Characteristics of Animal Slurry as a Key Biomass for Biogas Production in Denmark

Triolo, Jin Mi; Ward, Alastair James; Pedersen, Lene; Sommer, Sven G.

doi:10.5772/54424

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…The concentration of lignin in VS of most animal manure is larger than 10% except for pig fattening slurry, whereas the lignin may be higher for sow slurry [50].…”

Section: Pig Manurementioning

confidence: 95%

“…Due to the different diets, dairy cow manure generally shows the most abundant amount of lignin (i.e., around 18.0 %), whereas manure from beef breeds contains a lower amount of about 13% [50]. Other authors [47] found a content of lignin in dairy manure of 7.95%, while in pig and poultry manure it was 1.83% and 1.7%, respectively.…”

Section: Livestock Manurementioning

confidence: 99%

“…Lignocellulose is very difficult to be degraded in anaerobic environments due to its rigid structure produced by lignin. Numerous studies have reported that lignin content and the efficiency of enzymatic hydrolysis have an inverse relationship [50].…”

Section: Livestock Manurementioning

confidence: 99%

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Recent Updates on the Use of Agro-Food Waste for Biogas Production

et al. 2019

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The production of biogas from anaerobic digestion (AD) of residual agro-food biomasses represents an opportunity for alternative production of energy from renewable sources, according to the European Union legislation on renewable energy. This review provides an overview of the various aspects involved in this process with a focus on the best process conditions to be used for AD-based biogas production from residual agro-food biomasses. After a schematic description of the AD phases, the biogas plants with advanced technologies were described, pointing out the strengths and the weaknesses of the different digester technologies and indicating the main parameters and operating conditions to be monitored. Subsequently, a brief analysis of the factors affecting methane yield from manure AD was conducted and the AD of fruit and vegetables waste was examined. Particular attention was given to studies on co-digestion and pre-treatments as strategies to improve biogas yield. Finally, the selection of specific microorganisms and the genetic manipulation of anaerobic bacteria to speed up the AD process was illustrated. The open challenges concern the achievement of the highest renewable energy yields reusing agro-food waste with the lowest environmental impact and an increment of competitiveness of the agricultural sector in the perspective of a circular economy.

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“…The concentration of lignin in VS of most animal manure is larger than 10% except for pig fattening slurry, whereas the lignin may be higher for sow slurry [50].…”

Section: Pig Manurementioning

confidence: 95%

Section: Livestock Manurementioning

confidence: 99%

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Recent Updates on the Use of Agro-Food Waste for Biogas Production

et al. 2019

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“…To our knowledge, studies reporting the kinetics of CH 4 production and BMP prediction of pig slurries from commercial origins are scarce and those studies reporting kinetics of CH 4 emission include a relatively poor representation of pig slurries. Previous studies have also shown a wide variability in pig slurry composition and emissions, which in turn is affected by a multiple factors Triolo et al, 2013;Zhang et al, 2014b).…”

Section: Introductionmentioning

confidence: 99%

“…Similar considerations can be adopted for BMP prediction based on the substrate chemical composition Thomsen et al, 2014). Also, as evidenced by Strömberg et al (2015), these prediction methods do not provide information on the degradation kinetics of the substrates, which is essential for decision-making in slurry management processes (Chadwick et al, 2011;Mähnert and Linke, 2009;Moody et al, 2009;Pham et al, 2013), as well as in the design and operation of real scale biogas reactors (Triolo et al, 2013). Therefore, recent attempts in research aim to develop accurate kinetic models to predict methane emissions from an initial dataset of a BMP assay (Brulé et al, 2014;Strömberg et al, 2015;Sun et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%