Data Analytics and AI for Plant Operations

Most plant AI projects fail on data, not algorithms, and most of the rest fail because the operators do not trust an alert they cannot explain. This course gives engineers enough understanding of anomaly detection, predictive models and large-language-model agents to judge a vendor's claims, scope a use case, and set the guardrails a safety-critical facility needs. It uses Kairos as the worked example, from condition-monitoring data models to explained alerts and agent permissions.

• Describe the main analytics types — descriptive, anomaly detection, predictive, prescriptive — and match each to a plant problem
• Assess data readiness: historian coverage, tag quality, context data, lab and maintenance records
• Explain how asset and process health models are trained, validated and drift over time
• Read an explained alert and decide what action, if any, an operator should take
• Define guardrails for agentic AI: read-only vs write actions, approvals, audit trails, cybersecurity
• Build a business case and a pilot plan with measurable success criteria