Modeling And Optimization Of Leaching Process For Hydrometallurgy

As one of the two extractive metallurgy technologies, the remarkable advantages of hydrometallurgy are high degree of comprehensive recovery of valuable metals in raw materials, benefited for environmental protection, and easy to fulfill continuous and automated production. Therefore hydrometallurgy is more suitable for recovering low-grade metal resources. Leaching process is the central operation unit in the hydrometallurgical processes. Although leaching technic in hydrometallurgy is very advanced, the control of leaching production process is still according to the way of off-line analysis, experience adjustments and manual control, which leads to low efficiency, high resourses comsumption and unstable product quality. And it becomes a bottleneck for hydrometallurgy industrial development.According to the difficulties of on-line measursing leaching rate in hydrometallurgy leaching process, this thesis researches on prediction methods for leaching rate in hydrometallurgy leaching process comprehensively and systematically using hybrid modeling method which is composed of mechanism and data modeling methods. For multiple optimized objectives of leaching process, a multi-objective optimization model is proposed based on the hybrid model and an improved optimization algorithm is developed. The main researches are summarized as follows:1. Analyzing the basic kinetics principle of leaching process, the acid leaching process of cobalt compound ore which takes sulfur dioxide as the reducing agent is studied. A dynamic mechanism model for leaching process is proposed by analyzing the relationships of material balance and energy balance. The main parameters of model are decided using experimental analysis and identification. The validity and generalization of the model are verified by dynamic experiment and static experiment which are tested by practical data. Meanwhile, the effect of leading factors for leaching rate is analyzed by simulation experiment. The basis of hybrid model and process optimization is established by the dynamic mechanism model. 2. Aiming at the difficulity of applying the dynamic mechanism model to the industrial field directly, two hybrid modeling methods are proposed to build prediction model of leaching rate. One of them is an error compensation hybrid model for leaching rate. An improved bagging ensemble algorithm based on negative correlation learning is studied for the error compensation hybrid model. The model is composed of the mechanism model and error compensation model of data in parallel, therefore the advantages of different modeling methods can be exerted. The other one is a multi-model for reaching rate. A selected bagging ensemble algorithm based on binary PSO algorithm is studied for the multi-model. In this model, the bagging method is used to combine the data model with the mechanism model efficiently. The proposed method can improve the accuracy of data model and overcome the disadvantages of data model and mechanism model. By simulation experiment, the efficiency of the two hybrid modeling methods is verified.3. Aiming at the multi-objective problem of leaching process, the leaching problem is formulated as a constrained multi-objective optimization problem based on the hybrid models. Afterward, a multi-objective PSO algorithm based on two stages-guided is proposed in this thesis. There are four improved strategies in this algorithm:the strategy of constructing external data set based on combining strong predominance ranking and crowding distance ranking, the strategy of selecting guided particle based on two stages, the strategy of mutation based on combining gaussian distribution mutation and uniform distribution mutation, the strategy of updating based on personal best of neighborhood consciousness. Some benchmark functions are tested for comparing the performance of the algorithm with Sigma algorithm and MM-MOPSO algorithm. The results show the better search performance of the proposed algorithm. The proposed algorithm is applied to the multi-objective optimization problem of leaching process, and the results of experiment show that the proposed algorithm shows better performance than Sigma algorithm.4. The leaching section in a cobalt hydrometallurgy plant is regarded as a research example. For the low automation level of the production conditions, it is required to design basic automation system, which is composed of executive layer, control layer and optimization layer. Under the support of basic automation system, based on the above theory studies, prediction and optimization operating system software of leaching section is designed and developed. Prediction for leaching rate of leaching process is achieved. And the optimal operating guide for the process is provided. Applying the software to practical leaching section of some hydrometallurgy plant, good economic returns are yielded.