(20h) Chemical Engineering Modeling in Life and Work

Bill Russel’s research on colloidal dispersions provided important help early in my career, as I sought to understand aggregation and coalescence phenomena of small particles and droplets. However, I drew even more inspiration from Bill as a thoughtful, wise, and kind individual. I was especially inspired that he, as a top-flight researcher, made the time to serve as chair of chemical engineering and then dean of the graduate school at Princeton. Thus, this presentation is on chemical engineering principles that I applied to administration problems during my own time as a department chair and dean, using the calm, mathematical approach that I observed in Bill.

The first example is budgeting. The University of Colorado at the time used a mix of historical and resource-based budgeting, meaning each unit received the base budget from the year before, plus an adjustment based on inflation and growth. However, the fiscal year started in July but the adjustments did not come until October, after the enrollment numbers for the fall semester were in and long after spending decisions (like allocating teaching assistants and hiring extra lecturers) were needed. To solve this conundrum, I turned to the chemical engineering concept of a mass balance to predict in May the student enrollments for the fall semester and, hence, the expected budget allocation. The predictions were almost always within 1%.

A component in the mass (student) balance is the expected student persistence rate (or what fraction of enrolled students will return the next year). This need led to a statistical study of year-to-year student retention and graduation, with correlation to various factors. For example, it was found that men and women in our college had about the same persistence rate, but women were more likely to transfer to another (non-engineering) major whereas men were more likely to leave the University of Colorado altogether.

Another resource-related issue involves faculty retirements and replacements. The standard process was to wait until a faculty member retired, search the next year, and then the new hire start would arrive a year or so later (after finishing a postdoc, for example). While the two-year delay resulted in salary savings that might be applied to startup costs, it left a gap in teaching and (much more significant) an opportunity loss for research grants and the accompanying graduate-student support. Thus, a statistical analysis was performed to provide a predictive model of faculty retirements, so that searches for new faculty could be started earlier.

Finally, if time allows, I’ll speak a little about the rise in mental-health issues among students (and others). Although more in a qualitative than quantitative sense, mental health appears to be affected by social and media changes that can be described by diffusive mass transfer and chemical reactor principles. This understanding provides some insights on how to respond to mental-health concerns.