Water Treatment Datasets

Predictive modeling in water treatment is still in its infancy and the lack of open source water treatment data is slowing its potential. This repository is a collection of treatment datasets that can be used to investigate mechanistic (i.e., physically-based) models and train machine learning/AI models. Please send me info on datasets I might be missing in the Issues.

Jar Test Data

• Source: Paleolimbot's GitHub
• Summary: Jar tests (n=500) using aluminum and ferric coagulants. Includes source waters from across the United States.
• Applications: Dunnington et al. (in-prep)

Carbonaceous Sorbents Data

• Source: Figshare
• Summary: Carbonaceous sorbent dataset (n=329) with parameters commonly used for Fruendlich isotherms.
• Applications: Sigmund, Gabriel, Mehdi Gharasoo, Thorsten Hüffer, and Thilo Hofmann. “Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials.” Environmental Science & Technology, March 3, 2020. https://doi.org/10.1021/acs.est.9b06287.