Intelligent Integrated Modelling And Optimization Control Strategy For Lead-Zinc Sintering Blending Process

The imperial updrafted-sintering is an important loop of the Lead-Zinc Imperial Smelting Process. As the first working procedure of the Lead-Zinc sintering process, the blending process plays an important role, which exerts an influence on the sintering production cost, quantityt-quality and energy sources waste. As the chief tache for stabilization and optimization production, the role of Lead-Zinc sintering blending process has not been played at present and there are problems of low economics and accuracy in the sintering blending process. At the same time, high cost and low quantity-quality have resulted from the low level of the Lead-Zinc sintering blending process control, which result in energy sources squander and environment pollution. Amied at the above problems, the intelligent integrated modeling and optimization control strategy in the Lead-Zinc sintering blending process have been mainly studied and the main achievements in this dissertation include several aspects as follows:(1) Intelligent integrated prediction model for component of Pb-Zn agglomerateTo deal with the problem of the component prediction for Pb-Zn agglomerate, an intelligent integrated prediction model based on process neural network (PNN) and grey system theory (GST) was presented. First, the component of agglomerate was predicted by PNN and GST models. Then, from the viewpoint of the information theory, a kind of entropy method was proposed and the deviation of forecasting error series was defined renewal, then the optimal weightling coefficient of each prediction model was obtained. The exact prediction results for component of Pb-Zn agglomerate were acquired by integrating the reaults of two prediction models. The results show that the integrated model has high prediction precision, it predicts the component of agglomerate effectively, and it meets the requirements of the data completeness for proportioning computation.(2) Intelligent integrated prediction model for quantity of sintering return powdersBased on the fact that the complexity of return powders is affected by various factors and it is hard to predict accurately with a single prediction model, an intelligent integrated prediction model based on improved grey system (IGS) and support vector machine (SVM) was proposed. Firstly, the quantity of sintering return powders was respectively predicted by using IGS prediction model and SVM prediction model. Then an intelligent integrated prediction model based on two precision indicators of mean and deviate, was introduced to predict the quantity of sintering return powders by calculating optimal weighting coefficient. The prediction results show that the prediction precision of the integrated model is higher than that of single prediction model and it can predict the quantity of sintering return powders effectively. The prediction results can offer data support to the determination of blending ratio of return powders.(3) Qualitative and quantiative synthetic optimization algorithm for the primary blendingTo deal with the problem of high cost and low accuracy existed in conventional methods for the primary blending process in lead-zinc sintering, a kind of qualitative and quantiative synthetic optimization algorithm was proposed. First, based on the analysis for the characteristic and economy of the sintering material and establishment for the intelligent integrated prediction model for the agglomerate component, a blending optimization model was established for the purpose of minimizing the costs. The mixture ratios were optimized respectively by using the expert reasoning strategies and the immune genetic algorithm. Then, from the viewpoint of the system theory, the mixture ratios were optimized ulteriorly by using the qualitative and quantiative synthetic methodology integrated the process neural network technology, grey system theory, expert reasoning strategy and improved immune genetic algorithm. The blending accuracy was enhanced, the sintering cost was reduced and the considerable economic benefit was procured.(4) Intelligent optimization strategy for the secondary blending process based on comprehensive evaluation for sintering statusBased on the analysis of the relationship between parameters in the whole sintering process, the intelligent optimization strategy for the secondary blending process based on comprehensive evaluation for sintering status was proposed. The comprehensive evaluation model for sintering production status was established and the optimization algorithm for the operation parameters matching based on clustering analysis was presented. Firstly, based on the establishment of the models for sintering return powder, Pb, Zn and S component prediction, the outputs of these models were considered as factors for sintering status evaluation. The comprehensive evaluation for sintering status was implemented by using the fuzzy evaluation method. Secondly, according to the results of comprehensive status evaluation model, the optimum operation parameters were acquired by using matching optimization algorithm based on weighting fuzzy C-means clustering. The results show that the fluctuation of status can be meliorated efficiently and the quantity and quality of agglomerate were improved.(5) Intelligent integrated control strategy for sintering blending processThe material flow in the sintering blending process was impacted by many uncertain factors and fluctuates greatly with characters of high nonlinear and large lag. It is hard to solve the sintering blending control problem by conventional control theory and methods since the mathematic model is difficult to be established. In order to improve the accuracy and stability of the sintering blending, a fuzzy adaptive PID intelligent integrated control strategy for sintering blending based on weighing coefficients was proposed by combining with the features of the fuzzy control and PID control. The fuzzy controller and the adaptive PID controller were designed respectively. The outputs of the fuzzy controller and the adaptive PID controller were integrated by using the weighting coefficients. When the error of the material flow is greater, the fuzzy controller mostly determines the output so that fast response ability can be shown, and when the error is smaller, the adaptive PID controller mostly determines the output, so that a higher control precision can be obtained. The switch of the output between fuzzy controller and adaptive PID controller is continuous.