Study on blending of power coal based on sliding window and silos rating mechanism

Coal blending is an important link in coal preparation plants in sales of their commercial, yet wide fluctuation of quality of blended coal and high reliance on experience of operators are a general problem nowadays. To deal with this situation, the saleable coal blending process applied at Guqiao Mine is taken as the object for case study. The study is made in a procedure as follows: to start off, establish a dynamic database coal ash, moisture and calorific values based on collected saleable coal quality data; after that, partition the data using the sliding-window method for constructing the multivariate linear fitting optimal model of calorific values; then, establish the silo rating and grading mechanism based on coal levels in silos, the standard deviations of express ash analysis and calorific values of the coal loaded into silos; following that, setup a rule library for the coal out loaded from silos based on expert experience and number the silos in which the coal blends are stored; finally, calculate the required quantities of the coal blends from the numbered silo in which different grades of coal are stored, according to the mass and energy conservation principles. Practice shows the accuracy of the multivariate linear fitting model is up to 89.53%; the optimal numbers of the silos and the quantities of coal required to be discharged from them can be determined according to the coefficient of each term of the rating function calculated based on rating mechanism, the calorific values and actual volumes of coal in silos; the consistency between the decision made by the system and by onsite experts exceeds 80%, fully proving the effectiveness of the power coal blending technology.