As process industries accelerate their digital transformation through advanced technologies such as Machine Learning, Machine Vision, Artificial Intelligence (AI), and Large Language Models (LLMs), AI-driven visual monitoring emerges as a foundational enabler for enhancing both site safety and process safety performance at scale. This abstract outlines practical, field-proven applications of T-Pulse- a visual intelligence platform deployed across over 200 industrial sites worldwide, including refineries, petrochemical complexes, upstream facilities, and other large-scale industrial operations- demonstrating its role in advancing safety and operational excellence through AI-driven visual analytics.
System Overview: T-Pulse – Visual AI for Safety and Process Risk Monitoring
T-Pulse is an advanced Artificial Intelligence (AI) platform purpose-built to enhance safety and process monitoring across high-hazard industrial environments. It integrates state-of-the-art machine vision and deep learning technologies to identify indicators of operational deviations, non-compliance, and safety risks. The platform utilizes a comprehensive library of over 250 pre-trained AI models to interpret real-time data streams from multiple sources, including thermal cameras, RGB video, and other digital inputs.
Rather than supplanting existing process control and safety instrumentation, T-Pulse acts as a complementary layer- targeting visually perceptible hazards that are often beyond the detection scope of conventional sensors or manual inspections. By transforming unstructured visual data into structured, context-rich insights, the system enables timely recognition of high-impact scenarios such as Loss of Primary Containment (LOPC), unsafe acts, environmental anomalies, and other latent threats.
Through this approach, T-Pulse facilitates continuous, automated compliance monitoring and promotes scalable, consequence-based risk management.
Key Application Areas and Field Learnings:
T-Pulse enhances early detection of LOPC events by applying pixel-level analytics from RGB and thermal imaging. Its AI models enable real-time identification of hydrocarbon and chemical leaks in high-risk zones such as loading bays, drain points, oil traps, and known leak-prone areas. Field deployments have achieved a 94% reduction in undetected minor leaks, avoiding costly cleanups ($10,000–$200,000 per incident) and delivering up to $500,000 in annual savings per site. Critically, LOPC alerts can be mapped to Layer of Protection Analysis (LOPA) safeguards, supporting timely interventions and reinforcing the process of safety integrity.
T-Pulse enables continuous, real-time monitoring of environmental non-compliance through strategically positioned cameras in critical locations. It automatically detects events such as emissions, spills, flaring, effluent discharges, and unauthorized activities. The system produces time-stamped, verifiable records with contextual video snippets to support audits, incident documentation, legal proceedings, and insurance claims. By quantifying environmental conditions and human activity, T-Pulse prioritizes anomalies by risk severity and enhances root cause analysis with pre- and post-incident visuals. Scalable across multiple sites, it ensures consistent compliance, centralized reporting, and unbiased, fatigue-free inspections.
T-Pulse delivers continuous, real-time visual monitoring of key process areas, eliminating the need for periodic manual audits and inspections. By aligning with OSHA, IOGP, and site-specific protocols, it automatically detects and classifies unsafe acts and conditions with 95% compliance detection accuracy. This automation has driven up to 80% reductions in lost workdays due to safety and process safety-related incidents—translating to potential savings of up to $150,000 annually per site. Additionally, T-Pulse streamlines compliance verification and risk audit preparation, reducing audit-related costs by an estimated $50,000 annually and significantly minimizing exposure to regulatory penalties. With AI-driven process improvement insights, it also cuts dependence on external consultants, generating an additional $80,000 in savings.
T-Pulse can automate deviation monitoring following Management of Change (MOC) activities, addressing the limitations of manual observations and checklist-based validations. Its trained models may be configured to detect subtle, guideword-based deviations—such as "More Temperature" (e.g., unexpected steam plumes) or "Sudden Increase in Temperature and Pressure “by interpreting real-time visual cues. These detections can be mapped to consequence-prioritized alarms, supporting timely hazard identification and enabling data-driven feedback loops. T-Pulse can also capture near-miss visuals (e.g., vapor clouds, near-spills), enhancing LOPA validation and barrier management. This integrated approach could help ensure that changes introduced through MOC are both verifiable and aligned with effective risk controls while also supporting continuous improvement in process safety through actionable visual data.
T-Pulse has the potential to enhance Quantitative Risk Assessment (QRA) models by integrating real-time site-specific incident data, offering a more dynamic alternative to traditional reliance on generic historical assumptions. By continuously identifying minor leaks, near misses, and unsafe behaviors, the system can contribute to more precise risk quantification and spatial hazard mapping, improving the relevance and responsiveness of QRA outputs. This data-driven transparency can improve the insurer’s confidence in risk management maturity, leading to 10–20% reductions in insurance premiums—equating to ~$500,000 in annual savings per site. In parallel, automated compliance documentation, real-time monitoring, and AI-powered heatmaps strengthen regulatory alignment, reduce claims, and improve overall operational risk posture across sites.
Implementation Insights & Lessons Learned
While the technical capability of AI is well proven, the path to adoption requires robust stakeholder alignment and careful change management. This session will present candid lessons from the field—covering model calibration per site standards and strategies for scalable rollout across diverse industries.
Conclusion
AI-enabled digitalization is not a replacement for foundational safety and operational practices serve as a strategic enhancement. When integrated thoughtfully into site safety standards and core process safety structures, such as Management of Change (MOC), Layer of Protection frameworks, and risk assessment systems, AI can significantly improve hazard identification, compliance monitoring, and incident responsiveness.
The use cases and results presented in this abstract highlight a replicable and scalable model for aligning AI with site-level process safety management systems. Organizations may realize measurable improvements in safety performance, operational efficiency, and risk governance—translating to an estimated three-year return on investment of $9–$10 million per site.