Chapter-wise code repository for the book 'Machine Learning in Python for Process Systems Engineering'
- Google Play: https://play.google.com/store/books/details?id=K_NjEAAAQBAJ
- LeanPub: https://leanpub.com/machineLearningPSE
[Weblinks mentioned below may change or may no longer exist in future. Relevant data files have been provided in the respective folders in this repository. If you plan to share or use any dataset, please abide by the license policy (and/or the citation requests, if any) for the dataset.]
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Polymer Manufacturing Process Data:
Originally obtained from https://landing.umetrics.com/downloads-other-downloads (unfortunately this link no longer seems to work; data file is provided in the respective folder in this repository). Dataset also referenced at https://www.academia.edu/38630159/Multivariate_data_analysis_wiki -
Pulp & Paper Manufacturing Process Data:
Obtained from https://openmv.net. Citation: Dayal et al. "Application of feedforward neural networks and partial least squares regression for modelling Kappa number in a continuous Kamyr digester", Pulp and Paper Canada, 95, 1994, p T7-T13. -
Low-Density Polyethylene (LDPE) Process Data:
Obtained from https://openmv.net. -
Tennessee Eastman Process Data:
Available at https://github.com/camaramm/tennessee-eastman-profBraatz. Bigger dataset available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6C3JR1. Citation: Reith, C.A., B.D. Amsel, R. Tran., and B. Maia. Additional Tennessee Eastman process simulation data for anomaly detection evaluation. Harvard Dataverse, Version 1, 2017 -
Semiconductor Manufacturing Process Data:
Obtained from http://www.eigenvector.com/data/Etch/. Citation: B.M. Wise, N.B. Gallagher, S.W. Butler, D.D. White, Jr. and G.G. Barna, "A Comparison of Principal Components Analysis, Multi-way Principal Components analysis, Tri-linear Decomposition and Parallel Factor Analysis for Fault Detection in a Semiconductor Etch Process", J. Chemometrics (1999). -
Polymer Pilot Plant Data:
Originally obtained from ftp://ftp.cis.upenn.edu/pub/ungar/chemdata/ -
Debutanizer Column Data from a Petroleum Refinery:
Available as supplementary material at https://link.springer.com/book/10.1007/978-1-84628-480-9. Citation: Fortuna et. al., Soft sensors for monitoring and control of industrial processes, Springer, 2007 -
Concrete Compressive Strength Data:
Available at the UCI machine learning repository https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength Copyright: Prof. I-Cheng Yeh Citation: I-Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998) -
Wastewater Treatment Plant Data:
Available at the UCI machine learning repository https://archive.ics.uci.edu/ml/datasets/water+treatment+plant -
Combined Cycle Power Plant data:
Available at the UCI machine learning repository https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant Citation: Pınar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615 -
SISO Heater System Data:
Provided by Prof. John Hedengren at https://apmonitor.com/do/index.php/Main/LSTMNetwork. Direct links for the training and validation data: https://apmonitor.com/do/uploads/Main/tclab_dyn_data3.txt and https://apmonitor.com/pdc/uploads/Main/tclab_data4.txt. File names will need to be changed to match the ones used in the book. -
Gas Turbine Data:
Originally available at NASA prognostics data repository https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/. Data available at https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6/about_data. Training and validation data file names used in the text are different than the original file names. Citation: A. Saxena and K. Goebel (2008). "Turbofan Engine Degradation Simulation Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA License: CC0: Public Domain (https://creativecommons.org/publicdomain/zero/1.0/)