- Email: [email protected]
Advanced Control of a Rotary Dryer
Copyright to IFAC Automation in Mining, Mineral and Metal Processing, Cologne, Germany, 1998
ADVANCED CONTROL OF A ROTARY DRYER
Leena Yliniemi I, Jukka Koskinen 2 and Kauko Leiviskii 3
J Laboratory Manager, 2 Researcher. 3 Professor
University of Oulu lnfotech Oulu and Department of Process Engineering Control Engineering Laboratory BOX 444, Linnanmaa FlN-90571 Oulu http://ntsat.oulu·fi
Abstract: Two kinds of intelligent, hybrid control systems for a rotary dryer are presented. The main controlled variable is the output moisture of solids and the main manipulated variable is the input temperature of drying air which correlates to the fuel flow . The main disturbances of the process are the input moisture of solids and the feed flow. The one discussed control system includes a fuzzy logic controller (FLC) and a PIcontroller and the other a neural network controller and a PI -controller. In both cases the intelligent controller determines the set point value to a PI controller. The control results have been examined both with simulations and with pilot plant experiments. Copyright © 1998lFAC
Keywords: intelligent control, fuzzy control, neural control, rotary dryer
I .INTRODUCTION
results were compared with the results achieved by a conventional PID control. see ( Yliniemi and Uronen, 1983).
The control of the rotary dryer is difficult because of long delay times, long settling times, strong non linearities and unmeasurable disturbances in solids to be dried. The conventional PID control is not able to control the drying process so that the energy required would be minimized. Therefore other sophisticated control strategies as model based control, expert systems and intelligent control are of great interest in controlling the rotary dryer.
The research of the modeling and control of a rotary dryer based on intelligent methods was started in the middle of 90·s. The reasons to develop a fuzzy model and fuzzy or neural control to a dryer are due to that, firstly the mathematical model includes model parameters which are imprecise and vague and secondly much experience from the behavior of the dryer is available. The development of a fuzzy model based on a linguistic equation method is going on, and the first preliminary results have been published by Juuso et al. (1998) and Koskinen et al. (1998).
In the Control Engineering Laboratory at the University of Oulu the modeling and control of a rotary dryer have been examined during several years. The research has been made with simulations but also drying experiments with a pilot plant dryer located in the laboratory have been carried out.
In this paper two intelligent hybrid control systems are presented, together with results from simulations and control experiments with the pilot plant rotary dryer.
First the model of a rotary dryer based on mass and energy balances was developed, see (Yliniemi, et al., 1981). Based on this mathematical model a model based control system was developed and the control
119
2. DESCRIPTION OF THE PILOT PLANT ROTARY DRYER
The different input and output variables and their interactions have been presented in Fig. 2.
The schematic diagram of the pilot plant dryer used for drying experiments is presented in Fig. l . The material to be dried is fed to a rotating drum by a screw conveyor from a silo. The length of the drum is 3 m and the diameter 0.5 m. The drying air is supplied with a blower and it is heated by burning gases in a burning chamber. Propane gas is used as fue\. The dried product is fed back to the silo on a belt conveyor and is watered again.
3. DESIGN OF THE FLC FOR A ROTARY DRYER
3.1 Design of FLC The design procedure of a FLC controller according Manikopoulus et al. (1995) is as follows: Step 1: System functional requirements. Based on the investigations by Yliniemi and Uronen (1983) it has been found that the behavior of PlO is not satisfactory depending on the long delay time of the dryer. The mode based control gives the better control performance but the development of mathematical model is difficult and time consuming and therefore not easy to apply. The aim of the FLC combined with a PI-controller is to make the control easier and better by utilizing the operator' s expert knowledge . Step 2: Definition of system parameters. The main controlled variable is the output moisture of solids and the main manipulated variable is the input temperature of drying air which correlates to the fuel flow. The velocity of solids which correlates to the rotation speed of a screw conveyor can be used as an auxiliary manipulated variable. The main disturbances are the input moisture of solids and feed flow .
