Desi
g
n of Biomass Boiler Intelli
g
ent Heatin
g
S
y
stem Usin
g
Fuzz
y
PID
Dongxue Li
1
, Qiming Wang
2
and Linlin Zhao
1
1College of Energy and Powe
r
, Shenyang Institute o
f
Engineering, Shenyang, China
2Technology Department , Shenyang Institute of Engineering, Shenyang, China
Keywords: Biomass boiler, Fuzzy PID, Energy saving, Heating system.
Abstract: Fuzzy PID control scheme is proposed for the heating system of biomass hot water boiler characterized by
nonlinearity, large inertia, uncertainty and time delay. It is used in the heating system due to high control
accuracy and robustness. In this paper, fuzzy control rules are established to analyze the characteristics of
indoor temperature rise. Fuzzy inference method is adopted to realize on-line tuning of PID parameters. In
addition, the hardware and software design of Fuzzy PID controller are introduced in this paper. Fuzzy PID
control is obviously better than the conventional Proportional-Integral-Derivative (PID) control shown in
the experimental results. The indoor temperature is maintained at the comfortable human performance
indicators by parameters self-tuning Fuzzy PID control, which is an ideal energy saving control program.
1 INTRODUCTION
There are several reasons for using biomass energy.
China is a big agricultural country with large crop
straw production, wide distribution and many kinds
of crops (Wang, 2008). Therefore, it is a great
significance to make the crops into biomass fuel.
Biomass is a clean and renewable energy, and the
development and application of biomass energy is a
broad prospect. Biomass boilers are used in district
heating and power generation in developed countries
such as Denmark, the United States and Italy
(Dalólio,
2017). Biomass boilers instead of coal-fired boilers
in many aspects, for example, decentralized heating,
catering, bathing, swimming pool and other facilities,
which has more practical significance
(Herbert, 2016).
The heating process of hot water boiler has
always been the focus of research in the field of
process control
(Cai, 2014). In recent years, domestic
and foreign scholars have carried out research on
temperature control system based on fuzzy control,
Fuzzy PID control and PID control
(Duan, 2004).
Shen Guomin
(Lu, 2010) put forward the hot water
boiler heating control, and the water temperature is
controlled according to the outdoor temperature
changes, which did not take into account the specific
circumstances of indoor temperature changes.
Aiming at to solve the shortcomings of above
methods, biomass boiler intelligent heating system
based on Fuzzy PID control is proposed in this paper,
so that the indoor temperature is faster and more
stable to achieve the target temperature, to achieve
the energy saving purpose of biomass boiler
intelligent heating system
(Neath, 2014; Sharma, 2014).
2 FUZZY PID CONTROL
ALGORITHM PRINCIPLE
2.1 Fuzzy Control Theory
Fuzzy control has gained a wide acceptance, due to
make control decisions not depend on the model of
the object. The performance of fuzzy logic
controllers is largely determined by the fuzzy rules,
reasoning machines, and fuzzy decision making
methods (Sahu, 2015; Chen, 2014; Duan, 2013;
Sahu, 2014; Karasakal, 2013; Wu, 2003). It
discusses definite nature, fuzzy and imprecise
information system control in the real world. The
basic structure of fuzzy controller is as below.
Figure 1: Basic structure of fuzzy control
Li, D., Wang, Q. and Zhao, L.
Design of Biomass Boiler Intelligent Heating System Using Fuzzy PID.
In 3rd International Conference on Electromechanical Control Technology and Transportation (ICECTT 2018), pages 19-22
ISBN: 978-989-758-312-4
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
19

