Air Pollution Monitoring Using Fuzzy Logic in Industries

Vahdat, Seyed Ebrahim; Nakhaee, Foroogh Mofid

doi:10.5772/16947

Cited by 3 publications

(1 citation statement)

References 9 publications

(6 reference statements)

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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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AISI S1 Tool Steel after Deep Cryogenic Treatment: Tensile Properties and Microstructure

Niaki¹,

Vahdat²

2015

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Successful employment of advanced tool steel in engineering applications is related to its ability in terms of meeting service life requirements and fabrication with proper dimensions. Deep cryogenic treatment may be used to produce advanced tool steel by simultaneously increasing toughness, strength, and hardness. Twelve sets of specimens were tested, 9 of which were deep cryogenic treated and then tempered. Tensile properties, hardness, X-ray diffraction, and scanning transmission electron microscopy were applied for macroscopic and microscopic investigations. The best results of simultaneous improvement in tensile toughness, hardness, and strength were obtained for 36 h soaking and 1 h tempering times.

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Management of Pollutants in Industries: A Case Study

Vahdat¹

2015

JIM

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In this paper, using fuzzy logic, a model is presented to monitor region wise industrial pollutants in pyrometallurgy industries. The model has been used in a case study that will determine which industry, in which region, producing which and how much pollutants, is ecologically compatible. To assess the ecological compatibility, first, the sets of industries, regions, pollutants and ecology compatibility were defined. Then, to calculate the membership degree of the members of the ecology compatibility set, the membership function of ecology compatibility was defined. By ranking different industries in various regions, producing different pollutants, as continuous figures, the ecological compatibility of these industries was accurately compared. Given the degree of ecological compatibility in a region, the type of pollutant and the related industry, identification of the lowest degree of ecological compatibility was the first priority of this case study. Results of the conducted case study, without considering the region coefficient, show that in December 2005, member C 241 , with an ecological degree of compatibility equivalent to 0.0559, had the most critical condition in producing carbon dioxide. However, in the same period in 2005, on considering the region coefficient, member C 121 , with an ecological degree of compatibility equivalent to 0.0655, showed the most critical condition as far the production of carbon dioxide was concerned.

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Air Pollution Monitoring Using Fuzzy Logic in Industries

Cited by 3 publications

References 9 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

AISI S1 Tool Steel after Deep Cryogenic Treatment: Tensile Properties and Microstructure

Management of Pollutants in Industries: A Case Study

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