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Biogas from food waste through anaerobic digestion: optimization with response surface methodology

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Abstract

In the current study, anaerobic digestion method efficiency on biogas production and chemical oxygen demand (COD) degradation was assessed through a sequence of laboratory-scale batch experimentations to compute the role of chosen process parameters, viz., solid concentration (5–15%), pH (5–9), temperature (30–60 °C), and co-digestion (0–40% of poultry manure). Biogas production and COD degradation were significantly dependent on the selected process parameters with independent conditions to accomplish active performance of the process. Central composite design (CCD)-based response surface methodology (RSM) was adopted for evaluation and optimizing of the combined performance of system considering two responses. Among various combinations, it was observed that solid concentration of 7.38%, pH value as 7, temperature at 48.43 °C, and co-digestion as 29% produce biogas of 6344 ml and COD degradation as 38%. Confirmation experiment performed shows a deviation of 4.93% maximum between the predicted and experimental results.

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References

  1. Phan AN, Phan TM (2008) Biodiesel production from waste cooking oils. Fuel 87:3490–3496

    Article  Google Scholar 

  2. Huiru Z, Yunjun Y, Liberti F, Pietro B, Fantozzi F (2019) Technical and economic feasibility analysis of an anaerobic digestion plant fed with canteen food waste. Energy Convers Manag 180:938–948

    Article  Google Scholar 

  3. Deepanraj B, Sivasubramanian V, Jayaraj S (2014) Solid concentration influence on biogas yield from food waste in an anaerobic batch digester. International conference and utility exhibition 2014 on green energy for sustainable development (ICUE 2014), Thailand, 19-21 march 2014

  4. Alghoul O, El-Hassan Z, Ramadan M, Olabi AG (2019) Experimental investigation on the production of biogas from waste food. Energy Sources, Part A 41(17):2051–2060

    Article  Google Scholar 

  5. Zhang R, El-Mashad HM, Hartman K, Wang F, Liu G, Choate C, Gamble P (2007) Characterization of food waste as feedstock for anaerobic digestion. Bioresour Technol 98:929–935

    Article  Google Scholar 

  6. Food and Agriculture Organization of the United Nations (2011) Global food losses and food waste. Interpack, Dusseldorf

    Google Scholar 

  7. Veluchamy C, Gilroyed BH, Kalamdhad AS (2019) Process performance and biogas production optimizing of mesophilic plug flow anaerobic digestion of corn silage. Fuel 253:1097–1103

    Article  Google Scholar 

  8. Shin HS, Youn JH, Kim SH (2004) Hydrogen production from food waste in anaerobic mesophilic and thermophilic acidogenesis. Int J Hydrog Energy 29:1355–1363

    Article  Google Scholar 

  9. Raposo F, De la Rubia MA, Fernández-Cegri V, Borja R (2011) Anaerobic digestion of solid organic substrates in batch mode: an overview relating to methane yields and experimental procedures. Renew Sust Energ Rev 16:861–877

    Article  Google Scholar 

  10. Deepanraj B, Sivasubramanian V, Jayaraj S (2014) Biogas generation through anaerobic digestion process-an overview. Res J Chem Environ 18(5):80–93

    Google Scholar 

  11. Deepanraj B, Sivasubramanian V, Jayaraj S (2015) Kinetic study on the effect of temperature on biogas production using a lab scale batch reactor. Ecotoxicol Environ Saf 121:100–104

    Article  Google Scholar 

  12. Ghatak MD, Mahanta P (2014) Effect of temperature on anaerobic co-digestion of cattle dung with lignocellulosic biomass. J Adv Engng Res 1:1–7

    Google Scholar 

  13. Mukhopadhyay D, Prakas Sarkar J, Dutta S (2013) Optimization of process factors for the efficient generation of biogas from raw vegetable wastes under the direct influence of plastic materials using Taguchi methodology. Desalin Water Treat 51:2781–2790

    Article  Google Scholar 

  14. Antwi P, Li J, Boadi PO, Meng J, Shi E, Deng K, Bondinuba FK (2017) Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with back propagation artificial neural network. Bioresour Technol 228:106–115

    Article  Google Scholar 

  15. Venkata Mohan S, Veer Raghavulu S, Mohanakrishna G, Srikanth S, Sarma PN (2009) Optimization and evaluation of fermentative hydrogen production and wastewater treatment processes using data enveloping analysis (DEA) and Taguchi design of experimental (DOE) methodology. Int J Hydrog Energy 34:216–226

