Next Article in Journal
Terrestrial Protected Areas and Food Security: A Systematic Review of Research Approaches
Next Article in Special Issue
An Investigation of the Potential Adoption of Anaerobic Digestion for Energy Production in Irish Farms
Previous Article in Journal
Evaluating the Effectiveness of Urban Hedges as Air Pollution Barriers: Importance of Sampling Method, Species Characteristics and Site Location
Previous Article in Special Issue
Description of a Decentralized Small Scale Digester for Treating Organic Wastes
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of Temperature and Organic Load on the Performance of Anaerobic Bioreactors Treating Grasses

Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
*
Author to whom correspondence should be addressed.
Submission received: 8 September 2020 / Revised: 27 September 2020 / Accepted: 29 September 2020 / Published: 1 October 2020
(This article belongs to the Special Issue Small-Scale Anaerobic Digestion for Biogas Production)

Abstract

:
The organic residues generated in grasslands can be treated by adopting anaerobic digestion technology. This technology can enhance the efforts for sustainable waste management around the world. In the northern Netherlands, there is a vast amount of ditch clippings and canal grasses that can be used as a renewable source of energy; however, optimal bioenergy production from grasses is still under research and this study aims to evaluate biogas production from grassy residues at the local level in the context of a sustainable waste management scheme. Batch tests were facilitated to investigate the impact of temperature and organic load on the anaerobic digestion performance of grass mixtures (ditch clippings and canal grasses). The results showed that high temperature favors the degradation of high lignocellulosic materials like grasses. Specifically, bioreactors at 55 °C with an organic load of 30 g volatile solids (VS) L−1 reached 360.4 mL g VSsubstrate−1. Moreover, reactors with low organic loads resulted in a lower methane yield. The kinetics study also showed good fitting of the predicted and experimental values.

1. Introduction

Sustainable energy production is an urgent necessity due to the depletion of fossil fuels, the continuing increase in the world population, food security, and the augmentation in environmental problems [1,2,3]. Bioenergy from agricultural and farming waste plays a major role in research efforts [4,5,6,7]. Different treatment processes are implemented to treat organic waste, with the anaerobic digestion (AD) technology having among others economic merit in large-scale utilization [8,9,10,11]. The biochemical process of AD that converts organic waste into useful products is regarded as another choice for bioenergy production [12,13,14,15,16]. Biogas is an energy carrier and its composition consists of 66% CH4, 33% CO2, 0.5% N2, 0.1% O2, and 103 mg H2S (L biogas−1) [17]. Based on the application, the biogas may probably undergo post-treatment (upgrading) to reach the natural gas properties [18]. The multiple utilization of biogas for heat and electricity generation or vehicle fuel (upgrade biogas) can support the drive for its application [19,20,21]. It is also obvious that the AD antagonism will be affected by the use of other energy sources (e.g., wind, nuclear, shale oil/gas) [22,23,24,25,26].
Yet however, AD applicability can be recognized from both socioeconomic and ecological perspectives. Sustainable engineering has paved the way for AD technology to be widely used in the EU and, thus, biogas is a crucial factor for the transition to the bioeconomy [27,28,29,30]. The subsidization by the administration of each country has played a key role in the inexorable augmentation in the number of biogas plants around the globe. To date, it is essential to sustainably ameliorate rural areas’ life by materials recovery and diminished energy consumption [31,32]. The use of highly lignocellulosic waste streams may be a restriction for the applicability of AD technology because of their recalcitrance [33,34,35].
Many researchers agree to establish a competent bioreactor performance correlated to process conditions and microbiome dynamics. Nevertheless, the problems during the operation of the waste-treating bioreactors have made inquiring about the co-digestion process a matter of great concern. The co-digestion process has been formerly pointed out as an alternative to treat two or more substrates [36,37,38]. The carbon to nitrogen (C:N) ratio (ideal ratio ranges from 20 to 30) is an important player for the efficient simultaneous treatment of alternative substrates. Additionally, previous studies review the importance of temperature and organic load on the biogas production [39]. The farm-scale biogas plant increases across Europe and there is great potential for using grassy wastes to produce biomethane [40]. Ditch clippings and canal grasses can be collected in vast amounts and can be used for energy recovery. Optimal bioenergy production from grasses is still under research. The interest of using grasses as feedstock in biogas plants is due to its high yield of biogas produced. However, the use of grasses for biogas production in Europe still occurs at a moderate level compared to other waste resources [41].
This experimental study aims to appraise the AD performance from grasses mixtures (ditch clippings and canal grasses) under different process conditions. Specifically, the experimental study attempted to determine the influence of temperature and organic load (OL) in the AD efficiency in terms of methane yield. Ditch clippings and canal grasses were chosen as feedstocks for the experimental tests. This report particularly aims to (1) determine the methane yield of the grass mixtures, (2) examine the impact of temperature and organic load on the AD efficiency, and (3) predict methane production using the first order and cone models.

2. Materials and Methods

2.1. Inoculum and Substrates

The inoculum used in this study was obtained from a long-term operating anaerobic digester from the wastewater treatment plant of Garmervolde in Groningen, the Netherlands. The inoculum was stored at 4 °C to maintain freshness and microbial activity. It was reactivated at 37 °C for three days before use. The ditch clippings (DC) and canal grasses (CG) were collected from the suburban area in Groningen city. All substrates were stored in the freezer prior to the digestion tests. The characteristics of the anaerobic inoculum, canal grasses, and ditch clippings are summarized in Table 1.

