Skip to main content
Springer Nature Link
Log in

Artificial Neural Network Modeling in Pretreatment of Garden Biomass for Lignocellulose Degradation

  • Original Paper
  • Published:
Waste and Biomass Valorization Aims and scope Submit manuscript

Abstract

In this work, Artificial Neural Network (ANN) model was developed for the garden biomass pretreatment process from the available experimental data of previous work. ANN model was studied to compare the results obtained through Response Surface Methodology (RSM), both graphically and numerically. The influence of process variables such as reaction temperature, Fe2+ concentration and H2O2 concentration on lignocellulose degradation of garden biomass were investigated by this model. The ANN analysis using Matlab, R2012a has shown promising results for this nonlinear system with the correlation coefficient of 0.9663 for cellulose and 0.9699 for lignin, indicating good fit. ANN found to be the effective tool for modeling the experimental data of lignin and cellulose degradation from pretreated biomass and showed a good match with experimental data than RSM. We found that reaction temperature 50 °C, Fe2+ concentration 250 ppm and H2O2 concentration 10000 ppm are the optimum conditions for maximum lignin and cellulose degradation i.e. 47.688% of cellulose and 57.529% of lignin from pretreated garden biomass.

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

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Bourquin, J., Schmidli, H., Hoogevest, P.V., Leuenberger, H.: Advantages of artificial neural networks (ANNs) as alternative modeling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form. Eur. J. Pharm. Sci. 7, 5–16 (1998)

    Article  Google Scholar 

  2. Tamás, K.: The application of neural networks for solving complex optimization problems in modeling. Conference of Junior Researchers in Civil Engineering. pp. 97–102 (2012)

  3. Abilov, A., Zeybek, Z.: Use of neural network for modeling of non-linear process integration technology in chemical engineering. Chem. Eng. Process. Process Intensif. 39(5), 449–458 (2000)

    Article  Google Scholar 

  4. Polit, M., Estaben, M., Labat, P.: A fuzzy model for an anaerobic digester, comparison with experimental results. Eng. Appl. Artif. Intell. 15, 385–390 (2002)

    Article  Google Scholar 

  5. Wang, X., Chen, B., Zhao, Y., Yang, Z., Wang, F.: Chlorophyll a simulation in a lake ecosystem using a model with wavelet analysis and artificial neural network. Environ. Manage. 51(5), 1044–1054 (2013)

    Article  Google Scholar 

  6. Rammal, A., Perrin, E., Chabbert, B., Bertrand, I., Habrant, A., Lecart, B., Vrabie, V.: Evaluation of lignocellulosic biomass degradation by combining mid- and near-infrared spectra by the outer product and selecting discriminant wavenumbers using a genetic algorithm. Appl. Spectrosc. 69, 1303–1312 (2015)

    Article  Google Scholar 

  7. Dwyer, J.O., Zhu, L., Granda, C.B., Chang, V.S., Holtzapple, M.T.: Neural network prediction of biomass digestibility based on structural features. Biotechnol. Prog. 24, 283–292 (2008)

    Article  Google Scholar 

  8. Razi, M.A., Athappilly, K.: A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Syst. Appl. 29, 65–74 (2005)

    Article  Google Scholar 

  9. Subhabrata, D., Anamica, B., Amit, G., Apurba, D., Chatterjee Pradip, K.: Artificial neural network modeling of xylose yields from water hyacinth by dilute sulphuric acid hydrolysis for ethanol production. Int. J. Environ. Technol. Manage. 19(2), 150–166 (2016)

    Article  Google Scholar 

  10. Chen, W.K., Rivera, E., Rabelo, S., Garcia, D., Filho, R., Costa, A.: Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks. J. Chem. Technol. Biotechnol. 85(7), 983–992 (2000)

    Google Scholar 

  11. Rashida, R., Jamaluddinb, H., Amina, N.: Empirical and feed forward neural networks models of tapioca starch hydrolysis. Appl. Artif. Intell. 20, 79–97 (2006)

    Article  Google Scholar 

  12. Julian, D.: Olden: an artificial neural network approach for studying phytoplankton succession. Hydrobiologia. 436, 131–143 (2000)

    Article  Google Scholar 

  13. Ravikumar, R., Renuka, K., Sindhu, V., Malarmathi, K.B.: Response surface methodology and artificial neural network for modeling and optimization of distillery spent wash treatment using phormidium valderianum BDU 140441. Pol. J. Environ. Stud. 22(4), 1143–1152 (2013)

    Google Scholar 

  14. Rajendra, M., Jena, P.C., Raheman, H.: Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA. Fuel 88, 868–875. (2009)

    Article  Google Scholar 

  15. Elmolla, E.S., Chaudhuria, M., Eltoukhy, M.M.: The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the fenton process. J. Hazard. Mater. 179(1–3), 127–134 (2010)

