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Development of Air Quality Monitoring System for Preventive Maintenance in a Gas Plant

Authors

Abutu Silas

Electrical and Electronic Engineering Department, PTI, Effurun. (NG)

Article Information

DOI: 10.51583/IJLTEMAS.2025.1409000016

Subject Category: Electrical and Electronic Engineering

Volume/Issue: 14/9 | Page No: 118-122

Publication Timeline

Submitted: 2025-09-29

Published: 2025-09-29

Abstract

Abstract— Toxic gases and pollutants in gas plants pose serious risks to workers, equipment, and the environment. This research introduces the design and development of an Air Quality Monitoring System (AQMS) aimed at supporting preventive maintenance in such facilities. The system combines low-cost sensors, wireless communication, and data analytics to detect and measure pollutants in real time. While current monitoring systems typically offer limited coverage and track only a few key pollutants, our approach addresses these shortcomings by enabling broader, more detailed monitoring across the plant. We tested the AQMS in an operational gas plant, where it proved effective in detecting potential hazards early and supporting proactive maintenance. The findings show that the system not only reduces the risk of accidents, equipment failure, and environmental harm, but also helps streamline maintenance schedules and lower operational costs.

Keywords

Gas Plant, Preventive Maintenance, Pollutants, Environmental Pollution, Occupational Safety, Data Analytics, Real-time Monitoring, Wireless Communication

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References

1. World Health Organization (WHO). (2021). Air Pollution and Health Impacts. Geneva: WHO Press. Retrieved from www.who.int [Google Scholar] [Crossref]

2. United States Environmental Protection Agency (EPA). (2020). National Ambient Air Quality Standards (NAAQS). Washington, DC: EPA. Retrieved from www.epa.gov [Google Scholar] [Crossref]

3. Occupational Safety and Health Administration (OSHA). (2021). Industrial Air Quality Standards and Exposure Limits. Washington, DC: OSHA. Retrieved from www.osha.gov [Google Scholar] [Crossref]

4. Mousavi, A., & Wu, C. H. (2019). "Smart Sensors for Industrial Air Quality Monitoring: A Review." Sensors and Actuators B: Chemical, 281, 588-605. https://doi.org/10.1016/j.snb.2018.10.051 [Google Scholar] [Crossref]

5. Jiang, N., Lee, K., & Chen, R. (2020). "Internet of Things (IoT)-Based Air Quality Monitoring Systems: A Survey." Environmental Monitoring and Assessment, 192(8), 465-478. https://doi.org/10.1007/s10661-020-08560-2 [Google Scholar] [Crossref]

6. Patel, H., & Kumar, R. (2021). "Machine Learning Approaches for Predicting Air Pollution Levels in Industrial Environments." Journal of Environmental Informatics, 36(4), 530-548. https://doi.org/10.3808/jei.202100438 [Google Scholar] [Crossref]

7. European Environment Agency (EEA). (2020). Industrial Air Pollution and Its Impact on Human Health. Copenhagen: EEA. Retrieved from www.eea.europa.eu [Google Scholar] [Crossref]

8. Zhang, Y., & Li, X. (2018). "Wireless Sensor Networks for Real-Time Air Quality Monitoring in Petrochemical Plants." IEEE Transactions on Industrial Informatics, 14(3), 1152-1164. https://doi.org/10.1109/TII.2017.2730845 [Google Scholar] [Crossref]

9. Ghosh, S., & Banerjee, R. (2021). "Advances in Air Quality Prediction Using Artificial Intelligence Models." Applied Sciences, 11(5), 2334. https://doi.org/10.3390/app11052334 [Google Scholar] [Crossref]

10. International Organization for Standardization (ISO). (2019). ISO 14001: Environmental Management Systems - Requirements with Guidance for Use. Geneva: ISO. Retrieved from www.iso.org [Google Scholar] [Crossref]

11. Chen, X., Liu, Y., & Zhang, H. (2020). "IoT-Based Real-Time Air Quality Monitoring Systems for Industrial Safety." Journal of Industrial Safety Engineering, 5(2), 112-125. https://doi.org/10.1016/j.jise.2020.05.003 [Google Scholar] [Crossref]

12. International Energy Agency (IEA). (2021). Air Pollution from the Energy Sector: Global Trends and Policy Implications. Paris: IEA. Retrieved from www.iea.org [Google Scholar] [Crossref]

13. Gupta, R., & Sharma, P. (2019). "Predictive Maintenance Using Air Quality Data and Machine Learning in Industrial Plants." Journal of Sustainable Industrial Processes, 27(4), 287-301. https://doi.org/10.1016/j.jsip.2019.07.014 [Google Scholar] [Crossref]

14. Babu, S. K., & Kannan, R. (2020). "Development of Low-Cost Air Quality Monitoring Systems for Industrial Environments." International Journal of Environmental Science and Technology, 17(6), 4563-4578. https://doi.org/10.1007/s13762-020-02736-5 [Google Scholar] [Crossref]

15. World Bank. (2021). Industrial Air Pollution Control Strategies in Emerging Economies. Washington, DC: World Bank. Retrieved from www.worldbank.org [Google Scholar] [Crossref]

16. Kumar, N., & Singh, J. P. (2018). "Application of Big Data Analytics in Industrial Air Quality Monitoring." IEEE Transactions on Industrial Informatics, 15(6), 3392-3405. https://doi.org/10.1109/TII.2018.2853996 [Google Scholar] [Crossref]

17. Tang, Y., & Zhao, L. (2019). "Deep Learning for Air Pollution Prediction in Smart Industrial Cities." Environmental Science & Technology, 53(14), 8325-8335. https://doi.org/10.1021/acs.est.9b00857 [Google Scholar] [Crossref]

18. United Nations Environment Programme (UNEP). (2020). Air Pollution and Climate Change: Challenges and Policy Responses. Nairobi: UNEP. Retrieved from www.unep.org [Google Scholar] [Crossref]

19. Ramirez, J., & Torres, P. (2021). "Advancements in Wireless Sensor Networks for Real-Time Industrial Emissions Monitoring." Journal of Environmental Engineering, 147(2), 101-115. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001846 [Google Scholar] [Crossref]

20. European Commission. (2021). Best Available Techniques (BAT) for Industrial Air Pollution Control in the Petrochemical Industry. Brussels: European Commission. Retrieved from www.ec.europa.eu [Google Scholar] [Crossref]

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