Skip to main content

Advertisement

SpringerLink
Log in

Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation

  • Original Paper
  • Published:
Biophysical Economics and Sustainability Aims and scope Submit manuscript

Abstract

This paper explores the construction and validation of an artificial neural network (ANN) model to accurately and efficiently predict the performance of a downdraft biomass gasification integrated with syngas fermentation plant for ethanol production. The study aims to predict the specific mass flow rate of bioethanol product from the systems derived by various kinds of biomass feedstocks under atmospheric pressure and various operating conditions. The input parameters used in the models are elemental analysis compositions (C, O, H, N and S), proximate analysis compositions (moisture, ash, volatile material and fixed carbon) and operating parameters (gasifier temperature and air to fuel ratio). The architecture of the model consisted of one input, one hidden and one output layer. 1008 simulated data from 84 different types of biomasses in various operating conditions were used to train the ANN. The developed ANN shows agreement with simulated data with Root Mean Square Error (RMSE) less than 0.05 in the case of product bioethanol. Moreover, the relative influence of biomass characteristics and some specific operating parameters on output are determined. Finally, to have a more detailed assessment, the variations of all input variables with respect to carbon content are compared and analyzed together. The suggested integrated ANN based model can be applied as a very useful tool for optimization and control of the process through the downdraft biomass gasification integrated with bioethanol production unit.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Antonopoulos I-S, Karagiannidis A, Gkouletsos A, Perkoulidis G (2012) Modelling of a downdraft gasifier fed by agricultural residues. Waste Manage 32:710–718

    Article  Google Scholar 

  • Baruah D, Baruah D, Hazarika M (2017) Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers. Biomass Bioenergy 98:264–271

    Article  Google Scholar 

  • Bryers RW (1996) Fireside slagging, fouling, and high-temperature corrosion of heat-transfer surface due to impurities in steam-raising fuels. Prog Energy Combust Sci 22:29–120

    Article  Google Scholar 

  • Damartzis T, Michailos S, Zabaniotou A (2012) Energetic assessment of a combined heat and power integrated biomass gasification–internal combustion engine system by using Aspen Plus®. Fuel Process Technol 95:37–44

    Article  Google Scholar 

  • Demirbas A (2004) Combustion characteristics of different biomass fuels. Prog Energy Combust Sci 30:219–230

    Article  Google Scholar 

  • Demirbas A (2005) Potential applications of renewable energy sources, biomass combustion problems in boiler power systems and combustion related environmental issues. Prog Energy Combust Sci 31:171–192

    Article  Google Scholar 

  • Elmaz F, Yücel Ö, Mutlu AY (2020) Predictive modeling of biomass gasification with machine learning-based regression methods. Energy 191:116541

    Article  Google Scholar 

  • Gai C, Dong Y (2012) Experimental study on non-woody biomass gasification in a downdraft gasifier. Int J Hydrog Energy 37:4935–4944

    Article  Google Scholar 

  • George J, Arun P, Muraleedharan C (2018) Assessment of producer gas composition in air gasification of biomass using artificial neural network model. Int J Hydrog Energy 43:9558–9568

    Article  Google Scholar 

  • Haratian M, Amidpour M, Hamidi A (2019) Modeling and optimization of process fired heaters. Appl Therm Eng 157:113722

    Article  Google Scholar 

  • Hirschnitz-Garbers M, Gosens J (2015) Producing bio-ethanol from residues and wastes. A technology with enormous potential in need of further research and development. Policy Brief

  • Kobayashi N, Tanaka M, Piao G, Kobayashi J, Hatano S, Itaya Y, Mori S (2009) High temperature air-blown woody biomass gasification model for the estimation of an entrained down-flow gasifier. Waste Manage 29:245–251

    Article  Google Scholar 

  • Kumar B, Bhardwaj N, Agrawal K, Chaturvedi V, Verma P (2020) Current perspective on pretreatment technologies using lignocellulosic biomass: An emerging biorefinery concept. Fuel Process Technol 199:106244

    Article  Google Scholar 

  • Lapuerta M, Hernández JJ, Pazo A, López J (2008) Gasification and co-gasification of biomass wastes: effect of the biomass origin and the gasifier operating conditions. Fuel Process Technol 89:828–837

    Article  Google Scholar 

  • Li H et al (2020) Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network. Environ Sci Pollut Res 27:785–797

    Article  Google Scholar 

  • Liguori R, Soccol CR, Porto de Souza Vandenberghe L, Woiciechowski AL, Faraco V (2015) Second generation ethanol production from brewers’ spent grain. Energies 8:2575–2586

