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Simulation of thermal sanitization of air with heat recovery as applied to airborne pathogen deactivation.

M BustoE E TarifaM CristaldiJ M BadanoC R Vera
Published in: International journal of environmental science and technology : IJEST (2022)
The technique of air sterilization by thermal effect was revisited in this work. The impact of incorporating a high efficiency heat recovery exchanger to a sterilizing cell was especially assessed. A mathematical model was developed to study the dynamics and the steady state of the sterilizer. Computer simulation and reported data of thermal inactivation of pathogens permitted obtaining results for a proof-of-concept. The simulation results confirmed that the incorporation of a heat recovery exchanger permits saving more than 90% of the energy needed to heat the air to the temperature necessary for sterilization, i.e., sterilization without heat recovery consumes 10-20 times the energy of the same sterilization device with heat recovery. Sanitization temperature is the main process variable for sanitization, a fact related to the activated nature of the thermal inactivation of viruses and bacteria. Heat recovery efficiency was a strong function of the heat transfer parameters but also rather insensitive to the cell temperature. The heat transfer area determined the maximum capacity of the sterilizer (maximum air flowrate) given the restrictions of minimum sanitization efficiency and maximum power consumption. The proposed thermal sterilization device has important advantages of robustness and simplicity over other commercial sterilization devices, needing practically no maintenance and eliminating a big variety of microorganisms to any desired degree. For most pathogens, the inactivation can be total. This result is not affected by the uncertainties in thermal inactivation data, due to the Arrhenius-like dependence of inactivation. Temperature can be adjusted to achieve any removal degree.
Keyphrases
  • heat stress
  • high efficiency
  • single cell
  • big data
  • stem cells
  • cell therapy
  • electronic health record
  • bone marrow
  • artificial intelligence
  • deep learning
  • gram negative
  • particulate matter