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Silicon content in pig iron has long been used as one of the most important indices to represent the thermal state of a blast furnace. The control of silicon at a low level has been regarded as one of the most important operational techniques. In this paper, a mathematical program was developed to predict and control the silicon content in pig iron. The program includes three main models: the self-learning model, the prediction model and the control model. The first is to train the program by the intelligent method integrating artificial neural network with genetic algorithm according to the past processing variables. The second is to predict the next silicon content according to the current processing variables. The last is to control the subsequent behaviour of silicon based on the expert knowledge. From practical applications it was found that the program can provide both accurate predicted silicon content and corresponding operational guidance. It is very useful to stabilise silicon content and as low as possible. [ABSTRACT FROM AUTHOR]

Based on a coupled heat and stress model, an artificial intelligence optimisation program was developed to optimise the process parameters in the continuous casting of steel. The program can be used to identify the key factors for the cracks of the strand and obtain the optimum process parameters of continuous casting. The optimisation program contains two models, one is the thermomechanical model developed using the finite difference method, and the other is the optimisation model developed using the subproblem approximation method. The whole program works by automatic iteration between the two models. This study has taken the stress constraint in the solidified shell into consideration when studying the metallurgical constraints. The application of the achieved optimum parameters would make it possible to run the caster at maximum productivity, with minimum cost and fewer defects. After manufacturing verification of this optimisation project, the incidence of cracks has reduced from 8% to 2%, and water consumption in the secondary cooling zone has been decreased by 25%. [ABSTRACT FROM AUTHOR]

The present work aims to simulate the solidification process during beam blank continuous casting, to determine the causes of defects, and to optimise the process parameters. A transient finite element model has been developed to compute the temperature and stress profile in beam blank continuous casting. It has been found that the surface temperature fluctuated greatly and that the equivalent stress and strain have their highest values at the flange tip and the web. By comparing the calculated values with metallurgical constraints, the key factor causing surface cracks on the web was determined and an optimum cooling regime was proposed. Online verification of this optimisation project has led to a reduction in the incidence of web surface cracks. [ABSTRACT FROM AUTHOR]

Using a Gleeble 1500 hot simulator, the dynamic recrystallisation (DRX) behaviour and changes of DRX grain size of a Mn–Cr type gear steel have been investigated at deformation temperatures of 900–1150°C, strain rates of 0·1–1 s-1, and initial austenite grain sizes of 70–150μm. The experimental results show that the Mn–Cr gear steel exhibits a typical DRX behaviour under higher deformation temperatures, lower strain rates and smaller initial austenite grain sizes. The DRX takes place under conditions such that the Zener–Hollomon parameter Z is less than a critical value Zc. By regression analysis, activation energy and stress exponent were determined to be 378 kJ mol-1 and 5·8 respectively. The DRX grain size is a power law function of Z with an exponent of -0·25. The reason why the Z parameter determines uniquely the DRX grain size was analyzed. [ABSTRACT FROM AUTHOR]