2.2. Batch Tests

Batch tests were facilitated to determine the influence of temperature and organic load on the methane yield. Glass reactors with a working volume of 400 mL (500 mL total volume) were used in the experimental study. The inoculum-to-substrate ratio was set at two based on the preceding study [42]. Table 2 provides the process conditions applied in the test system. Reactors were filled with inoculum, grass mixture, and distilled water based on a predetermined ratio. All reactors (triplicate) were flushed with N2 for 5 min, sealed with butyl rubbers, and placed in a rotating incubator (150 rpm).
Blank reactors containing only inoculum were used to correct the methane production of the batch reactors containing the grass mixtures. Methane yields from the control were subtracted from the data obtained in the experiments with DC and CG. Biogas production and methane content were measured daily.

2.3. Analytical Methods

For the determination of total solids (TS), volatile solids (VS), and ash, the standard methods of APHA were applied. A pH meter (HI991001, Hanna Instruments, Woonsocket, RI, USA) was used to determine the pH of the bioreactors at the beginning and end of the experimental tests. The Nordmann titration method was applied to determine the total alkalinity based on a previous study [42]. A micro gas chromatographer (GC) was used to quantify the methane content of the biogas. The gas chromatographer has a single channel 2-stream selector system (Thermo Fisher Scientific Inc, USA) equipped with a chromatographic column (PLOT-U). Helium was used as carrier gas at a total flow of 10 mL min−1. The calibration gas used in GC device consisted of 50% (v/v) CH4, 20% (v/v) CO2, and 30% (v/v) N2. The daily methane volume (mL g VSsubstrate-1 day-1) was determined using the water displacement method.
The protocol described by Hames et al. [43] was used to estimate the protein content based on the nitrogen content (w/w% DM). According to this protocol, the protein content (w/w%) for biomass samples is calculated according to the equation:
P r o t e i n = N C × N F
where NC is the nitrogen content (w/w%) and NF the nitrogen factor, which is 6.25 for all types of biomass excluding wheat grains (NF = 5.70). The initial biomass oven-dry-weight corrected for the carbohydrate, ash, and protein contents, was assigned to the lignin content of the samples. Air-dried grass samples were used for elemental analysis with an elemental analyzer (Vario EL/microcube, Elementar, Langenselbode, Germany).

2.4. Kinetics Models

Two models (first-order and cone) were applied for the kinetic study using Microsoft Office Excel (Microsoft Office 2010). The two models determined the hydrolysis rates during the degradation of organic matter and the equations used are [42]:
M ( t ) = M O × ( 1 e ( Kt ) )
M ( t ) = M O 1 + ( Kt ) n
where M(t) is the cumulative methane yield at digestion time t days (mL methane g VSsubstrate−1), MO is the maximum methane potential of the substrate (mL methane g VSsubstrate−1), n is the shape factor, K is the methane production rate constant (d−1), and t is the time (days). To validate the models, the statistical indicators root mean square error (RMSE) and correlation coefficient (R2) were calculated from the formulas [44]:
RMSE = ( 1 n i = 1 n d i 2 ) 1 2
where di is the deviation between the ith measured and the predicted values and n is the number of data points; and
R 2 = [ n ( i = 1 n X i Y i ) ( i = 1 n X i ) ( i = 1 n Y i ) [ n i = 1 n X i 2 ( i = 1 n X i ) 2 ] [ n i = 1 n Y i 2 ( i = 1 n Y i ) 2 ] ] 2
where Xi is the ith predicted value.

2.5. Statistical Analysis

Differences in the methane yields obtained in co-digestion of DC and CG at different OLRs and temperatures were assessed by using one-way analysis of variances (ANOVA) in Microsoft Office Excel (Microsoft, USA). Statistical significance of the data was determined using Student’s t-test with a threshold p-value of 0.05.

3. Results and Discussion

3.1. Daily Methane Production

During the anaerobic digestion at 25 °C the individual substrates, R1, R2, and R3 began to produce ≥10 mL g VSsubstrate−1 day−1 on the 4th, 2nd, and 2nd day, respectively (Figure 1). Rapid methane production began in the reactors at 25 °C even though process speed did not show any clear dependence on the OL. The highest daily biogas production rates for R1, R2, and R3 were 11.1, 12.3, and 12.8 mL g VSsubstrate−1 on 4th, 5th, and 6th day, respectively. Methane production of R2 with 20 g VS L−1 (25 days) was terminated faster than those in R1 and R3 (34 and 30 days, respectively). The overall performance was at low ebb due to fast hydrolysis and the subsequent VFA (volatile fatty acids) acidification that inhibits the methanogenic activities. R4, R5, and R6 at mesophilic conditions (35 °C) showed a similar pattern of daily methane production.
Methane remained in the range of 10–15 mL g VSsubstrate−1 day−1 for 4, 7, and 7 days, respectively, and afterwards declined to a lower level until the methane production dropped to zero on day 35, 34 and 34, respectively. R4 and R5 reached the maximum daily methane production rate of 13.5 and 14.2 mL g VSsubstrate−1 on days 4 and 5, respectively, whereas R6 with OL of 20 g VS L−1 reached 16 mL g VSsubstrate−1 on day 4. In contrast, the maximum daily biogas derived from the reactors (R7→9) at 45 °C was 19.5, 20.8, and 18.6 mL g VSsubstrate−1 on days 4, 6, and 4, respectively.
Reactors with 30 g VS L−1 (R10, R11, and R12) began rapidly to produce a high amount of biogas reaching 20.5, 19.1, and 23.9 mL g VSsubstrate−1 on days 4, 6 and 4, respectively. The methane produced daily remained for the first 15 days in the range of 10–21 mL g VSsubstrate−1 day−1 and afterwards gradually declined to a lower level until the biogas production dropped to zero on day 35.

