Abstract
Anaerobic co-digestion (AcoD) is a solution to recover renewable energy, such as methane, from food waste (FW) and garden waste (GW). Methane yield efficiency can be improved through the optimization of the operating conditions, such as mixing ratio of substrates (MRS) and Mixing Ratio of Inocula (MRI). The objective was to optimize the AcoD of FW with GW by combining Artificial Neural Networks (ANN) and Particle Swarm Algorithm (PSO). The AcoD of FW and GW were initially evaluated experimentally at a laboratory scale through a central design composed of two factors, at three levels each: FW:GW MRS at 80:20, 70:30 and 60:40 (w/w) and MRI with sludge mixture Granular (GSL) and Flocculant sludge (FSL) equal to 10:90, 30:70 and 50:50 (GSL:FSL, v/v). The response variables were the biochemical methane potential (BMP), bicarbonate alkalinity (BA), volatile fatty acids (VFA), hydrolysis (kh) and process stability index (If). The optimization identified the best operational conditions, which were later validated with a second experiment. Results of ANN and PSO showed that the maximized methane yield (270 mL CH4/g VS) can occur at the ratios of MRS 64:36 (w/w) and at MRI 44:56 (v/v) that increase the methane yield by 26% compared to mono-digestion of FW (70 mL CH4/g VS), while BA of 1645 mg L−1, 1955 mg L−1 of VFA, kh of 0.32d−1 and a stability (If) of −59 are achieved. The second experiment showed the robustness and applicability of the optimization tools, which resulted in a production of 265 mL CH4/g VS as a maximum yield.
Highlights
• Simultaneous optimization of substrate and inoculum ratios increase methane yields • A mixing ratio of granular:flocculent sludge (44:56%) increased methane yields by 26% • The optimal neural network topology was 2-4-1 for predicting response parameters |
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding author on reasonable request.
Abbreviations
- AD:
-
Anaerobic digestion
- AcoD:
-
Anaerobic Co-digestion
- ANN:
-
Artificial neural networks
- BA:
-
Bicarbonate alkalinity
- BMP:
-
Biochemical methane potential
- CODt :
-
Total chemical oxygen demand
- CODS :
-
Soluble chemical oxygen demand
- HA:
-
Hydrolytic activity
- If :
-
Process stability index
- FSL:
-
Flocculant sludge
- \( {\mathcal{F}}_{\left(\uptheta \right)} \) :
-
Objective function
- FW:
-
Food waste
- GSL:
-
Granular sludge
- GW:
-
Garden waste
- k h :
-
Hydrolysis constant
- MLP:
-
Multi-layered perceptron
- MR:
-
Mixing ratio
- MRI:
-
Mixing Ratio of Inocula
- MRS:
-
Mixing ratio of substrates
- MSW:
-
Municipal solid waste
- NET:
-
Neuronic matrix
- OM:
-
Organic matter
- PSO:
-
Particle swarm optimization
- R2 :
-
Determination coefficient
- RMSE :
-
Root Mean Square Error
- SAA:
-
Specific acidogenic activity
- SMA:
-
Specific methanogenic activity
- TA:
-
Total alkalinity
- TN:
-
Total nitrogen
- TOC:
-
Total organic carbon
- TP:
-
Total phosphorus
- VFA:
-
Volatile fatty acids
- VS:
-
Volatile solids
- WWTP:
-
Wastewater treatment plant
- λ:
-
Lag phase
References
Ahmed B, Tyagi VK, Khan AA, Kazmi A (2020) Optimization of process parameters for enhanced biogas yield from anaerobic co-digestion of OFMSW and bio-solids. Biomass Conversion Biorefinery:1–12. https://doi.org/10.1007/s13399-020-00919-3
Aldin S, Nakhla G, Ray MB (2011) Modeling the influence of particulate protein size on hydrolysis in anaerobic digestion. Ind Eng Chem Res 50:10843–10849. https://doi.org/10.1021/ie200385e
Álvarez JA, Otero L, Lema JM (2010) A methodology for optimising feed composition for anaerobic co-digestion of agro-industrial wastes. Bioresour Technol 101:1153–1158. https://doi.org/10.1016/j.biortech.2009.09.061
APHA, AWWA, WEF (2005) Standard methods for examination of water and wastewater, 21 edn., Washington D.C.
