Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

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  • This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data.

    The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made.

    The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library.

    Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.     ... [Read More]

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4 files (329 MB)
Data Citation
  • Jin, Z., Huang, J., Hower, J. C., & Hsu-Kim, H. (2025). Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations. Duke Research Data Repository. https://doi.org/10.7924/r4vm4h42h
DOI
  • 10.7924/r4vm4h42h
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  • ark:/87924/r4vm4h42h
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Related Materials
  • Jin, Z., Huang, J., Hower, J. C., & Hsu-Kim, H. (2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.  https://doi.org/10.1021/acs.est.4c12705
Funding Agency
  • Alfred P. Sloan Foundation
  • Office of Fossil Energy
Grant Number
  • DE-FE0031748
  • G-2020-13922
Title
  • Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations
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Thumbnail Title Date Uploaded Visibility Actions
file details CoalSupplyDataset.csv 2025-02-20 Public Download
file details bayesian.pkl 2025-02-20 Public Download
file details readme.md 2025-02-20 Public Download
file details readme.txt 2025-02-20 Public Download

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