3.2. Cumulative Methane Production

Figure 1 describes the cumulative methane yield for all the reactors. Concerning the digestion at 25 °C, the reactor with OL of 30 g VS L−1 reached a methane yield of 166.7 mL g VSsubstrate−1. It was slightly higher (5.7%) than the methane yield produced in treatments with OL of 10 and 20 g VS L−1 (157.3 and 157.9 mL g VSsubstrate−1, respectively). Concerning the digestion at 35 °C, methane yield from the bioreactors was in correspondence with organic load reaching a methane yield of 193.3, 208.2, and 220.4 mL g VSsubstrate−1 for 10, 20, and 30 g VS L−1, respectively (Table 3). Nevertheless, the utilization of grass residues as the sole substrate may increase the VFA concentration due to the high lignocellulose content, and which perturbates the bioreactors’ stability. Low methane yields derived from highly lignocellulosic feedstocks have been formerly cited by Chiumenti et al. [45].
Treatments in thermophilic conditions (at 55 °C) showed a higher discrepancy in the methane yields. Treatment with the highest OL (30 g VS L−1) reached a methane yield of 360.4 mL g VSsubstrate−1 approximately 20.4 and 15.6% higher than the methane yield recovered from R10 and R11 (299.1 and 311.7 mL g VSsubstrate−1, respectively). The results from the experiments connoted that thermophilic tests resulted in higher methane yields and specifically R12 was 2.2-, 1.6- and 1.4-fold higher than those reactors (R3, R6, and R9) treating the grass mixture at 25 °C, 35 °C and 45 °C, respectively. A previous study examined the simultaneous digestion of clipped urban grasses collected from abandoned areas with sewage sludge and reported an average methane yield of 150 mL g VSsubstrate−1 [46]. They mentioned that the low methane yield is likely ascribed to the high fiber concentration contained in this type of grass. However, the material was derived from un-maintained areas, which presumably contained relatively high fiber concentrations.
A previous study reported high methane yield (approximately 70%) in the biogas produced from grass in a two-stage AD process [47]. Authors stated that this is due to the chemical composition of grass. Nizami et al. determined the methane yield from grass silage and reported that methane content can vary depending on the AD technique. Specifically, upflow anaerobic sludge blanket reactors resulted in 71% methane content in biogas, whereas, small scale methane potential systems resulted in 52% methane [48]
Additionally, temperature perturbation may influence the process performance and subsequently the biogas production. Ghatak and Mahanta investigated the biogas production in lignocellulosic biomasses (bamboo dust and saw dust) at 35 °C, 45 °C and 55 °C temperatures and they observed that biogas production increases with temperature [49]. However, Ramaraj and Unpaprom examined the biogas production from duckweed at 35 °C and 50 °C and they reported that thermophilic bioreactors produced less biogas and lower methane concentration [50]. Ahn et al. [51] evaluate the dry digestion of switchgrass (Panicum capillare L.) with swine manure resulting in 337 mL g VSsubstrate−1. Lehtomäki et al. [52] also investigated the methane potential of grass in a batch leach bed and reported a range of 141–0.204 mL g VSsubstrate−1. In a previous study, a positive effect on methane yield was observed from the co-digestion of organic wastes in high organic load [53]. An alternative to ameliorate the methane yield is a bioreactor intake with animal slurry, a fact that may influence the digesters’ stability. Świątek et al. [54] reported the addition of chicken manure increases the methane yield. Moreover, an extensive determination of a bioreactor’s microbiome is recommended as the high heterogeneity of microbial species can insinuate the discrepancies in methane yields derived using different inoculums [55,56].
Industrial and research efforts to overcome the technological constraints for lignocellulosic waste treatment have been previously reported [57]. Nonetheless, there are still issues that have to be examined like the value of the end-products, different waste sources, or the biology of bioreactors [58,59,60]. Considering the above results, a techno–economic assessment of the thermophilic anaerobic digestion of grasses in continuous mode would be interesting to evaluate the process in the context of viability and profitability in a pilot or full-scale application [61,62,63].

