Smart Biogas Plant Monitoring System Using Internet of Things

(IoT) Technology

Hendrik Elvian Prasetya, Ikke Dianita Sari, Rif’ah Amalia, Achmad Fawaidz Bintang Azisa,

Aulia Lailatul Fitri and Muhammad Rizal Jibran

Powerplant Engineering Departement, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia

aulialaila12345@gmail.com, rizaljibran210800@gmail.com

Keywords: Biogas, Digester, IoT, Biogas Sensor.

Abstract: Biogas is mixture of gases produced from fermentation process of organic matter derived from animal manure.

To produce optimal biogas, it is necessary to have composition of raw materials and appropriate

environmental conditions. This study used measurement system to maintain conditions in the biogas reactor.

Two types of tanks are used, first the digester tank functions as an anaerobic biogas production process, and

second storage tank functions to store biogas produced. Thermocouple sensor to detect temperature, MQ-4

sensor to detect methane gas, MG-811 sensor to detect carbon dioxide gas, and MQ-136 sensor to detect

hydrogen sulfide. Data from the readings of all sensors will be processed first into microcontroller, Arduino

Mega 2650, which will then be monitored via smartphone with the Android IoT platform in the form of Blynk.

Sensor data can be displayed on the blynk platform using communication module. Based on the measurement

results, it is known that the accuracy of measuring instrument is compared with secondary data, precision

value of measuring instrument is analyzed based on the standard deviation with the results of all measuring

instruments not more than 5.0, and the linearity value obtained from results of regression calculations with

linearity results not more than 0.15%.

1 INTRODUCTION

To carry out economic activities in Indonesia, energy

is needed for consumption and production activities

in various economic fields. Currently in line with

economic growth and increasing energy needs.

During the 2010-2019 period, total final energy

consumption (including biomass) increased from

777.3 million to 1.009 million BOE (Barrel Oil

Equivalent). Indonesia is a country that has renewable

and non-renewable resources. However, exploration

of energy resources is more focused on the energy

that is unrenewable resources. High dependence on

fossil energy sources is still a significant problem in

the national energy supply. In 2019 it was recorded

that 90.7% of the national primary energy supply was

met from coal, oil, and natural gas.

In 2017 the government issued the Rencana

Umum Energi Nasional (RUEN), with one of the

targets for developing bioenergy-based renewable

energy is the use of biogas. Biogas is one of the

environmentally friendly renewable energy, and its

availability is abundant in Indonesia. Utilizing biogas

is expected to reduce energy dependence on fossil

fuels. Biogas is produced from the fermentation of

organic materials by microorganisms under anaerobic

conditions. Materials containing organic compounds

can be used as biogas, be it organic waste, plantation

waste, or livestock manure such as cow dung.

Biogas contains a relatively high proportion of

methane (CH4). The complete composition of the

biogas content is as follows:

Table 1: Biogas Content Composition.

Gas T

e Amount

(

)

Metana (CH4) 50 - 70

Nitro

(

)

0 - 0,3

Karbondioksida

(

CO2

)

25 - 45

Hidro

(

)

1 - 5

Oksi

(

)

0,1 - 0,5

Hidro

en Sulfida

(

H2S

)

0 - 3

Prasetya, H., Sari, I., Amalia, R., Azisa, A., Fitri, A. and Jibran, M.

Smart Biogas Plant Monitoring System Using Internet of Things (IoT) Technology.

DOI: 10.5220/0011955600003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 915-920

ISBN: 978-989-758-619-4; ISSN: 2975-8246

915

To produce optimal biogas, it is necessary to have a

composition of raw materials and appropriate

environmental conditions. The changing environment

will affect and can reduce the quality of biogas.

Therefore, to maintain the conditions in the biogas

reactor as desired, it is necessary to add measurements

and monitoring in real-time. This study used the batch

type for the biogas production process. The control

system used is an ON-OFF control system, where

when the methane content in the digester has reached

70%, the valve will open so that the biogas will enter

the storage tank. Inside the digester tank, a stirrer will

operate for one hour four times a day.

The gas content and temperature in the digester

tank and storage tank will be measured using sensors

and monitored via a smartphone using IoT software,

namely Blynk. From this research, it is hoped that the

conditions in the digester will be monitored according

the desired conditions, and the biogas yield can be

more optimal.

