Gongye shui chuli (Mar 2024)

Analysis of the status and influencing factors of sludge yield coefficient of municipal wastewater treatment plants in China

  • CHEN Minmin,
  • LIU Jie,
  • LI Li’na,
  • QIU Lili,
  • YANG Weiwei,
  • JING Hong

DOI
https://doi.org/10.19965/j.cnki.iwt.2023-0236
Journal volume & issue
Vol. 44, no. 3
pp. 24 – 29

Abstract

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Based on the ecological environmental statistics and monitoring datas of municipal wastewater treatment plants in China, the empirical sludge yield coefficient(YQ) and the sludge yield coefficient(YCOD) calculated based on the sludge proliferation caused by the removal of COD in municipal wastewater treatment plants in China were analyzed,and the response relationships between single factor such as influent COD,design treatment scale,treatment process,regional distribution and sludge yield coefficient,as well as response relationships between multiple factors and sludge yield coefficient were studied. The results showed that the average of YQ of municipal wastewater treatment plants in China was 1.33×10-4 t/m3 and the average of YCOD was 0.81 kg/kg. YQ and YCOD showed positive skewed distribution in general. The average of YQ was positively correlated with COD in influent and design treatment scale,meanwhile the average of YCOD was negatively correlated with COD in influent and design treatment scale. The COD range corresponding to the average values of YQ and YCOD was 150-250 mg/L. YQ and YCOD for wastewater treatment plants with design treatment scale of 1×104-1×105 m3/d were the closest to the overall national average,with relative deviations of 2.8% and -4.3% respectively. YQ and YCOD of A2/O process,oxidation ditch type,ordinary activated sludge method and A/O process were close to the overall national average,with relative deviations ranging from -1.8%-1.9% and -2.3%-5.8% respectively. YQ of East China and Central China,YCOD of East China and Southwest China were basically the same as the national average. The COD in influent was the most significant influencing factor for YQ and YCOD through using multifactor analysis of variance.

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