2. 1 Jordan University of Science and Technology Chemical Engineering Department “Modeling & Control of Continuous Fluidized Bed Dryers” BY MOHAMMAD AL-HAJ ALI Supervisor: Dr. Nabil Abdel-Jabbar Coadvisor: Dr. Rami Jumah

3. 2 Outlines  Introduction.  Modeling of Fluidized Bed Dryers.  Model Identification.  Control System Design.  Conclusions.  Recommendations.

4. 3 Introduction What is Drying ?. Difficulties in Dryers’ Control: 1. The lack of direct, on-line and reliable methods for sensing product moisture content. sensing product moisture content. 2. The complex and highly nonlinear dynamics of 2. The complex and highly nonlinear dynamics of drying process, leading to difficulties in modeling drying process, leading to difficulties in modeling process adequately. process adequately.

5. 4 Research Objectives 1-Development of a rigorous mathematical model that describes drying in continuous fluidized bed dryer. 2-Propose a multivariable control system that can handle drying operation efficiently. 3-Design a state observer to estimate solid moisture content, the key controlled variable.

6. 5 Research Overview

7. 6 Assumptions: 1- Spherical particles with uniform size. 2- Water diffuses radialy inside particles. 3- Negligible temperature gradients within the particles. particles. 4- Thermal equilibrium between particles and air. 5- The solids are well-mixed inside the dryer. Model Development

8. 7 (1) Macroscopic Balance

9. 8 Model Development (2)Microscopic Balance

10. 9 Model Development (3) Auxiliary Equations

11. 10 Gas Gas Spouted Bed Fluidized Bed Distributors

12. 11 Continuous Model Solution Validation

13. 12 Unsteady State Simulation Unsteady State Simulation

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15. 14

16. 15

17. 16 Drying Process Variables FBD T M Y YiYiYiYi MiMiMiMi T gi G Load Variables Controlled Variables Manipulated Variables

18. 17 Step Testing

19. 18 Model Identification * Model identification is developing an empirical model directly from experimental data. * Model identification is used when: 1-The process is very complex. 2-Estimate unknown parameters. 3-The obtained model is very complex.

20. 19 Input Signals

21. 20

22. 21 Results & Discussion Models produced by Tai-Ji package Continuous Transfer Function: Continuous Transfer Function: Discrete Transfer Function: Discrete Transfer Function: Discrete State Space Model:

23. 22 Model Validation

24. 23 Control Systems Design  Control Systems Design: 1-Conventional Controllers 1-Conventional Controllers “Multiple Single-Loop Design.” “Multiple Single-Loop Design.” 2-Unconventional Controllers 2-Unconventional Controllers “Model Predictive Control (MPC) Design.” “Model Predictive Control (MPC) Design.”  State Observer Design.

25. 24

26. 25 Control Loop Interactions CV = [ T M Y ] CV = [ T M ] Drying Process T gi G M T

27. 26 Multiple Single-Loop  Control Loops Interactions. Relative Gain Array (RGA) Analysis: Relative Gain Array (RGA) Analysis:  Loop Pairings: Temperature of heating air-Temperature of the grains. Temperature of heating air-Temperature of the grains. Inlet grains flow rate-Humidity of the grains. Inlet grains flow rate-Humidity of the grains.

28. 27 Temperature of heating air-Temperature of the grains.

29. 28 Inlet grains flow rate-Humidity of the grains

30. 29 Multiloop Controller Design

31. 30

32. 31 Model Predictive Control (MPC) MPC depends on using dynamic model of the process in the control system. y sp Reference trajectory k k+p k k+c t t Manipulated variables

33. 32 Closed-Loop Simulation

34. 33

35. 34 State Observer The Problem  On-Line monitoring of unmeasurable properties. 1-Reliability. 2-Time. 3-Cost.  Approaches to solve the problem: 3 Remove it. 3 Estimate it.

36. 35 Kalman Filter Given: Filter equation:

37. 36 Observer Performance Incorrect estimates of initial conditions Incorrect estimates of initial conditions

38. 37 Observer Performance Providing estimates from noisy measurements Providing estimates from noisy measurements

39. 38

40. CONCLUSIONS vThe well-mixed model with internal diffusion control is suitable to simulate drying of grains in continuous fluidized bed dryers. vThe dynamic behavior of fluidized bed dryers can be approximated by linear, first order transfer functions via system identification. vThe multivariable system can be split up into two nearly autonomous input-output pairs: temperature of heating air-temperature of the grain, and inlet solid flow rate-humidity of the grain. vMPC strategy is more effective than PID strategy. vKalman filter shows excellent performance for both moisture content estimation and noise effects reduction.

41. RECOMMENDATIONS âDevelopment of new fluidized bed dryer model by taking temperature gradient inside the particles into account. âReal time implementation of control algorithms designed in this work. âDesign multivariable control systems for batch fluidized bed dryers.