Skip to main content
SpringerLink
Log in

Recovery of Organic Waste by Biogas Production-Mathematical Modeling of Anaerobic Digestion: A Short Literature Review

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 713))

  • 283 Accesses

Abstract

Anaerobic digestion (AD) is considered one of the most beneficial waste management methods. It is a process in which microorganisms break down organic matter to produce biogas, which can be used as a source of energy, and digestate, which can be used as a biofertilizer. Several by-products could inhibit the process such as volatile fatty acids, ammonia, and hydrogen. Therefore, monitoring and selection of key factors is a significant operation that contributes to the optimization of the AD process. The mathematical modeling of this process is crucial and has had significant effects for these purposes. It has been classified into two main types, mechanistic models and data-driven models. The purpose of this paper is to provide a comprehensive review of AD mathematical modeling, as well as to emphasize the critical role of trend optimization algorithms in AD mathematical modeling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Xu, F., Li, Y., Ge, X., Yang, L., Li, Y.: Anaerobic digestion of food waste–challenges and opportunities. Biores. Technol. 247, 1047–1058 (2018). https://doi.org/10.1016/j.biortech.2017.09.020

    Article  Google Scholar 

  2. Uthirakrishnan, U., et al.: Current advances and future outlook on pretreatment techniques to enhance biosolids disintegration and anaerobic digestion: a critical review. Chemosphere 288, 132553 (2022). https://doi.org/10.1016/j.chemosphere.2021.132553

    Article  Google Scholar 

  3. Benyahya, Y., Fail, A., Alali, A., Sadik, M.: Recovery of household waste by generation of biogas as energy and compost as bio-fertilizer: a review. Processes 10(11) (2022). https://doi.org/10.3390/pr10010081

  4. Wu, D., et al.: State indicators of anaerobic digestion: a critical review on process monitoring and diagnosis. Renew. Sustain. Energy Rev. 148, 111260 (2021). https://doi.org/10.1016/j.rser.2021.111260

    Article  Google Scholar 

  5. Kazemi, P., Bengoa, C., et al.: Data-driven techniques for fault detection in anaerobic digestion process. Process Saf. Environ. Prot. 146, 905–915 (2021). https://doi.org/10.1016/j.psep.2020.12.016

  6. Enitan, A.M., Adeyemo, J. et al.: Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches. Rev. Chem. Eng. 33, 309–335 (2017). https://doi.org/10.1515/revce-2015-0057

  7. Lafratta, M., Thorpe, R.B., Sabeha, K., et al.: Development and validation of a dynamic first order kinetics model of a periodically operated well-mixed vessel for anaerobic digestion. Chem. Eng. J. 426 (2021). https://doi.org/10.1016/j.cej.2021.131732

  8. James, M.W., Chandan, S., Karl, K., et al.: Definitions, methods, and applications in interpretable machine learning. In: Proceedings of the National Academy of Sciences, vol. 116, pp. 22071–22080 (2019). https://doi.org/10.1073/pnas.1900654116

  9. Emebu, S., Pecha, J., Janáčová, D.: Review on anaerobic digestion models: Model classification & elaboration of process phenomena. Renew. Sustain. Energy Rev. 160 (2022). https://doi.org/10.1016/j.rser.2022.112288

  10. Francesco, F., Aritra, D., Chanchal, M.: Comparative kinetic study of anaerobic treatment of thermally pretreated source-sorted organic market refuse. J. Eng. 2015 (2015). https://doi.org/10.1155/2015/684749

  11. Lo, H.M., Kurniawan, T.A., Sillanpää, M.E.T., et al.: Modeling biogas production from organic fraction of MSW co-digested with MSWI ashes in anaerobic bioreactors. Biores. Technol. 101, 6329–6335 (2010). https://doi.org/10.1016/j.biortech.2010.03.048

  12. Ketsub, N., Whatmore, P., Abbasabadi, M., et al.: Effects of pretreatment methods on biomethane production kinetics and microbial community by solid state anaerobic digestion of sugarcane trash. Bioresour. Technol. 352 (2022). https://doi.org/10.1016/j.biortech.2022.127112

  13. Abid, M., Wu, J., Seyedsalehi, M., et al.: Novel insights of impacts of solid content on high solid anaerobic digestion of cow manure: kinetics and microbial community dynamics. Bioresour. Technol. vol. 333, 2021. https://doi.org/10.1016/j.biortech.2021.125205

