AI-Driven Optimization System for Oil Refinery Workflow Automation

The client faces significant operational challenges due to manual management of crude oil supply, which impacts refinery throughput, cost-effectiveness, and risk mitigation. Specifically, managing multiple oil tanks, scheduling tanker discharges, and ensuring uninterrupted flow to processing units is complex, labor-intensive, and prone to errors—potentially causing costly downtimes, damage to equipment, and suboptimal product output. Additionally, unpredictability in external factors like shipment delays complicates decision-making and efficiency objectives.

A large-scale oil refinery seeking to enhance operational efficiency and profitability through AI-powered process automation and optimization.

• Develop an intelligent scheduling system to automate and optimize the flow of crude oil from tankers to storage tanks and subsequently to processing units.
• Reduce manual operational workload, lowering operator stress and minimizing human errors.
• Enhance refinery throughput and minimize downtime caused by supply interruptions.
• Increase profitability by optimizing product mix based on real-time market prices and demand forecasts.
• Train and validate reinforcement learning-based agents within a simulated environment to ensure safe deployment and continuous improvement.
• Achieve scalable, real-time operational decision support that adapts to dynamic conditions, including unpredictable event arrivals and delays.

• A simulated refinery environment representing tank layouts, pipeline topology, valves, and current operational data for testing and training.
• Static policy modules that assign tasks based on fixed rules, such as filling the most empty tank or prioritizing certain product types.
• Reinforcement learning agents trained via simulated interactions to develop adaptive, optimal decision policies for tank discharge scheduling and flow management.
• Real-time decision support interfaces for operators, with recommendations based on AI agent outputs.
• Data collection and logging capabilities to monitor agent performance, operational metrics, and market conditions during training.

• Industrial control systems (PLCs, SCADA) for real-time operational data
• Market data providers for commodity prices and demand forecasts
• Cargo scheduling and logistics systems for tracking tanker arrivals and delays
• Data storage solutions for logging and performance monitoring

• System must support real-time decision making with latency under 1 second
• Scalability to handle multiple refinery units and large data streams
• High availability and fault tolerance for critical decision support processes
• Secure data handling compliant with industry standards

By deploying the AI-driven workflow automation and scheduling system, the client is anticipated to significantly improve operational efficiency, leading to increased throughput and reduced downtime. Expected results include optimized product mix based on fluctuating market prices, decreased manual workload for operators, minimization of costly supply chain disruptions, and overall enhancement in refinery profitability. The approach is projected to simulate and train policies equivalent to decades of operational experience within hours, accelerating safe deployment and continuous improvement.