Utilizing XGBoost to Predict High-Performance Microbial Communities in Wastewater Treatment Plants


For a full-scale treatment plant, it is hard to identify the optimal microbial community assembly (MCA) for treating wastewater. Therefore, leveraging the power of the XGBoost model can help unlock the process performance. We used real-world data from a wastewater treatment plant in North Carolina to evaluate MCA and its treatment quality. The results revealed the relationships between alpha diversity, beta diversity, process performance, and MCA. The model can be applied to regional North Carolina wastewater treatment plants to help inform and monitor the changes in MCA and identify the optimal MCA for the process.


Yi-Chun Lai is a M.S. Candidate in Analytics at the Institute for Advanced Analytics, North Carolina State University. Prior to joining the master's program, she earned her PhD in Environmental Engineering at NC State and gained one year of experience as a research scientist in a biomedical device company. Her doctoral research primarily focused on microalgal bioprocessing for biodiesel production and participated projects aimed at optimizing anaerobic digestion performance in wastewater treatment plants. With a decade of hands-on laboratory research experience, Yi-Chun developed a passion for data science. She is now eager to leverage her scientific background and analytical skills to bridge the gap between research and practical applications.

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