Modern refineries operate in an increasingly complex environment where volatile feedstock prices, tightening environmental regulations, and aging infrastructure create unprecedented operational constraints.
With gas prices projected to increase and refineries facing mandatory toxic air pollutant reductions, traditional optimization approaches fall short of addressing multiple operational variables simultaneously while maintaining safety and profitability.
AI optimization emerges as the key solution for managing these interconnected constraints. Rather than treating each operational constraint in isolation, AI optimization solutions can optimize across multiple units simultaneously, delivering measurable improvements in yield, energy efficiency, and environmental compliance.
The following five critical constraints represent the most significant optimization opportunities where AI optimization delivers proven, quantifiable impact in operating refineries today.
1. Balancing Yield and Energy Consumption in Distillation Columns
Distillation columns consume approximately 20% of total refinery energy at 82 TBtu per barrel of product, making them the largest energy consumers in petrochemical operations. The fundamental constraint centers on optimizing reflux ratios to balance product purity against energy consumption while maximizing overall operational efficiency.
Traditional static control strategies cannot capture the continuous optimization potential as feed composition, product prices, and energy costs fluctuate throughout each day.
AI optimization technology learns the complex nonlinear relationships between reflux ratios, reboiler duties, feed temperatures, and pump-around flow rates, making real-time adjustments to maximize profitability while reducing energy intensity.
2. Managing Feed Variability Without Disrupting Operations
Changing crude slates and opportunity crudes create constant operational disruption in refineries designed for specific feedstock properties. Heavy crude blends trading at significant discounts offer substantial margin improvement opportunities, but feedstock transitions can dramatically increase catalyst deactivation rates and require extended transition periods using traditional manual operations.
The cascade effects impact every downstream unit: catalyst performance changes, product quality variations, and equipment fouling rates all shift with feed composition changes. Without proactive optimization, refineries face unplanned outages that can cost millions per day when transitions go wrong. For mid-size refineries, reliability-related production losses from aging infrastructure can reach substantial amounts annually, with unplanned outages potentially increasing margins per barrel during disruption events due to market tightness.
AI optimization predicts the impact of feed changes across all interconnected units, providing proactive adjustments to temperature profiles, catalyst circulation rates, and separation parameters that maintain stable operations.
This predictive capability allows refineries to capture significant value through distillate system optimization without the typical operational penalties, with documented improvements of $0.25/bbl while reducing transition times compared to conventional approaches.
3. Preventing Unplanned Downtime Through Equipment Health Monitoring
Equipment failures create significant downtime in refineries, resulting in substantial production losses. According to Deloitte, poor maintenance strategies can reduce an asset’s overall productive capacity by 5% to 20%. Critical equipment, including pumps, compressors, heat exchangers, and furnaces, experiences gradual degradation that traditional monitoring systems cannot detect until failure is imminent.
Key equipment failure patterns create different operational constraints:
- Centrifugal pumps present the highest failure frequency in refinery operations
- Fired heater tube failures create the longest outages, significantly impacting production
- Heat exchanger fouling causes chronic capacity reductions, forcing costly emergency cleaning cycles
These critical failure modes require proactive monitoring to minimize production impact.
AI optimization technology analyzes patterns in temperature, pressure, vibration, and efficiency data to identify early warning signs weeks before traditional monitoring flags issues. Advanced machine learning algorithms track equipment degradation curves and predict optimal maintenance timing, enabling refineries to shift from reactive firefighting to strategic maintenance planning that minimizes production impact while achieving mechanical availability.
4. Optimizing Complex Reaction Conditions in Real Time
Maintaining optimal conditions in reactors and crackers requires managing multiple variables that interact nonlinearly as catalyst activity declines, coking rates increase, and feedstock properties vary. Traditional static control strategies become suboptimal when catalyst deactivation requires systematic temperature compensation in diesel hydrotreaters, while reactor selectivity shifts throughout the catalyst run length.
The constraint intensifies with feedstock variations that can accelerate catalyst deactivation dramatically. Switching to heavier feedstocks or deasphalted oils can increase deactivation rates, fundamentally changing optimal operating conditions and requiring immediate adjustments to temperature profiles, residence times, and hydrogen-to-oil ratios.
AI optimization technology continuously learns the evolving relationship between operating conditions, catalyst age, feedstock properties, and product yields. The system makes micro-adjustments to reactor temperature profiles, catalyst circulation rates, and feed distribution every few minutes to maintain peak performance even as catalyst ages and feedstock varies.
This real-time optimization enables refineries to achieve profit margin improvements while extending catalyst run lengths and improving overall unit reliability through reduced process oscillations.
5. Meeting Environmental Targets While Protecting Margins
Refineries face intensifying pressure to reduce emissions, minimize flaring, and improve energy efficiency while maintaining profitability under tightening regulatory requirements. With petrochemical feedstocks accounting for 70% of the total volumetric increase in oil use, these facilities represent significant energy consumption in the industrial sector.
Environmental regulations continue to evolve, creating both compliance challenges and optimization opportunities for process industry leaders. If scaled up, digital technologies could reduce emissions by 20% by 2050 in the three highest-emitting sectors: energy, materials, and mobility. This significant potential underscores the importance of implementing advanced optimization solutions in refineries and petrochemical operations.
Traditional approaches treat environmental compliance as operational constraints rather than optimization opportunities, often sacrificing efficiency to meet regulatory limits:
- Manual coordination between fuel gas systems creates a suboptimal fuel balance across units
- Fired heater operations run with excess air ratios to ensure compliance margins
- Flare management relies on conservative approaches that increase emissions during normal operations
These approaches create conflicts where emissions targets compete directly with production objectives rather than finding synergistic solutions.
AI optimization solutions find operating strategies that simultaneously reduce emissions and improve economics by optimizing fuel gas balance across multiple units, minimizing excess air in furnaces, and coordinating turnaround planning to reduce overall flaring requirements.
The system balances steam generation, hydrogen production, and fuel gas consumption in real-time while maintaining production targets. Documented results show 15-30% natural gas consumption reductions and energy efficiency gains while supporting regulatory compliance objectives without compromising refinery margins.
How Imubit’s Closed Loop AI Optimization Tackles Refinery Complexity
Imubit’s Closed Loop AI Optimization solution addresses these five interconnected constraints through deep reinforcement learning (RL) that understands each refinery’s unique configuration and operational constraints.
The platform uses historical plant data and engineering expertise to create a unified optimization system that writes new setpoints to control system platforms in real-time, enabling continuous optimization across multiple units simultaneously.
Operating at many major U.S. refiners, the solution delivers significant, documented improvements in margins, yields, and energy efficiency that compound over time. Process industry leaders using Imubit’s technology report substantial annual value generation from various applications, including reformer optimization, debutanizer throughput increases, and liquid volume yield improvements.
The solution integrates seamlessly, operating independently or alongside current advanced process control (APC) systems without requiring capital-intensive equipment additions. Kickstart your AI journey at no cost with a complimentary, expert-led assessment of your refinery’s optimization potential and a clear path to value.
