Garbage In, Garbage Out: Why Early-Stage Numbers Fail (And What to Do About It)

Bad assumptions in early-stage techno-economic analysis don't just produce wrong numbers. They produce wrong decisions. And those decisions compound through FEL gates until the cost of correction is hundreds of thousands of dollars — or a shelved project.

In this webinar, Crystal Bleecher, P.E. (SVP Process Engineering, Roebling) and Frederick Twigg (UC Berkeley) unpack how it happens: how assumptions go untracked, how single-scenario models create false confidence, and what it actually means to layer AI on top of engineering analysis responsibly.

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Видео Garbage In, Garbage Out: Why Early-Stage Numbers Fail (And What to Do About It) канала Roebling