Bridging Safety and Process Control: AI-Enabled Mitigation, TAPPICon26
Industrial environments in the pulp and paper sector are characterized by high-speed machinery
and numerous pinch zones that pose significant entrapment and crushing risks. Conventional
safety measures—such as physical guarding, interlocks, warning signage, and lockout/tagout
(LOTO) procedures—remain essential but are inherently reactive and static, as defined in
established safety standards such as OSHA and ISO frameworks [1], [2]. These approaches
often lack the responsiveness required to address dynamic operational conditions in real time
and rely significantly on human compliance [3].
This paper presents an AI-driven safety approach that transforms traditional safeguards into an
active, adaptive control mechanism. An AI-augmented safety monitoring system is deployed at
a pulp and paper facility using existing camera infrastructure, computer vision techniques based
on modern object detection frameworks, and machine learning models to continuously monitor
designated pinch zones. When a potential hazard is detected, the system interfaces directly with
programmable logic controllers (PLCs) to initiate real-time enforcement actions.
In addition to real-time hazard mitigation, the system enables predictive insights into equipment
health and operational risks, aligning with established predictive maintenance methodologies.
Although not developed as a Safety Instrumented System (SIS) under IEC 61511, it functions
as a powerful monitoring system with integrated control capabilities, designed to improve safety
performance metrics as a part of the overall safety management system for mills. Results from a
case study deployment indicate measurable improvements in safety performance, reduced
downtime, and strong return on investment, supporting broader adoption of AI-enabled safety
within high-risk industrial environments.
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