School of Engineering
Showing 5,481-5,500 of 6,550 Results
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William Abraham Tarpeh
Assistant Professor of Chemical Engineering, by courtesy, of Civil and Environmental Engineering and Center Fellow at the Precourt Institute for Energy and, by courtesy, at the Woods Institute for the Environment
BioReimagining liquid waste streams as resources can lead to recovery of valuable products and more efficient, less costly approaches to reducing harmful discharges to the environment. Pollutants in effluent streams can be captured and used as valuable inputs to other processes. For example, municipal wastewater contains resources like energy, water, nutrients, and metals. The Tarpeh Lab develops and evaluates novel approaches to resource recovery from “waste” waters at several synergistic scales: molecular mechanisms of chemical transport and transformation; novel unit processes that increase resource efficiency; and systems-level assessments that identify optimization opportunities. We employ understanding of electrochemistry, separations, thermodynamics, kinetics, and reactor design to preferentially recover resources from waste. We leverage these molecular-scale insights to increase the sustainability of engineered processes in terms of energy, environmental impact, and cost.
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Daniel Tartakovsky
Professor of Energy Science Engineering
Current Research and Scholarly InterestsEnvironmental fluid mechanics, Applied and computational mathematics, Biomedical modeling.
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Clyde Tatum
Obayashi Professor in the School of Engineering, Emeritus
BioTatum's teaching interests are construction engineering and technical construction. His research focuses on construction process knowledge and integration and innovation in construction.
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Søren Henri Taverniers
Physical Science Research Scientist
Current Research and Scholarly InterestsDesign and implementation of novel statistical algorithms based on the Multilevel Monte Carlo method to accelerate the quantification of uncertainty in quantities of interest for multiphase systems such as reactive granular media and subsurface flows.
Development of neural-network based surrogate approaches to enable data-driven sensitivity analysis and uncertainty quantification for multiscale systems such as energy storage systems, and accelerate the design process of such devices.
