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Updated: Feb 7, 2026

Electrostatic Method to Remove Particulate Organic Matter from Soil
Published on: February 10, 2021
COD removal index - A mechanistic tool for predicting organics removal performance of vermifilters
Rajneesh Singh1, Puspendu Bhunia1, Rajesh R Dash1
1School of Infrastructure, Indian Institute of Technology, Bhubaneswar, India.
Abstract:
The present study was aimed at developing a mathematical COD removal index (CRI) based on the key variables of vermifiltration such as hydraulic retention time (HRT), organic concentration and earthworm density (EWD). In the maiden attempt of framing such an index, many experimental runs were conducted by varying the COD strength of the wastewater from 2.0-4.0 kg/m3, EWD and HLR were from 0 to 10,000 earthworms/m3 and 1.8-4.5 m3/m2·d, respectively. The sensitivity analysis of the developed CRI reveals that the EWD, bedding volume, concentration of organics and the flow applied to the vermifilter can immensely affect the model response. However, flow applied to the system is observed to be the most sensitive among other parameters incorporated in the model. In addition, from the sensitivity analysis, it was also revealed that the removal efficiencies were observed to increase with the increase in CRI values in the COD range of 0.2-6.0 kg/m3. In addition, the plot between the reported removals from literatures on vermifiltration of domestic wastewater and CRI values yielded an R2 value of 0.76. Such a high correlation suggests that the developed CRI index can be applied for other wastewaters also. The results of this study indicate that the developed index can successfully be applied in determining and predicting organics removal from vermifiltration of wastewaters.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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