  14. Prajapati, K.B., Singh, R., et al.: Co-digestion of sewage sludge and wheat straw in presence of iron scraps in mesophilic and thermophilic conditions for generating methane. Biomass Conv. Biorefinery (2022). https://doi.org/10.1007/s13399-022-02417-0

  15. Li, P., Li, W., et al.: Evaluation of biochemical methane potential and kinetics on the anaerobic digestion of vegetable crop residues. Energies 12(11) (2019). https://doi.org/10.3390/en12010026

  16. Pramanik, S.K., et al.: 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(19) (2019). https://doi.org/10.3390/pr7090600

  17. Wang, J., Cao, L., et al.: Multiple hydrolyses of rice straw by domesticated paddy soil microbes for methane production via liquid anaerobic digestion. Bioresour. Technol. 354 (2022). https://doi.org/10.1016/j.biortech.2022.127184

  18. Zhang, H., et al.: Modeling the methane production kinetics of anaerobic co-digestion of agricultural wastes using sigmoidal functions. Energies 14(11) (2021). https://doi.org/10.3390/en14020258

  19. Zhang, Y., Yang, Z., Xu, R., et al.: Enhanced mesophilic anaerobic digestion of waste sludge with the iron nanoparticles addition and kinetic analysis. Sci. Total Environ. 683, 124–133 (2019). https://doi.org/10.1016/j.scitotenv.2019.05.214

  20. Andriamanohiarisoamanana, F.J., Ihara, I., et al.: Kinetic study of oxytetracycline and chlortetracycline inhibition in the anaerobic digestion of dairy manure. Bioresour. Technol. 315 (2020). https://doi.org/10.1016/j.biortech.2020.123810

  21. Blasius, P., Contrera, R.C., et al.: Effects of temperature, proportion and organic loading rate on the performance of anaerobic digestion of food waste. Biotechnol. Rep. 27 (2020). https://doi.org/10.1016/j.btre.2020.e0050

  22. Karki, R., Chuenchart, W., et al.: Anaerobic co-digestion of various organic wastes: kinetic modeling and synergistic impact evaluation. Bioresour. Technol. 343 (2022). https://doi.org/10.1016/j.biortech.2021.126063

  23. Masih-Das, J., Tao, W.: Anaerobic co-digestion of foodwaste with liquid dairy manure or manure digestate: co-substrate limitation and inhibition. J. Environ. Manag. 223, 917–924 (2018). https://doi.org/10.1016/j.jenvman.2018.07.016

  24. Batstone, A., et al.: The IWA anaerobic digestion model no 1 (ADM1). Water Sci. Technol. 45(110), 65–73 (2002)

    Google Scholar 

  25. Olivier, B., Zakaria, H.-S., et al.: Dynamical model development and parameter identification for an anaerobic wastewater treatment process. Biotechnol. Bioeng. 75(14), 424–438 (2001). https://doi.org/10.1002/bit.10036

  26. Arzate, J.A., Kirstein, M., et al.: Anaerobic Digestion Model (AM2) for the description of biogas processes at dynamic feedstock loading rates. Chemie Ingenieur Technik 89(15), 686–695 (2017). https://doi.org/10.1002/cite.201600176

  27. Weinrich, S., Nelles, M.: Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – model development and stoichiometric analysis. Bioresour. Technol. 333 (2021). https://doi.org/10.1016/j.biortech.2021.125124

  28. Loganath, R., Mazumder, D.: Development of a simplified mathematical model for anaerobic digestion. In: Ghosh, S.K. (ed.) Sustainable Waste Management: Policies and Case Studies, pp. 571–578. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7071-7_51

    Chapter  Google Scholar 

  29. Li, D., Lee, I., Kim, H.: Application of the linearized ADM1 (LADM) to lab-scale anaerobic digestion system. J. Environ. Chem. Eng. 9(13) (2021). https://doi.org/10.1016/j.jece.2021.105193

  30. Waszkielis, K., Białobrzewski, I., et al.: Application of anaerobic digestion model No. 1 for simulating fermentation of maize silage, pig manure, cattle manure and digestate in the full-scale biogas plant. Fuel 317 (2022). https://doi.org/10.1016/j.fuel.2022.123491

  31. Li, P., Pei, Z., et al.: Application of Anaerobic Digestion Model No. 1 for modeling anaerobic digestion of vegetable crop residues: fractionation of crystalline cellulose, J. Clean. Prod. 285 (2021). https://doi.org/10.1016/j.jclepro.2020.124865