3.3. pH, Alkalinity and Volatile Solids Removal

PH variation is given in Figure 2 for all the glass bottles at the starting and ending point. The ending pH values ranged from 6.91 to 7.32, indicating a suitable environment for the decomposition of the feedstocks. Reactors at 25 °C and 35 °C resulted in a final pH lower than the starting pH in comparison to the reactors in higher temperatures. A pH range of 6.8–7.4 is highly favorable for the efficient degradation of the material. Nevertheless, lower pH values are indicated to increase the hydrolysis efficiency due to the enhanced bacteria activity in the pH range of 5.5–6.5.
Microbes are ‘nifty’ concerning pH tolerance and exposition to low pH may cause process perturbations that can lead the reactor to a ‘sour’ situation. Low pH languishes the activity and growth of methanogens resulting in a deficit of methane. This is due to the sensitivity of methanogens in acidic environments. Methanogenic species identification is a crucial factor for the stable behavior of anaerobic digesters due to the high sensitivity of methanogens in pH variation. A previous study ascertains that increasing the concentration of VFAs impedes the growth of methanogens, consequently lessening the methane content in biogas.
Alkalinity is also a parameter to assess the stability of the bioreactors. The total alkalinity of all the treatments is displayed shown in Figure 3. The ISR was set to two to provide sufficient alkalinity as it is regarded as an optimal value for the anaerobic digestion of lignocellulosic material. No additional buffers were added in the bioreactors as it was entirely provided by the inoculum. The inoculum being provided to the bioreactors as a nutrients supply is the essential condition for the microbial growth as mentioned by previous studies [64,65]. The preceding study also examined the impact of source-based inoculums in the digestion of lignocellulosic waste [66] and the authors elucidated that the source of the inoculum is critical for the degradation efficiency.
The determination of VS removal is also an indicator used to verify the soundness of the experimental tests and the correspondence of VS removal with the methane produced is expected. They ascertained the significant impact of macronutrients (i.e., nitrogen) on the microbial activity. The functional correlation of cumulative methane and VS reduction is shown is Figure 4. The plot displays the curved regression equation (y = −0.0002x2 + 0.1881x + 6.5975, R2 = 0.9567) and, as expected, methane yield shows an incremental tendency as the VS removal increases.

3.4. Kinetics Results

The results from the kinetic analysis using the first order and cone models are given in Table 3 and Table 4. To appraise the models’ soundness, the predicted methane values were plotted against the experimental values. Figure 5 demonstrates the picture of the kinetics indicating a good congruence of the models with the experimental study with a difference of less than 5% for the bioreactors. Lignin and its derivatives were present, and herein lies the observed low hydrolysis efficiency.
R8 at 45 °C with 20 g VS L−1 showed the highest hydrolysis rate 0.1826 (R2 = 0.9651) using the first-order model. In the cone model, treatments (R2 and R3) at 25 °C with 20 and 30 g VS L−1 showed the highest hydrolysis rates 0.1356 (R2 = 0.9775) and 0.1329 (R2 = 0.9857), respectively. However, the difference with the other reactors is not significant, indicating that there is no clear independence between methane yield and hydrolysis rates. Indeed, R12 showed lower hydrolysis rates, but it resulted in the highest methane yield reaching 360.4 mL g VSsubstrate−1.

4. Conclusions

This study examined the effect of organic load and temperature on the methane yield from the anaerobic treatment of ditch clippings and canal grasses. The positive impact of elevated temperature on methane yield was revealed from the experimental tests. Specifically, bioreactors at 55 °C with an organic load of 30 g VS L−1 reached 360.4 mL g VSsubstrate−1. Moreover, reactors with low organic loads resulted in lower methane yields. Additionally, the high organic load did not hinder the degradation of the grass, a fact that may overcome the impediments of high-rate digestion. Furthermore, from the kinetics analysis, both models were found to have a good fit with the experimental data. It is concluded that grass residues have the potential for bioenergy and their use as a substrate is recommended for farm-scale biogas production.

