Skip to main content

Advertisement

SpringerLink
Log in

Intelligent modelling of fermentable sugar concentration and combined severity factor (CSF) index from pretreated starch-based lignocellulosic biomass

  • Original Article
  • Published:
Biomass Conversion and Biorefinery Aims and scope Submit manuscript

Abstract

Fermentable sugar release is a crucial step in the fermentation of lignocellulosic biomass to biofuels. The efficiency of fermentable sugar release from pretreated biomass can be improved through acquiring pre-experimental information from predictive models that function as initial screening tools during lignocellulose bioprocessing. In this study, artificial neural networks, random forest, and decision tree were used to model fermentable sugar concentration (g/L) and combined severity factor (CSF) index from a pretreated starch-based lignocellulosic biomass-cassava peels. The input parameters for all the models include soaking temperature, soaking time, autoclave duration, hydrochloric acid concentration, and percentage solid loading. A backpropagation training algorithm was adopted for the ANN models and trained on experimental data while the random forest models were trained using a binary tree–based machine learning algorithm and the decision tree models were trained using an information entropy–based learning algorithm. For the fermentable sugar concentration, the highest coefficient of determination (R2 > 0.99) was achieved with the decision tree model while the highest prediction accuracy of 82% was achieved with the ANN model (R2 > 0.82). For the CSF index, the ANN model gave both the highest coefficient of determination (R2 > 0.93) and prediction accuracy (78%). Thus, the ANN models gave better predictions of the fermentable sugar concentration and the combined severity factor index compared to the other intelligent models. The study, therefore, provides an insight into intelligent predictive modelling towards the screening of pretreatment strategies for starch-based lignocellulosic biomass valorization.

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

Similar content being viewed by others

References

  1. Sivamani S, Chandrasekaran A.P, Balajii M, Shanmugaprakash M, Hosseini-Bandegharaei A, Baskar R (2018) Evaluation of the potential of cassava-based residues for biofuels production. Reviews in Environmental Science and Bio/Technology 1–18

  2. Sivamani S, Baskar R (2015) Optimization of bioethanol production from cassava peel using statistical experimental design. Environ Prog Sustain Energy 34(2):567–574

    Article  Google Scholar 

  3. Khawla BJ, Sameh M, Imen G, Donyes F, Dhouha G, Raoudha EG et al (2014) Potato peel as feedstock for bioethanol production: a comparison of acidic and enzymatic hydrolysis. Ind Crops Prod 52:144–149

    Article  Google Scholar 

  4. Den W, Sharma VK, Lee M, Nadadur G, Varma RS (2018) Lignocellulosic biomass transformations via greener oxidative pretreatment processes: access to energy and value-added chemicals. Front Chem 6:141

    Article  Google Scholar 

  5. Borisova AS, Pihlajaniemi V, Kont R, Niemelä K, Koitto T, Mikkelson A et al (2021) Effect of soluble phenolic compounds from hydrothermally pretreated wheat straw on cellulose degrading enzymes

  6. Ziegler-Devin I, Chrusciel L, Brosse N (2021) Steam Explosion pretreatment of lignocellulosic biomass: a mini-review of theorical and experimental approaches. Frontiers in Chemistry 9

  7. Mikulski D, Kłosowski G (2020) Microwave-assisted dilute acid pretreatment in bioethanol production from wheat and rye stillages. Biomass And Bioenergy 136:105528

    Article  Google Scholar 

  8. Nanssou PAK, Nono YJ, Kapseu C (2016) Pretreatment of cassava stems and peelings by thermohydrolysis to enhance hydrolysis yield of cellulose in bioethanol production process. Renewable Energy 97:252–265

    Article  Google Scholar 

  9. Mankar AR, Pandey A, Modak A, Pant K (2021) Pretreatment of lignocellulosic biomass: A review on recent advances. Biores Technol 334:125235

    Article  Google Scholar 

  10. García-Aparicio MP, Ballesteros I, González A, Oliva JM, Ballesteros M, Negro MJ (2006) Effect of inhibitors released during steam-explosion pretreatment of barley straw on enzymatic hydrolysis. Appl Biochem Biotechnol 129(1–3):278–288

    Article  Google Scholar 

  11. Hector RE, Bowman MJ, Skory CD, Cotta MA (2009) The Saccharomyces cerevisiae YMR315W gene encodes an NADP (H)-specific oxidoreductase regulated by the transcription factor Stb5p in response to NADPH limitation. New Biotechnol 26(3–4):171–180

    Article  Google Scholar 

  12. Baş D, Boyacı İH (2007) Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. J Food Eng 78(3):846–854

    Article  Google Scholar 

  13. Desai KM, Survase SA, Saudagar PS, Lele S, Singhal RS (2008) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan. Biochem Eng J 41(3):266–273

    Article  Google Scholar 

  14. Olatunji O, Akinlabi S, Madushele N (2020) Application of artificial intelligence in the prediction of thermal properties of biomass. Springer, Valorization of Biomass to Value-Added Commodities, pp 59–91

