Charging composition and structure optimisation in the sintering process (Part I)

Abstract

In this paper, an optimisation system for charging composition and structure in the sintering process was established in order to reduce the sinter cost in the ironmaking process. The system comprised four modules: sinter metallurgical performance testing and analysis, sintering burdening design and optimisation, sintering matching calculation, and sinter component and property prediction. The data for the first module came from actual production values of a steelworks and from testing in the laboratory. Based on material balance theory, the second module used a linear programming method to optimise sinter cost, quality and quantity. The third module was built to predict the sintering production data. The fourth module can be used to predict some composition and properties of the sinter based on a Back-Propagation neural network. The system integrated all of these modules using Visual Basic and MATLAB. As the result, the optimum charging composition and structure of sinter which satisfies all constraint conditions can be obtained. Compared with traditional production testing and hand calculation in the sintering process the system can reduce the sinter cost and greatly decrease the production risk. Industrial application proves that the system is very useful and efficient in reducing sinter cost while ensuring output quantity and quality.

Intelligent optimization system of burden structure in sintering and blast furnace ironmaking process based on improved genetic algorithm

“…Through the control of the blast furnace load distribution process, the stable operation of the blast furnace was realized, and the production index of the blast furnace was optimized [9]. WANG and others developed a mathematical model for sintering structure and composition optimization during sintering process, which made it possible to optimize sintering structure [10]. Nath and others have optimized the quality of sinter and improved the utilization rate of fuel by using a genetic algorithm [11].…”

In Situ Observation on the Heterogeneous Nucleation of MnS in Heavy Rail Steels with Varied Aluminum Content

“…[1][2][3] The number, size, composition, morphology, and spatial distribution of nonmetallic inclusions in steel have a crucial impact on the cleanliness and performance of the steel product. [4][5][6][7][8] Particularly, large and long MnS inclusions precipitated during heat treatment after rolling of the steel and have a detrimental effect on the strength and corrosion resistance of heavy rail steels. [9][10][11][12][13] Many studies were reported to control large-sized MnS inclusions according to the characteristic and source of MnS in the steel, such as slag refining, [14,15] calcium/magnesium addition, [16][17][18][19] zirconium/cerium addition, [20][21][22] heat treatment process, [23,24] and heterogeneous nucleation improvement.…”