Bradley Moorehead ’23
Operations Research and Financial Engineering
Data Mining Methods and Research on Environmental Literature
Certificate(s): Statistics and Machine Learning
I applied text mining methods to analyze more than 15,000 academic publications related to resource recovery from wastewater treatment processes. Text mining involves structuring, preprocessing and analyzing a set of textual information to identify patterns in data. My team focused on using article keywords and titles to: find research trends for wastewater resource recovery; determine how research topics have evolved over time; and identify underlying research topics, including water, energy, nutrients, heat, metals and biosolids recovery. I helped preprocess the information, which involved editing the raw text to combine similar terms, acronyms, chemical symbols, and other types of keywords to decrease noise and provide more meaningful results. I also developed a prototype of an online literature-information collection tool that can collect data from newly published environmental publications in order to build a database for in-time text mining analysis. In addition to learning about wastewater treatment and resource recovery, I learned about natural language processing techniques, data visualization, and application programming interface usages. This internship inspired me to continue learning about natural language processing and text mining, and to pursue similar research in the future.
Climate and Environmental Science
Princeton WET (Water and Energy Technologies) Lab, Department of Civil and Environmental Engineering and the Andlinger Center for Energy and the Environment, Princeton University
Z. Jason Ren, Professor of Civil and Environmental Engineering and the Andlinger Center for Energy and the Environment; Junjie Zhu, Associate Research Scholar, Civil and Environmental Engineering