  32. Shi, X.S., Yuan, X.Z., et al.: Modeling of the methane production and pH value during the anaerobic co-digestion of dairy manure and spent mushroom substrate. Chem. Eng. J. 244, 258–263 (2014). https://doi.org/10.1016/j.cej.2014.02.007

  33. Zhao, X., Li, L., Wu, D., et al.: Modified anaerobic digestion model no. 1 for modeling methane production from food waste in batch and semi-continuous anaerobic digestions. Bioresour. Technol. 271, 109–117 (2019). https://doi.org/10.1016/j.biortech.2018.09.091

  34. Li, H., Chen, Z., Fu, D., et al.: Improved ADM1 for modelling C, N, P fates in anaerobic digestion process of pig manure and optimization approaches to biogas production. Renew. Energy 146, 2330–2336 (2020). https://doi.org/10.1016/j.renene.2019.08.086

  35. Parra-Orobio, B.A., Donoso-Bravo, A., et al.: Energy balance and carbon dioxide emissions comparison through modified anaerobic digestion model No 1 for single-stage and two-stage anaerobic digestion of food waste. Biomass Bioenergy 142 (2020). https://doi.org/10.1016/j.biombioe.2020.105814

  36. Uhlenhut, F., Schlüter, K., et al.: Wet biowaste digestion: ADM1 model improvement by implementation of known genera and activity of propionate oxidizing bacteria. Water Res. 129, 384–393 (2018). https://doi.org/10.1016/j.watres.2017.11.012

  37. Wang, W., Kiik, M., et al.: A systematic review of machine learning models for predicting outcomes of stroke with structured data. PLOS ONE 15(16) (2020). https://doi.org/10.1371/journal.pone.0234722

  38. Walid, F., El Fkihi, S., et al.: Modeling and optimization of anaerobic digestion: a review. In: E3S Web Conference, vol. 229 (2021). https://doi.org/10.1051/e3sconf/202122901022

  39. Won, K., Seo, J., et al.: Prediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model. Bioresour. Technol. 341 (2021). https://doi.org/10.1016/j.biortech.2021.125829

  40. Cruz, I.A., Chuenchart, W., et al.: Application of machine learning in anaerobic digestion: perspectives and challenges. Bioresour.Technol. 345 (2022). https://doi.org/10.1016/j.biortech.2021.126433

  41. Naderloo, L., Alimardani, R., et al.: Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45(6), 1406–1413 (2012). https://doi.org/10.1016/j.measurement.2012.03.025

  42. Tufaner, F., Demirci, Y.: Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Technol. Environ. Policy 22(3), 713–724 (2020). https://doi.org/10.1007/s10098-020-01816-z

    Article  Google Scholar 

  43. Antwi, P., Li, J., et al.: Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Bioresour. Technol. 228, 106–115 (2017). https://doi.org/10.1016/j.biortech.2016.12.045

  44. Alejo, L., Atkinson, J., Guzmán-Fierro, V., Roeckel, M.: Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res. 25(21), 21149–21163 (2018). https://doi.org/10.1007/s11356-018-2224-7

    Article  Google Scholar 

  45. Dong, C., Chen, J.: Optimization of process parameters for anaerobic fermentation of corn stalk based on least squares support vector machine. Bioresour. Technol. 271, 174–181 (2019). https://doi.org/10.1016/j.biortech.2018.09.085

  46. Najafi, B., Ardabili, S.F.: Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC). Resour. Conserv. Recycl. 133, 169–178 (2018). https://doi.org/10.1016/j.resconrec.2018.02.025

  47. Zareei, S., Khodaei, J.: Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system. Renew. Energy 114, 423–427 (2017). https://doi.org/10.1016/j.renene.2017.07.050

  48. Qi, Y., et al.: Random forest similarity for protein-protein interaction prediction from multiple sources. Biocomputing 531–542 (2005). https://doi.org/10.1142/9789812702456_0050

  49. Wang, L., Long, F., et al.: Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms. Bioresour. Technol. 298 (2020). https://doi.org/10.1016/j.biortech.2019.122495

  50. Long, F., Wang, L., et al.: Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data. Water Res. 199 (2021). https://doi.org/10.1016/j.watres.2021.117182

