Abstract
The large-scale and resourceful utilization of solid waste is one of the important ways of sustainable development. The big data brings hope for further development in all walks of life, because huge amounts of data insist on the principle of “turning waste into treasure”. The steel big data has been taken as the research object in this paper. Firstly, a big data collection and storage system has been set up based on the Hadoop platform. Secondly, the steel slag prediction model based on the convolution neural network (CNN) is established. The material data of steelmaking, the operation data of steelmaking process, and the data of steel slag composition are put into the model from the Hadoop platform, and the prediction of the slag composition is further realized. Then, the alternatives for resource recovery are obtained according to the predicted composition of the steel slag. And considering the three aspects of economic feasibility, resource suitability, and environmental acceptance, the comprehensive evaluation system based on AHP is established to realize the recommendation of the optimal resource approach. Finally, taking a steel plant in Hebei as an example, the alternatives according to the prediction of the composition of steel slag are blast furnace iron-making, recycling waste steel, and cement admixture. The comprehensive evaluation values of the three resources are 0.48, 0.57, and 0.76, respectively, and the optimized resource of the steel slag produced by the steel plant is used as the cement admixture.
Highlights
In steel companies, steel slag accounts for 8% to 15% of crude steel production as a by-product, and more than 80 million tons of steel slag is discharged in China every year
Most of the studies of steel slag are limited to use as the raw material of calcining cement clinker, road-building material, concrete admixture, soil amendment, and a kind of adsorbent in wastewater treatment
The resource application technology of steel slag is usually based on the research and development of steel slag composition and structure
Summary
Steel slag accounts for 8% to 15% of crude steel production as a by-product, and more than 80 million tons of steel slag is discharged in China every year. A large data platform is built to collect and store real-time dynamic steelmaking production data, and the Hadoop distributed file system (HDFS) is used to realize the virtual resource storage of large data in the steelmaking process. The advantages of the HDFS system based on distributed NameNode nodes are (1) The fast handoff of NameNode/Secondary NameNode is realized, and the real-time invocation of HDFS is improved (2) The service performance of the distributed NameNode is improved, and the clustering service client is adopted (3) Hadoop clusters are extended to store large amounts of data. The coolant is mainly used to balance the heat in the furnace
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