Ezra's Round Table / Systems Seminar: Zheyu Jiang (Oklahoma St.)
Frank H. T. Rhodes Hall 253
Toward Sustainable Food and Chemical Productions via Systems Engineering Approaches
In 2050, the global population is expected to increase by 2 billion people to 10 billion. This puts unprecedented stress on food, energy, and water resources. Specifically, global food production must increase by at least 70% by 2050 to feed the world's growing population. Global consumption of both energy and water will also have to grow by at least 50% between now and 2050. To meet these growing needs sustainably, the ways food and chemicals are produced must be systematically reexamined and revolutionarily redesigned. These activities require innovations in systems-level thinking and systems engineering approaches in order to make our decision-making process more efficient, more accurate, and more comprehensive. Toward this goal, in this talk, I will shed some light on how new computational tools and systems-oriented solutions could help address some of the critical challenges in industrial decarbonization and digital agriculture. Specifically, I plan to introduce two ongoing projects in collaboration with systems engineering researchers: 1) sensor-based optimization and integrative learning of root-zone soil monitoring for sustainable agricultural irrigation, and 2) decarbonizing chemical process heating with electrification.
Zheyu Jiang joined the School of Chemical Engineering at Oklahoma State University as an assistant professor in 2021. He completed his B.S. at the University of Minnesota in 2014 and Ph.D. at Purdue University in 2018, both in chemical engineering. Between 2018 and 2021, Professor Jiang worked as a process development engineer in the Small Molecule Discovery and Development group at Dow Chemical/Corteva Agriscience, where he contributed to the market launch of Adavelt, the first broad-spectrum picolinamide fungicide product. At OSU, his research group focuses on developing new mathematical modeling, optimization and machine learning tools and systems-level insights for advanced separations, industrial decarbonization, digital agriculture and Industry 4.0.