M. M. Faruque Hasan

Assistant Professor


Office: 333 ERB
Phone: 979.862.1449
Email: hasan@tamu.edu

Research Website

Research Interests

  • Modeling and optimization of complex systems
  • Multi-scale systems engineering for energy and the environment
  • CO2 capture, utilization and storage
  • Grey-box and black-box systems
  • Nonlinear and nonconvex optimization, discrete/continuous optimization

We are interested in developing application-oriented theory, computational tools, algorithms, and optimization methods for complex and multi-scale systems. The methodologies and tools that we develop are applied to the design and discovery of advanced materials and processes for sustainable fuels and chemicals, carbon capture, oil & gas processing, and shale gas utilization, among others.

Multi-scale Systems Engineering for Energy and the Environment

Often the discovery and design of novel pathways depend on addressing systems which are complex and vary across different time- and length-scales. Trade-offs between various competing objectives (eg., selectivity vs. product purity vs. process cost) can be properly elucidated through multi-scale systems engineering. We develop methods to simultaneously address multiple and often contradicting factors at materials, process and supply chain levels.

Modeling and Optimization of Complex Systems

Complex systems can be chemical, biochemical or biological. Examples of complex systems vary from enzyme complexes to biological cells to process plants to supply networks. Compared to conventional process systems engineering, where systems are often considered to be well-described using suitable models and mathematical programs, complex systems are difficult to optimize due to their complexity, uncertainty, and the inability to generate accurate data using physical and/or computational experiments in reasonable time. Many fundamental challenges need to be resolved for accurate model prediction, control and optimization. We are currently working on developing theoretical, computational and algorithmic frameworks for complex systems optimization.

CO2 Capture, Utilization, and Storage (CCUS)

CCUS is an enabling technology toward clean energy from fossil fuels. While material selection is crucial for CO2 capture, the industrial scale deployment of CCUS requires material-, process- and supply chain network-level developments. Our work in CCUS extends across all three levels. Our aim is to design and discover materials and processes to reduce CO2 emissions in the most cost-effective manner.

Awards & Honors

  • Ralph E. Powe Junior Faculty Enhancement Award, 2015
  • World Future Foundation Ph.D. Prize in Environmental & Sustainability Research, 2010
  • Best Technical Paper Award, Annual Gas Symposium, Doha, Qatar, 2009
  • PhD Research Scholarship, National University of Singapore, 2005 – 2009


  • Postdoctoral Research, Princeton University, 2011 - 2014
  • Ph.D., Chemical Engineering, National University of Singapore, 2010
  • B.S., Chemical Engineering, Bangladesh University of Engineering & Technology, 2005

Selected Publications

First, E. L., Hasan, M. M. F., Floudas, C. A. Discovery of Novel Zeolites for Natural Gas Purification through Combined Material Selection and Process Optimization Approach. AIChE Journal, 2014, 60(5), 1767–1785.

Hasan, M. M. F., Boukouvala, F., Floudas, C. A. Nationwide, Regional and Statewide CO2 Capture, Utilization and Sequestration Supply Chain Network Optimization. Industrial & Engineering Chemistry Research, 2014, 53(18), 7489–7506.

Onel, O., Niziolek, A., Hasan, M. M. F., Floudas, C. A. Municipal Solid Waste to Liquid Transportation Fuels - Part I: Mathematical Modeling of a Municipal Solid Waste Gasifier. Computers and Chemical Engineering, 2014, DOI: 10.1016/j.compchemeng.2014.03.008.

Hasan, M. M. F., First, E. L., Floudas, C. A. Cost-effective CO2 Capture based on in silico Screening and Process Optimization. Physical Chemistry Chemical Physics, 2013, 15, 17601–17618.

Hasan, M. M. F., Karimi, I. A. Piecewise Linear Relaxation of Bilinear Programs using Bivariate Partitioning. AIChE Journal, 2010, 56, 1880–1893.