Browsing by Author "Komulainen, Tiina M."

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Novel modeling and control approach for performance improvement of an industrial copper solvent extraction process
(2007-11-30) Komulainen, Tiina M.
Doctoral dissertation (monograph)
More than 20% of the cathode copper is annually produced by copper leaching, solvent extraction and electrowinning processes. The focus in process technology has been on research and capital intensive development of the process equipment and chemicals. However, the financial benefits gained through an advanced control system would be significant. An advanced control system would maximize production by running the process closer to the optimal operating point, and increase the production of the first quality copper cathode by decreasing the variation in key process variables. The lack of adequate dynamic process models for industrial applications has, to date, prevented the development of advanced process control systems. Therefore, the first aim of this thesis is to develop dynamic models to describe the behaviour of an industrial copper solvent process and to facilitate control system development. The second aim of this thesis is to develop an advanced control system for the copper solvent extraction process, and to verify that the performance and profitability of an industrial copper solvent extraction process can be significantly increased by utilizing the advanced process control system. In the process model, the mass transfer of copper in the mixer-settler units is described by means of dynamic, modified ideal mixing and plug flow models. The equilibrium value for the ideal mixing model is calculated on the basis of the steady state McCabe-Thiele diagram. The model utilizes industrial online and offline measurements. The unit process models are combined according to the case plant flowsheet. Based on the process models, a control hierarchy is developed for the case copper solvent extraction process. The optimization level in the hierarchy consists of an optimization algorithm that maximizes the production of the copper solvent extraction process and provides setpoints for the stabilizing control level. The stabilizing control level consists of a single input-single output control strategy employing two PI controllers or, alternatively, a multi input-multi output control strategy using the model predictive control (MPC). The dynamic models are tested by comparing the simulated data with the industrial data. The controller performances are tested for setpoint tracking and disturbance rejection in the simulation environment with step input changes. The benefits of the control system are assessed by comparing the variation in the controlled variables and the total copper production to the data collected from the process under manual control. The dynamic models are tested with two data sets representing the normal operation of the industrial case copper solvent extraction plant. The models follow the output copper concentration trends smoothly for the major input changes in the flow rates and copper concentrations, and the residuals between the simulated data and the industrial measurements are sufficiently small. The average absolute error is 1-3 % of the mean value of the output copper concentrations. The performance of the control system for setpoint tracking and disturbance rejection is very good. As expected, the model predictive controller performs better than the PI controllers. The disturbance rejection capabilities are further improved by adding four feedforward compensators to the control strategies. Compared to manual control, the variation in the rich electrolyte copper concentration was decreased by 70-80 % with the PI controllers and 80-90 % with the model predictive controller. The copper mass production was increased by about 3-5 % with both control strategies. The modeling and control results are very encouraging for the further testing of the control system in an industrial copper solvent extraction plant.