Fuzzy Logic Control System Implementation on Solar and Gas
Energy Dryers
Ismail Ramli, Denny Hendra Cipta and Hamka Munir
Heavy Vehichle Study Program, Nunukan State Polytechnic, Jl. Ujang Dewa, Nunukan, Indonesia
Keywords: Fuzzy Logic, Drying, Moisture Content, LPG Gas, Control System.
Abstract: Savings in the use of power sources in tools receive special attention, for example by using hybrid power,
namely using two or more power sources to drive a device. The purpose of this research is to produce a
prototype control system that can be applied to a hybrid system using sunlight and LPG gas to keep it stable
and continuous with fuzzy logic method. The method used is fuzzy logic control program language using
codevision AVR program and matlab software. Parameters measured in the dryer test include air
temperature, air flow velocity, LPG gas energy consumption and material weight reduction. The
measurement results are used to calculate drying energy, dryer efficiency and moisture content. The use of
hybrid power, the dryer can save energy, small overshoot and stable temperature.
1 INTRODUCTION
Energy sources are getting depleted, energy savings
in tools or equipment receive special attention, for
example by using hybrids, namely using two or
more power sources to drive a tool. In this modern
era, energy sources are running low, saving on the
use of energy in tools or equipment needs special
attention and treatment. By using hybrid power,
namely using two or more power sources to drive a
tool. The requirement for hybridization energy for
equipment is renewable energy (water, wind, solar)
with sustain energy such as fossil energy (coal, gas
or oil). The aim of using hybridization energy is to
sustain the performance of the driven tool which it
does not decrease and in order to fossil energy can
be saved.
Combining two or more energy sources in the
system can cause the system to become
multivariable, non-linear, erratic, complex, uncertain
or fuzzy. According to (Negnevitsky 2005) a fuzzy
system can only be controlled by applying intelligent
systems such as neural network systems, experts, or
fuzzy logic.
(Azis and Sinadia, 2018) conducted studies
related to hybrid solar energy and LPG gas, namely
the design of a fuzzy-expert system on a solar and
LPG hybrid powered food control device with one
input and one output, (Satria et al., 2015) namely
designing a temperature control system on a hybrid
dryer using the fuzzy logic method, and (Arikundo
and Hazwi, 2014) designing a logic - based
temperature and humidity control system for drying
hybrid nutmeg (myristica sp.) using solar energy and
biomass.
Nowadays, on the one hand, the hybrid energy
system is unusual to implement because this kind of
technology has not fully ready yet. Moreover this
technology is quite expensive, so most of people still
using one the energy existed. On the other hand, all
existing energy are very limited. For this reason, it is
necessary to explore and to investigate the hybrid
energy system for innovate the sources energy
revolution.
2 RESEARCH METHODS
The materials used to make the dryer instrument
include U 13 mm mild steel, 0.9 mm thick aluminum
sheet, glass wool, 5 mm acrylic glass, screws, bolts,
rivets, pipes, glass glue, lighters, 3 inch Blawer, 3
inch paralon pipe, stainless steel doof plate, gas
stove, elbow iron, electronic components,
microcontroller, coax cable, electrical cable, PCB,
gas hose, 3 kg LPG gas, electronic lighter and
solenoid. The material used to test the performance
of the dryer is sago.
638
Ramli, I., Cipta, D. and Munir, H.
Fuzzy Logic Control System Implementation on Solar and Gas Energy Dryers.
DOI: 10.5220/0010950300003260
In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 638-643
ISBN: 978-989-758-615-6; ISSN: 2975-8246
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

The tools used to manufacture the dryer are
workshop equipment, solar power meter (0.1W/m²),
anemometer (0.1 m/s), digital scale (0.01g), data
logger, and software (Matlab, Codevision).
Figure 1: Research Diagram.
2.1 Determination of Tool Capacity
This tool is designed with a capacity of 40 kg. The
drying equipment in this study used an existing
drying chamber, consisting of 20 shelves with a
capacity of 2 kg per shelf. The wait is located at the
bottom of the Dryer chamber. Energy requirements
are needed to find out how much energy or power is
needed in waiting for the heater.
In this study, the type of collector used is a data
plate solar collector which has several main
components such as cover, acrylic, insulation, and
absorber.
Figure 2: Flat Plate collector design.
The corrugated type (V) on the data plate
collector is more widely used because of its higher
efficiency against the sun.
Figure 3: Flat Plate collector design.
2.2 Fuzzy Hardware Control System
Design
The design of fuzzy-based gas valve control
hardware whereas the gas valve (solenoid) is
assembled into a series and parallel circuit, so it can
supply various power for the energy needs of the
drying chamber. To produce power 0 to maximun
power, fuzzy control is used, and its use uses a
baypass on the solenoid.
Figure 4: Fuzzy Logic Control System Design.
The design of the valve control hardware is
shown in Figure 3, where the valve drive motor gets
a control signal from the microcontroller and its
action will move the air valve. The air rate of the
collector depends on the valve opening (0
0
, 45
0
,
90
0
). Motor drive used for the force acting on the
valve leaf. The driving motor used in this study is an
AC motor whose torque is caused by air in the valve.
2.3 Designing Fuzzy Control Software
2.3.1 Defining Input-Output
In developing a multivariable fuzzy logic control
system, so a fuzzy control method should be
designed, namely 2 inputs and 2 outputs. The 2-
description:
Ta = collector top surface height
Tb = collector area
Tc = width between collector cross sections
Ta = collector bottom surface height
Fuzzy Logic Control System Implementation on Solar and Gas Energy Dryers
639

