6th Int'l Conference on Electrical, Electronics & Civil Engineering (ICEECE'2014) Nov. 27-28, 2014 Cape Town (South Africa)
Control Of Biomass Boiler Water Temperature
Using Adaptive Control System
Ahmed S. Khamis, and Ali A. Lesewed
Abstract— Modern biomass boilers are seen as one of the most
promising renewable energy sources for reducing greenhouse gas
emission. Using biomass boiler to generate energy carries like heat
to produce electricity in small and medium scale (in units of MW)
power. Conventional PID control system cannot reach satisfaction
result for controlling the temperature of biomass boiler due to
nonlinearity, disturbances, uncertainty and time varying of the
dynamic behavior of this kind of the process. In this paper, an
advanced process control such as model reference adaptive control
(MRAC) is used to control the temperature of the boiler water. The
results show that MRAC has strong immunity for the disturbance
and the varying of system parameters.
II. BIOMASS BOILER STRUCTURE
The biomass boiler consists of the main parts as shown in
Fig.1. These parts include flue gas recirculation, combustion
air preheating HE2, combustion chamber and primary and
secondary air flows. Develop of the mathematical model for
temperature outlet of biomass boiler depends on experimental
data obtained by observation of the temperature when the
inclined moving of grate 1MW power designated for water
superheating.
Keywords— Biomass Boiler; mathematical model, PID, MRAC.
I. INTRODUCTION
C
OMPARED to classical boiler which operating on
natural gas or oil fuel, biomass boiler requires high
quality of combustion air due to non-homogenous of biomass
fuel and varying parameters such as humidity. In addition to
biomass boiler technology [1], control system design has also
major effect on economical and implementation of biomass
boiler. Due to such problems, we focus on develop and
implement more sophisticated control system such as adaptive
control system [2]. Usually, the control system of the existing
biomass boiler depends on classical PID control systems
which is not effective in controlling the outlet boiler
temperature due to process time delay and varying parameters
of the process dynamic. Although, the time delay can be
solved using smith predictor technique [3], but other impacts
such as uncertainty and varying process parameters cannot be
overcome using classical PID control system. Recently, new
methods of control systems such predictive control, fuzzy
logic control have been implemented for process control [4]
but few of them were applied for biomass technology due to
lack of real mathematical model for biomass boiler. For this
reason, we developed advanced control technique depends on
a mathematical model derived from real experiment which
had been done on observation of the operation of biomass
boiler for several years [5][ 6].
Fig. 1 Structure of biomass boiler
III.
BIOMASS BOILER MATHEMATICAL MODEL
The dynamic equation of temperature of heating water of
biomass boiler was derived from real experiments
observations done by three years of research [1]. The model
expressed the second order with extremely time delay .
(1)
IV. CONTROL SYSTEM DESIGN
Control of biomass boiler was designated for heating
water temperature which represents the core task of unit
control. In this case, PID control with smith predictor
technique was implemented to the system. Ziegler-Nichols
method [3] was used in order to obtain the following
controller parameters where: kp=72.3, ki=1/510, kd=472.5
Ahmed S. Khamis is with College of Electronic Technology, Bani-walid,
Libya; e-mail: ([email protected] ).
Ali A. Lesewed is with Faculty of Electrical and Electronic Eng. University of
Zaitoona, Tripoli, Libya; ([email protected]).
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6th Int'l Conference on Electrical, Electronics & Civil Engineering (ICEECE'2014) Nov. 27-28, 2014 Cape Town (South Africa)
According to these specifications, the reference model
becomes as the follows:
(2)
Now, the model reference adaptive control (MRAC) with
MIT rule [2] was applied to the system in order to improve
the quality of system response. Usually MRAC depends on
the error between the plant model output y and reference
model output ym, where:
(10)
And
(11)
(3)
Where r is unit step signal
Note that a third pole (S+ 8.7) has been added to equation
(10), so that the reference model will match the biomass
model multiplying PID pole. The third pole has been chosen
in such a way that cannot affect the performance of the
system response.
If we consider that the controller has only one adjustable
parameter Ɵ, then the control objective is to adjust the
controller parameter so that the error e(t) is minimized.
To do this, the cost function was chosen as:
V. NUMERICAL RESULTS
(4)
4B
The model is verified on environmental of SIMULINK/
MATLAB [7, 8] when PID and MRAC have been simulated
under some circumstances such as disturbances and
parameters changes. Figure 2 and 3 shows PID block diagram
and step response of the system when the temperature of the
water was set to 80oC.
In order to minimize the cost function, we need:
(5)
(6)
In similar way the change of controller parameters were
modified according to the rate of the error as the follows:
(7)
(8)
Fig. 2 Block diagram of PID control with smith predictor
Where, kp and ki are proportional gain and integral gain,
respectively.
Now, consider that the reference model is second order
system and has the general formula which written as:
(9)
Hence, Gm(s) can be calculated according to the following
specifications where: percentage of over shoot=5% , settling
time=2 sec and steady state error=1%.
Depends on the value of overshoot, the ratio of damping
was calculated as ζ=0.69. Similarly, the natural frequency ωn
was calculated from settling time and is equal to 2.17 rad/sec.
Fig. 3 Step response of the system using PID
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6th Int'l Conference on Electrical, Electronics & Civil Engineering (ICEECE'2014) Nov. 27-28, 2014 Cape Town (South Africa)
Figure 4 and 5 shows the block diagram of MRAC system
and its step response when the temperature of water was set to
80oC
Figure 6 shows the comparison between the step response
of the system by using PID and MRAC when the parameters
of the system changes.
Fig6. Step response of PID and MRAC control system
VI. CONCLUDING REMARKS
5B
One can see that employing PID control for biomass boiler
it quite possible and we can obtain good results as shown in
Fig. 3. The long time delay of the process can be cancelled by
adding smith predictor technique to our control loop.
Although the step response of MRAC looks oscillatory in
the transient region but its settling time is shorter than PID
control and was about 800 sec as shown in Fig. 5.
It can be noticed that the system performed better with the
adaptive PID control than classical PID in terms of settling
time and steady state error as shown in Fig. 6.
We conclude that the adaptive control gives amazing
results when the parameters of the process changes because it
has ability to adjust its parameters according to the state of
process dynamic. However, using of adaptive control was
quite complicated design and needs of a lot of sophisticated
calculations.
We plan further to synthesize biomass boiler control using
sliding mode control system to make simple design and
overcome the nonlinearity of the process.
Fig.4 Block diagram of MRAC system with smith predictor
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[2]
[3]
Fig.5 Step response of the system using MRAC control
[4]
In reality, the mathematical model of the biomass boiler
temperature is nonlinear or time varying parameters. In this
case, we tried to change the parameters of equation (1) to
emulate the nonlinear behavior model of biomass boiler, then
we compare PID and MRAC performance. We assumed the
changes of the model parameters as the follows:
[5]
[6]
[7]
[8]
(12)
92
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