40 YEARS OF PROCESS SIMULATION WITH A FOCUS ON ENERGY AND CARBON

Computer-based process simulation provides a toolkit to help make informed decisions and has evolved over the past half century to become an integral part of refinery technical management. The mass and energy balances from a model give access to a level of detail that cannot be measured in an operating plant, and the ease of modification allows for the exploration of countless scenarios. While steady-state modelling will always remain the workhorse for plant simulation, advancements in software capability and hardware resources have reached the point where detailed and comprehensive dynamic simulation is possible, e.g. high-fidelity dynamic simulation of the entire white side with full particle size distribution.
One of the ultimate goals of process simulation is a true Digital Twin, with bidirectional communication between plant and model. Historically, data exchange was via reports (e.g. Excel) to a human who then acted on that information. Software systems can now automatically communicate with models for various plant management tasks. It is also possible to integrate third-party software and model libraries into a simulation package – allowing high-accuracy chemistry, fluid mechanics, and properties packages to be used directly within a full plant simulation. API developments allow systems integration with other languages (e.g. Python), which opens access to a wide range of optimisation, reporting, graphics, and other routines that can be used seamlessly within or alongside a process model.
Modelling has always been based on solving a set of equations describing physical processes… until now. With the introduction of artificial intelligence (AI) and machine learning (ML) it is increasingly possible to model aspects of a process without a full knowledge of the physics. In applications with complicated processes, AI/ML can be used to give some useful predictive capability where traditional techniques fall short. This paper reviews the historical development of process simulation tools and explores future directions for software, hardware, integration APIs, dynamic applications, Digital Twins, and AI/ML, and how that ever-growing power and insight can be used to optimise energy utilisation and minimise carbon emissions into the future.