/treatment-datasets

Water treatment datasets for predictive modeling

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.