Fig. I. Pilot plant rotary dryer
Input variables
Disturbances
Moisture of solids
~
~
Output variables
Step 3: Definition of system parameters in terms of fuzzy sets. The input variables for FLC representing the contents of the rule-antecedent are as follows:
Feed flow
Moisture of
Temperature of solids Temperature of drying air
• • • Moisture of solids Temp.of solids
Input temperature of drying air (input 1) Input moisture of solids (input 2) Error in the output moisture of solids (input 3 )
The controller output representing the contents of the rule-consequent is as follow : •
Linear density of solids
Change in fuel flow (output)
Step 4: Formulation of control rules. The control rule based on the experience with the pilot plant dryer is of the form :
Temp. of drying air
IF input 1 is {low, ok, high} AND input 2 is {low, medium, high} AND input 3 is {negative, zero, positive} THEN output is {very small, small, zero, big, very big. The total number of rules is 27.
Velocity of drying air
Step S: Selection a method of defuzzification. The center of area method (COA) is used to transform the output of the fired rules into a crisp output. Fig. 2. Different variables of a drying process.
120
Step 6: Control experiments . Controller has been programmed as Matlab's functions in a HPworkstation which is connected to the automation system.
negative
zero
positive
3.2 Stru cture of the FLC and PI-controller The FLC part of the hybrid controller gives the set point value of the input temperature of drying air to the PI-controller as Figure 3 shows.
-0.1
0.1
0.4
Fig. 6 Membership function of the input 3. ve
Fuel flow at previous sampling
Input I InDut 2 Input 3
FLC
..
Output .... Set point to PI controller
-10
5
10
Fig. 7 Membership function of the output.
Fig. 3 Diagram of the FLC and PI controller. Trapezoidal membership functions of three input and one output variables have been presented in Figures 4 ... 7. Also the use of bell-shaped membership functions was examined with simulations, but no significant difference between control results could not been found . The simulation results have been reported by Yliniemi et.al (1995).
3.3 Tuning of the FLC The tuning of the FLC includes the determination of scaling factors, fuzzification and defuzzification method and the construction and representation of the rule base and membership functions . The scaling factors which describe the input and output normalization and denormalization correspond to a gain of a conventional controller. They have strong influence on the dynamics of a closed loop system, i.e. rise-time, amplitude of oscillation and overshoot. The determination of the scaling factors can be either heuristic or analytic The first one has a trial and error nature and has been used in this application.
The FLC controller has been designed to operate in the conditions, where the input moisture of solids is between 2.5 ... 4 m- % and the desired value of the output moisture of solids is 0.1 m-% . low
The other design parameter is the choice of membership functions . The shape and mapping affect the control performance. The tuning of the FLC can be on-line or off-line. The main difference between the above tuning methods is that the off-line tuning has no real time feedback- . The basic principle is to utilize the information received from process experts how to control the process. Because of the complexity of the drying process the tuning has been made off line.
190 Fig. 4 Membership function of the input 1.
In this application the tuning is based on the changing of rules and membership functions . The preliminary testing of rules and membership functions was made by FuzzyCon- program developed in Control Engineering Laboratory The control performance was estimated qualitatively.
Fig.5 Membership function of the input 2.
121
4 . CONTROL EXPERIMENTS
160 kglh at time 10.30. Also in this case the controller operates well.. It increases the fuel flow and this increases the temperatures of solids and drying air
Control experiments for the testing of the hybrid of FLC and PI- controller were carried out with the pilot plant dryer. The step disturbances were made to the input moisture of solids and to the feed flow. The responses have been presented in Figures 8 ... 11 .
---_
. .
_• ..
... _._._
--
_ 4
~~~L-_
_~~~_~
fA)
_ 4
0
E
;; (.)
0-
~
~
::l
....
~ 2 · -lnptit--fmislure-efsolids--- - --
'0
E
....
Cl)
50
~
~---.-.~~~---~
U
E Cl) E-
o ~--~~~~~~~~~ 9:35
11:35
30
13:35
Tirre 0~~~L4~~~~--~~· ~
9:00
ll:OO
13:00
15:00 Fig. 10 Responses for the step disturbance in the feed flow .
Tirre
Fig. 8 Responses of solids for step disturbances in the input moisture of solids.
l~'---~r------------T
I
ro
I
I O~l1 -+¥\a;:-----+~"o;_---=-----It'--........J.,rr.--l I ...