Beltramo T, Ranzan C, Hinrichs J, Hitzmann B (2016) Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm. Biosyst Eng 143:68–78. https://doi.org/10.1016/j.biosystemseng.2016.01.006
Boniecki P, Dach J, Mueller W, Koszela K, Przybyl J, Pilarski K, Olszewski T (2013) Neural prediction of heat loss in the pig manure composting process. Appl Therm Eng 58(1–2)650–655
Braguglia CM, Gallipoli A, Gianico A, Pagliaccia P (2018) Anaerobic bioconversion of food waste into energy: a critical review. Bioresour Technol 248:37–56. https://doi.org/10.1016/j.biortech.2017.06.145
Campuzano R, González-Martínez S (2016) Characteristics of the organic fraction of municipal solid waste and methane production: a review. Waste Manag 54:3–12. https://doi.org/10.1016/j.wasman.2016.05.016
Cárdenas-Cleves L, Marmolejo-Rebellón L, Torres-Lozada P (2018) Improvement of the biochemical methane potential of food waste by means of anaerobic co-digestion with swine manure. Braz J Chem Eng 35:1219–1229. https://doi.org/10.1590/0104-6632.20180354s20170297
Casallas-Ojeda MR, Marmolejo-Rebellón LF, Torres-Lozada P (2020) Identification of factors and variables that influence the anaerobic digestion of municipal biowaste and food waste. Waste Biomass Valoriz:1–16. https://doi.org/10.1007/s12649-020-01150-x
Chen X, Pang Y, Zou D, Yuan H, Liu Y, Zhu B, Li X (2014a) Optimizing acidogenic process for achieving high biomethane yield from anaerobic co-digestion of food waste and rice straw using response surface methodology. J Biobased Mater Bioenergy 8:512–518. https://doi.org/10.1166/jbmb.2014.1461
Chen X, Yan W, Sheng K, Sanati M (2014b) Comparison of high-solids to liquid anaerobic co-digestion of food waste and green waste. Bioresour Technol 154:215–221. https://doi.org/10.1016/j.biortech.2013.12.054
Chen T, Zhang S, Yuan Z (2020) Adoption of solid organic waste composting products: a critical review. J Clean Prod 272:122712. https://doi.org/10.1016/j.jclepro.2020.122712
Cristancho DE, Arellano AV (2006) Study of the operational conditions for anaerobic digestion of urban solid wastes. Waste Manag 26:546–556. https://doi.org/10.1016/j.wasman.2005.06.003
Dennehy C, Lawlor PG, Croize T, Jiang Y, Morrison L, Gardiner GE, Zhan X (2016) Synergism and effect of high initial volatile fatty acid concentrations during food waste and pig manure anaerobic co-digestion. Waste Manag 56:173–180. https://doi.org/10.1016/j.wasman.2016.06.032
Di Maria F, Segoloni E, Pezzolla D (2016) Solid anaerobic digestion batch of bio-waste as pre-treatment for improving amendment quality: the effect of inoculum recirculation. Waste Manag 56:106–112. https://doi.org/10.1016/j.wasman.2016.05.024
Ebner JH, Labatut RA, Lodge JS, Williamson AA, Trabold TA (2016) Anaerobic co-digestion of commercial food waste and dairy manure: characterizing biochemical parameters and synergistic effects. Waste Manag 52:286–294. https://doi.org/10.1016/j.wasman.2016.03.046
El-Fadel M, Saikaly P, Ghanimeh S (2013) Startup and stability of thermophilic anaerobic digestion of OFMSW. Crit Rev Environ Sci Technol 43:2685–2721. https://doi.org/10.1080/10643389.2012.694333
Faverial J, Boval M, Sierra J, Sauvant D (2016) End-product quality of composts produced under tropical and temperate climates using different raw materials: a meta-analysis. J Environ Manag 183:909–916. https://doi.org/10.1016/j.jenvman.2016.09.057
Fitamo T, Boldrin A, Boe K, Angelidaki I, Scheutz C (2016) Co-digestion of food and garden waste with mixed sludge from wastewater treatment in continuously stirred tank reactors. Bioresour Technol 206:245–254. https://doi.org/10.1016/j.biortech.2016.01.085
Gaur RZ, Suthar S (2017) Anaerobic digestion of activated sludge, anaerobic granular sludge and cow dung with food waste for enhanced methane production. J Clean Prod 164:557–566. https://doi.org/10.1016/j.jclepro.2017.06.201
