Board 442: Data-driven Approach to Problem Solving in Renewable Energy and Engineering Education

Mohammad Abu Rafe Biswas; Aaditya Khanal; Prabha Sundaravadivel

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Energy Conversion, Conservation and Nuclear Engineering Division (ECCNE) Poster Session
Tagged Divisions: Energy Conversion and Conservation and Nuclear Engineering Division (ECCNE)
Permanent URL: https://strategy.asee.org/47034

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Paper Authors

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Mohammad Abu Rafe Biswas The University of Texas at Tyler orcid.org/0000-0002-4077-7979

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Dr. Mohammad (Rafe) Biswas is an Associate Professor at the University of Texas at Tyler Houston Engineering Center in the Department of Mechanical Engineering. His expertise and interests include process dynamics and control, fuel cell systems and thermal fluid engineering education. He has taught courses in system dynamics and control, process control, energy conversion, and thermal fluids laboratory.

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Aaditya Khanal The University of Texas at Tyler

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Aaditya Khanal, PhD is an Assistant Professor of Chemical Engineering at the University of Texas at Tyler. His research interests fall within the energy and climate nexus, aiming to improve prosperity and sustainability through solutions in renewable energy, carbon sequestration, and underground hydrogen storage. He is certified in effective college instruction by The Association of College and University Educators and the American Council on Education.

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Prabha Sundaravadivel The University of Texas at Tyler

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Dr. Prabha Sundaravadivel received her Ph.D. Degree in Computer Science and Engineering at the University of North Texas, Denton, Texas in 2018. Her research interests are focused on developing Application-specific architectures for Smart Healthcare and Smart Cities, Sustainable Cyber-Physical Systems, Edge-Intelligent Embedded Systems for IoT Applications, Reconfigurable Computing, Bio-inspired Robotics, and Applied Machine Learning.

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Abstract

Courses based on experiential learning provide an excellent avenue to promote problem-solving and collaborative skills among the students in STEM. However, the current engineering curriculum does not have sufficient project-based learning emphasizing collaborative research on renewable energy to support the government’s goal of Net Zero emissions by 2050. So, we plan to develop and implement a project-based learning course to model and analyze renewable energy systems using machine learning methods. The course will be offered in modules covering several supervised and unsupervised machine learning algorithms. Moreover, video lectures and tutorials will be provided for analysis and evaluation of data sets of various technologies with computational tools for each machine learning model. This experiential learning experience will cater to our diverse student population of veterans and underrepresented groups (In 2019, Hispanic were 37% and Black were 26.9% at Houston Engineering Center). The proposed plan is designed to engage students in ways that have been shown to increase retention of students in STEM fields, therefore, the program will also help increase the number of and diversity of graduates fully prepared to pursue careers in engineering. Project outcomes are to (1) develop and implement effective machine learning instructional, and training modules for sustainability projects using experiential learning, (2) promote a data-driven approach to solving engineering problems for complex problems in science and engineering, and (3) train future leaders on topics related to sustainability. Moreover, the modules will become readily accessible to other students and faculty at the university, and other global academic institutions. The students will be provided the qualitative and quantitative data collected from the experiments to analyze using the machine learning modules prepared by the instructor for the class. These modules will enable students to acquire the relevant knowledge and skills for data-driven modeling. Various program assessment tools will be used, including pre-and post-course Likert-based quantitative surveys completed by students to measure the success of the project.

Citation
Format

Biswas, M. A. R., & Khanal, A., & Sundaravadivel, P. (2024, June), Board 442: Data-driven Approach to Problem Solving in Renewable Energy and Engineering Education Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://strategy.asee.org/47034

TY  - CPAPER
AB  - Courses based on experiential learning provide an excellent avenue to promote problem-solving and collaborative skills among the students in STEM. However, the current engineering curriculum does not have sufficient project-based learning emphasizing collaborative research on renewable energy to support the government’s goal of Net Zero emissions by 2050. So, we plan to develop and implement a project-based learning course to model and analyze renewable energy systems using machine learning methods. The course will be offered in modules covering several supervised and unsupervised machine learning algorithms. Moreover, video lectures and tutorials will be provided for analysis and evaluation of data sets of various technologies with computational tools for each machine learning model. This experiential learning experience will cater to our diverse student population of veterans and underrepresented groups (In 2019, Hispanic were 37% and Black were 26.9% at Houston Engineering Center). The proposed plan is designed to engage students in ways that have been shown to increase retention of students in STEM fields, therefore, the program will also help increase the number of and diversity of graduates fully prepared to pursue careers in engineering. 
Project outcomes are to (1) develop and implement effective machine learning instructional, and training modules for sustainability projects using experiential learning, (2) promote a data-driven approach to solving engineering problems for complex problems in science and engineering, and (3) train future leaders on topics related to sustainability. Moreover, the modules will become readily accessible to other students and faculty at the university, and other global academic institutions. The students will be provided the qualitative and quantitative data collected from the experiments to analyze using the machine learning modules prepared by the instructor for the class. These modules will enable students to acquire the relevant knowledge and skills for data-driven modeling. Various program assessment tools will be used, including pre-and post-course Likert-based quantitative surveys completed by students to measure the success of the project.

AU  - Mohammad Abu Rafe Biswas
AU  - Aaditya Khanal
AU  - Prabha Sundaravadivel
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Board 442: Data-driven Approach to Problem Solving in Renewable Energy and Engineering Education
UR  - https://strategy.asee.org/47034
ER  -