| abstract |
The invention discloses a multi-objective blast furnace batching and operation collaborative optimization method. Since it is difficult to describe the functional relationship between the decision variable and the objective function from the perspective of mechanism, the artificial neural network method is used to model it from the perspective of data driving. At the same time, according to the mass conservation, heat balance principle and blast furnace smelting mechanism, the constraints and objective functions of the blast furnace multi-objective optimization model are established, which ensures the effectiveness of the proposed method. The above modeling process fully considers the impact of batching and blast furnace operation on furnace conditions, including the originally relatively independent upper part of the blast furnace intermittent distribution control and the lower part of the continuous coal injection blast control, so it can effectively coordinate and optimize the upper and lower blast furnace operations. . |