F L O W C H A R T A N D T O O L

MAKING

2.1 Flowchart Monitoring Process

Figure 1: Schematic P&ID of the system on the digester.

In Figure 1, the process begins with the raw materials

being put in the digester tank, and then the stirrer will

rotate according to the set timer. After the methane is

formed, the sensor will detect the methane level and

process it using a controller. When the methane

content reaches 70%, the controller will instruct the

valve to open, and the biogas fills the storage tank.

The entire process will be monitored on a smartphone

using an IoT platform in the form of Blynk.

From the hardware design above, the methane

content is obtained from the MQ-4 sensor, the carbon

dioxide level from the MG-811 sensor, the hydrogen

sulfide content from the MQ-136, and the temperature

from the K type thermocouple. The sensor will then

be connected to the Arduino Mega microcontroller.

2560. Valves and agitation motors are also connected

to the microcontroller. The output is displayed on the

smartphone on the IoT platform, namely Blynk. To be

able to display the output on the Blynk, the

microcontroller will be connected to a

communication module in the form of an ESP8266.

2.2 Manufacturing Process

In this study, the process of making the tool follows

the directions on the flowchart shown in Figure 3. The

manufacture of hardware and software is carried out

in parallel by starting with hardware design by

drawing the circuit schematic. After that, the

schematic is checked and printed on the

PCB using

the help of the EAGLE software. Followed by laying

the components on the PCB by soldering, the results

of the hardware design can be seen in Figure 2.

Figure 2: Hardware Tool Design.

The data that has been processed on the

microcontroller will then be sent to the Blynk

software via the internet network that the

microcontroller requires a communication module so

that the microcontroller can connect to the internet

network. The communication module used is ESP826

Software development is done by programming each

sensor and programming it for smartphone needs. The

program is made with the help of Arduino software.

After that, check again to ensure the program is

correct so it can be integrated. Followed by testing for

the program as a whole. If successful, the program is

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

916

uploaded to the assembled hardware. The

communication module needs to be configured first

with the microcontroller, Arduino Mega 2650.

Figure 3: Tool making flowchart.

CHARACTERISTICS STATIC

MEASUREMENT SYSTEM

3.1 Accuracy

Accuracy is the accuracy of measuring instruments in

providing readings (Gunterus, 1994). Measurement

accuracy is how close the measured value of a quantity

is to the actual value. This quantity indicates the

number of deviations that occur in a measuring device

or system. There are several ways to express accuracy.

3.1.1 On the Measurement Variable

For example, a thermometer with a scale of O`F -

1OO'F is said to have an accuracy of 1'F. If the

thermometer shows a temperature of 40'F, then the

actual temperature is between 39'F and 41'F

3.1.2 In Percentage Span

A pressure transmitter has a of 100-400 psi range and

span accuracy. In other words, every signal that

comes to the transmitter can deviate as much as 0.5%

x 300 psi = 1.5 psi. For example, if the transmitter

emits 250 psi, the actual pressure will be between

248.5 psi and 251.5 psi.

3.1.3 In Percentage to the Maximum Scale

The term maximum scale is usually called full scale

(FS). Thus a voltmeter is said to have an accuracy of

1% FS, which means that if the meter is set to a

maximum reading scale of 300 volts, the accuracy in

that range is ±3 volts.

3.1.4 In Percentage of Reading

In this case, the accuracy depends on the value of the

reading measured. A level transmitter is said to have

an accurate output of 0.5%. The transmitter range is

0-100 cm. If the transmitter time shows the signal at

60cm, then the actual level range is 59.7 - 60.3cm.

3.2 Precision

Precision is the ability to produce the same value from

identical and repeated measurement results

(measurement points and relative time) (Obstfeld &

Taylor, 1997). The smaller the difference between

repeated measurements, the better the instrument's

performance. This can be seen from the standard

deviation obtained from the measurement.

The standard deviation is a statistic used to

determine how spread out the sample data is and how

close each point is to the mean. If the variance from

the mean is very large, the value of the 𝜎𝑥 will be

large, but if the variance of the data from the mean is

very small, then the value of 𝜎𝑥 will also be small.

This helps to determine whether the sample data

collected is representative of the population. Higher

precision means a smaller standard deviation. The

standard deviation can be calculated according to the

formula:

Smart Biogas Plant Monitoring System Using Internet of Things (IoT) Technology

917

(1)

3.3 Linearity

An element can be said to be linear if its input and

output curves form a straight line. However, finding

a graph with an ideal linear form is very rare. There

will be indentations that are usually slightly curved or

tortuous. However, in an ideal straight line, there is

still a nonlinearity called linearity (Gunterus, 1994).

