The BiOBr act as excellent supporting materials to improve the dispersion of the Cu2S QDs and the well-matched overlapping band-structures greatly accelerate the separation of photogenerated carriers.

Controlling the molten iron temperature plays an important role in the iron and steelmaking industry. The change of silicon content is adopted to reflect the temperature, however , the prediction of silicon content has been one of the hot and difficult problems. In this paper, a new model based on gray relational analysis (GRA) and extreme learning machine (ELM) is developed. Firstly, the GRA is used to get the high correlation indexes with the silicon content. Then the relevant indicators are taken as input and the silicon content is taken as output. The ELM model is constructed and the model is trained. Based on this, the silicon content is predicted. The results show that the hit rate reaches 87%(the error is less than 0.10). Compared with the traditional backpropagation or radial basis function neural network , this model has higher hit rate and faster running speed.

Under the premise of ensuring the smooth production of the blast furnace, the quality and output of molten iron, minimizing the cost of hot metal and reducing the impact of harmful elements are the most difficult problems faced by iron and steel enterprises at present. The intelligent optimization system of burden structure in the whole process of sintering and blast furnace ironmaking is developed based on the principle of blast furnace ironmaking and material balance, improved genetic algorithm and penalty function. It includes four parts: raw fuel database, intelligent sintering optimization, blast furnace burden structure intelligent optimization and company cost accounting. The industrial application shows that the system can not only guarantee the output and quality of molten iron, but also effectively reduce the cost of molten iron in the process of optimizing the sintering and blast furnace production of the steel company.

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