    Article  Google Scholar 

  16. Agatonovic-Kustrin, S., Beresford, R.: Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22(5), 717–727 (2000)

    Article  Google Scholar 

  17. Lei, H., Cybulska, I., Julson, J.: Hydrothermal pretreatment of lignocellulosic biomass and kinetics. J. Sustain. Bioenerg. Systems. 3, 250–259 (2013)

    Article  Google Scholar 

  18. Hendriks, A.T.W.M., Zeeman, G.: Pretreatments to enhance the digestibility of lignocellulosic biomass. Biores. Technol. 100(1), 10–18 (2008)

    Article  Google Scholar 

  19. Alvira, P., Tomas-Pejo, E., Ballesteros, M., Negro, M.J.: Pretreatment technologies for an efficient bioethanol production process based on enzymatic hydrolysis: a review. Biores. Technol. 101(13), 4851–4861 (2010)

    Article  Google Scholar 

  20. Maurya, D.P., Singla, A., Negi, S.: An overview of key pretreatment processes for biological conversion of lignocellulosic biomass to bioethanol. 3 Biotech. 5, 597–609 (2015)

    Article  Google Scholar 

  21. Hasegawa, I., Tabata, K., Okuma, O., Mae, K.: New pretreatment methods combining a hot water treatment and water/acetone extraction for thermo-chemical conversion of biomass. Energy Fuels 18(3), 755–760 (2004)

    Article  Google Scholar 

  22. Zheng, Y., Pan, Z., Zhang, R.: Overview of biomass pretreatment for cellulosic ethanol production. Int. J. Agric. Biol. Eng. 2(3), 51–68 (2009)

    Google Scholar 

  23. Shihani, N., Kumbhar, B., Kulshreshtha, M.: Modeling of extrusion process using response surface methodology and artificial neural networks. J. Eng. Sci. Technol. 1(1), 31–40 (2006)

    Google Scholar 

  24. Nikzad, M., Movagharnejad, K., Talebnia, F., Aghaiy, Z., Mighani, M.: Modeling of alkali pretreatment of rice husk using response surface methodology and artificial neural network. Chem. Eng. Commun. 202, 728–738 (2015)

    Article  Google Scholar 

  25. Mohaptra, S., Dash, P.K., Behera, S.S., Thatoi, H.: Optimization of delignification of two Pennisetum grass species by NaOH pretreatment using Taguchi and ANN statistical approach. Environ. Technol. 37(8), 1–12 (2015)

    Google Scholar 

  26. Redding, A.P., Keshwani, D.R., Cheng, J.J.: Process simulation of dilute acid pretreatment of Coastal Bermuda grass for bioethanol production. Conf. Present. White Papers Biol. Syst. Eng. 36, 2–11 (2009)

    Google Scholar 

  27. Bhange Vivek, P., William, S.P.M.P., Abhinav, S., Jagdish, G., Vaidya Atul, N., Wate Satish, R.: Pretreatment of garden biomass using Fenton’s reagent: influence of Fe2+ and H2O2 concentrations on lignocellulose degradation. J. Environ. Health Sci. Eng. 13(12), 1–7 (2015)

    Google Scholar 

  28. Betiku, E., Ajala, S.O.: Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: a case of artificial neural network vs. response surface methodology. Ind. Crops Prod. 53, 314–322 (2014)

    Article  Google Scholar 

  29. Das, S., Bhattacharya, A., Haldar, S., Ganguly, A., Sai, G., Tinga, Y.P., Chatterjee, P.K.: Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: comparison between artificial neural network and response surface methodology. Sustain. Mater. Technol. 3, 17–28 (2015)

    Google Scholar 

  30. Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)

    MATH  Google Scholar 

  31. Kurková, V.: Kolmogorov’s theorem and multilayer neural networks. Neural Netw. 5, 501–506 (1992)

    Article  Google Scholar 

  32. Priddy, K., Keller, P.: Artificial Neural Networks: An Introduction. WA:SPIE Press, Bellingham (2005)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biotechnology, Priyadarshini Institute of Engineering & Technology, Hingna Road, Nagpur, Maharashtra, 440019, India

    Vivek P. Bhange & Urvashi V. Bhivgade

  2. Solid Waste Management Division, National Environmental Engineering Research Institute, Nehru Marg, Nagpur, Maharashtra, 440020, India

    Atul N. Vaidya

Authors
  1. Vivek P. Bhange
    View author publications

    Search author on:PubMed Google Scholar

  2. Urvashi V. Bhivgade
    View author publications

    Search author on:PubMed Google Scholar

  3. Atul N. Vaidya
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Vivek P. Bhange.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhange, V.P., Bhivgade, U.V. & Vaidya, A.N. Artificial Neural Network Modeling in Pretreatment of Garden Biomass for Lignocellulose Degradation. Waste Biomass Valor 10, 1571–1583 (2019). https://doi.org/10.1007/s12649-017-0163-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12649-017-0163-z

Keywords