New Insights on Process Performance and Stability for
Anaerobic Co-Digestion through Modelling and Population
Analysis
A. Keucken *,**, M. Habagil *, D. Batstone *** and M. Arnell ****,***** * Vatten & Miljö i Väst AB, P.O. Box 110, SE-311 22 Falkenberg, Sweden. E-mail:
Alexander.Keucken@vivab.info, Phone: +46 75-727 40 37; E-mail: Moshe.Habagil@vivab.info. ** Water Resources Engineering, Faculty of Engineering, Lund technical University, Box 118, SE-221 00 Lund, Sweden.
*** Advanced Water Management Centre, The University of Queensland, Brisbane, 4072, QLD, Australia. E-mail: d.batstone@awmc.uq.edu.au.
**** Department of Biomedical Engineering (BME), Division of Industrial Electrical Engineering and Automation (IEA), Lund University, P.O. Box 118, SE-221 00 Lund, Sweden. E-mail:
magnus.arnell@iea.lth.se, Phone: +46 10 516 63 33.
***** RISE Research Institutes of Sweden, Gjuterigatan 1D, SE-582 73 Linköping, Sweden.
Abstract: Anaerobic co-digestion (AcoD) allows for underutilised digesters to increase biomethane production. The organic fraction of municipal solid waste (OFMSW), e.g. food waste, is an abundant substrate with high degradability and gas potential. This paper focuses on the implementation of co-digestion of mixed sludge from wastewater treatment and OFMSW through batch and continuous lab-scale experiments, modelling and microbial population analysis. The results show a rapid adaptation of the process and an increase of the biomethane production of 20 to 40% with 50% OFMSW and it has an impact on the microbial community. The methanogenic activity increases and changes towards acetate degradation while the community without co-substrate remains unaffected. The modelling results show that ammonium inhibition increases at elevated organic loads and that intermittent feeding causes fluctuations in digester performance due to varying inhibition. Modelling can be successfully used for designing feed strategies and experimental set-ups for anaerobic co-digestion.
Keywords: anaerobic digestion, mathematical modelling, microbial community, solid waste, wastewater treatment
INTRODUCTION
Following the strong focus on energy efficiency and climate change, WWTPs have started to valorise and optimise the biomethane production (Batstone & Virdis 2014). Many digesters at WWTPs are oversized, leaving redundant capacity for treating additional organic material, i.e. anaerobic co-digestion (AcoD) and thereby increasing the biomethane production. One substrate of interest, due to its availability and
characteristics, is the organic fraction of municipal solid waste (OFMSW). The practical aspects of co-digestion of OFMSW at WWTPs, such as biogas production and sludge production, has been extensively reviewed (Mata-Alvarez et al. 2011). However, only limited studies exist on the implementation (start-up, load increase, etc.) and impact on the AD processes by AcoD of these substrates (Fonoll et al. 2015). In this paper, implementation of AcoD for sewage sludge and OFMSW at WWTPs are investigated.
MATERIALS AND METHODS
In this study, experimental work in batch and continuous reactors has been extensively used together with modelling and simulation for assessment. The methodology is illustrated in Figure 1. Two substrates were used for co-digestion in this study: 1) mixed sludge (pre-thickened primary and secondary sludge) from the WWTP Getteröverket in Varberg, Sweden and 2) OFMSW collected from households in Varberg, Sweden. Physico-chemical analysis and biomethane potential (BMP) tests were performed on the two substrates, separately. Results were used to model feed characterisation, parameter estimation (following the methodology by Arnell et al.
(2016)) and for design of the continuous experiments (CSTRs). The CSTR tests were performed in two 5 l lab-scale reactors: one digesting 1:1 mixed sludge and OFMSW (R1) and the other digesting 100% mixed sludge as a reference (R2). The reactors were operated at mesophilic conditions and with 22 d hydraulic retention time (HRT). The reactors were fed daily starting at an organic loading rate (OLR) of 1 kg/m3/d for 33 d followed by a step change to 2 kg/m3/d for another 33 d. Substrate composition and HRT were kept constant throughout the experiment.
The Anaerobic Digestion Model No. 1 (ADM1) (Batstone et al. 2002) was used to model both the BMP tests and the two CSTRs (Arnell et al. 2016). The model was analysed for deeper understanding of the stability and process dynamics at start-up of AcoD. Furthermore, numerous population analyses of methanogenic organisms were performed on the anaerobic sludge.
RESULTS AND CONCLUSIONS
• The simulated BMP tests, substrate characteristics and influent fractionation as well as parameter estimations are shown in Figure 2 and Table 1. The input fractions and parameters were successfully used also for modelling continuous experiments (Figure 3). It can be concluded that, model characterisation based on BMP data is applicable for modelling continuous reactors.
• Applying AcoD with the two substrates increases the gas production at equivalent load. With a feed composition 1:1 of the two substrates the experiments show 22-42% more biogas than for a reference reactor fed with only mixed sludge (Figure 2 and Table 1).
• Implementation of co-digestion of sewage sludge and OFMWS shows rapid adaptation. Despite a short lag in the BMP for OFMSW (Figure 2), the response in gas production was immediate in continuous digestion (Figure 3). This conclusion is supported by the equally rapid increase in methanogenic microbial population for AcoD (Table 2).
