Agricultural & Biological Engineering

Students may apply to one or more of the below projects, indicating this in their statement of interest, or they may apply for "ABE: General," indicating in their statement of interest their skills and background and some faculty with whom they would be interested in working.  ABE Faculty List

Title Name Email Project Name Project Description Requirements
Assoc. Prof. Shweta Singh singh294@purdue.edu Machine Learning Methods for Sustainability Assessment of Emerging Technologies Sustainability of emerging technologies is difficult to model due to lack of established methods and predictive models available. In this project, the aim is to overcome this challenge by using novel machine learning methods and using the available heterogeneous data combined with mechanistic models such as process engineering models. Specific emerging technologies of interest are : bio-based manufacturing, automation in industries, renewable energy and waste-to-value added. The student will contribute to identification and development of appropriate machine learning algorithms for predictive modeling  and connect with mechanism based models. The technology selection will be decided as per the student interest since the emphasis is on method identification.  Chemical Engineering or Applied Mathematics major, Process Modeling training/Statistics, Ability/keen to learn a new software language or a software. Experience with open source software programming (R, Python)
Assoc. Prof. Mohit Verma msverma@purdue.edu Low-cost user-friendly biosensors Our lab develops biosensors that can be used in resource-limited settings. These biosensors are built using paper-based platforms so that they are lightweight, easily disposed, and cost-effective. We are currently building them for applications in animal health. The student working on this project will validate these devices and test them in the field.  Experience with microbiology is necessary. Experience with biosensors and programming is desirable. 
Prof. Michael Ladisch ladisch@purdue.edu Inverse Chromatography of Cellulolytic Proteins Theories and methods for liquid chromatographic separations of proteins are well-defined, and can now be used to probe stationary phases (material packed into liquid chromatography) with respect to retention behavior  of hydrolytic enzymes in the presence of enzyme inhibitors and activators, and therefore define characteristics of these materials.  Application is in direct study of productive and non-productive binding of enzymes to lignocellulose biomass, with objective of decreasing enzyme loading and cost required to obtain high cellulose conversion to fermentable sugars used for ethanol production. Introductory work in mass transfer, adsorption and chromatography.  Basic understanding of solution of differential equations. Some hands-on laboratory experience.

 

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