Control Method Of The Underflow Density For Thickening Process In Hydrometallurgy

There are lots of deposits of low-grade non-ferrous metal resources in our country. With the rapid growth of the national economy, and steady progress of its industrialization, it is very important for the sustainable development strategy of our country to use such resources effectively and economically. Hydrometallurgy being 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 it is more suitable to recover low-grade metal resources. Thickening process is an important unit operation that is widely used in hydrometallurgy. A thickener is the key equipment for the thickening process and the underflow concentration of thickener is the key quality index. However, at present, most thickening processes are still in the manual operation state, making its concentration and flow fluctuate largely, and influencing the subsequent production index. The automatic control of thickening process has been a difficult problem due to the harsh working conditions and other factors like the large time-delay and the slow time-varyingWith the aim to overcome the difficulties in the thickening underflow concentration control, and based on the deep insight of the hydrometallurgy procedure, this thesis puts forward a control strategy of the thickening process based on iterative linearization model predictive control and a nonlinear model predictive control strategy based on dynamic optimization algorithm. The main contents of this thesis are as follow:(1).Firstly, the operational and dynamics characteristics of the thickening process are analyzed and a hybrid model of the thickening process is established. And based on the review of the control strategies for the thickening process, the problems and difficulties in the actual production process which is the basis for the proposed control approach in the paper are analyzed.(2).Taking into consideration the thickening process control problem with high nonlinear and large variation of working condition, the control strategy based on iterative linearization model predictive control is applied. Online iterative linearization is processed by the hybrid model of the thickening process. Model predictive control method is applied based on the processed model.With feedback correction, rolling optimization and evaluated manipulated variable, the goal of thickening process underflow density control is rudimentarily achieved. Through further analysis of the above-described method,it can be established that the iterative linearization model is essentially a process of successive approximation processes changing the nonlinear model into a linear model while omitting partial information of the hybrid model. On the basis of rolling solving optimization problems, the optimal solution is not an actual exact optimal solution for the problem. This thesis applies dynamic optimization algorithm which combines simultaneous and sequential algorithm. The optimization problem of nonlinear model is directly solved and then actual optimal solution is obtained so that the goal of thickening process underflow density control is finally achieved. The capabilities of the control strategy are investigated through computer simulations.(3).Semi-physical simulation platform of underflow density control for thickening process is established by taking a thickening section in a hydrometallurgy plant as a research example. With the support of a computer simulation platform and based on the above theories, an optimal operating system software of thickening process is designed and developed; and optimal control for underflow concentration of thickening process is achieved.Finally, the thesis is concluded with a summary and discussions of problems which need to be further researched.