Today’s refinery optimization processes are highly manual and dependent on skilled staff with a strong knowledge of refinery processes and the relevant optimization technology. These staff are increasingly rare. Other challenges of refinery optimization today include:

• Linear models are often used and have a limited range of validity

• Maintaining the process simulation, production planning and production scheduling models is time-consuming and requires significant expertise

• The data reconciliation process is largely heuristic and simplistic

• Data is in silos and difficult to collect and manage

• The key constraints are not adequately identified and challenged

• Advanced control and real-time optimization strategies are not updated with plan and schedule changes

• Schedulers focus on finding a feasible solution with limited time to minimize the deviation to the plan or optimize the schedule

• The integration between the various applications is limited

In the future planning and scheduling optimization models will invoke machine learning and/or cognitive computing functions, using live data, to deliver a step change in refining optimization. The benefits of this include:

• Automated data driven optimization using AI

• Intelligent, automated work processes

• Automated data management and application integration

• Cloud deployment to facilitate collaboration, scalability and rapid development and delivery of enhancements

• Automated surfacing of optimization opportunities

• Automated backcasting to close the gap between the plan, schedule, actual and optimum

• Automated model updates to create a self-learning digital twin

• Automated and optimized production scheduling

• Multi-unit dynamic optimization

Similar opportunities exist in the optimization of oil and gas production and petrochemicals production. This technology has the potential to contribute greatly to the human resource and sustainability challenges facing the industry.