Microbiological Process Optimization of Biogas Synthesis

Microbiological Process 

Optimization of Biogas Synthesis 

Sabine Kleinsteuber, Dietmar Schlosser, Thomas Maskow, Susann Müller, Hauke Harms 

Department of Environmental Microbiology (UMB) 

Marcell Nikolausz, Heike Sträuber, Katharina Porsch 

Department of Bioenergy (BEN)

Helmholtz Research Field “Energy“ 

Research Program “Renewable Energies“ 

Topic: Refining Biomass into Chemical 

Energy 

Subtopic: Methane from Biochemical Processes 

Involved UFZ departments: 

Bioenergy (BEN), Environmental Microbiology (UMB), 

Environmental Engineering (TUCHEM), Centre for 

Environmental Biotechnology (UBZ) 

Collaboration partner: German Biomass 

Research Center (DBFZ), Dept. Biogas 

Technology

Methane from Biochemical Processes 

Challenges 

• More than 4700 biogas plants are 

operated in Germany (2.9% of gross 

power generation) 

• Process optimization potential for 

existing plants = 20-50% 

• Much higher biogas potential of still 

untapped substrates 

• Solutions for fermentation residues 

of biogas production 

Biogas plants in Germany

Objectives 

Improvement of efficiency and process stability of 

biomethane synthesis 

� Microbial indicators for process stability and dysfunction 

� Diagnostic tools for process control 

� Improve methane yield by process optimization and re-use of 

digestates 

Exploitation of novel feedstocks 

� Agricultural, industrial and municipal waste 

� In particular: lignocellulosic residues, re-use of digestates from 

biogas processes (currently only used as fertilizer) and 

residues from biofuel production 

� Learn from natural microbial processes: wood-decaying fungi, 

gut microbiota of xylophagous arthropods and mammals

3 

2 

2 

1 

1 

2 

4 

Microbiology of the 

biogas process: 

A Black Box? 

Functional groups: 

1: Primary fermenters 

2: Secondary fermenters 

(syntrophic consortia), 

homoacetogens 

3: Aceticlastic methanogens 

4: Hydrogenotrophic 

methanogens 

Competing: sulfate reducers

Specific research questions 

� Rate-limiting steps under different process conditions and depending 

on the feedstock 

� Key organisms of hydrolysis/acidogenesis (depending on feedstock) 

� Improving efficiency of hydrolysis by inoculation 

� Recovery of methanogenesis by pre-adapted consortia 

� Aceticlastic vs. hydrogenotrophic methanogenesis – which one 

performs better under which conditions (and which groups of archaea) 

� Key organisms of acetogenesis, role of homoacetogens 

� Competition for hydrogen and organic substrates (e.g. by SRB) 

� Stability under high ammonia load, low pH, high VFA, high organic 

load, short retention times … 

� Optimization of trace element supply (additives vs. substrate mixture 

optimization) 

� Constant vs. fluctuating process conditions: diversity – resilience – 

process stability 

� Single stage vs. two stage processes 

� Mesophilic vs. thermophilic processes 

� ….

Extending the utilization of lignocellulosic feedstocks for 

biochemical conversion into bioenergy 

Lignocellulosic materials not readily 

applicable in fermentation for 

biogas production 

Cellulose 

microfibrils 

Cellulose 

Hemicelluloses 

γ 

β 

α 

Lignin 

Pre-treatment with lignocellulose-decaying 

microorganisms / enzymes derived thereof 

• fungi with specific natural adaptations & capabilities 

• cellulases, hemicellulases, lignin-modifying enzymes 

Aquatic 

hyphomycetes 

as a still 

untapped 

bioresource 

Improved availability of lignocellulosic residues

Two-stage reactor system: 

