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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Author: Mathew Goethel
Bridging Safety and Process Control: AI-Enabled Mitigation,
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