Cleanroom Optimisation Through Machine Learning

Cleanroom Optimisation Through Machine Learning
Summary

Objective

  • Analyse the underlying potential for energy reduction in cleanrooms.
  • The possibility of reducing current air change rate without affecting critical quality parameters were tested in four cleanrooms using fixed air change rate reduction or dynamic air change control.
  • Cleanroom and HVAC data was collected, a simulation model was developed replicating each of the four cleanrooms and its associated HVAC systems to test with different air change rates and analyse its implications.
  • A machine learning algorithm was developed to implement the dynamic control and was integrated with the cleanroom simulation model.
More information
Country: IE
Address: Unit A, Aerodrome Business Park, Rathcoole, Co. Dublin D24 WCO4
Location
Attached files
File Type
IMR_steripack_stream.pdf PDF

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Cleanroom Optimisation Through Machine Learning
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