Source Journal of CSCD
Source Journal for Chinese Scientific and Technical Papers
Core Journal of RCCSE
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Volume 40 Issue 6
Sep.  2022
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XU Runze, CAO Jiashun, FANG Fang. RESEARCH PROGRESS ON N2O RECYCLING AND DATA-DRIVEN MODELING IN WASTEWATER TREATMENT PROCESSES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 107-115. doi: 10.13205/j.hjgc.202206014
Citation: XU Runze, CAO Jiashun, FANG Fang. RESEARCH PROGRESS ON N2O RECYCLING AND DATA-DRIVEN MODELING IN WASTEWATER TREATMENT PROCESSES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 107-115. doi: 10.13205/j.hjgc.202206014

RESEARCH PROGRESS ON N2O RECYCLING AND DATA-DRIVEN MODELING IN WASTEWATER TREATMENT PROCESSES

doi: 10.13205/j.hjgc.202206014
  • Received Date: 2022-01-01
    Available Online: 2022-09-01
  • Publish Date: 2022-09-01
  • Nitrous oxide (N2O) is a greenhouse gas and a strong oxidizing substance with the potential of energy recovery.This paper critically reviewed the novel nitrogen removal bioprocesses and methods for increasing N2O production from wastewater.The operating conditions and N2O conversion efficiencies in these bioprocesses were compared.Then,the shortages of these methods were pointed out.Moreover,this paper comprehensively reviewed the research progress on data-driven modeling of N2O emission in wastewater treatment processes from two perspectives:1) identifying key factors related to N2O,and 2) predicting N2O production.Currently,the main methods for recovering N2O include coupled aerobic-anoxic nitrous decomposition operation (CANDO),single reactor process,applications of recombinant strains or semiconductor modification strains.Big data of wastewater treatment plants could be utilized to establish the data-driven models for N2O emissions,whereas the existing N2O models mainly focus on reducing N2O emission.The future trends of N2O recovery include:1) developing new methods for recovering N2O;2) optimizing functional microbes in N2O recovering processes;3) establishing the relationships between data-driven modeling of N2O and N2O recovering processes.
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