Predicting the Properties of Materials with Machine Learning

Determining the properties of materials has always been one of the primary goals of research in materials science. Computational models for materials’ property determination are hindered by their high computational cost; it can take weeks (even years) to develop and evaluate a computational model for a single property of a single material.

New tools to study enzyme penetration and activity dynamics within wood cell walls

The main challenges in bioconversion of lignocellulose arise from our limited understanding of the heterogeneous chemistry of the substrate and poor accessibility of enzymes within dense wood cell walls. It is clear that our limited appreciation of enzyme penetration into cell walls and catalysis dynamics is partly due to the constraints of the techniques employed previously. Unlike these other studies, which addressed such questions by examining model substrates, I propose to examine and develop new tools for use on complex solid wood substrates.

Developing an on-line fluidization analysis probe

The main objective of this research is to develop an on-line fluidization analysis probe to be applied on a commercial fluidized bed. This project will focus on a combination of pressure differential and fiber optic reflection probes. Key objectives will be to establish a lab probe with dual fiberoptic and high frequency pressure readings.  Emphasis will be on establishing signal analysis for both fiber optics and pressure and then use the combined probe in a lab environment.

Application of Spatial Statistics to Quantify Mixing and the Potential for Reaction

Many chemical reactions can produce unwanted byproducts which require additional purification steps and lead to unwanted waste. Additional purification steps consume large amounts of energy, and waste products can have a significant environmental impact. The chemistry can be modified so that the desired products are favored over the unwanted byproducts, and the mixing can be intensified so that molecules are more rapidly and intimately mixed. Both of these approaches will reduce byproducts.

Simplified Mathematical Models to Describe Ethylene Oxide Catalyst Activity and Selectivity

The scope of this project is to develop alternative simplified mathematical models for the description of ethylene oxide catalyst activity and selectivity decline based on plant historical data. The models will be employed and evaluated for on line performance monitoring and prediction. Adaptive, online model estimation procedures may be required for these purposes.

Developing Microkinetics Oxidation Models

The emissions of NOx and SO2 are subject to increasingly severe environmental regulations. In an effort to reduce the amount of NOx and SO2 emitted by DuPont’s industrial facilities, the intern will develop new capabilities in NOx and SO2 absorption modeling. During the project, the intern will begin by learning the ChemKin™ software, which he will apply to simulate the absorption of NOx and SO2 into caustic. Caustic is a liquid solution currently used to “clean” exhaust gases from NOx and SO2 in wet scrubber equipment.

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