Gaviria CAO, Cabrera CCT, Losada LM (2014) Arranque de un reactor UASB para el tratamiento de aguas residuales domésticas en condiciones Andino Amazónicas. Revista Facultad de Ciencias Básicas 10:170–185. https://doi.org/10.18359/rfcb.328
Giménez J, Martí N, Ferrer J, Seco A (2012) Methane recovery efficiency in a submerged anaerobic membrane bioreactor (SAnMBR) treating sulphate-rich urban wastewater: Evaluation of methane losses with the effluent. Bioresour Technol 118:67–72
Ghatak MD, Ghatak A (2018) Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates. Fuel 232:178–189. https://doi.org/10.1016/j.fuel.2018.05.051
Götze R, Boldrin A, Scheutz C, Astrup TF (2016) Physico-chemical characterisation of material fractions in household waste: overview of data in literature. Waste Manag 49:3–14. https://doi.org/10.1016/j.wasman.2016.01.008
Gueguim Kana EB, Oloke JK, Lateef A, Adesiyan MO (2012) Modeling and optimization of biogas production on saw dust and other co-substrates using artificial neural network and genetic algorithm. Renew Energy 46:276–281. https://doi.org/10.1016/j.renene.2012.03.027
Hannan MA, Abdulla Al Mamun M, Hussain A, Basri H, Begum RA (2015) A review on technologies and their usage in solid waste monitoring and management systems: issues and challenges. Waste Manag 43:509–523. https://doi.org/10.1016/j.wasman.2015.05.033
Helenas-Perin JK, Biesdorf Borth PL, Torrecilhas AR, Santana da Cunha L, Kuroda EK, Fernandes F (2020) Optimization of methane production parameters during anaerobic co-digestion of food waste and garden waste. J Clean Prod 272:123–130. https://doi.org/10.1016/j.jclepro.2020.123130
Hernández-Gómez A, Calderón A, Medina C, Sanchez-Torres V, Oviedo-Ocaña ER (2020) Implementation of strategies to optimize the co-composting of green waste and food waste in developing countries. A case study: Colombia. Environ Sci Pollut Res 1:1–7. https://doi.org/10.1007/s11356-020-08103-w
Ho D, Jensen P, Batstone D (2013) Methanosarcinaceae and acetate-oxidizing pathways dominate in high-rate thermophilic anaerobic digestion of waste-activated sludge. Appl Environ Microbiol 79(20):6491–6500
Holliger C, Alves M, Andrade D, Angelidaki I, Astals S, Baier U, Bougrier C, Buffière P, Carballa M, de Wilde V, Ebertseder F, Fernández B, Ficara E, Fotidis I, Frigon J, de Laclos H, Ghasimi D, Hack G, Hartel M, Heerenklage J, Horvath I, Jenicek P, Koch K, Krautwald J, Lizasoain J, Liu J, Mosberger L, Nistor M, Oechsner H, Oliveira J, Paterson M, Pauss A, Pommier S, Porqueddu I, Raposo F, Ribeiro T, Rüsch Pfund F, Strömberg S, Torrijos M, van Eekert M, van Lier J, Wedwitschka H, Wierinck I (2016) Towards a standardization of biomethane potential tests. Water Sci Technol 74:2515–2522
Hu Z-H, Wang G, Yu H-Q (2004) Anaerobic degradation of cellulose by rumen microorganisms at various pH values. Biochem Eng J 21:59–62. https://doi.org/10.1016/j.bej.2004.05.004
ICONTEC (2011) Norma Técnica Colombiana 5167. Productos para la Industria Agrícola, Productos Orgánicos Usados como Abonos o Fertilizantes y Enmiendas de Suelo. Instituto Colombiano de Normas Técnicas y Certicación, Bogotá
Kainthola J, Kalamdhad AS, Goud VV (2019) Optimization of methane production during anaerobic co-digestion of rice straw and hydrilla verticillata using response surface methodology. Fuel 235:92–99. https://doi.org/10.1016/j.fuel.2018.07.094
Kaza S, Yao L, Bhada-Tata P, Van Woerden F (2018) What a waste 2.0: a global snapshot of solid waste management to 2050. The World Bank, USA, Washington DC
Liu G, Zhang R, El-Mashad HM, Dong R (2009) Effect of feed to inoculum ratios on biogas yields of food and green wastes. Bioresour Technol 100:5103–5108. https://doi.org/10.1016/j.biortech.2009.03.081
