A Statistical Intelligence (STI) Approach to Discovering Spurious Correlation in a Physical Model and Resolving the Problem With an Example of Designing a Pulse Jet Mixing System at Hanford

Pulse jet mixing tests were conducted to support the design of mixing systems for the Hanford Waste Treatment and Immobilization Plant. A physical approach (based on hydro-dynamic behavior) and two semi-empirical (SE) approaches were applied to the data to develop models for predicting two response variables (critical-suspension velocity and cloud height). Tests were conducted at three geometric scales using multiple noncohesive simulants and levels of possibly influential factors. The physical modeling approach based on hydrodynamic behavior was first attempted, but this approach can yield models with spurious correlation. To overcome this dilemma, two semi-empirical (SE) models were developed by generalizing the form of the physical model using dimensional and/or nondimensional (ND) variables. The results of applying statistical intelligence (STI) tools to resolve the spurious correlation problem via fitting the physical and SE models are presented and compared. Considering goodness-of-fit, prediction performance, spurious correlation, and the need to extrapolate, the SE models based on ND variables are recommended.