I ~
200 . . - - - - - - - - - - - - - - , - 80
I, ~10 +---~------=:----l (\)
E-< ~+----I-------'-----'-40
9:35
11:35
Trrre
13:35
80+---~-------~ ~
9:00
11:00
13:00
Fig. ll Responses for the step disturbance in the feed flow .
15:00
Tirre
5. DESIGN AND SIMULATION OF A NEURAL CONTROLLER OF A ROTARY DRYER
Fig. 9 Responses of drying air and fuel flow for step disturbances in the input moisture of solids.
The aim of the neural controller is to give the set point values of the input temperature of drying air and the feed flow to PI-controllers for controlling the valve of fuel flow and the rotation speed of the screw conveyor. The input temperature of drying air is the primary manipulated variable. The secondary manipulated variable is the feed flow. The main disturbance variable is the input moisture of solids.
As the figures show the controller is able quite well to keep the output moisture of solids at the desired value despite of the input moisture disturbances which occur at time 10.00 from 2.5 m-% to 3.3 m-% and at time 12.40 from 3.3. m-% to 2.7 m-% . Only some overshoot can be observed in the output. The following figures 10 and 11 show how the controller operates when the disturbance in the feed flow occur. The step disturbance is from 149 kglh to
122
The neural controller is a direct inverse controller utilizing the inverse process model. The tuning of weights is based on the backpropagation algorithm.
600 ~----------------------~
During the off line learning the inputs of the inverse process model are the present and past values of the inputs of the simulated process model and the past values of the output. The same inputs are used during the operation of the neural controller. The response of the neural controller is the input temperature of drying air. Because the weights are updated during the operation, the neural controller is adaptive and it has been implemented as Matlab's functions in the HP workstation ..
• • •
~ 300 ~----fJQ~--.JO_----l_
_ -=--------j 0,0072 ~ '" 0,0062 '0 &200 ~~~-+~~~~~~ O,0052 ·0 e u ~ 100 r - --tf-"==:::::::r----jI\r::-- -4- 0,0042..9 Cl) o 0,0032>
0,00
0,83
1,67
2,50
Time
The neural controller includes the input layer with 17 inputs , two hidden layers with 16 neurons and one output layer. The inputs of the neural controller are as follows : •
e
~ 500 ..J.....!.=~===-"lt=-'~C!!l:L.---l 0,0092 i 400 t---t----p==tt.;==:::J 0,0082 i
Fig.13 The simulated behavior of the velocity of velocity of solids and the input temperature of drying air.
Present and four previous values of the input moisture of solids Present and five previous values of the output moisture of solids Present and three previous values of the velocity of solids Present and previous values of the input temperature of drying air
The simulation results show that the neural controller based on the inverse process model together the PI controller seems to apply for controlling the drying process. Because the simulations results were encouraging, the neural controller was implemented also for controlling the pilot plant dryer.
All the inputs are scaled between 0 ... 1. It means that also the output of the neural controller is between O.. . 1.
6 .CONTROL EXPERIMENTS WITH THE NEURAL CONTROLLER
The operation of the above neural controller with the PI controller has been examined with simulations when a step disturbance to the input moisture of solids is made.
The structure of the neural controller developed for controlling the pilot plant dryer is not exactly the same as in the simulation application because the control loop of the velocity of solids was not in use in the pilot dryer. The inputs of the neural network are as follows :
Q2
•
4 ~
~
•
~
•
~
3
U15
E ·0
2
E Ul ·0 soJrloi :E
~
.§.
:E 0 UOO
•
uas U61
1.33
200
Present and two previous values of the input moisture of solids Present and two previous values of the output moisture of solids Present and one previous values of the output temperature of drying air Present and one previous values of the input temperature of drying air
The weights of the neural controller are updated during the operation. If the error which is the difference between the set point value and measured value of the output moisture of solids is zero, it is important to investigate if overdrying occurs. This is made by examining the output temperature of drying air. If this temperature exceeds the maximum temperature, the weights are determined based on the error of the output temperature of drying air.
261
Tare
Fig.12 The simulated behavior of the output moisture of solids when a step disturbance to the input moisture of solids is made.
123
REFERENCES
The control experiments were made when step disturbances in the input moisture of solids occur.
