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Advanced features in easier-to-use formats allow chemical engineers to take advantage of process modeling solutions for organization-wide optimization
Modeling and simulation are at the heart of chemical engineering design and operations today, whether it is using traditional process flowsheeting tools, computational fluid dynamics, advanced process-modeling environments, or specific equipment design packages (Figure 1). However, much of this modeling is currently performed in isolated "silos" within organizations.
"In oil production, for example, the reservoir is typically modeled using one simulation environment, the subsea production network using another, and the topside facilities, a third," says Mark Matzopoulos, marketing director with Process Systems Enterprise (PSE) Ltd. (London, England; www. psenterprise.com). "Operations like this could benefit significantly from an integrated modeling approach that would open up the possibility for largescale optimization" (Figure 2).
Matzopoulos adds that since much of today's modeling is in the form of traditional process steady-state flowsheet simulation, which calculates heatand-material-balance information and stream properties, and "optimization" tends to be by trial-and-error analysis, chemical process organizations could benefit significantly by taking advantage of more sophisticated advanced process-modeling techniques.
"The [other] chemical process industries [CPI] would do well to learn from the food and pharmaceutical industries where having an accurate predictive model of a plant or process allows chemical engineers and scientists to rapidly explore the decision space from laboratory to operating plant," he says.
Why? Because this expanded view enables chemical engineers to quickly rank and screen design alternatives, come up with optimized process designs, confidently scale up to industrial size, and perform plant-wide process optimization, to list just some of the benefits.
For new chemical processes, it is now possible to deploy modeling systematically across the process lifecycle, from R&D and conceptual design, through detailed design, and on to online operation. This can be done in a way that not only leverages knowledge contributed from different parts of the organization, but also provides a vehicle for knowledge transfer between groups or "silos."
Matzopoulos shares an example: "Early-stage experimental data can be used with a model of the experimental setup to estimate kinetic parameters and build a definitive reaction set. This forms the heart of a reactor model used by reaction engineers for ranking different reactor configurations. The model of the chosen reactor configuration is...