A linearity test is needed to find out whether two

variables have a linear relationship or not. The

measuring instrument can be said to have a linearity

level of 1% if the results of the input-output ratio

curve are still winding, but the difference in curvature

that is produced is still in the range of ± 1%. The

approach to a non-linear curve by cutting the curve

into smaller parts is called piecewise linear. The non-

linear shapes can be a parabola, serpentine, or curved.

The regression method is used to determine linearity.

Simple regression analysis is the relationship between

two variables: the independent variable and the

dependent variable. Multiple regression analysis is

the relationship between three or more variables,

with at least two independent variables and one

dependent variable. The purpose of regression is to

estimate the value of a variable when its value is

related to another specified variable. There are two

types of regression used: linear regression and

quadratic regression.

3.4 Linier Regression

Linear regression determines the effect between one

independent variable and one dependent variable.

The linear regression equation for a population based

on Yusuf (2009) is shown in the equation below:

𝑦

′

 𝑎

 𝑎

𝑥

(2)

3.5 Quadratic Regression

Quadratic regression is when the value of the

independent variable increases or decreases linearly,

or the form is displayed in a parabola if the data

results are formed in a scatter plot (the relationship

between the dependent and independent variables is

squared) and is a nonlinear regression method

(Wibisono, 2005). The mathematical model for

quadratic regression is:

𝑦

′

 𝑎

 𝑎

𝑥  𝑎

𝑥

(3)

4 RESULTS AND DISCUSSION

Accuracy is the precision of a measuring instrument

in providing readings (Gunterus, 1994). To determine

the accuracy of the measuring instrument, calibration

of the measuring instrument must be carried out.

Calibration is essential to determine the conventional

correctness of the designation value of measuring

instruments and measuring materials by comparing

traceable measuring standards to national standards

for units of measure. As explained in Chapter 2, there

are three types of measuring standards in calibrating.

In this study, the data produced by MQ-4 (methane

sensor), MG-811 (carbon dioxide sensor), and MQ-

136 (sulfide acid sensor) are compared with

measurement standards in the form of data from

Slamet's research, 2017. In comparison, the data

generated by the K- Type Thermocouple is compared

to NTC.

Table 2: Clean Air Composition.

Gas Type

Formula

Concentration Concentration

(

)

(pp

)

Nitrogen N

78,09 780.900

Oxygen O

20,95 209.500

Argon Ar 0,934 9.340

Carbon dioxide CO

0,032 320

Neon Ne 1,8 x 10

-3

Helium He 5,2 x 10

-4

5,2

Methane CH

1,5 x 10

-4

1,5

Krypton Kr 1,0 x 10

-4

Hydrogen H

5,0 x 10

-4

0,5

10 Water H

2,0 x 10

-5

0,2

Carbon

monoxide

CO 1,0 x 10

-5

0,1

12 Xenon Xe 1,0 x 10

-6

0,08

13 Ozone O

2,0 x 10

-6

0,02

14 Ammonia NH

6,0 x 10

-7

0,006

15 Nitogen dioxide NO

1,0 x 10-7 0,001

Nitrogen

monoxide

NO 6,0 x 10-8 0,0006

17 Sulfur dioxxide SO

2,0 x 10-8 0,0002

In this study, calibration was carried out three

times before the sensor was installed on the tank

to conduct the experiment. The results of sensor

readings on calibration are as follows:

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Table 4: Digester Tank Readings.

Sensor

Calibration to -

1 2 3

MQ-4 (ppm) 1,85 2,3 1,9

MG-811 (ppm) 315 324 366

MQ-136 (ppm) 0,00054 0,00098 0,00237

Thermocouple Type K

27,5℃ 28 ℃ 27,5 ℃

Table 5: Storage Tank Readings.

Calibration to

NT C Thermocouple type-

(Digester Tank)

Thermocouple type- K

(Storage Tank)

27,8 27,5 27,5

28,3 28 27,5

27,6 27,5 27,75

Table 6: Temperature Readings.

