Driving digital transformation in refineries

Heavy industries, including refineries, are traditionally slow in adopting digital transformation due to their scale, thin margins and industry specificity. However, recent supply chain disruptions from the pandemic and geopolitical events have accelerated digital investments to enhance asset utilization and plant longevity. Real-time analytics and artificial intelligence (AI)-driven decision-making are now key enablers of operations efficiency, as Rohit Khanna explores in this article.

The digital refinery: A game changer

The concept of the "digital refinery" emphasizes the integration of advanced technologies like AI to achieve unparalleled efficiency, safety and sustainability. Digital refineries integrate AI to optimize maintenance, enhance supply chains, ensure quality control and improve compliance. By adopting these technologies, refineries are future-proofing operations while setting new standards for efficiency, safety and sustainability.

The tech foundation of AI: Industry 4.0 meets 5.0

Before delving into AI applications, key Industry 4.0 technologies in refineries include:

  • IT-OT convergence: Sensors, SCADA systems, cloud computing and edge processing enable real-time monitoring and decision-making.

  • Digitalization of paper: ERP systems and digital applications have replaced paper-based workflows, improving efficiency and compliance.
  • Digital twins: Virtual refinery models allow for simulations, predictive analysis and streamlined troubleshooting.
  • Robotics: Autonomous robots and robotic dogs enhance hazardous inspections, maintenance and operations productivity.

Industry 5.0 adds:

  • Human and machine collaboration: With collaborative robots (Cobots, agentic AI-powered Digital Triplets), to leverage human creativity and problem-solving skills alongside machine efficiency, with the use of advanced tools such as exoskeletons, augmented reality and sophisticated multi-agent ecosystems.
  • Personalization: Enabling highly customized and on-demand manufacturing, where products are tailored to individual customer preferences through human-machine collaboration.

The power of operations data

AI thrives on high-quality data. Key refinery data sources include:

  • Process data (temperature, pressure, flow rates)
  • Equipment health data (vibration, sensor readings)
  • Feedstock and product quality data
  • Operations records (energy use, production volumes)
  • Maintenance logs and environmental monitoring (prices, demand forecasts)

Time-series data, particularly process data, is critical for AI-driven insights, enabling failure prediction and operations optimization. Poor data quality can lead to costly errors, making data management a priority.

AI in refinery operations: Reducing downtime

Minimizing downtime is a top business priority, as a two-day refinery shutdown can result in lost revenue. AI-driven predictive maintenance and IoT sensors can preempt failures, enabling proactive interventions and avoiding significant financial losses.

Example business case:

  • Throughput: 100K barrels/day
  • Shutdown duration: 2 days
  • Production loss: $15M
  • AI investment (IoT + Analytics): $1.5M
  • Savings from AI-driven early intervention: $7.5M–$12.5M

AI use cases in refinery maintenance

Predictive maintenance

AI-powered predictive Digital Twin maintenance detects anomalies, preventing failures in critical equipment like compressors, turbines, motors and pumps. Machine learning models refine failure predictions, optimizing maintenance schedules. Further, the addition of Digital Triplets, will provide cross process business KPI driven views to improve overall performance.

Real-time operations monitoring

AI Digital Twins enable real-time monitoring to:

  • Detect anomalies early, minimizing unexpected failures
  • Optimize performance by dynamically adjusting operating parameters
  • Reduce false positives, cutting unnecessary maintenance costs

The future of AI in refineries

AI-driven transformation is unlocking new efficiencies, reducing costs and improving reliability in refining operations. The next part of this series will explore additional AI-driven use cases shaping the future of the industry.