Dynamic Modeling for Design of Ion Exchange Systems. Khosrow Nikkhah

Transcription

1 <Title of Publication> <Edited by> TMS (The Minerals, Metals & Materials Society, <Year> Dynamic Modeling for Design of Ion Exchange Systems Khosrow Nikkhah AMEC Mining and Metals Consulting 111 Dunsmuir Street, Suite 400 Vancouver, British Columbia, Canada V6B 5W3 Abstract Increasingly ion exchange systems are being included in design of hydrometallurgical plants for metal extraction or impurity removal. Because such systems operate sequentially and are based on resins that exchange one ion for another, store it temporarily and release it to a regenerating solution, the usual steady state heat and mass balancing tools and spreadsheets cannot be used to design them successfully. This paper outlines the role of dynamic simulation in design of ion exchange systems. Examples are given for use of dynamic simulation in design and investigation of equipment based on consideration of the effect of input criteria such as expected ion loading and elution profiles. The methodology for dynamic modeling using IDEAS in design of multi column ion exchange trains operating in parallel is presented. Results shown include the non-steady state distribution of extracted metal and limitations imposed on operating conditions. These conditions include proposed sequencing of columns for loading and elution cycles, loading, elution and recycle flow rates as well as size and choice of related ion exchange plant equipment. Introduction Ion exchange (IX) technology has passed the pilot plant stage successfully in many hydrometallurgical projects in recent years. For example Eichrom Industries has developed an ion exchange process for control of iron in copper electrolytes [1]. CalEnergy Minerals is using an anionic DOWEX resin to recover zinc from geothermal brines in a series of ion exchange columns. The recovered zinc is eluted with pure water and transferred intermittently to be treated by further refining [2]. Goro nickel-cobalt project in New Caledonia includes the use of ion exchange technology at low ph [3]. This process uses the copper nickel selectable chelating resin DOWEX M4195 (formerly known as DOW 3N or XFS-4195) for the removal of small amounts of copper impurity at low ph. It is now recognized that successful application of pilot plant results to commercial scale production involves many complex criteria that requires the use of new tools such as dynamic process simulation. The simulation in this case has to be able to realistically link the intermittent flows in the ion exchange system to the continuous process flows in the rest of the plant. Once an accurate model has shown that the design choices in the ion exchange system and the adjacent plant areas can handle the desired capacity and flows, the operator can verify the design and be in a position to avoid surprises and start-up the integrated plant successfully.

2 Modeling Approach The greatest benefit from dynamic modeling can be found at the stage where pilot plant studies have proven the viability of IX process chemistry and the next step is to integrate the process in a commercial plant. At this stage the use of dynamic modeling will provide the necessary accuracy in feasibility capital cost estimates and will show the practicality of IX systems operation. It will also help operating companies avoid committing to equipment sizes for pumps, valves, piping and tank sizes that would impede the implementation of the successful pilot plant program at commercial scale. IDEAS Simulation is best suited for modeling ion exchange systems because it depicts the process material and energy flows as a function of time. Model Inputs Model inputs for dynamic simulation are typically as follows: The IX process flow specifications such as composition, temperature and flow rate The timing sequence. This should include both the loading and elution (or regeneration) program and the sequence in which flow rates and directions to various IX resin beds are changed. Resin based physical specification such as bed volume and void space Loading and elution profiles for the specific resin solution system The process solution and elution flow rates Product and intermediate solution storage tanks and other related equipment specifications such as filters, pumps and process control strategies When several IX trains or carousels use the same set of process equipment for solution storage, handling and transfer, the model should show how these systems interact. Model Outputs The following model outputs are used to provide valuable information on the IX system: The heat and mass balance across the ion exchange system and the adjacent plants Variation of metal species concentration in the IX columns and solution tanks Loading and elution recovery for the metal species The effect of sequencing of flows on the level of storage tanks used for various IX solutions and products IDEAS Ion Exchange Model IDEAS [4] is a high-fidelity dynamic modeling software that provides all the aforementioned capabilities for inputting design criteria and generating outputs for IX systems that are being designed for commercial use. IDEAS is based on a graphic user-interface. Instead of writing computer code the user can construct a dynamic model by using process units or objects from various libraries and build an interactive process flowsheet on the computer screen.

