How Predictive Control is changing the manufacturing landscape

Oil refining is a complex, multi-step process that demands precision, efficiency, and adaptability to fluctuating market conditions. Predictive control, particularly Model Predictive Control (MPC), is revolutionizing refining operations by optimizing production processes, reducing energy consumption, and enhancing product quality. By leveraging real-time data, advanced algorithms, and predictive analytics, refiners are achieving operational excellence and meeting environmental standards.

The crude distillation unit (CDU) is the heart of any refinery, where crude oil is separated into various fractions such as naphtha, kerosene, diesel, and heavier products. Predictive control systems analyze real-time feedstock variability, temperature, pressure, and flow rates to optimize the separation process.

Benefits:

• Improved product yield by maintaining optimal cut points.
• Enhanced energy efficiency by minimizing fuel use in fired heaters.
• Reduced operational costs by anticipating changes in crude quality and adjusting parameters dynamically.

Example: Predictive control can adjust reflux ratios and column pressures to ensure maximum recovery of valuable light hydrocarbons while minimizing energy losses.

Fluid Catalytic Cracking Units (FCCUs) and hydrocrackers are critical for converting heavier crude fractions into lighter, high-value products like gasoline, diesel, and jet fuel. These processes involve highly dynamic reactions influenced by temperature, pressure, and catalyst activity. Predictive control models optimize these variables to maximize yields and minimize coke formation or catalyst deactivation.

Key Applications:

• Predictive control fine-tunes reactor temperature profiles to enhance conversion rates.
• Real-time catalyst optimization ensures consistent product quality and extends catalyst lifespan.
• Energy consumption is reduced by optimizing steam and feed preheating processes.

Impact: A refinery can increase gasoline yield by several percentage points while reducing operational costs and improving catalyst efficiency.

Refineries are among the most energy-intensive industrial facilities. Predictive control optimizes energy consumption by managing heating, cooling, and energy recovery systems in real-time.

Applications:

• Optimizing furnace operations to maintain efficient heating without overfiring.
• Managing heat exchangers to minimize fouling and maximize heat recovery.
• Predicting steam demands and adjusting boiler performance dynamically.

Example: An MPC system can predict fluctuations in energy demand across the refinery and optimize steam production and distribution, leading to significant cost savings and reduced emissions.

Blending is the final step in refining, where different components are combined to produce final products like gasoline, diesel, and jet fuel that meet market specifications. Predictive control ensures products meet stringent quality standards while minimizing giveaway (producing higher-quality fuel than required).

Benefits:

• Accurate control over octane, cetane, sulfur content, and volatility.
• Reduced reliance on expensive additives by optimizing blending recipes.
• Faster response to changes in demand or product specifications.

Example: An MPC system dynamically adjusts the blend of reformate, alkylate, and FCC gasoline to produce high-octane fuels while minimizing the use of costly additives.

Refineries rely on critical equipment such as compressors, pumps, heat exchangers, and furnaces. Predictive control systems integrate with predictive maintenance strategies to monitor equipment health and forecast failures before they occur.

Applications:

• Monitoring vibration, pressure, and temperature in rotating machinery to detect wear and potential breakdowns.
• Predicting fouling in heat exchangers and scheduling cleaning to maintain efficiency.
• Forecasting furnace tube coking to prevent unplanned shutdowns.

Example: A refinery using predictive control and IoT sensors reduced furnace downtime by 30% by predicting tube fouling and scheduling cleaning at optimal intervals.

Crude oil quality can vary significantly based on its source, impacting refining processes. Predictive control systems dynamically adapt to feedstock variability, ensuring that processes like distillation, cracking, and blending remain efficient.

How It Works:

• Real-time analysis of crude assay data to predict yield changes.
• Adjusting process conditions to account for heavy or light crude inputs.
• Optimizing desalter performance to manage contaminants such as salt and water content.

Impact: Refiners can process lower-quality crudes profitably while maintaining product quality and throughput.

Refineries face strict environmental regulations regarding emissions, wastewater, and fuel quality. Predictive control helps operators meet these standards while maintaining efficiency.

Applications:

• Optimizing flare gas recovery systems to minimize flaring and emissions.
• Managing sulfur recovery units (SRUs) to maintain efficiency and compliance with SO₂ limits.
• Controlling nitrogen oxide (NOx) emissions in furnaces and boilers through real-time adjustments.

Example: Predictive control in an SRU ensures efficient sulfur extraction while minimizing energy use and reducing SO₂ emissions.

Predictive control integrates seamlessly with Real-Time Optimization (RTO) systems, which evaluate refinery-wide processes to maximize profitability. RTO uses economic models alongside predictive control algorithms to make decisions in real time.

Benefits:

• Maximized refinery margin by aligning operations with market pricing for crude and refined products.
• Rapid adjustments to production schedules in response to external changes, such as fuel demand fluctuations or price volatility.
• Optimal allocation of resources (e.g., energy, feedstocks, and additives) across the refinery.

1. AI and Machine Learning Integration: Predictive control systems are leveraging AI and ML to enhance their accuracy, allowing refiners to adapt more effectively to complex and non-linear process variations.
2. Digital Twins for Refinery Systems: Digital twins, virtual replicas of refinery units, use predictive control to simulate and optimize processes before implementation.
3. Sustainability and Net-Zero Goals: Predictive control plays a critical role in reducing emissions and energy use, helping refiners meet decarbonization targets.
4. Edge Computing for Faster Control: Deploying predictive control at the edge enables real-time decision-making, improving responsiveness in dynamic refining environments.

Predictive control is transforming oil refining into a smarter, more efficient, and environmentally sustainable industry. By optimizing processes, improving energy management, and ensuring compliance with regulatory standards, predictive control empowers refineries to maximize profitability while reducing their environmental footprint. As the technology continues to evolve, predictive control will remain at the core of modern, resilient, and future-ready refining operations.