Two-input and one-output fuzzy controller is
more common in practice, and also commonly
referred to as two-dimensional fuzzy controller.
From the theoretical analysis, the dimension of the
fuzzy controller is higher, the better its control effect
(Guo, 2002). The difficulty of the algorithm will be
greatly improved, while the deviation e and its rate
of change ec are taken as input. The overall
performance of the fuzzy dimension controller is
very good and the difficulty of realization is
moderate, so it is applied more extensively.
2.2 Research on Fuzzy PID Algorithm
The fuzzy control system block diagram of adaptive
fuzzy PID controller designed in this paper is shown
in the following figure, in which the fuzzy controller
is established in the dashed box
(LongMan, 2000).
The fuzzy controller module is two input and three
output, for example, the deviation e and its rate of
change ec as input, based on a series of fuzzy rules
and fuzzy decision making results, which ΔK
p
, ΔK
i
and ΔK
d
of real-time output is adjusted by fuzzy
controller. Furthermore, the adjustment amount and
the initial value of the PID parameter due to achieve
online parameters, so that it is maintained in the best
working point
(Zhang, 2006). Adaptive fuzzy PID
control block diagram is as below.
Figure 2: Block diagram of Fuzzy PID control
PID controller of indoor temperature is adjusted
by linear combination, its discrete general formula
(Pinsker, 2016) is as below:
(1)
The parameter e and the rate of change ec are
given by the following equation, which the predicted
value
(Azar, 2015).
(2)
3 INTELLIGENT HEATING
SYSTEM
According to the literature, the energy saving ratio
of the envelope and the heating system is about 1:1,
while it is not obvious to the effect in the
development and application of the energy saving
envelope. Therefore, it has great potential to
research on heating system. The design of biomass
hot water boiler intelligent heating system consists
of biomass hot water boiler CLHS0.035-85/65-M
and its ancillary equipment (such as draft fan, feeder,
water pump, ignition controller, LCD Display, keys,
temperature sensors, pressure sensors, water level
measuring devices, flow meters, etc.), and heating
systems (such as heat exchangers, radiators, water
tanks, etc.)
The proposed special optimization control
algorithm in this paper solves the system
optimization and energy saving operation problem
of the biomass water heating boiler system.
Furthermore, static and dynamic data combined with
analytical methods is used to ensure the biomass
boiler heating system operated safely,even in the
best state, real-time dynamic control. The intelligent
control system composed is as below:
Figure 3: Structure of the control system for small heating
equipment
In this paper, intelligent heating control system
based on embedded design,which consists of ARM,
DS18B20, the sensor(such as temperature, flow and
water level), and A/D is used to
converted continuous signal into digital signal which
is sent to the controller via I/O. And then get the
operation instructions of the water supply system,
the feeding system, the ignition system, the induced
draft dust removal system, the air supply system, the
flame out and the fire protection system. Instructions
are sent out through I/O output, to control the relay

)))1()(()()((
1
0




t
t
D
i
p
tete
T
T
te
T
T
teKtu
dd
d
ii
i
pp
p
KKK
KKK
KKK



/
/
/
ICECTT 2018 - 3rd International Conference on Electromechanical Control Technology and Transportation
20

closed and disconnected, the actuator to run and stop;
At the same time, the operating status of the various
actuators, such as temperature, liquid level and other
parameters are shown on LCD display. Physical map
is as below.
Figure 4: Physical map of Controller
In this paper, indoor temperature is collected,
and the deviation e and the rate of change ec are
calculated. Fuzzy-PID control algorithm is adopted,
and k
p
=0.6, k
i
=0.1 and k
d
=0.1. Intelligent heating
system algorithm flow chart is as below.
Figure 5: Flow chart of ntelligent heating system
algorithm
When the system in the steady state, the
amplitude interference signal observed is added to
the two systems,so that the anti-interference
performance is observed. It can be seen from the
simulation results that the response of the loop
where the adaptive fuzzy PID controller is small and
reaches a steady state fast again, which has a strong
anti-interference performance compared with the
conventional PID controller.
Figure 6: Curve of unit step response
4 RESULTS AND ANALYSIS
The room temperature variation trend is recorded in
this experiment, and the experimental data of
intelligent controller compared to conventional
controller. A temperature sensor (DS18B20) was
installed all around the laboratory. The four
temperature sensors were placed 0.5m away from
the wall, and the measured room temperature was
sent to the controller. The indoor heating target
temperature is 23℃in the experiment, then the
heating room temperature contrast curve is as below.
Figure 7: Curve of room temperature comparison
5 CONCLUSIONS
According to the characteristics of the heating
system, the intelligent heating system of biomass hot
water boiler based on fuzzy PID controller is
developed through the integrated application of
intelligent control technology such as embedded
technology, fuzzy control, PID controller and
cascade control, etc. In this experiment, the fuzzy
PID controller of heating system was compared
without controller measurement. The results are
Design of Biomass Boiler Intelligent Heating System Using Fuzzy PID
21

shown that the intelligent control system has the
ability to respond to the dynamic performance fast
and improve the steady-state accuracy. In order to
solve the complex problem of nonlinear and large
time-delay, a new control idea is provided .
In order to be realized accurate monitoring and
control of biomass boiler heating, the hardware and
software of the system are tested and perfected for a
long time and the performance of the whole system
is improved. According to the existing research
results, it is planned to do further research on the
function expansion of heating system of biomass hot
water boiler and intelligent adaptive heating control.
ACKNOWLEDGEMENTS
This work was financially supported by the
Shenyang Science and Technology Project(Z17-5-
062).Double hundred projects (major scientific and
technological achievements Converted). Project
Name: Ultra-Clean Emissions Biomass Energy
Conversion Technology.
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