  51. De Clercq, D., Wen, Z., et al.: Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion. Sci. Total Environ. 712 (2020). https://doi.org/10.1016/j.scitotenv.2019.134574

  52. Xu, W., Long, F., et al.: Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms. Waste Manag. 121, 59–66 (2021). https://doi.org/10.1016/j.wasman.2020.12.003

  53. Mamandipoor, B., Majd, M., Sheikhalishahi, S., Modena, C., Osmani, V.: Monitoring and detecting faults in wastewater treatment plants using deep learning. Environ. Monit. Assess. 192(2), 1–12 (2020). https://doi.org/10.1007/s10661-020-8064-1

    Article  Google Scholar 

  54. Xu, R.-Z., Cao, J.-S., et al.: An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process. Water Res. 184 (2020). https://doi.org/10.1016/j.watres.2020.116103

  55. Li, G., Ji, J., et al.: Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors. Sci. Total Environ. 813 (2022). https://doi.org/10.1016/j.scitotenv.2021.151920

  56. Akbaş, H., Bilgen, B., Turhan, A.M.: An integrated prediction and optimization model of biogas production system at a wastewater treatment facility. Bioresour. Technol. 196, 566–576 (2015). https://doi.org/10.1016/j.biortech.2015.08.017

  57. Saghouri, M., Abdi, R., Ebrahimi-Nik, M., et al.: Modeling and optimization of biomethane production from solid-state anaerobic co-digestion of organic fraction municipal solid waste and other co-substrates. Energy Sour. Part A: Recov. Utiliz. Environ. Effects 1–17, 2020 doi: https://doi.org/10.1080/15567036.2020.1767728

  58. Yang, J., et al.: Estimation of kinetic parameters of an anaerobic digestion model using particle swarm optimization. Biochem. Eng. J. 120, 25–32 (2017). https://doi.org/10.1016/j.bej.2016.12.022

    Article  Google Scholar 

  59. Wolf, C., McLoone, S., Bongards, M.: Biogas plant control and optimization using computational intelligence methods Biogasanlagenregelung und-optimierung mit. Comput. Intell. Methoden. at-Automatisierungstechnik 57(12), 638–649. https://doi.org/10.1524/auto.2009.0809

  60. Nguyen, D.D., Jeon, B.-H., et al.: Thermophilic anaerobic digestion of model organic wastes: Evaluation of biomethane production and multiple kinetic models analysis. Bioresour. Technol. 280, 269–276 (2019). https://doi.org/10.1016/j.biortech.2019.02.033

  61. Guo, H., Wu, S., et al.: Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: a review. Bioresour. Technol. 319 (2021). https://doi.org/10.1016/j.biortech.2020.124114

  62. Oloko-Oba, M.I., Taiwo, A.E., et al.: Performance evaluation of three different-shaped bio-digesters for biogas production and optimization by artificial neural network integrated with genetic algorithm. Sustain. Energy Technol. Assess. 26, 116–124 (2018). https://doi.org/10.1016/j.seta.2017.10.006

  63. Cervantes, J., Garcia-Lamont, F., et al.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189–215 (2020). https://doi.org/10.1016/j.neucom.2019.10.118

  64. Hu, C., Yan, B., et al.: Modeling the performance of anaerobic digestion reactor by the anaerobic digestion system model (ADSM). J. Environ. Chem. Eng. 6(12), 2095–2104 (2018). https://doi.org/10.1016/j.jece.2018.03.018

  65. Fatolahi, Z., Arab, G., Razaviarani, V.: Calibration of the anaerobic digestion model no. 1 for anaerobic digestion of organic fraction of municipal solid waste under mesophilic condition. Biomass Bioenergy 139 (2020). https://doi.org/10.1016/j.biombioe.2020.105661

Download references

Funding

This work was supported by the Moroccan Ministry of Higher Education and Scientific Research–National Centre for Scientific and Technical Research (CNRST).

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, NEST Research Group ENSEM, Hassan II University, Casablanca, Morocco

    Youssef Benyahya, Mohamed Sadik & Abderrahim Fail

Authors
  1. Youssef Benyahya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Sadik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abderrahim Fail
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youssef Benyahya .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Benyahya, Y., Sadik, M., Fail, A. (2023). Recovery of Organic Waste by Biogas Production-Mathematical Modeling of Anaerobic Digestion: A Short Literature Review. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_50

Download citation

Publish with us

Policies and ethics

Search

Navigation