| Ore jigging is one important method in physical preparation. About 50% of raw coal is processed in this method in China. As the key equipment on coal preparation, jig control includes the control of stratification and discharging. The separation efficiency of jig is related directly with its control results. As the conventional control is based on the precise mathematical model and used to solve problems of linear, invariant time system, it is difficult to get the satisfied effect on the complicated, nonlinear and great lag system like jig, for it is hard to find its precise mathematical model. The main work did in this paper includes:After analyzed the influencing factors of jig separation efficiency, it is pointed out that, although stratification is the precondition of discharging, but the jigging process is continuous, the stratifying and discharging is happening at the same time, i.e. the discharging good or bad is affecting the stratifying results, so it's the combined action of stratification and discharging deciding the efficiency of jig, jig control must be considered with this combination. However the control system we are using now is simple and extensive for the online measurement of jig bed status is not solved yet. Theγray Stratification Status Measuring System is analyzed in details and it shows that the jig bed online measuring problem has been solved and it provided a basic virtual platform for the construction of jig control system. Through taking typical samples from site, the integration model of stratification and discharging is reached by using ANN and SVM.It is pointed out that the Efficiency of Jig should be the final goal function (ultimate objective is the Coal Lost In Waste), i.e., the performance function is the goal function, and many variables in process make up of the inputs of the ANN. As the ANN has the advantages of distributed parallel treating, nonlinear mapping, self-adapting learning and robust, the topology structure, the number of hidden layer and the neuron number of hidden layer are determined. The weights and threshold is optimized through inherit arithmetic.The Support Vector Machine (SVM) arithmetic based on Statistical Learning Theory is studied on pattern recognition of waste segment of jig and it is found that the SVM based on limit samples shows the better results compared with ANN in test error. After compared the SVM kernel with linear, polynomial, RBF and S functions, using simulation of least-square SVM, it is found that the training error and the test error is smaller when the RBF as the kernel.As the predict control pay more attention to the model function rather than structure form, the description of process can be got through simple test and it is unnecessary to realize the inside mechanism of the process. So the predict control algorithm changed the strict request of model structure from the modern control theory. To build multiformity model in most convenient route is established according to the function desire. This method is so suitable to the jig process, which its theory got great behind the practice.Proceeding with model algorithm, dynamic matrix control, generalized predict control and the predict control based on NN analysis, the method is offered of how to use the off-line pretrained ANN to predict the Coal Lost In Waste to achieve the predict control of air valve cycle of jig. The predictor of Coal Lost In Waste and optimizer is constructed using BP Neural Network. The results of simulation using sample data shows that the Coal Lost In Waste is lower. |