Author Contributions

Conceptualization, S.A.; methodology, S.A. and G.J.W.E.; software, S.A.; writing—original draft preparation, S.A.; writing—review and editing, G.J.W.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bezama, A.; Agamuthu, P. Addressing the big issues in waste management. Waste Manag. Res. 2019, 37, 1–3. [Google Scholar] [CrossRef] [Green Version]
  2. Demichelis, F.; Piovano, F.; Fiore, S. Biowaste management in Italy: Challenges and perspectives. Sustainability 2019, 11, 4213. [Google Scholar] [CrossRef] [Green Version]
  3. Deletic, A.; Wang, H. Water pollution control for sustainable development. Engineering 2019, 5, 839–840. [Google Scholar] [CrossRef]
  4. Valenti, F.; Porto, S.M.C. Net electricity and heat generated by reusing Mediterranean agro-industrial by-products. Energies 2019, 12, 470. [Google Scholar] [CrossRef] [Green Version]
  5. Achinas, S.; Horjus, J.; Achinas, V.; Euverink, G.J.W. A PESTLE analysis of biofuels energy industry in Europe. Sustainability 2019, 11, 5981. [Google Scholar] [CrossRef] [Green Version]
  6. Zambon, I.; Colantoni, A.; Cecchini, M.; Mosconi, E.M. Rethinking sustainability within the viticulture realities integrating economy, landscape and energy. Sustainability 2018, 10, 320. [Google Scholar] [CrossRef] [Green Version]
  7. Provolo, G.; Mattachini, G.; Finzi, A.; Cattaneo, M.; Guido, V.; Riva, E. Global warming and acidification potential assessment of a collective manure management system for bioenergy production and nitrogen removal in northern Italy. Sustainability 2018, 10, 3653. [Google Scholar] [CrossRef] [Green Version]
  8. Ghanimeh, S.; Khalil, A.C.; Ibrahim, E. Anaerobic digestion of food waste with aerobic post-treatment: Effect of fruit and vegetable content. Waste Manag. Res. 2018, 36, 965–974. [Google Scholar] [CrossRef]
  9. Rosero-Henao, J.C.; Bueno, B.E.; De Souza, R.; Ribeiro, R.; De Oliveira, A.L.; Gomide, C.A.; Gomes, T.M.; Tommaso, G. Potential benefits of near critical and supercritical pre-treatment of lignocellulosic biomass towards anaerobic digestion. Waste Manag. Res. 2019, 37, 74–82. [Google Scholar] [CrossRef]
  10. Lemões, J.S.; Silva, E.C.F.L.; Avila, S.P.F.; Montero, C.R.S.; Silva, S.D.D.A.E.; Samios, D.; Peralba, M.D.C.R. Chemical pretreatment of Arundo donax L. for second-generation ethanol production. Electron. J. Biotechnol. 2018, 31, 67–74. [Google Scholar]
  11. Reißmann, D.; Thrän, D.; Bezama, A. How to identify suitable ways for the hydrothermal treatment of wet bio-waste? A critical review and methods proposal. Waste Manag. Res. 2018, 36, 912–923. [Google Scholar] [CrossRef] [PubMed]
  12. Makarichi, L.; Kan, R.; Jutidamrongphan, W.; Techato, K.-A. Suitability of municipal solid waste in African cities for thermochemical waste-to-energy conversion: The case of Harare Metropolitan City, Zimbabwe. Waste Manag. Res. 2019, 37, 83–94. [Google Scholar] [CrossRef] [PubMed]
  13. Efferth, T. Biotechnology applications of plant Callus cultures. Engineering 2019, 5, 50–59. [Google Scholar] [CrossRef]
  14. Hildebrandt, J.; Bezama, A. Cross-fertilisation of ideas for a more sustainable fertiliser market: The need to incubate business concepts for harnessing organic residues and fertilisers on biotechnological conversion platforms in a circular bioeconomy. Waste Manag. Res. 2018, 36, 1125–1126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Morales-Polo, C.; Cledera-Castro, M.D.M.; Moratilla Soria, B.Y. Reviewing the anaerobic digestion of food waste: From waste generation and anaerobic process to its perspectives. Appl. Sci. 2018, 8, 1804. [Google Scholar] [CrossRef] [Green Version]
  16. Ruggero, F.; Gori, R.; Lubello, C. Methodologies to assess biodegradation of bioplastics during aerobic composting and anaerobic digestion: A review. Waste Manag. Res. 2019, 37, 959–975. [Google Scholar] [CrossRef] [Green Version]
  17. Sahajwalla, V. Green processes: Transforming waste into valuable resources. Engineering 2018, 4, 309–310. [Google Scholar] [CrossRef]
  18. Solarte-Toro, J.C.; Chacón-Pérez, Y.; Cardona-Alzate, C.A. Evaluation of biogas and syngas as energy vectors for heat and power generation using lignocellulosic biomass as raw material. Electron. J. Biotechnol. 2018, 33, 52–62. [Google Scholar] [CrossRef]
  19. Chatzikonstantinou, D.; Tremouli, A.; Papadopoulou, K.; Kanellos, G.; Lampropoulos, I.; Lyberatos, G. Bioelectricity production from fermentable household waste in a dual-chamber microbial fuel cell. Waste Manag. Res. 2018, 1037–1042. [Google Scholar] [CrossRef]
