The Data Mining Theory Of Non-ferrous Metallurgical Process And Its Application In Copper-matte Converting

The non-ferrous metallurgical production concerns economy, national defense, aerospace engineering and some other fields. The improvement of operation and control of the non-ferrous metallurgical process has a great meaning in saving energy, increasing the raw material utilization ratio, improving the production environment and reducing the operator's labor intensity. But the non-ferrous metallurgical process is difficult to operate and control because it is often multivariable, nonlinear, largely delayed, strongly coupling, very difficult to measure some process parameters and intermittent in some production process. At present, most of the operation and control in the non-ferrous metallurgical process depend on the operator's experience. And also the optimized operation rules are given by the operator's experience. Due to multi-factors, the rules have great casualness. On the other hand, most operation parameters are recorded during the practical production, and these data are regarded as running log and left unused, though the rules of system running and controlling are hidden in those. With the data mining methods employed, the rules of optimized operating and control are extracted from running data of the non-ferrous metallurgical process. Furthermore, the methods are proved to be feasible and practical by the simulation of copper-matt converting.The main work is as follows:1. The characteristics of data mining are studied in non-ferrous metallurgical process. The non-ferrous metallurgical process is generally nonlinear, high dimensional, and the data is mostly continuous and noisy, so it has a great difference from the commerce data mining.2. In order to develop and compare the algorithms, the viewpoint of components is introduced in the data mining of non-ferrous metallurgical process. The view of components divides the algorithms into five modules: task, model, score function, optimization method and data management, thus the data mining algorithms from every area have a uniform research framework.3. The framework of data mining in the non-ferrous metallurgical process is constructed and some examples of data mining in copper-matte converting are given. The parameters of the copper-matte converting process are multivariable, nonlinearity and noisy. In the paper, several data mining algorithms are successfully applied to copper-matte converting. It is proved that the data mining theory and technology can be applied to the optimization decision of non-ferrous metallurgical process, and to achieve the target of saving energy and reducing consumption.4. The multimodal optimization algorithm based on improved particle swarm optimization is developed. At present there is no effective algorithm of multimodal optimization. The algorithm proposed in this paper is simple and effective in low-dimensional.5. The chaos particle swarm optimization algorithm is proposed. Chaos motion has ergodic property and inherent randomness. In PSO algorithm, the initial particles produced by the chaos sequence can be distributed reasonablely, which makes it favorably find the optimization points.6. The mountain clustering algorithm based on improved particle swarm optimization is proposed. Compared with the existing clustering algorithm, the algorithm presented in this paper has fewer determining parameters and the clustering effect is better.7. The fast mountain clustering algorithm based on local particle swarm optimization is proposed. Compared with the mountain clustering algorithm based on particle swarm optimization, the algorithm can save more than 80% amount of calculation with little precision loss.8. The discretization algorithm based on PSO mountain clustering is proposed. Compared with the present other discretization method, determining parameters are fewer and adjusting attribute value is more convenient.