Machine_Learning_for_DPS

Code repository for the book 'Machine Learning in Python for Dynamic Process Systems'

Book links:

• Google Play: https://play.google.com/store/books/details?id=fBC-EAAAQBAJ
• LeanPub: https://leanpub.com/ML-Python-for-DPS

Data / Model sources for datasets used in this book:

• Box-Jenkins Gas Furnace Data:

  Obtained from https://openmv.net/info/gas-furnace

• Continuously Stirred Tank Reactor (CSTR) Model:

  Obtained and adapted from https://github.com/APMonitor/pdc/blob/master/CSTR_Control.ipynb

• Industrial Fired Heater Model:

  Obtained and adapted from https://apmonitor.com/dde/index.php/Main/FiredHeaterSimulation

• Distillation Column Model:

  Obtained and adapted from ‘Digital control systems: design, identification and implementation. Springer, 2006’

• Glass Furnace Data:

  Obtained from https://homes.esat.kuleuven.be/~smc/daisy/daisydata.html’

  Citation: De Moor B.L.R. (ed.), DaISy: Database for the Identification of Systems, Department of Electrical Engineering, ESAT/STADIUS, KU Leuven, Belgium, URL: http://homes.esat.kuleuven.be/~smc/daisy/.

• Tennessee Eastman Process Data:

  Available at https://github.com/camaramm/tennessee-eastman-profBraatz

• Liquid-saturated Steam Heat Exchanger Data:

  Obtained from https://homes.esat.kuleuven.be/~smc/daisy/daisydata.html’

  Citation: De Moor B.L.R. (ed.), DaISy: Database for the Identification of Systems, Department of Electrical Engineering, ESAT/STADIUS, KU Leuven, Belgium, URL: http://homes.esat.kuleuven.be/~smc/daisy/.