    Google Scholar 

  15. da Silva Bispo VD, Scheid CM, Calçada LA, da Cruz Meleiro LA (2017) Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids. J Pet Sci Eng 150:69–73

    Article  Google Scholar 

  16. Obafemi O, Stephen A, Ajayi O, Nkosinathi M (2019) A survey of artificial neural network-based prediction models for thermal properties of biomass. Procedia Manuf 33:184–191

    Article  Google Scholar 

  17. Moodley P, Rorke DC, Kana EBG (2019) Development of artificial neural network tools for predicting sugar yields from inorganic salt-based pretreatment of lignocellulosic biomass. Biores Technol 273:682–686

    Article  Google Scholar 

  18. Mascaro J, Asner GP, Knapp DE, Kennedy-Bowdoin T, Martin RE, Anderson C et al (2014) A tale of two “forests”: random forest machine learning aids tropical forest carbon mapping. PLoS ONE 9(1):e85993

    Article  Google Scholar 

  19. Adebayo, R.A.; Moyo, M.; Gueguim-Kana, E.B.;Nsahlai, I.V (2020) The use of artificial neural networks for modelling rumen fill. Canadian Journal of Animal Science ja

  20. Aruwajoye GS, Faloye FD, Kana EG (2017) Soaking assisted thermal pretreatment of cassava peels wastes for fermentable sugar production: process modelling and optimization. Energy Convers Manage 150:558–566

    Article  Google Scholar 

  21. Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31(3):426–428

    Article  Google Scholar 

  22. Lee J-W, Jeffries TW (2011) Efficiencies of acid catalysts in the hydrolysis of lignocellulosic biomass over a range of combined severity factors. Biores Technol 102(10):5884–5890

    Article  Google Scholar 

  23. Patil-Shinde V, Kulkarni T, Kulkarni R, Chavan PD, Sharma T, Sharma BK et al (2014) Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier. Ind Eng Chem Res 53(49):18678–18689

    Article  Google Scholar 

  24. Song Y-Y, Ying L (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27(2):130

    Google Scholar 

  25. Elmaz F, Yücel Ö, Mutlu AY (2020) Machine learning based approach for predicting of higher heating values of solid fuels using proximity and ultimate analysis. Int J Adv Eng Pure Sci 32(2):145–151

    Google Scholar 

  26. Nwosu-Obieogu K, Dzarma GW, Ugwuodo C, Chiemenem L (2022) Environmental sustenance via melon seed peel conversion to fermentable sugars using soft computing models. Clean Eng Technol 7:100452

    Article  Google Scholar 

  27. Nwosu-Obieogu K, Aguele F.O, Agu C.M, Onyekwulu C.S, Akatobi K.N (2021) Optimisation of the hydrolysis of melon seed (Citrullus lanatus) husk with hydrochloric acid using response surface methodology. Indian chemical engineer 1–10

  28. Kana EG, Oloke J, Lateef A, Adesiyan M (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

    Article  Google Scholar 

  29. Vani S, Sukumaran RK, Savithri S (2015) Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling. Biores Technol 188:128–135

    Article  Google Scholar 

  30. Galvão RM, Santana TS, Fontes CH, Sales EA (2013) Modeling of biomass production of Haematococcus pluvialis. Appl Math 4(8A):50

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Discipline of Microbiology, School of Life Sciences, University of KwaZulu-Natal, Private bag X01, Scottsville, Pietermaritzburg, 3209, South Africa

    Gabriel Sanjo Aruwajoye & Evariste Gueguim Kana

  2. Centre for Renewable and Sustainable Energy Studies, Stellenbosch University, Stellenbosch, South Africa

    Funmilayo Dorcas Faloye

  3. Bioresources Engineering, School of Engineering, Pietermaritzburg Campus, University of KwaZulu-Natal, Scottsville, Pietermaritzburg, 3209, South Africa

    Alaika Kassim

  4. Discipline of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-Natal, Durban, 4041, South Africa

    Akshay Kumar Saha

Authors
  1. Gabriel Sanjo Aruwajoye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Funmilayo Dorcas Faloye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alaika Kassim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akshay Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Evariste Gueguim Kana
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: G.S.A. and E.G.K. Methodology: G.S.A., F.D.F., and E.G.K. Data curation: G.S.A. Modelling and validation: G.S.A. Resources: A.K., A.K.S., and E.G.K. Writing—original draft preparation: G.S.A. Writing—review and editing: G.S.A., A.K., F.D.F., A.K.S., and E.G.K. Supervision: A.K., A.K.S., and E.G.K.

Corresponding author

Correspondence to Gabriel Sanjo Aruwajoye.

Ethics declarations

Conflict of interest

The authors declare no competing 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

Aruwajoye, G.S., Faloye, F.D., Kassim, A. et al. Intelligent modelling of fermentable sugar concentration and combined severity factor (CSF) index from pretreated starch-based lignocellulosic biomass. Biomass Conv. Bioref. (2022). https://doi.org/10.1007/s13399-022-03013-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13399-022-03013-y

Keywords

Search

Navigation