By unifying planning/scheduling and APC and coordinating the controllers’ objectives, plantwide optimisation provides additional benefits beyond APC. AI/ML techniques can be used to simplify plantwide optimisation tools that are complex to operate and strenuous to maintain, but these algorithms need a large amount of data and have to be developed, guided, and monitored by engineers with rigorous knowledge of the process and the operation.

In addition, in an increasingly connected global market context, refining and petrochemical schemes are more and more complex and integrated, complicating plantwide optimisation.

As a result, this is not only a matter of data science but also of process expertise, as it is of real importance to understand the interactions between the different processing units across the plant.

Regarding tools and techniques, hybrid modelling, meaning a combination of historical operating data and first-principles models, must be taken into consideration for developing such solutions. It is worth mentioning that some mandatory project phases must be respected.

• The first milestone is to express the objectives of the optimisation solution clearly;
• The second milestone is to accordingly define the strategy to achieve these objectives and make available all needed resources.

Consider that agility is also key to redefining objectives or resizing resources as necessary. Agility is also a way to extend the scope of the solution over time by starting plantwide optimisation, for instance, of the utilities and hydrogen network, then including pools management, and so on.

In conclusion, data availability and monitoring, professional expertise (process, operation, control, data science), efficient project management, and resources (on the one hand, people and, on the other hand, the tools and techniques) are necessary for the successful implementation of plantwide optimisation using AI/ML.