Modeling And Optimization For Cementation Process Of Hydrometallurgy

There are lots of deposits of non-ferrous metal resources in our country. With the rapid growth of the national economy, and steady progress of industrialization, it is very important to use such resources effectively and economically for sustainable development strategy of our country. As one of the two extractive metallurgy technologies, the remarkable advantages of hydrometallurgy are high comprehensive recovery rates of valuable metals in raw materials, benefit for environmental protection, and easy fulfillment of continuous and automated production processes. Therefore hydrometallurgy is more suitable for recovering low-grade metal resources. The hydrometallurgical cementation process one of important processes in hydrometallurgy. The control of cementation production process still remains off-line analysis, experience adjustments and manual control, which lead to low efficiency, high recourses consumption and unstable product quality. And it becomes a bottleneck for hydrometallurgy industrial development in our country.This thesis aims at the difficulty of on-line measuring cementation rate in hydrometallu--rgy cementation process. Based on deeply analyzing the characteristics of cementation production process, it is researched comprehensively and systematically on mechanism modeling, data modeling and optimization. The main researches are summarized as follows:1. Based on deeply analyzing cementation production process, the dynamic mechanism model of cementation process was established according to kinetics principle, material balance and the filter equation of filter press.2. To aim at the difficulty of applying the dynamic first principle model to the industrial field directly, the data modeling method was put forward to solve prediction problem of the gold cementation rate in hydrometallurgy cementation process. A nonlinear PLS method based on RBF neural networks was adopted to solve the nonlinear problem of cementation process. The nonlinear problem in the low-dimensional space was change into linear one in the high-dimensional space by RBF transforming. Then the PLS algorithms was used to train the parameters in the model. To address the problem that the mismatch between the NPLS model and the actual plant, data from the current batch were used to update the NPLS model. Finally, simulation results show the effectiveness of the above modeling method.3. Due to model plant mismatches and the batch-to-batch change, the end of the product quality may deviate from the expected quality, which leads to the improvement of production cost. To solve this problem, the iterative optimization method was proposed for the optimization of the addition amount of zinc powder between batch, which makes final product quality gradually converge to expectation.4. Finally, the prediction and optimization platform system for the hydrometallurgical cementation process was designed and built in this paper. And every part’s structure and function of the platform system were also introduced.