• The methanogenic microbial population in the CSTRs increases when commencing co-digestion of sewage sludge and OFMSW on a WWTP inoculum. This effect is further pronounced at an increased load, which also promotes a change in the methanogenic microorganisms towards acetate (Table 2).
• The feeding strategy of continuous lab-scale digestion experiments has impact on digester performance instantaneously. The simulation results show that intermittent feeding lead to short-term inhibition of the process (Figure 4).
REFERENCES
Arnell M., Astals S., Amand L., Batstone D. J., Jensen P. D. and Jeppsson U. (2016). Modelling anaerobic co-digestion in Benchmark Simulation Model No. 2: Parameter estimation, substrate characterisation and plant-wide integration. Water Research 98, 138-46.
Batstone D. J., Keller J., Angelidaki R. I., Kalyuzhnyi S. V., Pavlostathis S. G., Rozzi A., Sanders W. T. M., Siegrist H. and Vavilin V. A. (2002). Anaerobic Digestion Model No. 1,
IWA Scientific and Technical Report No. 13, IWA Publishing, London, UK.
Batstone D. J. and Virdis B. (2014). The role of anaerobic digestion in the emerging energy economy.
Current Opinion in Biotechnology 27, 142-9.
Fonoll X., Astals S., Dosta J. and Mata-Alvarez J. (2015). Anaerobic co-digestion of sewage sludge and fruit wastes: Evaluation of the transitory states when the co-substrate is changed.
Chemical Engineering Journal 262, 1268-74.
Mata-Alvarez J., Dosta J., Macé S. and Astals S. (2011). Codigestion of solid wastes: A review of its uses and perspectives including modeling. Critical Reviews in Biotechnology 31(2), 99-111.
Output Modelling/Simulation Physico-chemical substrate analysis BMP tests Continuous experiments Substrate characterisation
Model calibration ADM1
Analysis/experimental work Substrate chatacteristics BMP data/model Parameter estimates khyd, fd Continuous reactor data/model Biosludge analysis Methanogenic
activity data
Figure 1. Work flow diagram for the applied methodology.
a)
b)
Figure 2. Cumulative methane production for biomethane potential tests of mixed sludge (a) and OFMSW (b). For each substrate, the total biomethane production (top) is displayed as well as net production corrected for inoculum (bottom). Markers represent data and lines are model results (ADM1).
Table 1. Resulting substrate characteristics.
Mixed sludge OFMSW Measurements DS [kg DS/ton] 73.6 186 VS [kg VS/ton] 59.6 173 Model estimates Ultimate methane potential (B0) [m3 CH4/ton VS] 287 475 Hydrolysis rate (khyd) [d-1] 0.34 0.25 Total dissolved COD [g COD/m3] 10 52.1 Carbohydrates (Xch) [g COD/m3] 2.60 69.9 Protein (Xpr) [g COD/m3] 20.2 56.7 Lipids (Xli) [g COD/m3] 24.6 85.6
a) 0 10 20 30 40 50 60 70 0 50 100 150 200 250 300 350 400 Qraw g a s [Nml.h -1] OLR=1 OLR=2 0 10 20 30 40 50 60 70 time [d] 0 50 100 150 200 250 QCH4 [Nml.h -1] OLR=1 OLR=2 b) 0 10 20 30 40 50 60 70 0 50 100 150 200 250 300 350 400 Qraw g a s [Nml.h -1] OLR=1 OLR=2 0 10 20 30 40 50 60 70 time [d] 0 50 100 150 200 250 QCH4 [Nml.h -1] OLR=1 OLR=2
Figure 3. Gas production for continuous lab scale reactor R1 (a) and reference reactor R2 (b). Total gas flow rate (top) and methane flow rate (bottom). Markers represent data for daily production, blue lines represent modelled production and grey lines show modelled daily production. The red dashed lines mark time for load increase.
Table 2. Quantitative measurements of methanogenic bacteria in continuous lab scale reactors R1 and R2 at OLRs of 1 kg/m3/d and 2 kg/m3/d (n.d. - not detected).
Percentage of the taxonomic group
Orders and families of methanogenic bacteria Inoculum, start point of both reactors R1 OLR 1.0 R2 OLR 1.0 R1 OLR 2.0 R2 OLR 2.0 Methanomicrobiales in sum 8 12 9 12 15 Thereof: Methanocorpusculaceae n.d. n.d. n.d. n.d. n.d. Thereof: Methanospirillaceae n.d. n.d. n.d. 2 1 Methanobacteriales n.d. n.d. n.d. n.d. n.d. Methanosarcinales in sum 8 15 10 20 15 Thereof: Methanosaetaceae 8 15 10 20 15 Methanococcaceae n.d. n.d. n.d. n.d. n.d.
All methanogenic bacteria 16 27 19 32 30
a) 0 10 20 30 40 50 60 70 time [d] 0.65 0.7 0.75 0.8 0.85 0.9 INH4 -N [-] b) 67 67.5 68 68.5 69 69.5 70 time [d] 0.68 0.69 0.7 0.71 0.72 0.73 0.74 0.75 0.76 INH4 -N [-]
Figure 4. Modelled ammonium inhibition for reactor R1. Simulation period 70 days (a) and a three-day selection (b). The red dashed line marks time for load increase. Yellow dashed lines mark time for feeding.