Metabolite and energy flow 

substrate 

substrate 

pump 1 

Temp 

Control 1 

Gas 

analysis 1 

Efflux 1 

Reactor 1 

Hydrolysis and 

Acidogenesis 

pH‐ 

controlled 

substrate 

pump 

Temp 

Control 2 

Gas 

analysis 2 

Reactor 2 

Efflux 2 

Acetogenesis and 

Methanogenesis

Methods to study metabolite 

and energy flow: 

Calorimetry 

� Non-invasive 

� Real-time measure of metabolic activity 

� Real-time indication of metabolic changes 

Aceticlastic vs. hydrogenotrophic methanogenesis 

CH 3COOH � CO 2 + CH 4 

endothermic reaction 

4 H 2 + CO 2 � CH 4 + 2 H 2O extremely exothermic reaction 

� Ratio between both reactions under different conditions 

� Ratio between methanogenesis and competing reactions (e.g. 

sulfate reduction) 

� Influence of hydrogen partial pressure 

Combination with metabolomics / modeling

Methods to study metabolite and energy flow: 

Isotope signature analysis and isotope tracers 

Whiticar (1999) 

� Determine 

proportions of 

aceticlastic and 

hydrogenotrophic 

methanogenesis 

� Metabolite 

analysis by isotope 

tracers 

� Combination with 

molecular methods 

� Data for modeling

Müller & Nebe-von-Caron (2010) 

FEMS Microbiol. Rev. 

Flow Cytometry 

� Studying community 

dynamics 

� High resolution analysis 

of key sub-communities 

based on single cell 

characteristics (e.g. cellular 

DNA content, scatter 

behaviour) 

� Combination with 

molecular methods and 

statistical pattern analysis 

� Data for modeling

Müller et al. (2010) 

Fluorescence 

Assisted Cell 

Sorting (FACS) 

Unravel complex microbial 

communities using 

multiparametric flow cytometry 

Phylogenetic and functional 

profiling of sub-communities 

� Genomics 

� Transcriptomics 

� Proteomics 

� Isolation and cultivation

Molecular approaches for community analysis 

Phylogenetic 

identification 

In situ detection and 

quantification 

PCR 

Bacterial 

and 

archaeal 

groups 

Community 

profiling 

100% 

Functional 

diversity 

50% 

0% 

mcrA 

fhs 

Zz 5-7 Zz 53-56 Zz 9-00

T-RFLP profiles of the methanogenic community in DDGS-fed digesters 

Methanoculleus 

Gas production: 4.46 L/d 

4462 ppm H 2 S 


281 ppm H 2 S (FeCl 3 ) 

Propionic acid: 0 mg/L 


47 ppm H 2 S (FeCl 3 ) 



69 ppm H 2 S (FeCl 3 ) 


Propionic acid: 64.3 mg/L 


31 ppm H 2 S (Ferrosorp) 


Methanosarcinales 

Methanosaeta 

Methanosarcina 

Methanomicrobiales 

#6.2 

#6.3 

#6.4 

#6.5 

#6.6 

Ziganshin et al., in prep.

Improved molecular strategies to assess 

community activity and dynamics 

� RNA is a more sensitive marker of activity changes 

compared to DNA 

� Functional gene approach provides information on 

the structure of certain functional guilds 

�Methanogens – mcrA (Methyl coenzym M reductase) 

�Acetogens – fhs (Formyltetrahydrofolate synthetase) 

�Sulfate reduction – dsrAB (Dissimilatory sulfite reductase) 

� Cellulase and hydrogenase genes 

� … 

�Strategy: Study expression of functional genes 

Combination with flow cytometry, isotope tools, 

metabolomics, modeling

Analysis of syntrophic consortia by fluorescence-based methods: 

CARD-FISH, Magneto-FISH, CLSM 

©2008 by National Academy of Sciences 

Pernthaler et al. (2008) PNAS 105:7052-7057

Functional Description of Biogas Communities 

Epi-metabolome and thermodynamic analysis Community dynamics and interactions 

Metabolic network modeling Disturbance regimes – process optimization

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Microbiological Process Optimization of Biogas Synthesis

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