3 Particular equipment specifications as well as feed characteristics can be specified using dialog box inputs for each object. These objects readily interact with each other and can be interconnected in many numbers of combinations to create complex process flowsheet models. IDEAS software has a comprehensive material properties database. Each of the simulation objects has access to thermo-chemical and physical properties of components used in the model. These properties are already available as text files for each individual aqueous, solid and gaseous component. Using the Materials Properties Object, the user can compile the components necessary for the modeling exercise. Figure 1 shows the IDEAS Material Properties Object and its various tabs and features for defining the model material properties. Figure 1: IDEAS Material Properties Object for Defining Material and Their Properties in the Process Model. IDEAS can define the flow, pressure and composition of the feed streams for the IX system. Figure 2 shows a typical Source Object that defines the flow rate as well as solution composition to a series of ion exchange resin columns.

4 Figure 2: IDEAS Source Object Used for Defining the Composition and State of Process Streams. In addition to ordinary icon based objects that are available in IDEAS for modeling hydrometallurgical process equipment, there is another type of object, called a Hierarchical Object. A Hierarchical Object is a combination of other objects to represent a more complex piece of equipment or area of the process. Major uses of Hierarchical Objects are to create more complex models from simpler objects and simplify the process flowsheet by representing complex areas of a model in a simpler form. The interactive graphic interface of IDEAS allows the user to save hierarchical objects as graphic icons and reuse them for simulation of similar equipment. Modeling of IX columns is a good example of where the benefits of hierarchical modeling can be realized. After an IX column is modeled, it can be saved and reused for simulating other IX columns in the system without having to repeat the work each time. Simulation of IX columns in IDEAS is based on using a Plug Flow Tank Object to model the flow and displacement of both the process solution flow and the elution flows in the column. Also Dynamic Tanks that have specific volumes are used to simulate the storage of resin and metal-ion loaded resin in the column. Rates of loading of metal ions on the resin and washing of the loaded resin are controlled by loading and elution data (curves) obtained from samples taken in the field for that species. These curves represent time dependent variation of the ion species concentration at the discharge from the IX column. They can only be used as variable set points in a dynamic simulation tool where the ion exchange process can be represented as a function of time. Case Study Figure 3 shows a schematic diagram of a 4-column IX train operating sequentially in series in a multi train IX system. Here the desired metal ions are sequentially loaded on the IX columns and then eluted with flows from regenerating, lean and intermediate strength aqueous solutions of the same metal ion. Each column is sequentially used as a lead, tail and polishing column. The fourth column having been eluted and regenerated under yet another sequence of flows

5 from various elution tanks enters the loading train as the last (polishing) column. As the sequence of loading and elution continues, the high strength metal species solution builds up in Figure 3: Schematic of Multi-Column IX Systems Operating in Series

6 the IX product tank and is transferred for further processing. IDEAS Sequencer Object is used to model the sequence of valve positions and flow variations in this system that are necessary for changes in the loading order as well as the special sequence of flows used in the elution and regeneration program in the fourth column. For flow of process solution through the three loading columns and the elution flows through the fourth column, there is a unique state (set of valve positions and flow rates) that corresponds to each unique flow configuration. Starting with the original valve positions the changes that bring about these flow directions and rates are stored in the IDEAS Sequencer Object. The Sequencer Object relays this information to the pumps and valves in the model. The Sequencer Object is itself controlled by a Master Sequencer that repeats the program of various valve positions in the system. In the example shown in Figure 3, solution tanks for elution, regeneration and IX product are shared between several IX trains. During the elution stages various flows are used to displace the process solution from the resin bed, elute the loaded metal species and regenerate the resin for further loading. The solutions leaving and entering the columns may include recycle streams, regeneration streams and recovered metal solution that report to intermediate and highgrade IX product surge tanks prior to further processing in down stream process areas. When the process plant is comprised of several IX trains, it is necessary for these trains to share the same source and destination solution surge tanks to ensure economical construction and operation of the plant. Having excessively large tanks to avoid any staggering of the operation would be too costly and uneconomical. On the other hand, undersized tanks will result in the IX flows to be interrupted due to tanks overflowing or operating at very low levels causing discharge pumps to cavitate. This necessitates simulating the IX process flows in all its detail to demonstrate the effect of flows on tank levels. In this way one can arrive at optimized tank sizes based on a practical staggering of the operation of the parallel IX column trains. Using the IDEAS software in addition to the flows, stream composition and IX process chemistry, other process details such as solution tank sizes, their low and high level control set points and process control logic can be included in the model. In this way before finalizing plant design and operation strategy, it can be shown that despite the complexity of the necessary sequence of events, the IX system is operable in a continuous fashion. Figure 4 shows the solution level plots generated by IDEAS for elution tanks that are used as both source and storage for IX solution flows. Generation of these plots for different loading, elution and regeneration programs can verify the practical limits for continuous operation. If the sequence of operation for various IX columns is staggered in an optimized manner, one can benefit the most from the tank volumes available. On the other hand certain tank volumes can be estimated first and then using IDEAS model, one can evaluate the possibility of implementing certain loading and elution programs. Figure 5 shows an analysis output depicting the effect of staggering of IX trains operating in parallel for different options in elution tank volumes. By using the IDAS model with different tank dimensions, one can find the acceptable range for staggering of the flows in different IX trains to ensure operability of the plant. This technique provides accurate information for timing of the operation for each train and their effect on the tank levels.

7 Figure 4: IDEAS Plots for Elution and IX Product Tank Levels For example for the scenario under consideration in Figure 5, the process sequencing would be viable, if the various trains were synchronized to operate with a lag times between each of them of 6.5 to 9 minutes. If the time lag in operation for whatever reason went outside this 2.5- minute window, certain tanks would overflow or run near empty causing discharge pumps to cavitate. Options 1 to 4 show the effect of adopting various alternative tank sizes that were quickly simulated using IDEAS software to arrive at a more practical window of operation and as a result decrease the chances of pausing the whole IX process. In this case option 4 is the optimum choice giving more flexibility in plant operation. Another problem encountered in the design of IX systems is that adoption of new IX elution programs may impact process flows adversely during the loading of the metal ion on the resin beds. For example in a case that recycling of elution and resin regeneration flows are necessary to capture displaced metal ions from the IX bed, highly corrosive species may be recycled and affect the loading process and damage the resin columns. Therefore in the process of optimizing the elution and regeneration flows program to achieve higher metal loading and extraction from in the IX columns, it is important to be able to analyze the choice of different options on process solution properties. IDEAS in addition to performing a dynamic mass and energy balance, monitors any stream or vessel contents property that would be closely associated with the process mass balance. For example IDEAS can calculate and plot ph for both theoretical and estimated values based on titrations performed on solution samples taken from the process plant. Consideration of changes to the elution program may involve recycling of different quantities of acidic solution to the loading solution feed to the columns. In such a case by modeling the alternative elution programs; the user can accurately determine the column feed ph. In this way an elution program can be adopted, that would optimize the loading capability and be appropriate for the materials of construction in the IX system.

8 Option 4 Option 3 Option 2 Option 1 Current Tank Volumes Time, Minutes Minimum Lag Maximum Lag Operating Window Figure 5. Lag Time and Operating Window Plots for Parallel IX Trains Figures 6 and 7 show typical loading and elution curves for the metal ionic species at exit of the three loading columns operating in series in a four-column system. Concentration values from these plots are used in the IDEAS model as dynamic set-points for controlling the quantity of the ionic species that leaves the column; what does not leave the column during the loading cycle is loaded on the resin by assigning a 100% conversion for a chemical reaction involving the metal species and the resin that at its simplest form would be: Metal Ion + Resin Metal Loaded Resin During elution or regeneration of the resin bed, a reverse reaction is used in the IDEAS Reactor Object to model the elution of the metal species from the resin. The resin remains in the ion exchange column, while the metal ion enters the solution on contact with the eluting aqueous stream and is discharged from the column. For modeling the elution and regenerating process rates of the resin, elution concentration profiles such as that shown in Figure 7 experienced at the pilot plant or commercial scale operation are used. Knowing the quantity of resin used in each bed, concentration of the loaded metal per unit volume of the resin is calculated from a preliminary run of the IDEAS model. This information is then used to represent the concentration of metal ion in depleted solution leaving the column as a function of its concentration on the resin bed. In this way the original concentration data can be replaced with a unique mathematical function independent of time. This function is then used to replace the time dependent loading profile data in the model. Such functions would only be applicable to the prevailing process conditions such as resin type and condition, temperature, process solution concentrations and flow rates.

9 300 Process Feed Metal g/l 250 Metal Concentration - mg/l Time, Minutes Metal, mg/l - Polishing Metal, mg/l - Tail Metal, mg/l - Lead Metal, mg/l - Process Feed Figure 6: Loading Curves Showing Discharge Metal Concentrations for Three Ion Exchange Columns Operating in Series Relative to the Process Feed Concentration 1,400 1,200 Process Solution Displacement Elution Stage 1 Elution Stage 2 Resin Regeneration 1, Metal -mg/l Time, Minutes Figure 7: Elution Curve showing Metal Ion Concentration for the Regeneration and Elution Process Discharges. The same approach is used to relate the concentration at the discharge of an IX column during elution to the concentration of the species remaining on the IX column resin. Here the ion exchange process of loading is reversed and the chemistry in the model is represented by:

10 Metal Loaded Resin Metal Ion + Resin IDEAS software can monitor and transmit the concentration of the metal species in various IX columns, tanks and streams throughout the process plant that is being modeled. Using data for tank levels and composition, IDEAS provides a dynamic (time-dependent) distribution of the metal species of interest in the process plant. Using IDEAS Integrator Object the mass flow of any component in the flowsheet is totalized in terms of absolute mass of component and total productions and recoveries are calculated as the process simulation proceeds. Figures 8 (a) to 8 (c) show part of the IDEAS Notebook where an example of the aforementioned distribution for a metal species in IX columns and the adjacent solution tanks is continuously monitored and recorded. Three cases are presented where changes in loading time and elution program have affected the product distribution and recoveries. In the first case shown in Figure 8 (a) a standard loading time of 50 minutes was used. In order to improve the recovery in the second case the load time was increased to 52 minutes. In the third case more solution volume was used for elution; this improved the recovery to above 90% of the metal species reporting to the IX product, but the IX product dropped in concentration slightly. Figure 8 (a) Overall IX Product Distribution and Recovery for Variations in Loading and Elution Times and Program, Showing an Overall Recovery of 79.83% to the IX Product

11 Figure 8 (b) Overall IX Product Distribution and Recovery for Variations in Loading and Elution Times and Program, showing an Overall Recovery of 87.74% to the IX Product Figure 8 (c) Overall IX Product Distribution and Recovery for Variations in Loading and Elution Times and Program showing an Overall Recovery of 90.29% to the IX product

12 Conclusions 1. For hydrometallurgical plants where sequential ion exchange systems and other continuous operations interact and are inter-dependent, it is necessary to perform dynamic computer modeling of the process to arrive at practical design choices for IX sequence of operation. 2. Modeling is also necessary to confirm tank surge capacities within the IX system and in areas adjacent to continuous process operations. 3. Other useful information obtained form dynamic modeling include better understanding of stream properties such as ph and operation scheduling for several trains working in parallel to ensure continuous operation without pauses and correct materials selection. 4. Loading and elution reactions are also dependent on process stream concentrations, temperature and the nature of contacting the solution with resin. If enough information is available from test work, it can be incorporated in a dynamic IX model to provide some degree of predictive capability. In absence of such extensive data, process specific loading and elution concentration data should be used to model the IX system for the specific conditions only. 5. In the same fashion as described for IX systems in metal recovery or impurity removal, dynamic simulation can be applied to modeling of other combinations of continuous and batch process operation. Examples of these in hydrometallurgical processing are filtration, batch reactors and hydrogen reduction autoclaves used in the nickel-cobalt industry. References 1. M.J. Gula and D. Dreisinger, The Ion Exchange Control of Iron in Copper Electrolyte Streams Using Eichrom s Diponix Resin, SME Annual Meeting, Phoenix, Arizona, Pre-print (1996). 2. T.J Clutter, Mining Economic Benefits from Geothermal Brine, GHC Bulletin, (June 2000), I. Mihaylov, E. Krause, D.F. Colton, Y. Okita, J-P. Duterque, and J-J. Perraud, The Development of a Novel Hydrometallurgical Process for Nickel and Cobalt Recovery from Goro Laterite Ore, CIM Bulletin, 93. (1041), (June 2000), K. Nikkhah, Role of Simulation Software in Design and Operation of Metallurgical Plants: A Case Study, SME Annual Meeting, Denver Colorado, Pre-print (2001).