AI in Process Safety Management and Alarm Rationalization
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Program Professional Framework
AI in Process Safety Management and Alarm Rationalization
Program Overview
Alarm floods and nuisance alarms remain a recognized contributor to process incidents in refineries, gas plants and petrochemical sites. This course combines alarm management practice under ISA-18.2, IEC 62682 and EEMUA 191 with the analytical power of machine learning. Participants learn how to analyse historian and alarm logs to find chattering and stale alarms, apply data-driven methods to support rationalization and priority setting, use anomaly detection and predictive models as early warning for abnormal situations, and integrate these tools into process safety management elements such as management of change, HAZOP follow-up and safety instrumented system performance under IEC 61511.
Learning Outcomes
- ✓Analyse alarm system performance against ISA-18.2 and EEMUA 191 metrics such as alarm rate, flood periods and standing alarms.
- ✓Apply clustering and pattern analysis to alarm logs to identify chattering, duplicate and consequential alarm groups.
- ✓Support alarm rationalization decisions by documenting cause, consequence, response time and priority for selected alarms.
- ✓Evaluate anomaly detection models that give early warning of abnormal process conditions before alarm limits are reached.
- ✓Define governance controls so that AI tools in process safety remain subject to management of change and human verification.
Target Audience
- Process safety and loss prevention engineers
- Control room supervisors and senior panel operators
- Instrumentation, control and alarm management engineers
- Operations and reliability engineers in oil, gas and petrochemical plants
Detailed Program Modules
01Process Safety and Alarm Management Foundations
- Role of alarms as a layer of protection
- ISA-18.2 and IEC 62682 alarm lifecycle
- Alarm-related lessons from major process incidents
02Alarm System Performance Analysis
- EEMUA 191 benchmark metrics
- Extracting and cleaning alarm and event logs
- Identifying bad actors, floods and standing alarms
03Data-Driven Alarm Rationalization
- Master alarm database and rationalization records
- Clustering and sequence analysis of related alarms
- Priority setting based on consequence and response time
04AI for Abnormal Situation Management
- Anomaly detection on process historian data
- Predictive models for equipment and process deviations
- Operator decision support and advisory displays
05Integration with Process Safety Elements
- Linking AI insights to HAZOP and LOPA findings
- Safety instrumented systems and IEC 61511 boundaries
- Management of change for alarms and AI models
06Governance and Human Factors
- Validation, explainability and operator trust
- Cybersecurity of control system data
- Monitoring model performance and alarm KPIs
Training Methodology
Applied Project & Assessment
Applied Project
Participants analyse an alarm log sample from their unit or a provided dataset, identify the top bad-actor alarms, propose rationalization actions with priorities, and outline an AI-based early warning use case with its validation and governance requirements.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.
University Certificate & International Accreditation
Professional Certificate accredited by Twintech International University College of Technology in AI in Process Safety Management and Alarm Rationalization
International accreditation: American Association of Innovation (AAI)