  20. Mahdisoozani, H.; Mohsenizadeh, M.; Bahiraei, M.; Kasaeian, A.; Daneshvar, A.; Goodarzi, M.; Safaei, M.R. Performance enhancement of internal combustion engines through vibration control: State of the art and challenges. Appl. Sci. 2019, 9, 406. [Google Scholar] [CrossRef] [Green Version]
  21. Wang, H.; Fu, P.; Li, J.; Huang, Y.; Zhao, Y.; Jiang, L.; Fang, X.; Yang, T.; Huang, Z.; Huang, C. Separation-and recovery technology for organic waste liquid with a high concentration of inorganic particles. Engineering 2018, 4, 406–415. [Google Scholar] [CrossRef]
  22. Wang, J.; Wang, H.; Fan, Y. Techno-economic challenges of fuel cell commercialization. Engineering 2018, 4, 352–360. [Google Scholar] [CrossRef]
  23. Koçer, A.T.; Özçimen, D. Investigation of the biogas production potential from algal wastes. Waste Manag. Res. 2018, 36, 1100–1105. [Google Scholar] [CrossRef] [PubMed]
  24. Vaskalis, I.; Skoulou, V.; Stavropoulos, G.; Zabaniotou, A. Towards circular economy solutions for the management of rice processing residues to bioenergy via gasification. Sustainability 2019, 11, 6433. [Google Scholar] [CrossRef] [Green Version]
  25. Davis, L.A. The shale oil and gas revolution. Engineering 2018, 4, 438–439. [Google Scholar] [CrossRef]
  26. Liu, A.; Teo, I.; Chen, D.; Lu, S.; Wuest, T.; Zhang, Z.; Tao, F. Biologically inspired design of context-aware smart products. Engineering 2019, 5, 637–645. [Google Scholar] [CrossRef]
  27. Chen, P.; Anderson, E.; Addy, M.; Zhang, R.; Cheng, Y.; Peng, P.; Ma, Y.; Fan, L.; Zhang, Y.; Lu, Q.; et al. Breakthrough technologies for the biorefining of organic solid and liquid wastes. Engineering 2018, 4, 574–580. [Google Scholar] [CrossRef]
  28. Achinas, S.; Leenders, N.; Krooneman, J.; Euverink, G.J.W. Feasibility assessment of a bioethanol plant in the northern Netherlands. Appl. Sci. 2019, 9, 4586. [Google Scholar] [CrossRef] [Green Version]
  29. Franco, R.T.; Coarita, H.; Bayard, R.; Buffière, P. An improved procedure to assess the organic biodegradability and the biomethane potential of organic wastes for anaerobic digestion. Waste Manag. Res. 2019, 37, 746–754. [Google Scholar] [CrossRef] [Green Version]
  30. RedCorn, R.; Fatemi, S.; Engelberth, A.S. Comparing end-use potential for industrial food-waste sources. Engineering 2018, 4, 371–380. [Google Scholar] [CrossRef]
  31. Balkau, F.; Bezama, A. Life cycle methodologies for building circular economy in cities and regions. Waste Manag. Res. 2019, 37, 765–766. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Pérez-Gimeno, A.; Navarro-Pedreño, J.; Almendro-Candel, M.B.; Gómez, I.; Zorpas, A.A. The use of wastes (organic and inorganic) in land restoration in relation to their characteristics and cost. Waste Manag. Res. 2019, 37, 502–507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Smuga-Kogut, M.; Piskier, T.; Walendzik, B.; Szymanowska-Powałowska, D. Assessment of wasteland derived biomass for bioethanol production. Electron. J. Biotechnol. 2019, 41, 1–8. [Google Scholar] [CrossRef]
  34. Stanton, B.J.; Gustafson, R.R. Advanced hardwood biofuels northwest: Commercialization challenges for the renewable aviation fuel industry. Appl. Sci. 2019, 9, 4644. [Google Scholar] [CrossRef] [Green Version]
  35. Dalmo, F.C.; Simao, N.; Nebra, S.; Santana, P.D.M. Energy recovery from municipal solid waste of intermunicipal public consortia identified in São Paulo State. Waste Manag. Res. 2019, 37, 301–310. [Google Scholar] [CrossRef]
  36. Borowski, S.; Kucner, M. The use of sugar beet pulp stillage for co-digestion with sewage sludge and poultry manure. Waste Manag. Res. 2019, 7, 1025–1032. [Google Scholar] [CrossRef]
  37. Rabii, A.; Aldin, S.; Dahman, Y.; Elbeshbishy, E. A review on anaerobic co-digestion with a focus on the microbial populations and the effect of multi-stage digester configuration. Energies 2019, 12, 1106. [Google Scholar] [CrossRef] [Green Version]
  38. Luo, L.; Kaur, G.; Wong, J.W.C. A mini-review on the metabolic pathways of food waste two-phase anaerobic digestion system. Waste Manag. Res. 2019, 37, 333–346. [Google Scholar] [CrossRef]
  39. Vikrant, U.D.; Ajit, C.C.; Yogesh, V.A. Temperature, pH and Loading Rate Effect on Biogas Generation From Domestic Waste; International Conference on Advances in Engineering and Technology (ICAET): Nagapattinam, India, 2014; pp. 1–6. [Google Scholar]
  40. Achinas, S.; Euverink, G.J.W. Rambling facets of manure-based biogas production in Europe: A briefing. Renew. Sustain. Energy Rev. 2020, 119, 109566. [Google Scholar] [CrossRef]
  41. Kromus, S.; Wachter, B.; Koschuh, W.; Mandle, M.; Krotscheck, C.; Narodoslawsky, M. The green biorefinery Austria-development of an integrated system for green biomass utilization. Chem. Biochem. Eng. Q. 2004, 18, 7–12. [Google Scholar]
