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Mohammad Biswas

Mohammad Biswas

Assistant Professor

Phone: 903.566.6115
Email: mbiswas@uttyler.edu
Building:   HEC A214
Department: Mechanical Engineering

Degrees

  • Ph.D., University of Florida, 2013
  • B.Che., Auburn University, 2008

Biography

Courses Taught

  • MENG 3211 - Thermal-Fluids Laboratory
  • MENG 4215 – Senior Design I
  • MENG 4216 - Senior Design II
  • MENG 4312 - System Dynamics & Control
  • MENG 4330/5330 – Process Control
  • MENG 4345/5345– Energy Conversion  

Research Interests

My research focuses on process and system dynamics and control, with applications in fuel cell systems and thermal-fluid systems. I am particularly interested in the modeling, analysis, and control of fuel cell and electrolyzer systems—such as PEM fuel cells and regenerative fuel cells—for sustainable portable, vehicular and space-based applications. Moreover, I am engaged in engineering education through the development of interactive, remotely accessible laboratory systems that support experiential learning in thermal-fluid sciences and renewable energy systems as well as supporting engineering focused bridge programs and outreach.

Research Page

Awards & Honors

  • Runners Up Team, Inaugural EnergyTech University Prize - Faculty Track, Department of Energy Office of Technology Transitions, 2024
  • Certificate in Effective College Instruction, The Association of College and University Educators (ACUE) and The American Council on Education
  • Top 8 Best Paper Award, Best Paper Nomination, The 12th International Conference on Computer Science and Education, IEEE, Houston, TX
  • 2nd place best paper Award, 2016 ASEE Gulf Southwest Annual Conference, Texas Christian University, Fort Worth, TX

Selected Publications

  1. Wilberforce, T. & Biswas, M. A. R. (2022). A Study into Proton Exchange Membrane Fuel Cell Power and Voltage Prediction Using Artificial Neural Network. Energy Reports, 8, 12843–12852. https://doi.org/10.1016/j.egyr.2022.09.104
  2. Biswas, M.A.R., Robinson, M. D. & Fumo, N. (2016). Prediction of residential building energy consumption: A neural network approach. Energy, 117(1), 84-92.
  3. Mudiraj, S., Crisalle, O., Biswas, M.A.R., & Lear, W. (2015). Comprehensive mass transport modeling technique for the cathode side of an open-cathode direct methanol fuel cell. International Journal of Hydrogen Energy, 40(25), 8137–8159.
  4. Fumo, N., & Biswas, M.A.R. (2015). Regression analysis for prediction of residential energy consumption. Renewable & Sustainable Energy Reviews, 47, 332–343.
  5. Biswas, M.A.R., Crisalle, O., Mudiraj, S., & Lear, W. (2014). Systematic approach for modeling methanol mass transport on the anode side of direct methanol fuel cells. International Journal of Hydrogen Energy, 39(15), 8009–8025.

My research focuses on process and system dynamics and control, with applications in fuel cell systems and thermal-fluid systems. I am particularly interested in the modeling, analysis, and control of fuel cell and electrolyzer systems—such as PEM fuel cells and regenerative fuel cells—for sustainable portable, vehicular and space-based applications. Moreover, I am engaged in engineering education through the development of interactive, remotely accessible laboratory systems that support experiential learning in thermal-fluid sciences and renewable energy systems as well as supporting engineering focused bridge programs and outreach.

My research focuses on process and system dynamics and control, with applications in fuel cell systems and thermal-fluid systems. I am particularly interested in the modeling, analysis, and control of fuel cell and electrolyzer systems—such as PEM fuel cells and regenerative fuel cells—for sustainable portable, vehicular and space-based applications. Moreover, I am engaged in engineering education through the development of interactive, remotely accessible laboratory systems that support experiential learning in thermal-fluid sciences and renewable energy systems as well as supporting engineering focused bridge programs and outreach.

Thermal Fluid Engineering Laboratory Education

  • Development of laboratory-scale heat exchanger system with, user-friendly interface and data acquisition system
  • Interactive learning experience via physical & remote access
  • Development of open education resources for thermal fluid laboratory course
  • Development of heat exchanger and fuel cell models with user-friendly interface and simulated data collection and analysis

Modular Heat ExchangerResearch ItemCross Flow Heat Exchanger InterfaceSimulated Fuel Cell System

Process dynamics and control of fuel cell and solar systems

  • Semi-analytical and analytical process model development for control synthesis for various energy systems including regenerative fuel cell systems
  • Performance prediction using machine learning based modeling approach for energy and power applications
  • Design control strategy for electrochemical systems including solar fuel cell and battery systems

Research Item

Research PictureResearch PictureResearch Picture
Awards and Honors

  • Runners Up Team, Inaugural EnergyTech University Prize - Faculty Track, Department of Energy Office of Technology Transitions, 2024
  • Certificate in Effective College Instruction, The Association of College and University Educators (ACUE) and The American Council on Education
  • Top 8 Best Paper Award, Best Paper Nomination, The 12th International Conference on Computer Science and Education, IEEE, Houston, TX
  • 2nd place best paper Award, 2016 ASEE Gulf Southwest Annual Conference, Texas Christian University, Fort Worth, TX

Selected Publications

  1. Wilberforce, T. & Biswas, M. A. R. (2022). A Study into Proton Exchange Membrane Fuel Cell Power and Voltage Prediction Using Artificial Neural Network. Energy Reports, 8, 12843–12852. https://doi.org/10.1016/j.egyr.2022.09.104
  2. Biswas, M.A.R., Robinson, M. D. & Fumo, N. (2016). Prediction of residential building energy consumption: A neural network approach. Energy, 117(1), 84-92.
  3. Mudiraj, S., Crisalle, O., Biswas, M.A.R., & Lear, W. (2015). Comprehensive mass transport modeling technique for the cathode side of an open-cathode direct methanol fuel cell. International Journal of Hydrogen Energy, 40(25), 8137–8159.
  4. Fumo, N., & Biswas, M.A.R. (2015). Regression analysis for prediction of residential energy consumption. Renewable & Sustainable Energy Reviews, 47, 332–343.
  5. Biswas, M.A.R., Crisalle, O., Mudiraj, S., & Lear, W. (2014). Systematic approach for modeling methanol mass transport on the anode side of direct methanol fuel cells. International Journal of Hydrogen Energy, 39(15), 8009–8025.

Curriculum Vitae
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