Lü F, Hao L, Zhu M, Shao L, He P (2012) Initiating methanogenesis of vegetable waste at low inoculum-to-substrate ratio: importance of spatial separation. Bioresour Technol 105:169–173. https://doi.org/10.1016/j.biortech.2011.11.104
Marañón E, Negral L, Suárez-Peña B, Fernández-Nava Y, Ormaechea P, Díaz-Caneja P, Castrillón L (2020) Evaluation of the methane potential and kinetics of supermarket food waste. Waste Biomass Valoriz:1–15. https://doi.org/10.1007/s12649-020-01131-0
Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165. https://doi.org/10.1016/j.chemolab.2015.08.020
Mirmohamadsadeghi S, Karimi K, Tabatabaei M, Aghbashlo M (2019) Biogas production from food wastes: a review on recent developments and future perspectives. Bioresource Technology Reports 7:100202. https://doi.org/10.1016/j.biteb.2019.100202
Mosquera J, Varela L, Santis A, Villamizar S, Acevedo P, Cabeza I (2020) Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization. Biomass Bioenergy 142:105790. https://doi.org/10.1016/j.biombioe.2020.105790
Mudhoo A, Kumar S (2013) Effects of heavy metals as stress factors on anaerobic digestion processes and biogas production from biomass. Int J Environ Sci Technol 10:1383–1398. https://doi.org/10.1007/s13762-012-0167-y
Neugebauer M, Sołowiej P (2017) The use of green waste to overcome the difficulty in small-scale composting of organic household waste. J Clean Prod 156:865–875. https://doi.org/10.1016/j.jclepro.2017.04.095
Oladejo O, Dahunsi S, Adesulu-Dahunsi A, Ojo O, Lawal A, Idowu E, Olanipekun A, Ibikunle R, Osueke C, Ajayi O, Osueke N, Evbuomwan (2020) Energy generation from anaerobic co-digestion of food waste, cow dung and piggery dung. Bioresour Technol 313:123694. https://doi.org/10.1016/j.biortech.2020.123694
Onodera T, Syutsubo K, Hatamoto M, Nakahara N, Yamaguchi T (2017) Evaluation of cation inhibition and adaptation based on microbial activity and community structure in anaerobic wastewater treatment under elevated saline concentration. Chem Eng J 325:442–448. https://doi.org/10.1016/j.cej.2017.05.081
Oviedo R, Marmolejo L, Torres P (2017) Advances in research on biowaste composting in small municipalities of developing countries. Lessons from Colombia. Revista Ingenieria Investigacion y Tecnologia 18:31–42
Owamah HI, Izinyon OC (2015) Development of simple-to-apply biogas kinetic models for the co-digestion of food waste and maize husk. Bioresour Technol 194:83–90. https://doi.org/10.1016/j.biortech.2015.06.136
Parra-Orobio BA, Angulo-Mosquera LS, Loaiza-Gualtero JS, Torres-López WA, Torres-Lozada P (2018) Inoculum mixture optimization as strategy to improve the anaerobic digestion of food waste for the methane production. J Environ Chem Eng 6:1529–1535. https://doi.org/10.1016/j.jece.2018.01.048
Pobeheim H, Munk B, Johansson J, Guebitz GM (2010) Influence of trace elements on methane formation from a synthetic model substrate for maize silage. Bioresour Technol 101:836–839. https://doi.org/10.1016/j.biortech.2009.08.076
Pramanik SK, Suja FB, Porhemmat M, Pramanik BK (2019) Performance and kinetic model of a single-stage anaerobic digestion system operated at different successive operating stages for the treatment of food waste. Processes 7:600–611. https://doi.org/10.3390/pr7090600
Raposo F, De la Rubia M, Fernández-Cegrí V, Borja R (2012) Anaerobic digestion of solid organic substrates in batch mode: An overview relating to methane yields and experimental procedures. Renew Sustain Energy Rev 16(1):861–877
Regueiro L, Veiga P, Figueroa M, Alonso-Gutierrez J, Stams AJM, Lema JM, Carballa M (2012) Relationship between microbial activity and microbial community structure in six full-scale anaerobic digesters. Microbiol Res 167:581–589. https://doi.org/10.1016/j.micres.2012.06.002
