REAL-TIME CLOUD-BASED FRAMEWORK FOR MONITORING AND PREDICTION ANALYSIS OF ACETONE IN AN AMBIENT AIR USING DEEP LEARNING

Objective:Acetone is one of the hazardous compounds available in ambient aircausingan impact on human health and environmental safety, making its monitoring and analysis more vital. An IoT-based, real-time framework presented deals with the development of a portable and low-cost system for acetone monitoring and analysis via deep learning (DL). Since it's a cost-effective, compact system, it can be installed in any industry as compared with the traditional monitoring systems that are highly expensive and difficult to install anywhere. Methodology: This integrated System was developed using an ESP-32 IoT Controller and a Multi Sensor Module (ZPHS01B)and deployed in an industry and the real-time data samples of the acetone were collected. The data samples were analyzed using the XGBoost algorithm (XGBOOST) – A deep learning model for the system performance evaluation. Findings:The system performance evaluated based on the XGBOOST algorithm indicates that the developed system is highly capable of monitoring acetone concentration in ambient air. The analysis revealed that the VOC grade and acetone concentration (0.96) has a very strong correlation. Moreover, Strong Positive correlations among VOCsi.e.,Acetone vs Benzene: 0.95, Acetone vs Toluene: 0.94, Acetone vs Methane: 0.94. Acetone shows that the developed device is highly capable of detecting the Acetone concentration. The weak relations between Acetone Vs Temperature (-0.06) and Acetone Vs Humidity (0.11) indicate that environmental variables do not strongly influence acetone concentration directly. Novelty: The IoT-based real-time device developed for monitoring hazardous exhaust compounds is cost-effective, compact and easily deployable at a fraction of the cost of traditional systems. The dataset generated by the system can be useful to the regulating / external agencies for the pollution audit, framing the future policies, ensuring increased workplace safety and regulatory compliance in industrial settings. This device can be employed in industrial settings that can ensure increased workplace safety and regulatory compliance by providing an affordable and scalable environmental monitoring solution.

Authors

Publication Details

Journal
Lex localis - Journal of Local Self-Government
Published
2025-05-15
DOI
https://doi.org/10.52152/800207
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

REAL-TIME CLOUD-BASED FRAMEWORK FOR MONITORING AND PREDICTION ANALYSIS OF ACETONE IN AN AMBIENT AIR USING DEEP LEARNING

N. K. Choudhari, Shishir A. Bagal, A. R. Chaudhari
Lex localis - Journal of Local Self-Government
Air Quality Monitoring and Forecasting
article

REAL-TIME CLOUD-BASED FRAMEWORK FOR MONITORING AND PREDICTION ANALYSIS OF ACETONE IN AN AMBIENT AIR USING DEEP LEARNING

N. K. Choudhari, Shishir A. Bagal, A. R. Chaudhari
article en

Abstract

Objective:Acetone is one of the hazardous compounds available in ambient aircausingan impact on human health and environmental safety, making its monitoring and analysis more vital. An IoT-based, real-time framework presented deals with the development of a portable and low-cost system for acetone monitoring and analysis via deep learning (DL). Since it's a cost-effective, compact system, it can be installed in any industry as compared with the traditional monitoring systems that are highly expensive and difficult to install anywhere. Methodology: This integrated System was developed using an ESP-32 IoT Controller and a Multi Sensor Module (ZPHS01B)and deployed in an industry and the real-time data samples of the acetone were collected. The data samples were analyzed using the XGBoost algorithm (XGBOOST) – A deep learning model for the system performance evaluation. Findings:The system performance evaluated based on the XGBOOST algorithm indicates that the developed system is highly capable of monitoring acetone concentration in ambient air. The analysis revealed that the VOC grade and acetone concentration (0.96) has a very strong correlation. Moreover, Strong Positive correlations among VOCsi.e.,Acetone vs Benzene: 0.95, Acetone vs Toluene: 0.94, Acetone vs Methane: 0.94. Acetone shows that the developed device is highly capable of detecting the Acetone concentration. The weak relations between Acetone Vs Temperature (-0.06) and Acetone Vs Humidity (0.11) indicate that environmental variables do not strongly influence acetone concentration directly. Novelty: The IoT-based real-time device developed for monitoring hazardous exhaust compounds is cost-effective, compact and easily deployable at a fraction of the cost of traditional systems. The dataset generated by the system can be useful to the regulating / external agencies for the pollution audit, framing the future policies, ensuring increased workplace safety and regulatory compliance in industrial settings. This device can be employed in industrial settings that can ensure increased workplace safety and regulatory compliance by providing an affordable and scalable environmental monitoring solution.

Lex localis - Journal of Local Self-GovernmentVol. 23(S1)
Openalex Percentile: Top 76%
Air Quality Monitoring and Forecasting
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