Method of decarburizing molten metal in the refining of steel using neural networks

A method for refining steel by controlling the decarburization of a predetermined molten metal bath having a known composition of elements including carbon and having a known or estimated initial temperature and weight at the outset of decarburization of a molten metal bath in a refractory vessel with a process of decarburization performed through the injection of oxygen and a diluting gas into said bath under adjustable conditions of gas flow, comprising the steps of:

• (a) training a first neural network to analyze input and output data representative of many process periods of one or more decarburization operations, from data including the bath chemistry, weight and temperature at the outset of each process period, the gas ratio of oxygen to diluent gas used during each process period, the counts of oxygen injected into the bath for each process period, and the final temperature obtained at the conclusion of each process period, until said first neural network is able to provide a substantially accurate output representing the counts of oxygen required to be injected into said predetermined bath at any preselected gas ratio to cause the temperature of the bath to rise to a specified aim temperature level as a result of such gas injection;
• (b) training a second neural network to analyze input and output data representative of many process periods of one or more decarburization operations, from data including the bath chemistry, weight and temperature at the outset of each process period, the gas ratio of oxygen to diluent gas used during each process period, the counts of oxygen injected into the bath for each process period and the final carbon content obtained at the conclusion of each process period until said second neural network is able to provide a substantially accurate output schedule of oxygen counts to be injected into said predetermined bath to reduce the carbon level to a predetermined aim level in one or more successive stages corresponding to a preselected schedule of ratios of oxygen to diluent gas;
• (c) employing said first neural network to compute the oxygen counts to be injected into said predetermined bath, from its known initial chemistry, weight and temperature at a first preselected ratio of oxygen to diluent gas to raise the bath temperature to a specified aim temperature level.
• (d) injecting oxygen and diluent gas into said bath at said first preselected ratio until the oxygen counts computed by said first neural network are satisfied;
• (e) employing said second neural network to provide an output schedule of oxygen counts to be injected into said predetermined bath from its known initial chemistry, weight and temperature to successively reduce the carbon level in said bath to a predetermined aim carbon level in one or more stages corresponding to a preselected schedule of ratios of oxygen to diluent gas;
• (f) injecting oxygen and diluent gas into said bath at said preselected schedule of oxygen counts corresponding to said output schedule as computed by said second neural network;
• (g) training a third neural network to analyze data from the bath chemistry, weight and temperature at the outset of each process period, the weight of each solid addition, if any, made during each process period, the counts of oxygen injected during each process period, the corresponding ratio of oxygen to diluent gas used during each process period and the resulting carbon content at the conclusion of each process period of the purpose of predicting an output representing the carbon content that would be obtained as a result of such oxygen injection;
• and
• (h) employing said third neural network to compute the carbon content in the bath upon completion of the injection of oxygen intended as a result of computations performed in at least one of the steps (c) and (e).