Historically, chemical processes have been developed using iterative experimentation: discover a new reaction, (2) fit it into a laboratory process including separations/purifications and usually several other reactions to accomplish the overall transformation of interest, (3) then conduct several iterations of scale-up and optimization of reaction conditions, culminating in a pilot plant campaign. A kinetic model is fitted to the data measured in the pilot plant, and that model is used to make the predictions needed to design the full industrial scale unit needed to commercialize the process, Fig. . The full-scale unit is then modestly tweaked to fine-tune its performance. This conventional procedure works, but has several drawbacks. First, it is expensive: the pilot plant experiments typically cost several million dollars, and this high cost significantly discourages innovations that involve introducing new chemical reactions. Since the pilot plant experiments are expensive, only a limited number are conducted, and so the final process model can only involve a small number of adjustable parameters tuned to match the limited data. Typically this is done by using a reduced simplistic model that only contains the major species, rather than a realistic kinetic model that includes the reactive intermediates. Also, this procedure locks in on particular process design and reaction conditions, since it is usually not practical to consider a wide range of possible processes or reaction conditions experimentally. So in the end, the process that is modeled and built to full industrial scale is typically a local optimum in the limited region of process space tested, not the best possible process. And the simplistic model we have for that process, while usually accurate for interpolation between the reaction conditions of the pilot plan experiments, is usually unreliable in extrapolation. Often the simplistic model cannot represent minor byproducts, or any of several phenomena that can cause upsets in a large reactor.