Juuso. E .. 1.Koskinen. L.Yliniemi and K.. Leiviska (1998). Linguistic equation method applied to fuzzy modelling of a rotary dryer. In: Preprints of TOOLMET '98 Symposium-Tool Methods for Environments Environments and Development Intelligent Systems (Leena Yliniemi and Esko Juuso. (Ed)) , 145-155. University of Oulu. Qulu. Koskinen, 1. . L. Yliniemi and K. Leiviska, (1998). Fuzz), modelling of a pilot plant rotary dryer. Submitted (January J998) to International Conference on Control '98. 1--4 September 1998. University of Wales, Swansea. UK Manikopoulus, CN ., M.Zhou and S.S Nerurkar (1995) Design and Implementation of FuzzyLogic Controllers for A Heat Exchanger in a Water-for Injection System . Journal of Intelligent and Fuzzy SYstems. Vol 3, 43-57. Yliniemi, L. ,L . A1aimo and 1 . Koskinen (1995) . Development and Tuning of a Fuzzy Controller For a Rotary Dryer. Report A No I. University of OuIu, Oulu. Yliniemi,L. , E.A.A. lutila and P. Uronen (1981). Modelling and Control of a Pilot - Plant Rotary Dryer used for Drying of Industrial Concentrates Preprints of the IFAC 8th Triennal World Congress, 198-203. Kyoto. Yliniemi,L and P.Uronen (1983). A Comparison of Different Control Strategies f or a Rotary Dryer. In: Preprints of 4th Symposium on Automation in Mining and Mineral and Metal Processing (T. Westerlund (Ed)), 403 - 411. lnsinooritieto Oy ,Helsinki.
100 4~
oU
"F
~
8)
i
a
~
Cl
~
2 a '" ·15
"
~
E ([) f-
«)
9:55
0
1155
1355
Ture
Fig. 14. Responses given by the neural controller for a step disturbance in the input moisture of solids.
100
-r----------r-------r 4
~O +-~~~~~~~~~ o
3
~
E
...
"i)
~ 2 ·0
::E
9 :55
13 :55
11 :55
Time
Fig.15. Responses given by the neural controller for a step disturbance in the input moisture of solids.
7.CONCLUSION In this paper two intelligent hybrid controllers have been developed for the rotary dryer. The controllers consist of the fuzzy and neural controllers with the PI controller. The behavior of the controllers have been examined with simulations and with control experiments using the pilot plant rotary dryer located in Control Engineering Laboratory. The results show that both intelligent controllers with the conventional PID controllers seem to apply well for controlling the drying process and the experimental work will continue, especially with neural controller.
124
ADVANCED CONTROL OF A ROTARY DRYER
Leena Yliniemi I, Jukka Koskinen 2 and Kauko Leiviskii 3
J Laboratory Manager, 2 Researcher. 3 Professor
University of Oulu lnfotech Oulu and Department of Process Engineering Control Engineering Laboratory BOX 444, Linnanmaa FlN-90571 Oulu http://ntsat.oulu·fi
Abstract: Two kinds of intelligent, hybrid control systems for a rotary dryer are presented. The main controlled variable is the output moisture of solids and the main manipulated variable is the input temperature of drying air which correlates to the fuel flow . The main disturbances of the process are the input moisture of solids and the feed flow. The one discussed control system includes a fuzzy logic controller (FLC) and a PIcontroller and the other a neural network controller and a PI -controller. In both cases the intelligent controller determines the set point value to a PI controller. The control results have been examined both with simulations and with pilot plant experiments. Copyright © 1998lFAC
Keywords: intelligent control, fuzzy control, neural control, rotary dryer
I .INTRODUCTION
results were compared with the results achieved by a conventional PID control. see ( Yliniemi and Uronen, 1983).
The control of the rotary dryer is difficult because of long delay times, long settling times, strong non linearities and unmeasurable disturbances in solids to be dried. The conventional PID control is not able to control the drying process so that the energy required would be minimized. Therefore other sophisticated control strategies as model based control, expert systems and intelligent control are of great interest in controlling the rotary dryer.
The research of the modeling and control of a rotary dryer based on intelligent methods was started in the middle of 90·s. The reasons to develop a fuzzy model and fuzzy or neural control to a dryer are due to that, firstly the mathematical model includes model parameters which are imprecise and vague and secondly much experience from the behavior of the dryer is available. The development of a fuzzy model based on a linguistic equation method is going on, and the first preliminary results have been published by Juuso et al. (1998) and Koskinen et al. (1998).
