Comparison Biomethane Potential (BMP) Test of Sewage Sludge Recovered from Different Treatment Processes

Odey, Emmanuel Alepu; Wang, Kaijun; Jin, Zhengyu; Segun, Giwa Abdulmoseen; Li, Zifu; Ikhumhen, Harrison Odion

doi:10.4172/2252-5211.1000233

Cited by 6 publications

(1 citation statement)

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“…The analytical method describes the procedure for characterization. Characterization of the substrate is an important parameter reflecting the potential of methane production from the BMP test [22]. The analytical methods used for this study were presented in Table 2.…”

Section: Methodsmentioning

confidence: 99%

The Performance Evaluation on Co-Digestion of Domestic Sewage Sludge and Food Waste for Methane Yield and Kinetics Analysis

Sulaiman

Seswoya

2021

IJIE

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Sewage sludge and food waste; are organic wastes suitable for the anaerobic digestion. However, the digestion of sewage sludge and food waste as solely substrate is having a drawback in term of methane yield. Therefore, many researchers combined these two wastes as a co-substrate and used in co-digestion. This study focused to evaluate the anaerobic co-digestion of domestic sewage sludge (in form of primary and secondary sewage sludge) with food waste under mesophilic temperature in a batch assay. Two series of batch biochemical methane potential (BMP) test were conducted using the Automatic Methane Potential Test System (AMPTS II). Each set are labelled with BMP 1(PSS:FW) and BMP 2 (SSS:FW). The BMP tests were monitored automatically until the methane production is insignificant. Using the data observed in the laboratory, the kinetic paremeters were calculated. Also, the First-order and Modified Gompertz modeling were included to predict the anaerobic digestion performance. Finding showed that BMP 1(PSS:FW) have better performance with respect to the higher ultimate methane yield and methane production rate as compared to BMP 2 (SSS:FW). Besides, the kinetic parameters from laboratory work and modeling were slightly different. In which the kinetic paremetes from modelling is lesser. However, both modelling are well fitted to the experimental data with high correlation coefficient, R2 ranged from 0.993 to 0.997.

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Section: Methodsmentioning

confidence: 99%