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Machine_Learning_for_DPS

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

Book links:

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/.
    

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

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