In the Control Engineering Laboratory at the University of Oulu the modeling and control of a rotary dryer have been examined during several years. The research has been made with simulations but also drying experiments with a pilot plant dryer located in the laboratory have been carried out.
In this paper two intelligent hybrid control systems are presented, together with results from simulations and control experiments with the pilot plant rotary dryer.
First the model of a rotary dryer based on mass and energy balances was developed, see (Yliniemi, et al., 1981). Based on this mathematical model a model based control system was developed and the control
119
2. DESCRIPTION OF THE PILOT PLANT ROTARY DRYER
The different input and output variables and their interactions have been presented in Fig. 2.
The schematic diagram of the pilot plant dryer used for drying experiments is presented in Fig. l . The material to be dried is fed to a rotating drum by a screw conveyor from a silo. The length of the drum is 3 m and the diameter 0.5 m. The drying air is supplied with a blower and it is heated by burning gases in a burning chamber. Propane gas is used as fue\. The dried product is fed back to the silo on a belt conveyor and is watered again.
3. DESIGN OF THE FLC FOR A ROTARY DRYER
3.1 Design of FLC The design procedure of a FLC controller according Manikopoulus et al. (1995) is as follows: Step 1: System functional requirements. Based on the investigations by Yliniemi and Uronen (1983) it has been found that the behavior of PlO is not satisfactory depending on the long delay time of the dryer. The mode based control gives the better control performance but the development of mathematical model is difficult and time consuming and therefore not easy to apply. The aim of the FLC combined with a PI-controller is to make the control easier and better by utilizing the operator' s expert knowledge . Step 2: Definition of system parameters. The main controlled variable is the output moisture of solids and the main manipulated variable is the input temperature of drying air which correlates to the fuel flow. The velocity of solids which correlates to the rotation speed of a screw conveyor can be used as an auxiliary manipulated variable. The main disturbances are the input moisture of solids and feed flow .
Fig. I. Pilot plant rotary dryer
Input variables
Disturbances
Moisture of solids
~
~
Output variables
Step 3: Definition of system parameters in terms of fuzzy sets. The input variables for FLC representing the contents of the rule-antecedent are as follows:
Feed flow
Moisture of
Temperature of solids Temperature of drying air
• • • Moisture of solids Temp.of solids
Input temperature of drying air (input 1) Input moisture of solids (input 2) Error in the output moisture of solids (input 3 )
The controller output representing the contents of the rule-consequent is as follow : •
Linear density of solids
Change in fuel flow (output)
Step 4: Formulation of control rules. The control rule based on the experience with the pilot plant dryer is of the form :
Temp. of drying air
IF input 1 is {low, ok, high} AND input 2 is {low, medium, high} AND input 3 is {negative, zero, positive} THEN output is {very small, small, zero, big, very big. The total number of rules is 27.
Velocity of drying air
Step S: Selection a method of defuzzification. The center of area method (COA) is used to transform the output of the fired rules into a crisp output. Fig. 2. Different variables of a drying process.
120
Step 6: Control experiments . Controller has been programmed as Matlab's functions in a HPworkstation which is connected to the automation system.
negative
zero
positive
3.2 Stru cture of the FLC and PI-controller The FLC part of the hybrid controller gives the set point value of the input temperature of drying air to the PI-controller as Figure 3 shows.
-0.1
0.1
0.4
Fig. 6 Membership function of the input 3. ve
Fuel flow at previous sampling
Input I InDut 2 Input 3
FLC
..
Output .... Set point to PI controller
-10
5
10
Fig. 7 Membership function of the output.
Fig. 3 Diagram of the FLC and PI controller. Trapezoidal membership functions of three input and one output variables have been presented in Figures 4 ... 7. Also the use of bell-shaped membership functions was examined with simulations, but no significant difference between control results could not been found . The simulation results have been reported by Yliniemi et.al (1995).
3.3 Tuning of the FLC The tuning of the FLC includes the determination of scaling factors, fuzzification and defuzzification method and the construction and representation of the rule base and membership functions . The scaling factors which describe the input and output normalization and denormalization correspond to a gain of a conventional controller. They have strong influence on the dynamics of a closed loop system, i.e. rise-time, amplitude of oscillation and overshoot. The determination of the scaling factors can be either heuristic or analytic The first one has a trial and error nature and has been used in this application.