input system
consists of Dryer Room Temperature
(T
1
) and Collector Temperature (T
2
). While the
output is power
(W) and valve opening (
0
).
2.3.2 Defining the Universe of Speech and
Values
In this study, the input temperature of the collector
used ranges from 25
O
C to 90
O
C, at the input of
drying room temperature 30
O
C to 60
O
C. This range
is the actual condition of the collector temperature
and drying chamber temperature. The universe talks
about fuzzy singleton output in the range of 0 – max
watt for power and 0
O
to 90
O
for opening and
closing the valve.
2.3.3 Determining the Membership Function
and Fuzzy Set
The function used for input is a triangular function
and a set of three fuzzy sets for each input variable.
For membership function. The system has two
inputs and two outputs of fuzzy sets, the drying
chamber input
temperature (T
1
) is defined as r1 =
low, s
1
= medium, t
1
= high. For the fuzzy set, the
input collector temperature (T
2
) is defined as r
2
=
low, s
2
= hot, t
2
=
high.
Figure 5: Temperature input membership function (
o
C).
Mean while, the power output fuzzy set for the
hybrid system is defined as kc=small, ak=rather
small, sd=medium, bs=large, and sb=very large. The
fuzzy set of valve open output is tt=close (0
0
),
bs=open (45
0
) and bk=open (90
0
)
Figure 6: Output membership function.
2.3.4 Formulation of Conversational Rules
In this study, the rules are arranged based on the
loop system that is in accordance with the desired
conditions. The following are the results of the
compilation of the rules for the 2 input 2 output
system proposed by (Y Wang at al., 1997) in Figure
19. The results are as follows:
The rules made from the control matrix are:
R
1
: if (T
1
is r
1
) and (T
2
is t
2
) then (K
1
is bk) (P
1
is sd)
R
2
: if (T
1
is r
1
) and (T
2
is s
2
) then (K
2
is bt) (P
2
is bs)
R
3
: if (T
1
is r
1
) and (T
2
is r
2
) then (K
3
is tt) (P
3
is sb)
R
4
: if (T
1
is s
1
) and (T
2
is t
2
) then (K
4
is bs) (P
4
is ak)
R
5
: if (T
1
is s
1
) and (T
2
is s
2
) then (K
5
is bs) (P
5
is sd)
R
6
: if (T
1
is s
1
) and (T
2
is r
2
) then (K
6
is tt) (P
6
is bs)
R
7
: if (T
1
is t
1
) and (T
2
is t
2
) then (K
7
is bk) (P
7
is kc)
R
8
: if (T
1
is t
1
) and (T
2
is s
2
) then (K
8
is bs) (P
8
is ak)
R
9
: if (T
1
is t
1
) and (T
2
is r
2
) then (K
9
is tt) (P
9
is sd)
2.4 Instrument Examining
Testing of the tool aims is to determine whether the
results of the hardware design and fuzzy rules can
run a hybrid solar and gas system simulation tool in
accordance with the expected function and it cand
produce good performance. The implementation of
this testing is carried out in two stages, namely
functional testing and performance testing
3 RESULTS AND DISCUSSION
3.1 Dryer Machine Description
The hybrid system dryer consists of a collector, an
LPG furnace, a dryer box and a control system. As
seen in the image below.
Grade
Grade
kc
ak
sd
bs
bs
t
t
b
k
k
b
s
1
0
Wat
t
0
Dera
j
ad
Da
y
a
Valve
(
o
)
r
2
s
2
t
2
30
45
Collector
Temperatur
60
r
1
s
1
t
1
25
45
Dryer Room
Temperatur
90
Figure 7: Fuzzy control matrix for hybrid systems.
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
640

