Strategies to Optimize Thermal Cracking Units with AI

One of our clients recently shared a striking figure: a delayed coker generates about $1 million in product per day. A single 10-day shutdown can erase $10–15 million in value. Avoiding even one premature outage per year pays back an AI investment many times over.

Unplanned shutdowns in thermal cracking units are notoriously costly, driven by coke buildup that chokes heat transfer and disrupts pressure balance until a forced decoke is unavoidable. Despite decades of incremental improvements, fouling remains one of the biggest barriers to consistent profitability, especially in these energy-intensive operations.

Traditional optimization tools target yield or furnace efficiency, but rarely both together. Industrial AI changes that dynamic by turning historian data into real-time control, shifting operations from reactive maintenance to predictive optimization. The outcome is longer runs, safer operations, and higher margins across cracking units.

With the stakes this high, refiners need practical ways to capture value today. The following six strategies show how AI can extend run lengths, curb energy waste, and unlock safer, more profitable thermal cracking operations.

1. Balance Furnace Passes to Prevent Early Fouling

Uneven flows or coil-outlet temperatures across furnace passes create local hotspots where coke forms fastest, forcing you to decoke sooner and sacrifice throughput. When one pass runs hotter, its tube metal temperature rises, accelerating fouling while other passes still have margin. The critical metric that suffers is days between decokes—the moment the hottest pass reaches its pressure-drop limit, the entire furnace must shut down.

AI keeps every pass marching in step. By streaming temperature, ΔP, and firing-rate data from the distributed control system (DCS), an adaptive model learns the nonlinear coupling among passes and continuously trims individual flow valves or firing registers to equalize heat duty. Traditional PID loops react pass by pass; AI models consider all passes simultaneously, reducing temperature fluctuation.

To deploy this strategy effectively, verify flow-orifice sizing and correct any mismatches, install or calibrate ΔP transmitters so the model sees true pressure balance, and connect the AI to the DCS with guard rails that respect existing safety and emissions limits.

With balanced passes, fouling progresses uniformly and predictably, adding valuable production days between mandatory decokes while protecting tube integrity.

2. Detect Coke Formation Early with Predictive Monitoring

Fouling rarely announces itself with a single alarm; it creeps in as subtle shifts in tube-metal temperature, pressure drop, or steam ratio. AI inferentials ingest these high-frequency signals and learn the faint combinations that precede coke laydown. 

In practice, the model can alert you eight to twelve hours before conventional thresholds would trigger, turning a scramble to decoke into a planned intervention. Training begins with months of historian data—ΔP trends, product composition, and lab results, paired with timestamped decoke records. 

Variable-importance rankings show which signals drive each alert, and what-if sliders let you test how a modest steam-to-hydrocarbon adjustment slows fouling progression. Combining these data-driven insights with rule-based logic delivers both high recall and transparent reasoning. Planned maintenance becomes safer, run lengths stretch, and your furnace stays online instead of on fire watch.

3. Automate Real-Time Control Adjustments

Keeping coil outlet temperature, steam ratio, pressure, and feed flow on target simultaneously creates a constant balancing act. The reaction network in ethylene production is highly nonlinear, so adjusting one pass can ripple through the entire furnace and upset product selectivity. An industrial AI model built with reinforcement learning (RL) studies months of high-frequency historian data, then learns control moves that raise margin while honoring safety and emissions constraints.

Deployment begins by streaming process tags into the model and operating it in advisory mode alongside your DCS. Once its recommendations consistently outperform manual adjustments, operators grant closed-loop authority. The model then writes setpoints back to the DCS every few seconds, always within guard rails defined by engineering standards.

The AI surfaces variable-importance rankings and what-if visualizations, so operators see exactly why each move is made. This transparency builds trust, sharpens skill sets, and resolves the classic tug-of-war between yield maximization and energy efficiency without requiring around-the-clock manual tuning.

4. Stabilize Feed & Cut-Point Transitions

Switching feed slates or tightening cut-points creates chaos in cracking units: temperatures drift, pressures swing wildly, and coke builds faster than operators can respond. AI prevents these disruptions by learning from thousands of past transitions, then adjusting coil outlet temperature, steam ratio, and residence time in real-time to keep the furnace within its operating window.

The technology tracks critical variables continuously, catching disturbances that manual control or traditional advanced process control (APC) solutions miss entirely. Field deployments show continuous AI optimization cuts temperature variation during transitions and eliminates the pressure swings that typically force emergency rate cuts.

Stability drives direct business value. Smoother cut-point moves keep product on-spec, protect run length, and let you push throughput instead of waiting to stabilize. The AI models adapt as market demands force more frequent feed changes, giving your plant the agility to respond without sacrificing furnace health.

5. Make Counter-Intuitive Moves That Reduce Fouling

Pushing furnace duty feels instinctive when you are chasing daily yield targets, yet the extra heat accelerates coke growth and shortens the run. Advanced industrial AI shows that doing a little less at the right moment can earn you much more. 

By mining years of historian, lab, and economic data, the model simulates profit versus fouling curves and spots inflection points invisible to traditional control logic. When the curve turns, a modest trim in coil outlet temperature or a temporary reduction in feed rate can delay the next decoke by several days, time worth well over a million dollars in avoided downtime and maintenance.

Examples include easing duty during transient sulfur spikes, holding steam-to-hydrocarbon ratio steady instead of chasing lab noise, or reducing early-run severity to preserve tube skin margins for later. Because the algorithm highlights the economic impact of each recommendation, operators quickly see that these suggestions are not guesswork but data-backed ways to balance today’s throughput with long-term asset health.

6. Capture & Retain Operator Knowledge with AI

Seasoned control-room operators rely on years of intuition to keep furnaces stable, yet retirements and turnover threaten to let that hard-won insight walk out the door. By pairing natural language processing with high-frequency historian data, an industrial AI model can turn daily logs, event notes, and shift comments into structured knowledge that stays with your plant.

Together, these inputs train a model that explains its recommendations through variable-importance charts and what-if visuals, so every operator can see how a suggested adjustment aligns with safety limits and economic targets. Once deployed, the model documents each successful intervention, effectively writing a living playbook that shortens onboarding time for new staff and keeps practices consistent across shifts.

Plants already using this approach report significant reductions in unplanned downtime hours, thanks to earlier, data-backed decisions that mirror veteran instincts while continuously learning from fresh data streams. The result is a virtuous cycle: operators teach the AI, the AI reinforces best practices, and your front-line operations retain their collective expertise even as personnel changes occur.

Transform Your Cracking Operations with AI

These six AI strategies collectively transform how you optimize cracking units. Together, they extend run lengths, boost safety, and heighten profitability. Implementing even a couple of these approaches can yield substantial benefits, illustrating the transformative potential of artificial intelligence in refining operations.

For those ready to harness AI optimization, a plant assessment provides an excellent starting point. Extended run times with AI interventions deliver considerable financial impact, essentially transforming operational efficiency and profitability.

AI deployment can be a phased journey, starting with the areas of highest potential impact. This flexibility ensures that refineries can align improvements with their unique operational constraints. In a market where competitiveness hinges on efficiency and adaptability, these strategies offer a significant edge.