(145b) Challenges in Applying Data Analysis to Industrial Processing: Inverse Problem Viewpoint
AIChE Spring Meeting and Global Congress on Process Safety
Wednesday, March 29, 2017 - 8:30am to 9:00am
The underlying physics can provide insights to data analysis and is probably necessary to solve the âInverse Problemâ. For example, the physics of a fixed bed reactor will produce relationships about the flows, pressure drop, temperature, heat generated (absorb), and stream composition changes. Applying these physics in an industrial setting will require numerous assumptions about the process. There may be competing assumptions that need to be considered. For the reactor example, these assumptions will be capture in parameters that designate the flow resistance and reaction kinetics. The competing assumptions will generate different model structures and possibly different types of parameters. The analysis of the production data will drive refinements of these parameters.
This presentation examines the challenges and tools for analyzing production data and for determining the appropriate physic driven models that can capture the behavior of the production units. This will include approaches for doing time series analysis, uncertainty quantification, principle component analysis, machine learning, objective function determination and data structure analysis. In particular, we will look at how these tools can provide different insights into the underlying production behavior.