The FLC controller has been designed to operate in the conditions, where the input moisture of solids is between 2.5 ... 4 m- % and the desired value of the output moisture of solids is 0.1 m-% . low
The other design parameter is the choice of membership functions . The shape and mapping affect the control performance. The tuning of the FLC can be on-line or off-line. The main difference between the above tuning methods is that the off-line tuning has no real time feedback- . The basic principle is to utilize the information received from process experts how to control the process. Because of the complexity of the drying process the tuning has been made off line.
190 Fig. 4 Membership function of the input 1.
In this application the tuning is based on the changing of rules and membership functions . The preliminary testing of rules and membership functions was made by FuzzyCon- program developed in Control Engineering Laboratory The control performance was estimated qualitatively.
Fig.5 Membership function of the input 2.
121
4 . CONTROL EXPERIMENTS
160 kglh at time 10.30. Also in this case the controller operates well.. It increases the fuel flow and this increases the temperatures of solids and drying air
Control experiments for the testing of the hybrid of FLC and PI- controller were carried out with the pilot plant dryer. The step disturbances were made to the input moisture of solids and to the feed flow. The responses have been presented in Figures 8 ... 11 .
---_
. .
_• ..
... _._._
--
_ 4
~~~L-_
_~~~_~
fA)
_ 4
0
E
;; (.)
0-
~
~
::l
....
~ 2 · -lnptit--fmislure-efsolids--- - --
'0
E
....
Cl)
50
~
~---.-.~~~---~
U
E Cl) E-
o ~--~~~~~~~~~ 9:35
11:35
30
13:35
Tirre 0~~~L4~~~~--~~· ~
9:00
ll:OO
13:00
15:00 Fig. 10 Responses for the step disturbance in the feed flow .
Tirre
Fig. 8 Responses of solids for step disturbances in the input moisture of solids.
l~'---~r------------T
I
ro
I
I O~l1 -+¥\a;:-----+~"o;_---=-----It'--........J.,rr.--l I ...
I ~
200 . . - - - - - - - - - - - - - - , - 80
I, ~10 +---~------=:----l (\)
E-< ~+----I-------'-----'-40
9:35
11:35
Trrre
13:35
80+---~-------~ ~
9:00
11:00
13:00
Fig. ll Responses for the step disturbance in the feed flow .
15:00
Tirre
5. DESIGN AND SIMULATION OF A NEURAL CONTROLLER OF A ROTARY DRYER
Fig. 9 Responses of drying air and fuel flow for step disturbances in the input moisture of solids.
The aim of the neural controller is to give the set point values of the input temperature of drying air and the feed flow to PI-controllers for controlling the valve of fuel flow and the rotation speed of the screw conveyor. The input temperature of drying air is the primary manipulated variable. The secondary manipulated variable is the feed flow. The main disturbance variable is the input moisture of solids.
As the figures show the controller is able quite well to keep the output moisture of solids at the desired value despite of the input moisture disturbances which occur at time 10.00 from 2.5 m-% to 3.3 m-% and at time 12.40 from 3.3. m-% to 2.7 m-% . Only some overshoot can be observed in the output. The following figures 10 and 11 show how the controller operates when the disturbance in the feed flow occur. The step disturbance is from 149 kglh to
122
The neural controller is a direct inverse controller utilizing the inverse process model. The tuning of weights is based on the backpropagation algorithm.
600 ~----------------------~
During the off line learning the inputs of the inverse process model are the present and past values of the inputs of the simulated process model and the past values of the output. The same inputs are used during the operation of the neural controller. The response of the neural controller is the input temperature of drying air. Because the weights are updated during the operation, the neural controller is adaptive and it has been implemented as Matlab's functions in the HP workstation ..
• • •
~ 300 ~----fJQ~--.JO_----l_
_ -=--------j 0,0072 ~ '" 0,0062 '0 &200 ~~~-+~~~~~~ O,0052 ·0 e u ~ 100 r - --tf-"==:::::::r----jI\r::-- -4- 0,0042..9 Cl) o 0,0032>
0,00
0,83
1,67
2,50
Time
The neural controller includes the input layer with 17 inputs , two hidden layers with 16 neurons and one output layer. The inputs of the neural controller are as follows : •
e
~ 500 ..J.....!.=~===-"lt=-'~C!!l:L.---l 0,0092 i 400 t---t----p==tt.;==:::J 0,0082 i
Fig.13 The simulated behavior of the velocity of velocity of solids and the input temperature of drying air.
