Operation optimization of cement clinker production line based on neural network and genetic algorithm

The operation control plays a great role in the running performance of an industrial process. To achieve a successful control, the key point is searching for the target value of the control parameters. The relationship between operation parameters and performance parameters is usually nonlinear and complex, so it is hard to control an industrial process excellently with workers' experience. Based on a large amount of actual operation data, a bridge between control parameters and performance parameters might be built by the neural network. Prediction model is established by using neural network, genetic algorithm was then used to refine these parameters, the optimal condition can be further achieved by combining the relation bridge and genetic algorithm (GA). Paying attention to a cement clinker production process, this article established an optimizing approach based on the neural network and genetic algorithm and one-year operation data (615 sets). The feeding mass rate of raw meal and coal at the precalciner and the feeding coal mass rate at the rotary kiln are selected as the main independent variable and control parameters, and the specific standard coal consumption is determined as the key performance parameter and the optimization objective function. The mean square error and the correlation coefficient of the established neural network model are 12.84 and 0.89, respectively. The relative errors of approximately all prediction data (92.52 %) are within ±5 %. The optimal values for the raw meal feeding rate, the coal feeding rate into precalciner, and the coal feeding rate into rotary kiln are 259.57 t/h, 7.84 t/h, and 7.40 t/h, respectively. In that optimal condition, the specific standard coal consumption reaches 80.00 kgstandard coal/tclinker (This is a relative value, as there is a slight drift in zero point of the factory's instruments). A highly accurate neural network model is developed, which can significantly reduce standard coal consumption and improve industrial energy efficiency.