Research On Model Migration And Its Application On Leaching Process Modeling

In the industrial production, the original process model built on one particular process becomes invalid when working conditions change. Model reconstruction not only consumes lots of labor and material, but also takes a long time, which will lead to prediction and optimization control of the production process difficult to conduct during this period. Model migration is an effective way to solve this problem. Leaching as an essential process plays an important role in the hydrometallurgical production, so researching on the issue of leaching process modeling and model tuning is the requirement of the theoritical and practical applications. Obviously, the study of model migration and its application on leaching process modeling has great theoretical and practical significance.At the beginning, this thesis takes the acid leaching of cobalt compound ore as research background, describes leaching process, and presents a mechanism model for leaching process. Meanwhile, the effect for leaching rate of leading factors is analyzed by simulation experiment. As for frequent changes of working conditions in hydrometallurgy leaching process, model migration is proposed to build leaching process model. Then, the feasibility of model migration, the types of process similarity and migration strategy based on several typical kinds of process similarity are briefly introduced. The process parameters such as mineral size, high-valence mineral content and low-valence mineral content have significant impact on actual leaching process. According to the important process parameters mentioned above, modeling and model tuning method based on model migration are studied. For the change of a single process parameter, the family similarity in leaching process is defined and the appropriate strategy is designed to develop static and dynamic predicting model of leaching process. Simulation results verify the effectiveness of the method.For the changes of multiple process parameters, the inclusive similarity in leaching process is defined, and the neural network based on integrated training algorithm (bagging) is used to develop new models. Simulation results verify the effectiveness of the method. Finally, online application of model migration is studied. This thesis aims at the difficulty of on-line measuring leaching parameters in hydrometallurgy leaching process, a working condition identified way is proposed based on data mining. Thus the problem of model migration for the online application is solved. Simulation results verify the effectiveness of the method.