Optimization of Biomethane Production in Mono-Cardboard Digestion: Key Parameters Influence, Batch Test Kinetic Evaluation, and DOM Indicators Variation

Dunjie Li, Liuying Song, Hongli Fang, Yue Teng, Hongfei Cui, Yu You Li, Rutao Liu, Qigui Niu

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Mono-cardboard waste digestion in batch tests associated with different impact factors was investigated. The maximum methane generation was 394 mL/gVSadd with the best F/M of 0.5 at mesophilic conditions. The highest methane content reached 75% in the dynamic water bath feeding with an average particle size of 1-3 mm. Hydrolysis and methanogenesis were significantly different between static and dynamic states, especially at particle size over 3 mm. The modified Gompertz model (R2 > 0.98) and the modified Aiba model (R2 > 0.88) were the most appropriate models for methane generation among the six kinds of models. At different TS, the variation of dissolved organic matters reflects the metabolic rate of the microbial community. The soluble microbial product-like and protein-like components half split by excitation-emission matrix-parallel factors significantly negatively corresponded to biomethane production. Moreover, a rapid loss of methanogenesis was observed with high organics concentration. A strong correlation between the F/M ratio and the CH4 generation ability was observed with an optimized F/M of 0.5. The maximum energy production was also investigated based on the optimized particle size of 2-5 mm and F/M of 0.5, in which long-term stability was maintained.

Original languageEnglish
Pages (from-to)4340-4351
Number of pages12
JournalEnergy and Fuels
Volume33
Issue number5
DOIs
Publication statusPublished - 2019 May 16

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Fuel Technology
  • Energy Engineering and Power Technology

Fingerprint

Dive into the research topics of 'Optimization of Biomethane Production in Mono-Cardboard Digestion: Key Parameters Influence, Batch Test Kinetic Evaluation, and DOM Indicators Variation'. Together they form a unique fingerprint.

Cite this