    Article  Google Scholar 

  • Luo H, Lin W, Song W, Li S, Dam-Johansen K, Wu H (2019) Three dimensional full-loop CFD simulation of hydrodynamics in a pilot-scale dual fluidized bed system for biomass gasification. Fuel Process Technol 195:106146

    Article  Google Scholar 

  • Madhiyanon T, Sathitruangsak P, Soponronnarit S (2009) Co-combustion of rice husk with coal in a cyclonic fluidized-bed combustor (ψ-FBC). Fuel 88:132–138

    Article  Google Scholar 

  • Masiá AT, Buhre B, Gupta R, Wall T (2007) Characterising ash of biomass and waste. Fuel Process Technol 88:1071–1081

    Article  Google Scholar 

  • Michailos S, Parker D, Webb C (2017) Design, sustainability analysis and multiobjective optimisation of ethanol production via syngas fermentation. Waste Biomass Valoriz 10:1–12

    Google Scholar 

  • Miles T, Baxter L, Bryers R, Jenkins B, Oden L (1995) Alkali deposits found in biomass power plants: a preliminary investigation of their extent and nature

  • Moilanen A (2006) Thermogravimetric characterisations of biomass and waste for gasification processes. VTT, Espoo

    Google Scholar 

  • Mu D, Seager T, Rao PS, Zhao F (2010) Comparative life cycle assessment of lignocellulosic ethanol production: biochemical versus thermochemical conversion. Environ Manage 46:565–578

    Article  Google Scholar 

  • Neill SP, Hashemi MR (2018) Fundamentals of ocean renewable energy: generating electricity from the sea. Academic Press, London

    Google Scholar 

  • Nguyen TLT, Hermansen JE, Nielsen RG (2013) Environmental assessment of gasification technology for biomass conversion to energy in comparison with other alternatives: the case of wheat straw. J Clean Prod 53:138–148

    Article  Google Scholar 

  • Nutalapati D, Gupta R, Moghtaderi B, Wall T (2007) Assessing slagging and fouling during biomass combustion: a thermodynamic approach allowing for alkali/ash reactions. Fuel Process Technol 88:1044–1052

    Article  Google Scholar 

  • Pandey DS, Das S, Pan I, Leahy JJ, Kwapinski W (2016) Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor. Waste Manage 58:202–213

    Article  Google Scholar 

  • Pardo-Planas O, Atiyeh HK, Phillips JR, Aichele CP, Mohammad S (2017) Process simulation of ethanol production from biomass gasification and syngas fermentation. Biores Technol 245:925–932

    Article  Google Scholar 

  • Pettersson A, Zevenhoven M, Steenari B-M, Åmand L-E (2008) Application of chemical fractionation methods for characterisation of biofuels, waste derived fuels and CFB co-combustion fly ashes. Fuel 87:3183–3193

    Article  Google Scholar 

  • Porcu A, Sollai S, Marotto D, Mureddu M, Ferrara F, Pettinau A (2019) Techno-economic analysis of a small-scale biomass-to-energy BFB gasification-based system. Energies 12:494

    Article  Google Scholar 

  • Puig-Arnavat M, Hernández JA, Bruno JC, Coronas A (2013) Artificial neural network models for biomass gasification in fluidized bed gasifiers. Biomass Bioenergy 49:279–289

    Article  Google Scholar 

  • Rajaeifar MA, Akram A, Ghobadian B, Rafiee S, Heijungs R, Tabatabaei M (2016) Environmental impact assessment of olive pomace oil biodiesel production and consumption: a comparative lifecycle assessment. Energy 106:87–102

    Article  Google Scholar 

  • Ray RC, Ramachandran S (2018) Bioethanol production from food crops: sustainable sources, interventions, and challenges. Academic Press, London

    Google Scholar 

  • Risnes H et al. (2003) Calcium addition in straw gasification☆ Fuel 82:641–651

  • Ross A, Jones J, Kubacki M, Bridgeman T (2008) Classification of macroalgae as fuel and its thermochemical behaviour. Biores Technol 99:6494–6504

    Article  Google Scholar 

  • Rostampour V, Motlagh AM, Komarizadeh MH, Sadeghi M, Bernousi I, Ghanbari T (2013) Using Artificial Neural Network (ANN) technique for prediction of apple bruise damage. Austral J Crop Sci 7:1442

    Google Scholar 

  • Roy D, Samanta S, Ghosh S (2019) Thermo-economic assessment of biomass gasification-based power generation system consists of solid oxide fuel cell, supercritical carbon dioxide cycle and indirectly heated air turbine. Clean Technol Environ Policy 21:827–845

    Article  Google Scholar 

  • Safarian S, Bararzadeh M (2012) Exergy analysis of high-performance cycles for gas turbine with air-bottoming. Journal of Mechanical Engineering Research 5:38–49