  42. Achinas, S.; Krooneman, J.; Euverink, G.J.W. Enhanced biogas production from the anaerobic batch treatment of banana peels. Engineering 2019, 5, 970–978. [Google Scholar] [CrossRef]
  43. Hames, B.; Scarlata, C.; Sluiter, A. Determination of protein content in biomass. In Laboratory Analytical Procedure (LAP); Technical Report NREL/TP-510-42625; National Renewable Energy Laborator: Golden, CO, USA, 2008. [Google Scholar]
  44. Achinas, S.; Euverink, G.J.W. Elevated biogas production from the anaerobic co-digestion of farmhouse waste: Insight into the process performance and kinetics. Waste Manag. Res. 2019, 37, 1240–1249. [Google Scholar] [CrossRef] [PubMed]
  45. Chiumenti, A.; Boscaro, D.; Da Borso, F.; Sartori, L.; Pezzuolo, A. Biogas from fresh spring and summer grass: Effect of the harvesting period. Energies 2018, 11, 1466. [Google Scholar] [CrossRef] [Green Version]
  46. Hidaka, T.; Arai, S.; Okamoto, S.; Uchida, T. Anaerobic co-digestion of sewage sludge with shredded grass from public green spaces. Bioresour. Technol. 2013, 130, 667–672. [Google Scholar] [CrossRef] [PubMed]
  47. Yu, H.W.; Samani, Z.; Hanson, A.; Smith, G. Energy recovery from grass using two-phase anaerobic digestion. Waste Manag. 2002, 22, 1–5. [Google Scholar] [CrossRef]
  48. Nizami, A.S.; Orozco, A.; Groom, E.; Dieterich, B.; Murphy, J.D. How much gas can we get from grass. Appl. Energy 2012, 92, 783–790. [Google Scholar] [CrossRef]
  49. Ghatak, M.D.; Mahanta, P. Effect of temperature on biogas production from lignocellulosic biomasses. In Proceedings of the 1st International Conference on Non Conventional Energy (ICONCE 2014), Kalyani, India, 16–17 January 2014; pp. 117–121. [Google Scholar]
  50. Ramaraj, R.; Unpaprom, Y. Effect of temperature on the performance of biogas production from Duckweed. Chem. Res. J. 2016, 1, 58–66. [Google Scholar]
  51. Ahn, H.K.; Smith, M.C.; Kondrad, S.L.; White, J.W. Evaluation of biogas production potential by dry anaerobic digestion of switchgrass-animal manure mixtures. Appl. Biochem. Biotechnol. 2010, 160, 965–975. [Google Scholar] [CrossRef]
  52. Lehtomäki, A.; Huttunen, S.; Lehtinen, T.; Rintala, J. Anaerobic digestion of grass silage in batch leach bed processes for methane production. Bioresour. Technol. 2008, 99, 3267–3278. [Google Scholar] [CrossRef]
  53. Önen, S.; Nsair, A.; Kuchta, K. Innovative operational strategies for biogas plant including temperature and stirring management. Waste Manag. Res. 2019, 37, 237–246. [Google Scholar] [CrossRef] [PubMed]
  54. Świątek, M.; Lewicki, A.; Szymanowska-Powałowska, D.; Kubiak, P. The effect of introduction of chicken manure on the biodiversity and performance of an anaerobic digester. Electron. J. Biotechnol. 2019, 37, 25–33. [Google Scholar] [CrossRef]
  55. Guo, X.; Kang, K.; Shang, G.; Yu, X.; Qiu, L.; Sun, G. Influence of mesophilic and thermophilic conditions on the anaerobic digestion of food waste: Focus on the microbial activity and removal of long chain fatty acids. Waste Manag. Res. 2018, 36, 1106–1112. [Google Scholar] [CrossRef] [PubMed]
  56. Reyes-Contreras, C.; Leiva, A.M.; Vidal, G. Evaluation of triclosan toxic effects on the methanogenic activity. Electron. J. Biotechnol. 2019, 39, 61–66. [Google Scholar] [CrossRef]
  57. Llewellyn, D. Does global agriculture need another green revolution? Engineering 2018, 4, 449–451. [Google Scholar] [CrossRef]
  58. Longjan, G.G.; Dehouche, Z. Nutrient characterisation and bioenergy potential of common Nigerian food wastes. Waste Manag. Res. 2018, 36, 426–435. [Google Scholar] [CrossRef] [Green Version]
  59. Weber, R.S.; Holladay, J.E. Modularized production of value-added products and fuels from distributed waste carbon-rich feedstocks. Engineering 2018, 4, 330–335. [Google Scholar] [CrossRef]
  60. Dal’ Magro, G.P.; Talamini, E. Estimating the magnitude of the food loss and waste generated in Brazil. Waste Manag. Res. 2019, 37, 706–716. [Google Scholar] [CrossRef]
  61. Baccioli, A.; Ferrari, L.; Guiller, R.; Yousfi, O.; Vizza, F.; Desideri, U. Feasibility analysis of bio-methane production in a biogas plant: A case study. Energies 2019, 12, 473. [Google Scholar] [CrossRef] [Green Version]
  62. Benato, A.; Macor, A. Italian biogas plants: Trend, subsidies, cost, biogas composition and engine emissions. Energies 2019, 12, 979. [Google Scholar] [CrossRef] [Green Version]
  63. Salas, L.D.M.; González, E.C.; Giraldi, M.R.; Jamed-Boza, L.O. Valorisation of the organic fraction of municipal solid waste. Waste Manag. Res. 2019, 37, 59–73. [Google Scholar] [CrossRef] [Green Version]