Reyes-Torres M, Oviedo-Ocaña ER, Dominguez I, Komilis D, Sánchez A (2018) A systematic review on the composting of green waste: feedstock quality and optimization strategies. Waste Manag 77:486–499. https://doi.org/10.1016/j.wasman.2018.04.037
Schattauer A, Abdoun E, Weiland P, Plöchl M, Heiermann M (2011) Abundance of trace elements in demonstration biogas plants. Biosyst Eng 108:57–65. https://doi.org/10.1016/j.biosystemseng.2010.10.010
Soto-Paz J, Oviedo-Ocaña ER, Manyoma-Velásquez PC, Torres-Lozada P, Gea T (2019) Evaluation of mixing ratio and frequency of turning in the co-composting of biowaste with sugarcane filter cake and star grass. Waste Manag 96:86–95. https://doi.org/10.1016/j.wasman.2019.07.015
Suárez A, Nielsen K, Köhler S, Merencio D, Reyes IP (2014) Enhancement of anaerobic digestion of microcrystalline cellulose (MCC) using natural micronutrient sources. Braz J Chem Eng 31:393–401. https://doi.org/10.1590/0104-6632.20140312s00002689
Thi N, Kumar G, Lin C-Y (2015) An overview of food waste management in developing countries: current status and future perspective. J Environ Manag 157:220–229. https://doi.org/10.1016/j.jenvman.2015.04.022
Van Soest PJ, Wine R (1967) Uso de detergentes en el análisis de alimentos fibrosos. IV. Determinación de permanganato. Assoc Oficial Anal Chem 50:1–6
Wainaina S, Awasthi MK, Sarsaiya S, Chen H, Singh E, Kumar A, Ravindran B, Awasthi SK, Liu T, Duan Y, Kumar S, Zhang Z, Taherzadeh MJ (2020) Resource recovery and circular economy from organic solid waste using aerobic and anaerobic digestion technologies. Bioresour Technol 301:122778. https://doi.org/10.1016/j.biortech.2020.122778
Wang G, Mu Y, Yu H-Q (2005) Response surface analysis to evaluate the influence of pH, temperature and substrate concentration on the acidogenesis of sucrose-rich wastewater. Biochem Eng J 23:175–184. https://doi.org/10.1016/j.bej.2005.01.002
Wang K, Yin J, Shen D, Li N (2014) Anaerobic digestion of food waste for volatile fatty acids (VFAs) production with different types of inoculum: Effect of pH. Bioresour Technol 161:395–401
Xu F, Li Y, Ge X, Yang L, Li Y (2018) Anaerobic digestion of food waste – challenges and opportunities. Bioresour Technol 247:1047–1058. https://doi.org/10.1016/j.biortech.2017.09.020
Zhang L, Sun X (2016) Influence of bulking agents on physical, chemical, and microbiological properties during the two-stage composting of green waste. Waste Manag 48:115–126. https://doi.org/10.1016/j.wasman.2015.11.032
Acknowledgments
The authors thank Universidad Industrial de Santander (UIS) for the support provided doing the development of this research. Jonathan Soto thank Universidad Industrial de Santander for financing of the postdoctoral research: Convocatoria Programa Vicerrectoria de Investigación y Extensión – Apoyo a la formación de posdoctorados (VIE 2020-II).
Code Availability
The code used was developed on the MatLAB®2020a platform and continues to be improved through its validation in other experiments.
Funding
The authors thank the Universidad Industrial de Santander for funding this research.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Miguel Casallas-Ojeda, Jonathan Soto-Paz, Wilfredo Alfonso-Morales, Edgar Ricardo Oviedo-Ocaña and Dimitrios Komilis. The first draft of the manuscript was written by Jonathan Soto-Paz and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics Approval
The study did not involve humans or animals.
Consent to Participate
Not applicable.
Consent for Publication
The authors consent to the publication of this article.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Casallas-Ojeda, M., Soto-Paz, J., Alfonso-Morales, W. et al. Optimization of Operational Parameters during Anaerobic Co-digestion of Food and Garden Waste. Environ. Process. 8, 769–791 (2021). https://doi.org/10.1007/s40710-021-00506-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40710-021-00506-2