    Google Scholar 

  • Safarian S, Unnthorsson R (2018) An assessment of the sustainability of lignocellulosic bioethanol production from wastes in Iceland. Energies 11:1493

    Article  Google Scholar 

  • Safarian S, Khodaparast P, Kateb M (2014) Modeling and technical-economic optimization of electricity supply network by three photovoltaic systems. J SolEnergy Eng 136:024501

    Google Scholar 

  • Safarian S, Sattari S, Hamidzadeh Z (2018) Sustainability assessment of biodiesel supply chain from various biomasses and conversion technologies BioPhysical Economics and Resource. Quality 3:6

    Google Scholar 

  • Safarian S, Richter C, Unnthorsson R (2019a) Waste biomass gasification simulation using Aspen Plus: performance evaluation of wood chips sawdust and mixed paper wastes. J Power Energy Eng 7:12–30

    Article  Google Scholar 

  • Safarian S, Sattari S, Unnthorsson R, Hamidzadeh Z (2019b) Prioritization of bioethanol production systems from agricultural and waste agricultural biomass using multi-criteria decision making biophysical economics and resource. Quality 4:4

    Google Scholar 

  • Safarian S, Unnþórsson R, Richter C (2019c) A review of biomass gasification modelling. Renew Sustain Energy Rev 110:378–391

    Article  Google Scholar 

  • Safarian S, Ebrahimi Saryazdi SM, Unnthorsson R, Richter C (2020a) Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant. Energy 213:118800

    Article  Google Scholar 

  • Safarian S, Ebrahimi Saryazdi SM, Unnthorsson R, Richter C (2020b) Dataset of biomass characteristics and net output power from downdraft biomass gasifier integrated power production unit. Data Brief 33:106390

    Article  Google Scholar 

  • Safarian S, Unnthorsson R, Richter C (2020c) The equivalence of stoichiometric and non-stoichiometric methods for modeling gasification and other reaction equilibria. Renew Sustain Energy Rev 131:109982

    Article  Google Scholar 

  • Safarian S, Unnthorsson R, Richter C (2020d) Performance analysis and environmental assessment of small-scale waste biomass gasification integrated CHP in Iceland. Energy 197:117268

    Article  Google Scholar 

  • Safarian S, Unnthorsson R, Richter C (2020e) Simulation and performance analysis of integrated gasification-syngas fermentation plant for lignocellulosic ethanol production. Fermentation 6:68

    Article  Google Scholar 

  • Safarian S, Unnthorsson R, Richter C (2020f) Simulation of small-scale waste biomass gasification integrated power production: a comparative performance analysis for timber and wood waste. Int J Appl Power Eng (IJAPE) 09:147–152

    Article  Google Scholar 

  • Safarian S, Unnthorsson R, Richter C (2020g) Techno-economic analysis of power production by using waste biomass gasification. J Power Energy Eng 8:1

    Article  Google Scholar 

  • Safarian S, Unnthorsson R, Richter C (2020h) Techno-economic and environmental assessment of power supply chain by using waste biomass gasification in Iceland. BioPhys Econ Sustain 5:7

    Article  Google Scholar 

  • Safarianbana S, Unnthorsson R, Richter C Development of a new stoichiometric equilibrium-based model for wood chips and mixed paper wastes gasification by ASPEN Plus. In: ASME International Mechanical Engineering Congress and Exposition, 2019. American Society of Mechanical Engineers, p V006T006A002

  • Scurlock JM, Dayton DC, Hames B (2000) Bamboo: an overlooked biomass resource? Biomass Bioenergy 19:229–244

    Article  Google Scholar 

  • Serrano D, Golpour I, Sánchez-Delgado S (2020) Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach. Fuel 266:117021

    Article  Google Scholar 

  • Srikanth S, Das SK, Ravikumar B, Rao D, Nandakumar K, Vijayan P (2004) Nature of fireside deposits in a bagasse and groundnut shell fired 20 MW thermal boiler. Biomass Bioenergy 27:375–384

    Article  Google Scholar 

  • Talebnia F, Karakashev D, Angelidaki I (2010) Production of bioethanol from wheat straw: an overview on pretreatment, hydrolysis and fermentation. Bioresour Technol 101:4744–4753

    Article  Google Scholar 

  • Tauqir W, Zubair M, Nazir H (2019) Parametric analysis of a steady state equilibrium-based biomass gasification model for syngas and biochar production and heat generation. Energy Convers Manage 199:111954

