AI Optimization Tips to Reduce Downtime in a Coker Unit

Unplanned outages in a delayed coker can trigger a domino effect, leading to millions in losses—part of a broader industry challenge. The world’s 500 largest companies now lose 11% of their annual revenue due to unplanned downtime, totaling around $1.4 trillion. These costly disruptions often arise from various factors, including equipment failure and maintenance delays, with significant losses stemming from shutdowns.

An insight shared by a customer highlights that a delayed coker produces approximately $1 million worth of product per day. A 10-day unplanned shutdown can result in $10–15 million in lost value.

While uneven tube fouling can contribute to operational challenges, the primary causes of these disruptions are more complex, involving a range of equipment failures and operational inefficiencies. However, advanced predictive analytics and industrial AI are changing the game. The following AI-powered strategies show how refineries can balance tube flows, predict fouling patterns, and automate corrective actions—all before a shutdown becomes inevitable.

1. Balance Furnace-Tube Flows With AI

Uneven fouling in a single radiant-coil tube can trigger an unplanned decoke that idles the entire coker for weeks and burns millions in lost margin. AI-driven optimization tackles the problem by continuously redistributing heater duty so every coil ages at the same pace, removing the weak-link tube from the equation. Closed Loop AI Optimization learns from thousands of operating scenarios and writes new setpoints back to the distributed control system (DCS) in real time.

Furnace run length can be significantly extended, with more moderate improvements in delayed-coker throughput and maintenance intervals; specific percentages vary by application. Fewer hotspots not only boost production but also help avoid the prolonged emergency outages that once felt inevitable.

Before you go live, validate tube-level temperature, pressure, and flow signals. The algorithms need clean data to spot the subtle patterns that signal early fouling.

2. Replace Reactive Fixes With AI-Guided Adjustments

Traditional control approaches depend on operators spotting temperature spikes or pressure-drop alarms, then dialing back firing rates or adding steam. Those moves happen after the deviation appears, so they often overcorrect and push the unit away from optimum conditions, setting up the next cycle of instability.

AI-guided optimization reverses this sequence: models trained on historical operating data recognize the subtle patterns that precede runaway fouling and propose corrective moves minutes—sometimes hours—before you’d normally react. Instead of tweaking a single handle, the solution simultaneously balances feed rate, coil-outlet temperature, and steam, keeping every variable within its constraint window.

Sites deploying Closed Loop AI Optimization can see millions in annual benefits from avoided emergency decokes and smoother shift-to-shift operations. Because the platform learns from each outcome, move proposals sharpen over time. 

Embedding those proposals into your existing procedures and workflows ensures every recommendation is reviewed, logged, and auditable, turning reactive firefighting into a disciplined, predictive practice.

3. Predict Fouling Before It Becomes Critical

Rather than waiting for problems to emerge, predictive models help identify issues before they impact operations. Soft-sensor models detect early signs of coke formation hours or even days before they would show up on routine trend screens. These models learn the furnace’s normal patterns, flagging deviations early enough to allow maintenance teams to act proactively, on their terms rather than reacting to the unit’s needs.

To achieve this level of foresight, the models rely on key inputs such as temperature data, pressure readings, flow rates, and historical performance data. By combining these signals, the model can create a clearer picture of tube health and help predict potential fouling risks.

When these signals are fed into a trained algorithm, the model functions like a digital twin of tube health, converting raw sensor data into a single “coke risk” score that can be tracked from shift to shift.

Before deploying the model, it’s helpful to check a few things: Ensure critical transmitters are calibrated and mapped, compare forecasts with the upcoming turnaround plan to confirm manpower and parts are aligned, and set alarm thresholds that are sensitive enough to act early but not so low that they cause unnecessary alerts.

4. Automate Real-Time Optimization 24/7

Building on predictive insights, the next level involves continuous automated adjustment of operating parameters. Closed-loop systems keep your coker furnace on target even when feed quality, ambient temperature, or equipment health shift unexpectedly. By learning plant-specific behavior from historian data and live sensor feeds, the model adapts minute by minute, automatically steering the unit back toward its economic sweet spot.

Reinforcement learning looks across the entire residue chain instead of chasing single tags. It simultaneously manipulates key handles to prevent runaway fouling and energy waste: steam to manage velocity and residence time, recycle to fine-tune thermal severity, heater duty to balance coil-outlet temperature, and quench timing to control downstream stability.

Every few minutes, the platform reads fresh process data, calculates optimal setpoints, and writes them back to the distributed control system (DCS). Operators can open a move log that explains each adjustment, turning the so-called black box into a practical coaching tool that raises team expertise.

Pair the controller with a live economic dashboard so reliability teams see dollar gains accumulate in real-time; an instant feedback loop that builds confidence and accelerates further optimization.

5. Stabilize Connected Units Across the Residue Chain

The next step in optimization goes beyond individual units to manage entire process chains. By treating the vacuum heater, visbreaker, and delayed coker as an interconnected system rather than separate units, industrial AI can coordinate thousands of variables together in real-time.

A closed-loop controller powered by reinforcement learning takes in live data from each unit, learns their cause-and-effect relationships, and continuously adjusts setpoints in the DCS to keep the whole residue chain on track, even as feed quality fluctuates or equipment ages.

For example, suppose there’s a surge in visbreaker outlet temperature that could over-pressurize the coker drums. In that case, the system responds immediately by adjusting the coker heater duty, while also tweaking downstream recycle and steam ratios. This helps keep pressure, yields, and metallurgy within safe limits. These coordinated adjustments routinely add margin per barrel and reduce flare episodes and energy waste.

Traditional loop-by-loop control systems can’t anticipate these cross-unit effects, often leading operators to use large safety margins that reduce profitability. To get started with your own rollout, begin by mapping historian tags across every residue-handling unit. A shared data foundation will help the multi-unit model learn faster and unlock more value across the entire plant.

Transform Reactive Maintenance into AI-Powered Optimization

The evolution from reactive maintenance to predictive optimization represents a fundamental shift in how refineries approach coker operations. These five strategies demonstrate how intelligent systems transform costly disruptions into manageable, planned events. The financial case for implementation becomes clear when examining the substantial annual savings and throughput improvements documented across multiple refineries.

For those exploring closed-loop optimization, the journey starts not with expensive equipment upgrades, but with leveraging existing process data through more sophisticated analytical tools. The system learns your plant’s unique operating patterns, creating a customized solution that gets sharper with every control move and every avoided upset.

Ready to see how these strategies could transform your coker operations? Get a complimentary, expert-led assessment of your unit’s optimization potential. This no-cost session includes a review of your specific constraints and goals, benchmarking against successful applications, and identification of high-impact opportunities unique to your operations.