    Article  Google Scholar 

  16. Deepanraj B, Sivasubramanian V, Jayaraj S (2017) Multi-response optimization of process parameters in biogas production from food waste using Taguchi - grey relational analysis. Energy Convers Manag 141:72–76

    Article  Google Scholar 

  17. Senthilkumar N, Deepanraj B, Vasantharaj K, Sivasubramanian V (2016) Optimization and performance analysis of process parameters during anaerobic digestion of food waste using hybrid GRA-PCA technique. J Renew Sustain Energy 8:063107

    Article  Google Scholar 

  18. Wang K-S, Chen J-H, Huang Y-H, Huang S-L (2013) Integrated Taguchi method and response surface methodology to confirm hydrogen production by anaerobic fermentation of cow manure. Int J Hydrog Energy 38:45–53

    Article  Google Scholar 

  19. Qdais HA, Hani KB, Shatnawi N (2010) Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resour. Conserv Recycl 54:359–363

    Article  Google Scholar 

  20. Karichappan T, Venkatachalama S, Jeganathan PM (2014) Investigation on biogas production process from chicken processing industry wastewater using statistical analysis: modelling and optimization. J Renew Sustain Energy 6:043117

    Article  Google Scholar 

  21. Yang M, Wang G, Yu H-Q (2006) Response surface methodological analysis on biohydrogen production by enriched anaerobic cultures. Enzym Microb Technol 38:905–913

    Article  Google Scholar 

  22. Xie E, Ding A, Dou J, Zheng L, Yang J (2014) Study on decaying characteristics of activated sludge from a circular plug-flow reactor using response surface methodology. Bioresour Technol 170:428–4353

    Article  Google Scholar 

  23. Senthilkumar N, Tamizharasan T, Gobikannan S (2014) Application of response surface methodology and firefly algorithm for optimizing multiple responses in turning AISI 1045 steel. Arab J Sci Eng 39:8015–8030

    Article  Google Scholar 

  24. Montgomery DC (2013) Design and analysis of experiments, 8th edn. Wiley, USA

    Google Scholar 

  25. Liu M, Niu S, Lu C, Cheng S (2015) An optimization study on transesterification catalyzed by the activated carbide slag through the response surface methodology. Energy Convers Manag 92:498–506

    Article  Google Scholar 

  26. Sivakumar P, Bhagiyalakshmi M, Anbarasu K (2012) Anaerobic treatment of spoiled milk from milk processing industry for energy recovery – a laboratory to pilot scale study. Fuel 96:482–486

    Article  Google Scholar 

  27. Nayono SE, Gallert C, Winter J (2010) Co-digestion of press water and food waste in a biowaste digester for improvement of biogas production. Bioresour Technol 101:6987–6993

    Article  Google Scholar 

  28. Babatope A, John JM, Farouk F (2012) Proximate composition and postproduction stability of poultry waste fertilizer pellets production stability of poultry waste fertilizer pellets. Int J Appl Biol Res 4:25–31

    Google Scholar 

  29. Ghatak MD, Mahanta P (2014) Effect of temperature and total solid on biomethanation of sugarcane bagasse. IUP J Mech Eng 7:68–75

    Google Scholar 

  30. Gamst G, Meyers LS, Guarino AJ (2008) Analysis of variance designs - a conceptual and computational approach with SPSS and SAS. Cambridge University Press, Cambridge

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Jyothi Engineering College, Cheruthuruthy, Thrissur, India

    B. Deepanraj

  2. Department of Mechanical Engineering, Adhiparasakthi Engineering College, Melmaruvathur, India

    N. Senthilkumar

  3. CO2 Research and Green Technologies Centre, Vellore Institute of Technology, Vellore, India

    J. Ranjitha

  4. Department of Mechanical Engineering, National Institute of Technology Calicut, Kozhikode, India

    S. Jayaraj

  5. School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

    Hwai Chyuan Ong

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  1. B. Deepanraj
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  2. N. Senthilkumar
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  3. J. Ranjitha
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  4. S. Jayaraj
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  5. Hwai Chyuan Ong
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Correspondence to N. Senthilkumar.

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Deepanraj, B., Senthilkumar, N., Ranjitha, J. et al. Biogas from food waste through anaerobic digestion: optimization with response surface methodology. Biomass Conv. Bioref. 11, 227–239 (2021). https://doi.org/10.1007/s13399-020-00646-9

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  • DOI: https://doi.org/10.1007/s13399-020-00646-9

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