Citation

Chew, Boon Keat and Mahmud, Azwan and Singh, Harjit (2025) Autonomous Hazardous Gas Detection Systems: A Systematic Review. Sensors, 25 (21). p. 6618. ISSN 1424-8220

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Abstract

Gas Detection Systems (GDSs) are critical safety technologies deployed in semiconductor wafer fabrication facilities to monitor the presence of hazardous gases. A GDS receives input from gas detectors equipped with consumable gas sensors, such as electrochemical (EC) and metal oxide semiconductor (MOS) types, which are used to detect toxic, flammable, or reactive gases. However, over time, sensors degradations, accuracy drift, and cross-sensitivity to interference gases compromise their intended performance. To maintain sensor accuracy and reliability, routine manual calibration is required—an approach that is resource-intensive, time-consuming, and prone to human error, especially in facilities with extensive networks of gas detectors. This systematic review (PROSPERO on 11th October 2025 Registration number: 1166004) explored minimizing or eliminating the dependency on manual calibration. Findings indicate that using properly calibrated gas sensor data can support advanced data analytics and machine learning algorithms to correct accuracy drift and improve gas selectivity. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVMs), multivariate regression, and calibration transfer have been effectively applied to differentiate target gases from interferences and compensate for sensor aging and environmental variability. The paper also explores the emerging potential for integrating calibration-free or self-correcting gas sensor systems into existing GDS infrastructures. Despite significant progress, key research challenges persist. These include understanding the dynamics of sensor response drift due to prolonged gas exposure, synchronizing multi-sensor data collection to minimize time-related drift, and aligning ambient sensor signals with gas analytical references. Future research should prioritize the development of application-specific datasets, adaptive environmental compensation models, and hybrid validation frameworks. These advancements will contribute to the realization of intelligent, autonomous, and data-driven gas detection solutions that are robust, scalable, and well-suited to the operational complexities of modern industrial environments.

Item Type:	Article
Uncontrolled Keywords:	Autonomous calibration, autonomous calibration, autonomous systems, calibration drift, cross-sensitivity, gas detection algorithms
Subjects:	T Technology > TD Environmental technology. Sanitary engineering > TD201-500 Water supply for domestic and industrial purposes > TD429.5-480.7 Water purification. Water treatment and conditioning. Saline water conversion
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	09 Dec 2025 04:13
Last Modified:	09 Dec 2025 04:13
URII:	http://shdl.mmu.edu.my/id/eprint/14983

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