Calibration to

NT C Thermocouple type-

(Digester Tank)

Thermocouple type- K

(Storage Tank)

27,8 27,5 27,5

28,3 28 27,5

27,6 27,5 27,75

Figure 4: Calibration of the MQ-4 Sensor.

In the first calibration, the results of the MQ-4 sensor

on the digester tank and storage tank showed a

difference of 0.35 ppm by reference. In the second

calibration, the difference in the readings of the MQ-

4 sensor increased. The sensor on the digester tank

shows a difference of 0.8 ppm, while in the storage

tank, it is 0.6 ppm. The increase in the second

experiment was due to the onset of rust on the sensor.

The higher the difference between the reading and the

reference, the accuracy of the measuring instrument

decreases, so maintenance must be carried out in the

form of a large enough sensor cleaning so that the

sensor is better replaced with a new sensor. After the

MQ-4 sensor attached to the digester tank was

replaced with a new sensor, the reading difference

dropped to 0.4 ppm. Meanwhile, in storage tanks, the

difference is higher than before, which is 1.0 ppm.

There are differences in the sensors attached to the

digester tank and the storage tank. This is due to the

sensor attached to the digester tank being replaced

with a new one, while the sensor on the storage tank

is only cleaned if the reading still shows the

difference.

Figure 5: Calibration of the MG-811 Sensor.

From the graph in Figure 5, the readings of the MG-

811 sensor on the digester tank and the storage tank

are very different. In the first calibration, the readings

on the two tanks are the same at 315 ppm, a difference

of 5 ppm from the reference. For the second

calibration, the sensor readings are equal to the

increase. The sensor on the digester tank shows a

difference of 4 ppm, while the digester tank shows a

difference of 2 ppm. There is an increase in graphics,

and this is due to the onset of rust on the sensors. In

the third calibration, the sensor on the digester tank

has increased significantly enough to produce a

difference of 46 ppm, while in the storage tank, the

sensor shows a difference of 5 ppm. In the third

calibration, the sensor located on the digester tank

experienced a considerable increase in difference; this

is due to the sensor located in the digester tank being

exposed to the gas produced by the biogas longer than

the sensor located in the storage tank.

Figure 6: Calibration of the MQ-136 Sensor.

Figure 6 shows the calibration graph on the MQ- 136

sensor. It can be seen in the picture that the sensor

readings on the storage tank tend to be constant, while

the digester tank, it is constantly increasing. In the first

Smart Biogas Plant Monitoring System Using Internet of Things (IoT) Technology

919

calibration, the magnitude of the difference in

readings with a reference of 0.00034 ppm for the

digester tank and 0.00033 ppm for the storage tank.

In the second calibration, the difference in readings is

0.00078 ppm in the digester tank and 0.00038 ppm in

the storage tank. In the third calibration, the MQ-136

sensor readings on the digester tank experienced a

high enough increase to produce a difference of

0.00217 ppm, while the sensors on the storage tank

showed a difference of 0.00035 ppm. The existence

of considerable differences between the two sensors

can be caused by the environmental conditions in the

tank where the sensor is installed. The sensor attached

the digester tank is longer exposed to the gases

produced by the biogas, so the sensor undergoes more

ratification than the sensor on the storage tank.

Figure 7: Calibration of K-Type Thermocouple.

Figure 7 is a calibration chart on the K-Type

Thermocouple. Unlike the previous sensors, the

thermocouple reading results are not compared with

secondary data but rather with NTC. In the first

calibration, the thermocouples on the digester tank

and the storage tank showed the same result, so it is

known that the difference is 0.3ºC. In the second

calibration, the digester tank shows a difference of

0.3ºC and in the storage tank 0.8ºC. In the third

calibration, the reading difference between the sensor

and the NTC is 0.1ºC for the digester tank and 0.15ºC

for the storage tank.

5 CONCLUSION

Based on the results of the analysis of the

measurement system in the biogas reactor, it can be

concluded that the measurement results are analyzed

based on the static characteristics of the measuring

instrument, namely accuracy, precision, and linearity.

It is known that the accuracy of the measuring

instrument is compared to secondary data with a not-

so-significant difference; the precision value of the

measuring instrument is analyzed based on the

standard deviation with the results of all measuring

instruments not more than 5.0 so that it can be

concluded that the precision measuring instrument

and linearity value are obtained from the results of

regression calculations with linearity results are not

more than 0.15%, which means that the sensor

reading output shows a deviation of 0.15%

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