  64. Feng, S.; Hou, S.X.; Huang, X.; Fang, Z.; Tong, Y.; Yang, H. Insights into the microbial community structure of anaerobic digestion of municipal solid waste landfill leachate for methane production by adaptive thermophilic granular sludge. Electron. J. Biotechnol. 2019, 39, 98–106. [Google Scholar] [CrossRef]
  65. Franchi, O.; Rosenkranz, F.; Chamy, R. Key microbial populations involved in anaerobic degradation of phenol and p-cresol using different inocula. Electron. J. Biotechnol. 2018, 35, 33–38. [Google Scholar] [CrossRef]
  66. Achinas, S.; Euverink, G.J.W. Feasibility study of biogas production from hardly degradable material in co-inoculated bioreactor. Energies 2019, 12, 1040. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Daily and cumulative biogas production for all the reactors.
Figure 1. Daily and cumulative biogas production for all the reactors.
Environments 07 00082 g001
Figure 2. pH for all the reactors.
Figure 2. pH for all the reactors.
Environments 07 00082 g002
Figure 3. Alkalinity for all the reactors.
Figure 3. Alkalinity for all the reactors.
Environments 07 00082 g003
Figure 4. Correlation of biogas produced per g volatile solids (VS) and percentage of VS removal for all the experiments.
Figure 4. Correlation of biogas produced per g volatile solids (VS) and percentage of VS removal for all the experiments.
Environments 07 00082 g004
Figure 5. Plot of measured and predicted methane yields for all the reactors.
Figure 5. Plot of measured and predicted methane yields for all the reactors.
Environments 07 00082 g005
Table 1. Physico–chemical characteristics of the inoculum and substrates used in the batch tests. DC: ditch clippings; CG: canal grasses.
Table 1. Physico–chemical characteristics of the inoculum and substrates used in the batch tests. DC: ditch clippings; CG: canal grasses.
ParameterUnitInoculumDCCG
pH-7.23 (0.21)n.d.n.d.
VSg VS/kg biomass31.9 (1.1)153.9 (11.5)244.5 (8.5)
TSg TS/kg biomass77.5 (3.6)165.2 (9.7)268.7 (9.9)
C% (based on TS)n.d.42.6 (0.1)43.0 (0.1)
O% (based on TS)n.d.47.1 (0.1)46.7 (0.2)
H% (based on TS)n.d.6.0 (0.0)6.1 (0.1)
N% (based on TS)n.d.3.9 (0.1)4.0 (0.00)
S% (based on TS)n.d.0.5 (0.2)0.3 (0.1)
Total carbohydrates% w/wn.d.34.6 (4.5)51.3 (2.3)
Protein% w/wn.d.24.3 (0.3)24.7 (0.0)
Lignin% w/wn.d.31.911.5
Ash% w/wn.d.9.2 (0.8)12.5 (1.6)
Table 2. Process conditions applied in the batch tests.
Table 2. Process conditions applied in the batch tests.
ReactorsTemperature (°C)Organic Load (g VSsubstrate L−1I/S RatioReplicates
R1251023
R2252023
R3253023
R4351023
R5352023
R6353023
R7451023
R8452023
R9453023
R10551023
R11552023
R12553023
Table 3. Results of the kinetic study using the first order and cone model.
Table 3. Results of the kinetic study using the first order and cone model.
ReactorMeasured (mL g VSsubstrate−1)K (day−1)R2RMSEPredicted (mL g VSsubstrate−1)
R1157.30.08030.99898.16154.7
R2157.90.10180.994510.77156.7
R3166.70.11160.9959.76163.6
R4193.30.08170.998310.62193.2
R5208.20.08670.996212.19198.2
R6220.40.08590.996611.66209.5
R7268.40.09000.991812.26255.5
R8238.60.18260.995112.90228.4
R9260.50.08010.997413.65249.2
R10299.10.08150.99915.25281.8
R11311.70.06940.998520.43284.2
R12360.40.08110.99918.34339.3
Table 4. Results of the kinetic study using the cone model.
Table 4. Results of the kinetic study using the cone model.
ReactorMeasured (mL g VSsubstrate−1)K (day−1)nR2RMSEPredicted (mL g VSsubstrate−1)
R1157.30.11122.040.97836.90148.0
R2157.90.13562.380.97757.22154.1
R3166.70.13292.180.98575.91161.0
R4193.30.11232.110.98058.11183.1
R5208.20.10852.100.98128.63196.3
R6220.40.11092.040.97859.63207.3
R7268.40.11132.030.978311.76252.5
R8238.60.11572.030.98259.37225.4
R9260.50.11052.050.979111.25245.2
R10299.10.11092.040.979212.84281.4
R11311.70.09882.100.973315.44290.2
R12360.40.11072.040.978915.59339.0

Share and Cite

MDPI and ACS Style

Achinas, S.; Euverink, G.J.W. Effect of Temperature and Organic Load on the Performance of Anaerobic Bioreactors Treating Grasses. Environments 2020, 7, 82. https://0-doi-org.brum.beds.ac.uk/10.3390/environments7100082

AMA Style

Achinas S, Euverink GJW. Effect of Temperature and Organic Load on the Performance of Anaerobic Bioreactors Treating Grasses. Environments. 2020; 7(10):82. https://0-doi-org.brum.beds.ac.uk/10.3390/environments7100082

Chicago/Turabian Style

Achinas, Spyridon, and Gerrit Jan Willem Euverink. 2020. "Effect of Temperature and Organic Load on the Performance of Anaerobic Bioreactors Treating Grasses" Environments 7, no. 10: 82. https://0-doi-org.brum.beds.ac.uk/10.3390/environments7100082

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop