1. Controllability of a Granulation Process
My thesis temporarly has the name ”controllability of a granulation process”. It is based on the project work last semester and my supervisors are still Sigurd and Vidar Alstad at Yara in Porsgrunn.
Christer Haugland
Supervisors:
Sigurd Skogestad
Vidar Alstad (Yara Technology Center)
Christer Haugland, Modeling and Control of fertilizer granulation process

2. Outline Summary process description
Obtaining limit cycle behaviour as seen at the Yara plant
Fitting model to plant data
Simulink model
Further work
In this presentation I will try to point out my main results so far. First I will give a quick recap of the process. Then I will explain what has been done in the model to obtain the limit cycles and fit the model to plant data. Also a simulink model has been created and finally I will discuss further work.

3. Process description Feed: Slurry melt Spherodizer: Drying Granulation
Screens
Separation
based on size
Crusher
Crush oversize
particles
As you may remember, the spherodizer is a granulation and drying drum. Slurry feed is fed onto recycled solid particles and dries in the drum before its transported and analyzed. Then the solid particles are separated into over, under and poductsize particles and a recycle controller is adjusting the product and undersize flows to either product or recycle. The crusher is milling oversize particles to small new particles.

4. Recycle mass flow controller
Split range controller
The recycle controller is a split range controller. To increase the total recycle it sends product size particles to recycle and to decrease the total recycle it sends undersize particles to product via a second screen.

5. Model Model developed in MATLAB
First looked at discretizations in particle sizes
Increased particle classes (no. of discretizations) until the effect on the model was no longer seen.
Limit cycles occurred!
The model is the same as used in the project and is developed in MATLAB. I first looked at the discretizations in particle sizes to see if this affected the model. In the model particle size was discretized into 10 classes, ranging from 0.5 to 8 mm in particle diameter. The picture on the right is simulations of 10 particle classes with the particle sizes out of the crusher as low as possible. Still a steady state solution was achieved.

6. I started increasing the number of particle classes in the model to see the effect. This figure shows from 10 to 70 particle classes. As you can see limit cycles started occuring and increasing from 60 to 70 particles did not affect the model. Therefore 60 particle classes was chosen.

7. Stable system. Crushed particle sizes = 2.55 mm
With 60 particle classes the system was stable for low crushing grade. Here the particle sizes out of the crusher was 2.55 mm and a steady state solution was achieved.

8. Unstable system. Crushed particle sizes = 1.55 mm
In this simulations the crushed particles was 1.55 mm and the system is unstable. The cycles are caused by periods of growth followed by periods of crushing.

9. Bifurcation plots d50_mill = 1.95 mm
Bifurcation plots has also been created to show for which regions the system is stable. At about 2.3 mm in crushed particle sizes and at about 10 t/h feed to the crusher, instability occurs. The figure to the right shows stability with the recycle controller on. The system was started in a unstable region and the controller was turned on to see if the controller could stabilize the system. For some setpoints the controller was able to stabilize the system.

10. Plant data compared to model
Too large particle sizes in the system!
Only closed loop data available
Recycle mass flow controller output should also be in the same range as plant data.
Unknown controller tunings. Many unknown parameters
I recieved plant data from Yara some weeks ago and then I started comparing the model to the plant data. As you can see in the figure to the right, the particle sizes in the system was to large. The controller output amplitude was also to large.

11. Model fitted to plant data
Trial and error approach
Based on average values of plant data d50 and controller output values
Not able to get the same amplitude in d50 oscillations
Controller output and d50 in the same region as plant data.
By many trial and error attempts the model has been adjusted to try and match the plant data. However it is difficult to get the same amplitude in the particle size oscillations and have the same controller output. There seems to be a unknown disturbance causing the high amplitude oscillations at the plant.

12. Model fitted to plant data
Screen 2
Screen 1
New parameters
Old parameters
The screen separation probabilities were adjusted. Also parameters like transport delay, residence time in the granulator and maximum mass in the granulator was also adjusted.

13. Simulink Model Particle balance. Recycle controller off.
Particles inn-> Crusher , Particles out -> Product
A simple model for the particle balance has also been created in Simulink. These are the open loop simulation results. Oscillations are ocurring and the particles in the system either decreases or increases, like an integrating process.

14. Simulink Model Particle balance. Recycle controller on.
Keeping recycled particles constant -> Stable particle balance
Closed loop simulations shows that the particle in the system can be stabilized with the recycle mass flow controller.

15. Further work Bifurcation plots with the new model fitted to plant data
Controllability analysis
Look at control structure
My first task will be to redo bifrucation plots and analyzations with the new model. The plan is to do a controllability analysis and look at the control structure. Thank you!