the total cost. The scope and boundaries of the system were also discussed at the same time. Boilers and gasholders were buffer users to solve the fluctuation of byproduct gases. The priority of gasholders should be ranked the last. The allocation of surplus ga- ses among gasholders and boilers was also discussed to make full use of gases and realize zero emission targets. Case study shows that the proposed model made good use of byproduct gases and at least 7.8 ~//00 operation cost was re- duced, compared with real data in iron and steel industry.

Prediction of blast furnace gas generation based on data quality improvement strategy

The real-time energy flow data obtained in industrial production processes are usually of low quality.It is difficult to accurately predict the short-term energy flow profile by using these field data,which diminishes the effect of industrial big data and artificial intelligence in industrial energy system.The real-time data of blast furnace gas(BFG)generation collected in iron and steel sites are also of low quality.In order to tackle this problem,a three-stage data quality improvement strategy was proposed to predict the BFG generation.In the first stage,correlation principle was used to test the sample set.In the second stage,the original sample set was rectified and updated.In the third stage,Kalman filter was employed to eliminate the noise of the updated sample set.The method was verified by autoregressive integrated moving average model,back propagation neural network model and long short-term memory model.The results show that the prediction model based on the proposed three-stage data quality improvement method performs well.Long short-term memory model has the best prediction performance,with a mean absolute error of 17.85 m3/min,a mean absolute percentage error of 0.21%,and an R squared of 95.17%.

Pretreatment of metallurgical sewage via vacuum distillation driven by low-temperature exhausted gas from steel plants

The metallurgical sewage has very complex component and a significant environmental perniciousness and needs high treatment costs. In addition, too much low-temperature waste heat is emitted owing to the lack of suitable users. Considering these concerns, a low-temperature-driven pretreatment method via vacuum distillation was proposed to treat the sewage from the metallurgical production. It uses the sensible heat carried by low-temperature exhausted gases to drive the distillation of sewage. The distilled water can be reused into the process as new water supply, while the enriched wastewater is discharged into the sewage treatment center for subsequent treatment. Converter dust removal sewage was chosen to perform an experimental observation. The variations of chemical oxygen demand, ammonia nitrogen, suspended solids, electrical conductivity, and pH of the condensate under different vacuum degrees and evaporation rates were mainly investigated. It can be found that the quality of the condensate gets better under certain conditions, which validates the feasibility of the proposed approach. Furthermore, by comprehensively analyzing the water quality indices and their influencing factors, the optimal vacuum degree was suggested to be controlled between 0.07 and 0.09 MPa, and the best evaporation rate was between 40 and 60%.

Multi-boiler System Optimization in Integrated Steelworks Based on Decomposition and Coordination Method

As for the existing problems of boilers in integrated steelworks, the multi-boiler system could be quantitatively optimized with the decomposition and coordination method. Then, case studies were carried out based on the data of an integrated steelworks. Two groups of actual production records were contrastively analyzed, and the calculation results from the optimized program of these two groups indicated that for groups 1 and 2, the costs fall by 5.06% and 3.79%and the fuel consumptions decrease by 2.72% and 1.45%, respectively, compared with the actual data. To analyze the cost and fuel consumption change under the same condition of total load demand, assigned fuel consumption and water temperature, five sets of data were selected for further analysis. It was shown that the total cost and fuel consumption of the optimized program could fall by 3.5% and 1.6% respectively, compared with the actual production records. The optimal allocation significantly contributed to energy conservation and cost reduction. The effects of the system energy conservation cannot be realized by single equipment energy conservation. They were complementary to each other, and should be put on the same stage.