Design of Indoor Room Gas CO and SO2 Detection Based on Microcontroller Using Fuzzy Logic

Widodo, Slamet; Amin, Mohamad; Supani, Ahyar

doi:10.1051/e3sconf/201912523013

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…According to Javid et al [15] and the remaining summary in Table 1, TVOC and CO2 are gases that are frequently used to construct IAQI. Additionally, some studies use Fuzzy Logic to classify the various types of gas parameters [14], [17], [18], but only a few IAQI studies employ forecasting techniques, either statistical or machine learning based. TVOC and CO2 were used as gas parameters in this study to calculate the IAQI.…”

Section: A Principle Of Indoor Air Quality Indexmentioning

confidence: 99%

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto

Rahmatsyah²

2022

JOIV : Int. J. Inform. Visualization

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Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.

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Section: A Principle Of Indoor Air Quality Indexmentioning

confidence: 99%

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto

Rahmatsyah²

2022

JOIV : Int. J. Inform. Visualization

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“…Inhaling them can increase the chance that you'll have health problems [2]. People with lung or heart disease, children and older adults are at greater risk from air pollution [3]. Air pollution isn't just outside-the air inside building can also be polluted and effect our health [4].…”

Section: Introductionmentioning

confidence: 99%

Geolocation based air pollution mobile monitoring system

Talib

Jasim

2021

IJEECS

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Air pollution is conducted to harmful substances like solid particles, gases or liquid droplets. More pollutants CO, SO2, NOx, CO2.This research is proposed the design and implementation of mobile, low cost and accurate air pollution monitoring system using Arduino microcontroller and gas sensor like MQ2, MQ131, MQ135, MQ136, DHT22, measuring materials mentioned above, smoke, Acetone, Alcohol, LPG, Toluene, temperature, humidity and GPS sensor”NEO-6M” that track the location of air pollution data, and display the analysis result on ESRI maps. The system also save the results on SQL server DB. The data is classified using data mining algorithms, presenting the result on a map helps governmental organizations, nature guards, and ecologists to analyze data in real time to simplify the decision making process. The proposed system uses J48 pruning tree classifier generated using cross validation of fold (10) with highest accuracy 100%, while IBK ≈99.67, Naïve bays ≈90.89, and SVM ≈81.4. It’s found that the common air quality for Baghdad (study area) is between (“Good”, “Satisfactory”, and “Moderately”) for 1835 records of air samples during (January and February 2021) time period.

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“…Therefore, there are various kinds of gas sensors with various types of materials according to the target gas to be detected. One example of a CO2 gas sensor used is the MQ-135 [13]- [15]. This sensor has advantages such as high sensitivity, a more affordable price and relatively easy to integrate into a circuit system [16]- [19].…”

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confidence: 99%

CO2 detection system in mixed gas using MQ-135 sensor

Salamah

Hidayah²,

Kusuma³

2022

NMJ

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The increase in CO2 gas occurred very significantly due to the use of fossil fuels, industry, forest fires, volcanic eruptions and natural gas resulting in air pollution. The dangers of CO? gas for humans can cause headaches, sweating, blurred vision, tremors, and loss of consciousness after exposure for five to ten minutes. CO2 is a poisonous gas which is physically colorless and odorless. This characteristic causes many humans to be unaware of the existence of this gas, so that it is exposed and endangers health. Therefore we need a CO2 gas detection system. The difficulty that occurs in the detection of CO2 gas is when the gas is mixed with other gases that have similar material properties. In this research, a CO2 gas detection system is designed to detect the gas when it mixes with other gases using the MQ-135 sensor. The system that has been designed and built is then compared with a standard tool, namely the xm-7000 dragger. The test data analyzed using linear regression showed the results in accordance with 2the value of R = 0.9896 with the conversion equation y = 0.0389x-76.086. With these results provide new opportunities in testing CO2 on gas mixtures that are easier and cheaper.

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Design of Indoor Room Gas CO and SO2 Detection Based on Microcontroller Using Fuzzy Logic

Cited by 4 publications

References 3 publications

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Geolocation based air pollution mobile monitoring system

CO2 detection system in mixed gas using MQ-135 sensor

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