| Ore grinding is a complex process, the links of the process have a good correlation, for lack of instruments to measure the internal work state of ball mill and hydro cyclone, the regulation of grinding process is not completely known, it is necessary to understand and grasp the mathematics model in order to carry out the online control of grinding process.At present, internal condition of ball mill and hydro cyclone that can not be directly measured. Application of the Soft-measure Technology, key parameters about quality index of the grinding and classification system be directly determined and correlation model of soft measurement be established based on RS Theory and RBF neural network, which is helpful to production technical personnel and operation workers to technically analysis and control system of grinding and classification.As the intelligence technologies continue to develop, products of these technologies represented by fuzzy system and neural network show the powerful processing capability to complex nonlinear systems. A series of advanced control systems based on intelligent control theories and methods are continuously put forward and improved, great breakthrough and plentiful fruits are derived for the control problem of complex systems. However, due to the immaturity of the foundation theory of intelligent control, there are still many aspects and key points need to be improved when applying the control methods. In view of the combined design of the advanced intelligent control system in future, the main research work of this dissertation can be described as follows:1,A control model of fuzzy RBF neural network is presented, and the future process action of the grinding ore is predicted based on RBF neural network and available information, With two additional layers, fuzzy layer and fuzzy inference rules layer on the hidden layer in RBF neural network, it analyzes how the parameters and process characteristics of grinding ore affect controlling effect by receding horizon optimization methods and minimizing the error between model output and experimental data; A intelligent PID control method is presented based on the fuzzy RBF neural network, which realizes decoupling control of multi variable, nonlinear and time-variation by adjusting parameters of PID controller on-line; The analysis course is briefness, the time of network learning and training is little, learning precision is high, estimate value very close in upon analysis value; The simulation researches have verified the proposed approach which can be control systems where it is difficult to build accurate math model.2,A control technology on predicting dynamic branches for large dead-time processes was put forwarded to keep the control loops being optimal all time and improve efficiency of process equipments as well, by the coordination and decision level of prediction control variables, a form on predicting dynamic branches was formed by treatment of relevance of input and output on controlled objects in control software and optimal tracing of rolling optimal strategy based on feedback correction, the technology makes compromise between transient response and steady state performance. It is shown that the control effect was improved after field practical application. The technology not only emphasizes regularity of input controlled variables, but also improves the accuracy and fast of the response. Simulation example shows that the online identified model is accurate and it can guarantee both desired robustness and control performance.3,Attributes reduction of multidimensional data were given on certain ore plant by using Rough Set Theory, on the basis of RBF neural network prediction model, least knowledge expression is presented about ore production processing's inherence rule, and the metallurgical performance was built by applying the model. The results showed that: attributes reduction of multidimensional data is applicable on grinding and classification system; the model is assistant for production personnel or operation worker when they use RBF neural network putting up analysis and control, and it make them understand ore production processing's inherence rule, and it can provide theoretical basis for experiential operating; key parameters of ball mill and hydrocyclone were presented by soft measurement technology; the analysis course is briefness, the time of network learning and training is little, learning precision is high, estimate value very close in upon analysis value.4,Parallel segmentation module technology of grinding and classification system is proposed, Firstly, each component of the grinding and classification system is regarded as functional unit module, RBF neural network of system components was established, the most influential modules on grinding concentration and overflow particle size of the grinding and classification system were identified. Then RBF neural module of one series of the grinding and classification was established, his own opinion is proposed and improved on a functional unit of the grinding and classification so that performance index of the grinding and classification is improved. At last, the performance indexes of the grinding and classification were performed comparatively for the situations with and without the improvement.5,Based on big flotation machine that have highly request for liquid level of automation control.In this paper,we designed control valve of liquid level of automation control from the locate,and gave a soft measurement technique based on neural networks. By using different form of feedback cam, which brought different non-linear flow rate, and which influenced character of flow rate of control valve in different processing of change deal and comparison coefficient, we put out control valve's function and it satisfied linear character of flow rate. Optimal curve fitting is determined based on replenishment quantity of flotation process and model of BP neural network by using the different shape of valve core curve. By experiment and operation on the locate,it worked well.
|
PDF Full Text Request