TY - GEN
T1 - Improving Critical Controls Using IoT and Computer Vision
AU - Kainola, Michael
AU - Esmahi, Larbi
AU - Dewan, M. Ali Akber
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - In risk management, critical controls are processes that are put in place to prevent or mitigate the effects of material unwanted events (MUEs). One of the least effective categories of critical controls is administrative controls. This category of critical control encompasses manual, policy-based, and procedural controls. These manual inspections are ineffective as they are subjective and only provide point-in-time assurance. The application of computer vision, IoT, and predictive AI can help automate this type of controls and significantly reduce safety risks. In this research study, two applied experiments are performed in an industrial nickel refining plant to validate the effectiveness of this technology. For the first experiment, a system is developed to monitor the flow of molted material during granulation and detect any buildup that could potentially cause a risk-event. For the second experiment, a system was developed to continuously monitor the ambient brightness of the reduction processing area and use the collected data to predict potential risk-events before they happen.
AB - In risk management, critical controls are processes that are put in place to prevent or mitigate the effects of material unwanted events (MUEs). One of the least effective categories of critical controls is administrative controls. This category of critical control encompasses manual, policy-based, and procedural controls. These manual inspections are ineffective as they are subjective and only provide point-in-time assurance. The application of computer vision, IoT, and predictive AI can help automate this type of controls and significantly reduce safety risks. In this research study, two applied experiments are performed in an industrial nickel refining plant to validate the effectiveness of this technology. For the first experiment, a system is developed to monitor the flow of molted material during granulation and detect any buildup that could potentially cause a risk-event. For the second experiment, a system was developed to continuously monitor the ambient brightness of the reduction processing area and use the collected data to predict potential risk-events before they happen.
KW - Computer vision
KW - Critical controls
KW - IOT
KW - Machine learning
KW - Mining industry
KW - Risk Management
UR - http://www.scopus.com/inward/record.url?scp=105003860568&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-85923-6_5
DO - 10.1007/978-3-031-85923-6_5
M3 - Published Conference contribution
AN - SCOPUS:105003860568
SN - 9783031859229
T3 - Communications in Computer and Information Science
SP - 59
EP - 72
BT - Internet Computing and IoT and Embedded Systems, Cyber-physical Systems, and Applications - 25th International Conference, ICOMP 2024, and 22nd International Conference, ESCS 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Revised Selected Papers
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
A2 - Shenavarmasouleh, Farzan
T2 - 25th International Conference on Internet Computing and IoT, ICOMP 2024, and 22nd International Conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024
Y2 - 22 July 2024 through 25 July 2024
ER -