(2ke) Sustainable Materials and Process Design through Multi-Scale Systems Engineering and Hybrid Mechanistic/Data-Driven Modeling

Vision

My research goal is to design sustainable materials and processes using multi-scale systems engineering and hybrid mechanistic/data-driven modeling to address energy security and transition, environmental protection, supply chain management, process safety through computation and simulation, and data science, especially machine learning and AI. In the next five years, I want to develop a strong PSE research group focusing on Process Optimization, Design Integration, and Informatics (PRODIGI). The PRODIGI group will do the fundamental research to lay the groundwork for new energy technologies, new material discoveries and design, and a greater understanding of the physical universe, all of which will result in advancements in energy, environment, and national security. This will allow me to write research grants targeting DOE-Basic Energy Sciences (BES), DOD-Basic Scientific Research, NSF-Civil, Mechanical and Manufacturing Innovation (CMMI), DARPA-Artificial Intelligence Exploration (AIE). I will do the foundational research on: (1) sustainable process design, integration, optimization, and control, (2) multi-scale hybrid mechanistic/data-driven modeling, (3) blockchains and game-theoretic approaches for resilient supply chains, and (4) mixed-integer and constrained optimization using quantum computing (QC) or hybrid QC/classical computing. These methodologies have a wide range of applications, including: (1) sustainable hydrogen infrastructure for equitable low-carbon development, (2) carbon capture, utilization, and storage (CCUS) in clean energy transitions, and (3) smart additive manufacturing. The vision of the PRODIGI group will be to harness the potential of the big data generated from numerous Industry 4.0 elements by developing hybrid mechanistic/data-driven predictive models for multi-scale processes. The resiliency and sustainability of the integrated processes under known and unknown uncertainties will be assured through classical global optimization solvers or by the newly developed quantum computing, blockchain, and game-theory-inspired optimization.

Current and Past Research

Currently, as a member of the carbon management alliance team at the Texas A&M Energy Institute, USA, I am actively involved in researching the future of global energy (e.g., hydrogen and renewable energy) and the energy transition (e.g., decarbonization of traditional process routes through carbon capture, utilization, and storage (CCUS)), leveraging my mathematical modeling and simulation, and machine learning programming skills. I am using traditional PSE tools and methods for multi-scale systems engineering for decarbonization, integration of renewable energy, emission reduction, efficiency improvement, and technological advancements in environment and energy systems. For instance, in my current postdoctoral tenure, I am working in strategic planning, multi-objective optimization and RNN-LSTM model analysis of decarbonization pathways, electrification of process heat, intensified CCUS technology innovations, sustainable hydrogen economy, game-theory-informed peer-to-peer carbon emission trading system on blockchain, and quantum optimization technique for solving linear, quadratic and mixed-integer quadratic programs. I have a unique blend of research experience that combines computation and experiment. For example, my Ph.D. dissertation was on implementing an in-house-developed optimizing controller of a continuous chromatographic separation process at the lab scale. Later, I joined Qatar University as a postdoctoral fellow and worked in diversified research areas, such as data-driven modeling for smart manufacturing, optimization of industrial supply chains, process design under uncertainty, abnormal situation management, and inherently safer design for robust process operations. I develop new machine learning, quantum computing, game-theory-based optimization, and blockchain-enabled concepts, tools, and solutions. Through these methods, my research addresses the resiliency and sustainability of the integrated processes under uncertainties, cost-effective smart manufacturing, and faster material discovery.

Future Research Directions

In the first several years, my initial research directions will be in the following areas: (1) Hydrogen Economy: SHIELD - Sustainable hydrogen infrastructure for the multi-sector decarbonization stakeholders' equitable development through an upgraded PSE framework integrating blockchain, quantum computing and game theory; (2) Decarbonization: P2PACT - Peer-to-peer autonomous carbon emission trading system on blockchain enabling game-theoretically fair multi-party dynamic auction; (3) Smart Manufacturing: HyDDrim - Hybrid data-driven modeling and control for smart additive manufacturing of composite materials for industry 4.0 applications using sparse identification of nonlinear dynamical systems. The targeted research works will be accomplished by winning nationally and internationally competitive research grants (i.e., NSF-SAI, NSF-LEAP HI, DOE FECM, DOE-HPC4Mfg, NSF-BRAID, NSF-GOALI, DOD-ONR -STAMP, USDA-NIFA-AFRI, DARPA–AIE, Qatar National Research Fund (QNRF)), publishing in highly ranked peer-reviewed journals, and graduating Ph.D. and MS students in the chemical and relevant engineering fields. I will also actively recruit and educate minority graduate students in my PRODIGI group. I will offer mentorship for female and minority students to encourage them to continue academic careers since this is a crucial component of NSF Broadening Participation in my research project applications. As a member of the Texas A&M Energy Institute's staff, I have the opportunity to interact with institutions such as AIChE RAPID and its future endeavors, Greater Houston Partnership, Permian Energy Development Lab (PEDL), and many more. I may use the Texas A&M Energy Institute's network as an affiliate member. Additionally, the institution enables me to write research proposals with their connections.

Teaching Statement

Teaching Philosophy

“The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires.”– William Arthur Ward

I consider teaching to be an integral part of my entire academic activity. Students at the university get access to some of the world's most extensive information resources. As an educator, I must make students aware of these resources and encourage them to seek them out, examine them, and use them. As the quotation says, my objective is not just to teach them but also to open their eyes and encourage them to address problems independently. I believe that five essential elements are conducive to learning. (1) The teacher's role is to act as a guide. (2) Students must access the practical problems for hands-on activities. (3) Students should be able to have choices and let their curiosity direct their learning. (4) Students need the opportunity to practice skills in realistic scenarios. (5) Cutting-edge technology must be incorporated into the classroom. I will present a curriculum that will incorporate each learning style (e.g., visual, auditory, and kinesthetic) and make the content relevant to the student's life. I will incorporate hands-on learning, cooperative learning, projects, themes, and individual work that engage and activate students' learning. My classroom teaching will lay the foundation for critical problem-solving abilities via hands-on experience. Besides solving classroom problems, I would encourage my students' minds to think bigger for cross-boundary and out-of-the-box thinking. This will allow their minds to maneuver freely to think critically and creatively to solve any practical issue using the fundamental classroom learning and the impromptu decision-making abilities, just like mechanistic/hybrid data-driven models.

Teaching Experience & Plan

I have a passion for teaching, and I take every opportunity to teach. I consider teaching to be an integral part of my entire academic activity. Currently, I’m co-teaching an MS course (ICPE 618 - Carbon Capture, Utilization, and Storage) with Dr. Hasan at Texas A&M Energy Institute, Texas A&M University on the fundamental concepts of various CO₂ capture and utilization processes, methods for technology comparison, and the basic economics of carbon management and relevant PSE issues for CCUS implementation. I also co-designed a graduate course (EEMP 626 – Clean Energy Resources) and the evaluation process with Dr. Eljack at Qatar University. I was also invited for two guest lectures on the usage of database for research and on Hydrogen as clean energy source. I'll be comfortable teaching core courses and specialized modules in chemical engineering at both the undergraduate and graduate levels. I also appreciate the need for faculty flexibility and embrace the opportunity to cover a broad spectrum of courses. Furthermore, I am interested in developing (i) an introductory undergraduate course on big data analysis through machine/deep/reinforcement learning related to chemical engineering applications and (ii) a graduate course on future clean energy resources and its resilient supply chain management.