Present and four previous values of the input moisture of solids Present and five previous values of the output moisture of solids Present and three previous values of the velocity of solids Present and previous values of the input temperature of drying air
The simulation results show that the neural controller based on the inverse process model together the PI controller seems to apply for controlling the drying process. Because the simulations results were encouraging, the neural controller was implemented also for controlling the pilot plant dryer.
All the inputs are scaled between 0 ... 1. It means that also the output of the neural controller is between O.. . 1.
6 .CONTROL EXPERIMENTS WITH THE NEURAL CONTROLLER
The operation of the above neural controller with the PI controller has been examined with simulations when a step disturbance to the input moisture of solids is made.
The structure of the neural controller developed for controlling the pilot plant dryer is not exactly the same as in the simulation application because the control loop of the velocity of solids was not in use in the pilot dryer. The inputs of the neural network are as follows :
Q2
•
4 ~
~
•
~
•
~
3
U15
E ·0
2
E Ul ·0 soJrloi :E
~
.§.
:E 0 UOO
•
uas U61
1.33
200
Present and two previous values of the input moisture of solids Present and two previous values of the output moisture of solids Present and one previous values of the output temperature of drying air Present and one previous values of the input temperature of drying air
The weights of the neural controller are updated during the operation. If the error which is the difference between the set point value and measured value of the output moisture of solids is zero, it is important to investigate if overdrying occurs. This is made by examining the output temperature of drying air. If this temperature exceeds the maximum temperature, the weights are determined based on the error of the output temperature of drying air.
261
Tare
Fig.12 The simulated behavior of the output moisture of solids when a step disturbance to the input moisture of solids is made.
123
REFERENCES
The control experiments were made when step disturbances in the input moisture of solids occur.
Juuso. E .. 1.Koskinen. L.Yliniemi and K.. Leiviska (1998). Linguistic equation method applied to fuzzy modelling of a rotary dryer. In: Preprints of TOOLMET '98 Symposium-Tool Methods for Environments Environments and Development Intelligent Systems (Leena Yliniemi and Esko Juuso. (Ed)) , 145-155. University of Oulu. Qulu. Koskinen, 1. . L. Yliniemi and K. Leiviska, (1998). Fuzz), modelling of a pilot plant rotary dryer. Submitted (January J998) to International Conference on Control '98. 1--4 September 1998. University of Wales, Swansea. UK Manikopoulus, CN ., M.Zhou and S.S Nerurkar (1995) Design and Implementation of FuzzyLogic Controllers for A Heat Exchanger in a Water-for Injection System . Journal of Intelligent and Fuzzy SYstems. Vol 3, 43-57. Yliniemi, L. ,L . A1aimo and 1 . Koskinen (1995) . Development and Tuning of a Fuzzy Controller For a Rotary Dryer. Report A No I. University of OuIu, Oulu. Yliniemi,L. , E.A.A. lutila and P. Uronen (1981). Modelling and Control of a Pilot - Plant Rotary Dryer used for Drying of Industrial Concentrates Preprints of the IFAC 8th Triennal World Congress, 198-203. Kyoto. Yliniemi,L and P.Uronen (1983). A Comparison of Different Control Strategies f or a Rotary Dryer. In: Preprints of 4th Symposium on Automation in Mining and Mineral and Metal Processing (T. Westerlund (Ed)), 403 - 411. lnsinooritieto Oy ,Helsinki.
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Fig.15. Responses given by the neural controller for a step disturbance in the input moisture of solids.
7.CONCLUSION In this paper two intelligent hybrid controllers have been developed for the rotary dryer. The controllers consist of the fuzzy and neural controllers with the PI controller. The behavior of the controllers have been examined with simulations and with control experiments using the pilot plant rotary dryer located in Control Engineering Laboratory. The results show that both intelligent controllers with the conventional PID controllers seem to apply well for controlling the drying process and the experimental work will continue, especially with neural controller.
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