*Jordan University of Science and Technology Chemical Engineering DepartmentModeling & Control of Continuous Fluidized Bed Dryers

BYMOHAMMAD AL-HAJ ALI

Supervisor: Dr. Nabil Abdel-JabbarCoadvisor: Dr. Rami Jumah

*OutlinesIntroduction.

Modeling of Fluidized Bed Dryers.

Model Identification.

Control System Design.

Conclusions.

Recommendations.

*IntroductionWhat is Drying ?.

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

*Research Objectives1-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.

*Research Overview

*

Assumptions:1- Spherical particles with uniform size.

2- Water diffuses radialy inside particles.

3- Negligible temperature gradients within the particles.

4- Thermal equilibrium between particles and air.

5- The solids are well-mixed inside the dryer.Model Development

*Model Development

*Model Development(2)Microscopic Balance

*Model Development

*Spouted Bed Fluidized Bed

*Continuous Model Solution Validation

Run

No.

Water Removal Rate g/min

outlet Air Temperature (C

Exp.

Model

Deviation

%

Exp.

Model

Deviation,

%

M.A.

H.A.

5

6

11

12

13

14

121.2

94.9

101.4

88.0

106.4

71.0

121.1

90.4

98.6

90.6

109.5

70.0

122.2

94.4

94

86.4

113

66.1

+0.87

+1.95

-5.98

-3.23

+4.7

-6.24

49

49.9

45.3

45.6

46.9

41.8

50.4

45.7

45.1

44.2

50.7

41

+2.86

-2.56

-0.44

-3.07

+8.10

-1.91

* Unsteady State Simulation

*

*

*

*Drying Process Variables

FBD

TMYYiMiTgiGLoad VariablesControlled VariablesManipulated Variables

*Step Testing

*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.

*Input Signals

*

*Results & DiscussionModels produced by Tai-Ji package

Continuous Transfer Function:

Discrete Transfer Function: Discrete State Space Model:

*Model Validation

*Control Systems DesignControl Systems Design: 1-Conventional Controllers Multiple Single-Loop Design. 2-Unconventional Controllers Model Predictive Control (MPC) Design.

State Observer Design.

*

*Control Loop Interactions

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

*Temperature of heating air-Temperature of the grains.

*Inlet grains flow rate-Humidity of the grains

*Multiloop Controller Design

*

*Model Predictive Control (MPC)MPC depends on using dynamic model of the process in the control system.

*Closed-Loop Simulation

*

*State Observer

The ProblemOn-Line monitoring of unmeasurable properties. 1-Reliability. 2-Time. 3-Cost.

Approaches to solve the problem:

Remove it. Estimate it.

*Kalman FilterGiven:

Filter equation:

*Observer Performance

Incorrect estimates of initial conditions

*Observer Performance Providing estimates from noisy measurements

*

CONCLUSIONSThe well-mixed model with internal diffusion control is suitable to simulate drying of grains in continuous fluidized bed dryers.

The dynamic behavior of fluidized bed dryers can be approximated by linear, first order transfer functions via system identification.

The 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.

MPC strategy is more effective than PID strategy.

Kalman filter shows excellent performance for both moisture content estimation and noise effects reduction.

RECOMMENDATIONS

Development of new fluidized bed dryer model by taking temperature gradient inside the particles into account.

Real time implementation of control algorithms designed in this work.

Design multivariable control systems for batch fluidized bed dryers.

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