Operations Laboratory Module on Heat Exchangers

Courtney Pfluger; Dayna Lee Martínez

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Conference: 2020 ASEE Virtual Annual Conference Content Access
Location: Virtual On line
Publication Date: June 22, 2020
Start Date: June 22, 2020
End Date: June 26, 2021
Conference Session: Experimentation and Laboratory-oriented Studies Division Technical Session 5
Tagged Division: Experimentation and Laboratory-Oriented Studies
Page Count: 8
DOI: 10.18260/1-2--35012
Permanent URL: https://strategy.asee.org/35012
Download Count: 469

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Paper Authors

biography

Courtney Pfluger Northeastern University

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In the fall of 2011, Dr. Pfluger took a position as an Assistant Teaching Professor at Northeastern University in the College of Engineering as a part of the First Year Engineering Faculty with a focus on chemical engineering. In the summer of 2013, she developed and ran a faculty led Dialogue of Civilizations program to Brazil where she taught two courses that focused on Sustainable Energy Technologies and Brazilian Culture. This program has successful ran for 7 consecutive years gaining popularity among a variety of engineering students. She was instrumental in the development of the curriculum redesign of the Cornerstones of Engineering for the first-year program in 2014. In the fall of 2017, she moved into teaching full time in the Chemical Engineering department where she has implemented improvements in the Transport 2 Lab and Capstone 2 courses. Dr. Pfluger has also spent her time volunteering as Faculty Advisor for the American Institute of Chemical Engineers (AIChE) and ChemE Car student groups on campus. In 2018, the AIChE student group won Outstanding Student chapter for its numerous activities within the Chemical Engineering community both on campus, regionally, and nationally.

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Dayna Lee Martínez Northeastern University

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Abstract

As engineers, it is important to have statistics as part of the tools we use to solve problems of interest to society considering that statistics support the creative process by collecting, analyzing and using data to make decisions, solve problems, and design processes and products. In addition, employers in chemical engineering fields ask for students to understand statistical tools prevalent in industry, however statistics is not a required course in Chemical Engineering (ChemE) curriculum at this large, private R1 university. Therefore, there is a need for students to learn and implement statistical tools into Chemical Engineering courses which are currently being taught in the Industrial Engineering (IE) curriculum. This led to the interdisciplinary course module to implement IE statistical tools into a ChemE Unit Operations (UO) laboratory course at a large, private, R1 university. Specifically, students applied hands-on and experiential learning to implement and analyze data using the statistical method Design of Experiments (DOE) to a ChemE UO laboratory module on heat exchangers.

One section (N=19) of ChemE UO course were surveyed before the heat exchanger module to find out their previous knowledge of statistical tools. It was found that 71% of the class had never run a DOE before and 100% of the students surveyed had not performed a DOE statistical analysis in an engineering course. It was found that 29% (~5 students) had performed a DOE on coop in industry. Proving once more that there is a need to teach these statistical tools in the ChemE curriculum. It was also found that only 40% said they believed the statistical tools would provide a more detailed analysis for heat exchanger conditions compared to simple heat transfer calculations in excel. This indicates that prior to learning and implementing these IE statistical concepts, ChemE students do not understand the benefits of IE statistical tools.

The students determined 3 variables to vary in their respective heat exchanger experiments to achieve their target temperature output as part of a real-world themed problem statement. They ran a full factorial 2^3 experiment with 3 replicates and then analyzed the experiment using Minitab, were they quantified main and interaction effects between variables and interpreted those results in the context of heat transfer theory.

After performing the DOE and statistical analysis, the ChemE students found that the use of the IE statistical tools helped them determine variable interactions that they would not have discovered without the knowledge of these applications. The students commented that the DOE helped them understand impacts of heat transfer on certain variable conditions they did not expect. This interdisciplinary course module of applying IE statistical tools to a ChemE UO course demonstrated enhanced student understanding of heat exchanger experimental variables effect on heat transfer and how that data compares to theory. The understanding and knowledge of these IE statistical tools prepares ChemE students for their future careers in industry and research.

Citation
Format

Pfluger, C., & Martínez, D. L. (2020, June), Operations Laboratory Module on Heat Exchangers Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35012

TY - CPAPER
AB - As engineers, it is important to have statistics as part of the tools we use to solve problems of interest to society considering that statistics support the creative process by collecting, analyzing and using data to make decisions, solve problems, and design processes and products. In addition, employers in chemical engineering fields ask for students to understand statistical tools prevalent in industry, however statistics is not a required course in Chemical Engineering (ChemE) curriculum at this large, private R1 university. Therefore, there is a need for students to learn and implement statistical tools into Chemical Engineering courses which are currently being taught in the Industrial Engineering (IE) curriculum. This led to the interdisciplinary course module to implement IE statistical tools into a ChemE Unit Operations (UO) laboratory course at a large, private, R1 university. Specifically, students applied hands-on and experiential learning to implement and analyze data using the statistical method Design of Experiments (DOE) to a ChemE UO laboratory module on heat exchangers.

One section (N=19) of ChemE UO course were surveyed before the heat exchanger module to find out their previous knowledge of statistical tools. It was found that 71% of the class had never run a DOE before and 100% of the students surveyed had not performed a DOE statistical analysis in an engineering course. It was found that 29% (~5 students) had performed a DOE on coop in industry. Proving once more that there is a need to teach these statistical tools in the ChemE curriculum. It was also found that only 40% said they believed the statistical tools would provide a more detailed analysis for heat exchanger conditions compared to simple heat transfer calculations in excel. This indicates that prior to learning and implementing these IE statistical concepts, ChemE students do not understand the benefits of IE statistical tools.

The students determined 3 variables to vary in their respective heat exchanger experiments to achieve their target temperature output as part of a real-world themed problem statement. They ran a full factorial 2^3 experiment with 3 replicates and then analyzed the experiment using Minitab, were they quantified main and interaction effects between variables and interpreted those results in the context of heat transfer theory.

After performing the DOE and statistical analysis, the ChemE students found that the use of the IE statistical tools helped them determine variable interactions that they would not have discovered without the knowledge of these applications. The students commented that the DOE helped them understand impacts of heat transfer on certain variable conditions they did not expect. This interdisciplinary course module of applying IE statistical tools to a ChemE UO course demonstrated enhanced student understanding of heat exchanger experimental variables effect on heat transfer and how that data compares to theory. The understanding and knowledge of these IE statistical tools prepares ChemE students for their future careers in industry and research.

AU - Courtney Pfluger
AU - Dayna Lee Martínez
CY - Virtual On line
DA - 2020/06/22
PB - ASEE Conferences
TI - Operations Laboratory Module on Heat Exchangers
UR - https://strategy.asee.org/35012
DO - 10.18260/1-2--35012
ER -