AI Refinery Process Optimization
Optimizes refinery unit setpoints and constraints using AI to improve throughput, energy intensity, and product quality.
The Problem
“Reduce refinery yield loss and energy intensity”
Organizations face these key challenges:
Frequent operating upsets and constraint chasing due to changing crude slates, ambient conditions, and equipment fouling, leading to yield loss and energy spikes
Delayed or sparse quality measurements (lab turnaround times of hours) causing off-spec giveaways, reblending, and conservative operating margins
Siloed optimization across units (CDU/VDU, FCC, hydrocrackers, hydrotreaters, reformers) that misses site-wide interactions like hydrogen balance, steam/power limits, and flare constraints
Impact When Solved
The Shift
Human Does
- •Review historian trends, lab results, and unit KPIs to assess current performance
- •Manually adjust unit setpoints and operating targets based on experience and alarms
- •Coordinate trade-offs across units for yield, energy, hydrogen, and product quality
- •Investigate off-spec events, energy spikes, and yield losses through retrospective analysis
Automation
- •Provide basic alarms and APC or RTO model outputs from existing rule-based workflows
- •Aggregate routine process measurements and historical operating data for review
- •Surface standard reports on throughput, energy use, and constraint performance
Human Does
- •Approve recommended operating targets and constraint changes for each unit
- •Decide margin, energy, emissions, and quality trade-offs during changing operating conditions
- •Handle safety, reliability, and compliance exceptions when recommendations conflict with plant realities
AI Handles
- •Continuously monitor process conditions, sensor health, and likely measurement reliability issues
- •Predict yields, product quality, energy use, and constraint risks from current operating conditions
- •Recommend coordinated setpoint and severity adjustments across interacting refinery units
- •Detect emerging underperformance or off-spec risk and explain likely drivers and actions
Operating Intelligence
How AI Refinery Process Optimization runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not change unit operating targets or constraints without operator or supervisor approval unless the site has explicitly authorized closed-loop operation for that unit. [S1][S2]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Refinery Process Optimization implementations:
Key Players
Companies actively working on AI Refinery Process Optimization solutions:
Real-World Use Cases
ML-based parts life extension from customer-specific usage patterns
AI studies how each customer actually runs equipment and estimates whether parts can safely last longer before replacement.
AI for Optimizing Power Plant Operations
AI helps power plants run better and save money.