Role of simulation & testing in scaling-up new technologies

The views and opinions expressed in this blog are those of the author’s and do not necessarily reflect the official policy or position of Hydrocarbon Processing.

Design can be a challenging journey, starting with bench-scale reaction chemistry in a laboratory to a pilot plant, then building a demonstration facility, before commercially viable production facility can be confidently developed. The issue of scale presents many challenges: vessel wall effects at a lab scale masking other effects; challenges in uniform distribution of liquids and gases across a large-diameter vessel; potential problems of channeling within packed beds; and correctly sizing the equipment around a reactor that supports heating and cooling, condensation or evaporation. Add to this the complexity of solids handling, mechanical options for removing and adding catalyst while in operation.

Simulation of process chemistry and phase behavior is now better supported by 3D modeling of computational fluid dynamics (CFD), such as redistribution systems for packed columns. Computer-aided engineering (CAE) is being used to design better control valves, other components subject to high thermal stresses or erosion. Computer-aided manufacturing (CAM) enables new component designs using 3D printers sintering metallic or ceramic powders.

An exciting development in process design is the ability to automatically run many variations of a design case, changing just a parameter at a time, to develop an optimal design based on the desired outcome e.g. minimizing energy consumption, maximizing conversion, achieving minimal impurities. This can be applied to test data from pilot plants, and extend those results to cases not yet tested.

This is not an argument to eliminate all testing, rather to have it focus on key parameters that become important for commercial success. Tests that are done should be well instrumented with sensors that capture data that may impact scale-up, e.g. wall effects, flow distribution, temperature distribution.

The process simulation needs to demonstrate that it can accurately reproduce all the pilot plant data, before beginning case study analyses, using tools like Hierarchical Evolutionary Engineering Design System (HEEDS) with MSU’s intelligent, hybrid, adaptive design optimization SHERPA algorithm. This work can then inform not only the demonstration plant design, but also what additional data will need to be collected. Done properly, this can also shorten the amount of testing at demo stage.

What do you think of reduced testing vs increased simulation case studies? We invite your comments on this article.

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