    Article  Google Scholar 

  • Teo WK, Ruthven DM (1986) Adsorption of water from aqueous ethanol using 3-. ANG. molecular sieves. Ind Eng Chem Process Design Dev 25:17–21

    Article  Google Scholar 

  • Theis M, Skrifvars B-J, Hupa M, Tran H (2006a) Fouling tendency of ash resulting from burning mixtures of biofuels. Part 1: deposition rates. Fuel 85:1125–1130

    Article  Google Scholar 

  • Theis M, Skrifvars B-J, Zevenhoven M, Hupa M, Tran H (2006b) Fouling tendency of ash resulting from burning mixtures of biofuels. Part 2: deposit chemistry. Fuel 85:1992–2001

    Article  Google Scholar 

  • Thy P, Lesher C, Jenkins B (2000) Experimental determination of high-temperature elemental losses from biomass slag. Fuel 79:693–700

    Article  Google Scholar 

  • Thy P, Jenkins B, Grundvig S, Shiraki R, Lesher C (2006) High temperature elemental losses and mineralogical changes in common biomass ashes. Fuel 85:783–795

    Article  Google Scholar 

  • Tillman DA (2000) Biomass cofiring: the technology, the experience, the combustion consequences. Biomass Bioenergy 19:365–384

    Article  Google Scholar 

  • Tite MS, Shortland A, Maniatis Y, Kavoussanaki D, Harris S (2006) The composition of the soda-rich and mixed alkali plant ashes used in the production of glass. J Archaeol Sci 33:1284–1292

    Article  Google Scholar 

  • Vamvuka D, Zografos D (2004) Predicting the behaviour of ash from agricultural wastes during combustion. Fuel 83:2051–2057

    Article  Google Scholar 

  • Vamvuka D, Zografos D, Alevizos G (2008) Control methods for mitigating biomass ash-related problems in fluidized beds. Biores Technol 99:3534–3544

    Article  Google Scholar 

  • Vassilev SV, Vassileva CG (2009) A new approach for the combined chemical and mineral classification of the inorganic matter in coal. 1 Chemical and mineral classification systems. Fuel 88:235–245

    Article  Google Scholar 

  • Vassilev SV, Baxter D, Andersen LK, Vassileva CG (2010) An overview of the chemical composition of biomass. Fuel 89:913–933

    Article  Google Scholar 

  • Wei X, Schnell U, Hein KR (2005) Behaviour of gaseous chlorine and alkali metals during biomass thermal utilisation. Fuel 84:841–848

    Article  Google Scholar 

  • Werther J, Saenger M, Hartge E-U, Ogada T, Siagi Z (2000) Combustion of agricultural residues. Prog Energy Combust Sci 26:1–27

    Article  Google Scholar 

  • Wieck-Hansen K, Overgaard P, Larsen OH (2000) Cofiring coal and straw in a 150 MWe power boiler experiences. Biomass Bioenergy 19:395–409

    Article  Google Scholar 

  • Zeng J, Xiao R, Zhang H, Wang Y, Zeng D, Ma Z (2017) Chemical looping pyrolysis-gasification of biomass for high H2/CO syngas production. Fuel Process Technol 168:116–122

    Article  Google Scholar 

  • Zevenhoven-Onderwater M, Blomquist J-P, Skrifvars B-J, Backman R, Hupa M (2000) The prediction of behaviour of ashes from five different solid fuels in fluidised bed combustion. Fuel 79:1353–1361

    Article  Google Scholar 

  • Zevenhoven-Onderwater M, Backman R, Skrifvars B-J, Hupa M (2001) The ash chemistry in fluidised bed gasification of biomass fuels. Part I: predicting the chemistry of melting ashes and ash–bed material interaction. Fuel 80:1489–1502

    Article  Google Scholar 

Download references

Acknowledgements

This paper was a part of the project funded by Icelandic Research Fund (IRF), (in Icelandic: Rannsoknasjodur) and the Grant Number is 196458-051.

Author information

Authors and Affiliations

  1. Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Hjardarhagi 6, 107, Reykjavik, Iceland

    Sahar Safarian, Runar Unnthorsson & Christiaan Richter

  2. Department of Energy Systems Engineering, Sharif University of Technologies, Tehran, Iran

    Seyed Mohammad Ebrahimi Saryazdi

Authors
  1. Sahar Safarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Mohammad Ebrahimi Saryazdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Runar Unnthorsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christiaan Richter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sahar Safarian or Seyed Mohammad Ebrahimi Saryazdi.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safarian, S., Saryazdi, S.M.E., Unnthorsson, R. et al. Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation. Biophys Econ Sust 6, 1 (2021). https://doi.org/10.1007/s41247-020-00083-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41247-020-00083-2

Keywords

Search

Navigation