Figure 8: Hybrid Dryer.
Figure 9: Dryer Control Point.
The figure above is the control panel of the
drying machine which is controlled automatically
using fuzzy logic solar energy is the main source
while LPG gas is an alternative source that is used if
sunlight cannot reach room temperature.
Figure 10: LPG Gas Stove Design.
The figure above is an LPG gas stove design
which uses a selenod as an LPG flame regulator,
adjusted for use with the temperature of the drying
room.
Drying machine specifications can be seen in the
table below:
Table 1: Specification of Dryer Machine with Fuzzy Logic
Control System.
Kategori Keterangan
Tool Capacity 20 rack
Dryer Dimensions 122 cm x 57 cm x 175
Collector Dimension 122cm x 244 cm
Collector Efficiency 79,24 %
material Steinless stell
Blower 3 inci
Driving force Listrik 220 V, 2,5 A
Spower source LPG dan Surya
Control System Fuzzy Logic
3.2 Functional Test Tool
This test is carried out by heating the temperature
sensor at a temperature of 30
0
C to 60
o
C and
observing which gas solenoid is working. The
solenoid valve is used to supply energy from LPG,
there are 4 solenoids that are installed with the
markings Solenoid 1, Solenoid 2, Solenoid 3 and
Solenoid 4. Solenoid 1 will continue to provide gas
supply to keep the fire burning while solenoid 2,
solenoid 3 and solenoid 4 will lights up depending
on the energy requirements calculated by fuzzy. The
test results in table 2 show that when the temperature
is between 29
0
C and 50
0
C the power required is
large to increase the temperature in the drying
chamber, while at a temperature > 50
o
C the power
will decrease according to the energy requirement
until it reaches the desired temperature.
Table 2: Solenoid Valve Test Results Against Temperature
and Power.
Solenoid Valve
Temperature
(
O
C)
power
(Watt)
I
(300)
II
(1000)
III
(2000)
IV
(2000)
open close open open
30 -34 4300
open open close open
35-41 3300
open close open close
42-50 2300
open open close close
51-55 1300
open close close close >56 300
Fuzzy Logic Control System Implementation on Solar and Gas Energy Dryers
641

3.3 Simulation Results of Fuzzy
Control Rules 2 Inputs, 2 Outputs
in the Matlab Program
To ensure that the rules made are correct, it is
necessary to look at the surface results in Matlab as
shown in Figure 34. Figure 34 shows that the
decrease in gas power (LPG) decreases gradually so
that the rules that have been made are as desired.
The above rules are the basis for developing a fuzzy
logic control program language using the AVR
codevision program.
Figure 11: Fuzzy Surface Display in Matlab.
3.4 Tool Performance Test
Performance tests were carried out to determine the
performance of the tool when drying sago using
solar energy sources, LPG gas, and hybrids.
Parameters observed were drying chamber
temperature, drying time, moisture content and
energy use.
3.4.1 Using Solar Energy
The temperature in the drying chamber using solar
energy is strongly influenced by weather conditions.
conducted for 2 days from 10.00 AM to 15.00 PM.
The test results can be seen in Figure 12. The figure
shows that on the first day the highest air
temperature in the collector was 62
0
C (12.00 PM)
and the highest air temperature in the drying room
was 46
0
C (12.30 PM), while on the second day the
highest temperature in the collector was 68
0
C.
(13.00 PM) and the highest temperature in the
drying room is 51
0
C (13.00 PM).
The difference between the collector air
temperature and room temperature is caused by the
loss of energy in the air duct and the mixing between
the air temperature and the collector temperature. In
the picture, the temperature looks unstable, the air
temperature in the room ranges from 30
0
C to 51
0
C
on the second day of drying.
3.4.2 Using LPG
From the observations it is proved that the fuzzy
control system can control the temperature based on
the setting point, the time is quite short, the offer
shoot is 64
0
C minutes and it does not exceed 69
0
C,
and it could running well because at that temperature
gelatinization will occur (Cecil et al. 1982).
Although there is an offset of 58
0
C (3% of the
setting point) but it is smaller than 5%, according to
(Ogata et al., 1996) that the system offset occurs
between 2% - 5%.
Figure 12: Dryer Room Temperature Response to Solar
Intensity.
Figure 13: Dryer Room Temperature Response to Time
Using LPG.
While the observation of the temperature in the
drying room during the drying process can be seen
in Figure 13. It can be seen in the figure that the
temperature reaches the Setting Point (60
0
C) in 6.75
minutes and overshoot occurs at 4
0
C for 11.5
minutes after that the temperature is constant at
58
0
C.
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
642

Figure 14: Dryer Room Temperature Response to Time
Using Hybrid Power.
To meet the water content of sago from the
Indonesian national standard, in this study, the water
content of 6% was taken. This aims to extend the
shelf life of sago flour, because the moisture content
is less than 7%, the microbiological damage is much
slower. According to (Winarno, 1992) the water
content of the material between 3% to 7% will
achieve optimum food stability.
4 CONCLUSIONS
Based on this research that has been done on the
hybrid powered dryer, it can be concluded that:
1. A multivariable control system has been
successfully designed that works with the
expected results, such as relatively small
overshoot, stable temperature and relatively
small offset.
2. The drying time using the hybrid and non-
hybrid methods (LPG gas) is same, but the use
of the solar energy method is longer.
3. The dryer can save 50% of LPG gas with the
hybrid method in varying sunlight conditions
(cloudy).
ACKNOWLEDGEMENTS
There are many obstacles in completing this
research, and this work would not have been
possible without the support of several parties. For
that, I would like to thank all parties who have been
willing to work so far and other related writings.
Director of the Nunukan State Polytechnic who has
provided support to me in completing this research. I
also want to thank my family and friends who have
always supported me in completing this research.
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