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Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

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Abstract

A hybrid neural model (HNM) and particle swarm optimization (PSO) was used to optimize ethanol production by a flocculating yeast, grown on cashew apple juice. HNM was obtained by combining artificial neural network (ANN), which predicted reaction specific rates, to mass balance equations for substrate (S), product and biomass (X) concentration, being an alternative method for predicting the behavior of complex systems. ANNs training was conducted using an experimental set of data of X and S, temperature and stirring speed. The HNM was statistically validated against a new dataset, being capable of representing the system behavior. The model was optimized based on a multiobjective function relating efficiency and productivity by applying the PSO. Optimal estimated conditions were: S0 = 127 g L−1, X0 = 5.8 g L−1, 35 °C and 111 rpm. In this condition, an efficiency of 91.5% with a productivity of 8.0 g L−1 h−1 was obtained at approximately 7 h of fermentation.

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Abbreviations

A1 :

Initial value of horizontal asymptote

A2 :

Final value of horizontal asymptote

ANN:

Artificial neural network

dx:

Model increment

Ftab :

Tabled value for the Fisher Test F

HNM:

Hybrid neural model

n:

Number of samples

nv :

Number of variables estimated

N:

Stirring speed (rpm)

p:

Number of model parameters

P:

Product concentration (g L−1)

Pf :

Final product concentration (g L−1)

PSO:

Particle Swarm Optimization

RSD:

Residual standard deviation (%)

S:

Substrate concentration (g L−1)

S0 :

Initial substrate concentration (g L−1)

Sf :

Final substrate concentration (g L−1)

T:

Time (h)

tf :

Final time (h)

x:

Model variable

x0 :

Average value between horizontal asymptotes

X:

Cell concentration (g L−1)

X0 :

Initial cell concentration (g L−1)

ε:

Error (%)

μS :

Specific substrate consumption rate (gsubs.g −1cell .h−1)

μP :

Specific product production rate (gproduct.g −1cell .h−1)

μX :

Specific growth rate of cells (h−1)

cal:

Calculated

exp:

Experimental

min:

Minimum

max:

Maximum

n:

Normalized

References

  1. Anderson ST (2012) The demand for ethanol as a gasoline substitute. J Environ Econ Manage 63(2):151–168. https://doi.org/10.1016/j.jeem.2011.08.002

    Article  Google Scholar 

  2. (IEA), I.E. A. (2015) Medium-term renewable energy market report. OECD/IEA, Paris

    Google Scholar 

  3. International Energy Agency (2017) Renewables 2017: analysis and forecasts to 2022—executive summary. J Qual Participat. https://doi.org/10.1073/pnas.0603395103

    Article  Google Scholar 

  4. Neelakandan T, Usharani G (2009) Optimization and production of bioethanol from cashew Apple juice using immobilized yeast cells by Saccharomyces cerevisiae. Am Eur J Sci Res 4:85–88

    CAS  Google Scholar 

  5. Karuppaya M, Sasikumar E, Viruthagiri T, Vijayagopal V (2010) Optimization of process variables using response surface methodology (RMS) for ethanol production from cashew apple juice by Saccharomyces cerevisiae. Asian J Food Agro Ind 3:462–473

    Google Scholar 

  6. Pinheiro ÁDT, da Silva Pereira A, Barros EM, Antonini SRC, Cartaxo SJM, Rocha MVP, Gonçalves LRB (2017) Mathematical modeling of the ethanol fermentation of cashew apple juice by a flocculent yeast: the effect of initial substrate concentration and temperature. Bioprocess Biosyst Eng 40(8):1221–1235. https://doi.org/10.1007/s00449-017-1782-2

    Article  CAS  PubMed  Google Scholar 

  7. Pereira AD, Pinheiro ÁD, Rocha MV, Gonçalves LR, Cartaxo SJ (2019) A new approach to model the influence of stirring intensity on ethanol production by a flocculant yeast grown on cashew apple juice. Canad J Chem Eng 97:1253–1262. https://doi.org/10.1002/cjce.23419

    Article  CAS  Google Scholar 

  8. Pinheiro ADT, Rocha MVP, Macedo GR, Gonçalves LRB (2008) Evaluation of cashew apple juice for the production of fuel ethanol. Appl Biochem Biotechnol 148(1–3):227–234. https://doi.org/10.1007/s12010-007-8118-7

    Article  CAS  PubMed  Google Scholar 

  9. Marques WL, Raghavendran V, Ugarte B (2015) Sucrose and Saccharomyces cerevisiae: a relationship most sweet. FEMS Yeast Res. https://doi.org/10.1093/femsyr/fov107

    Article  PubMed  Google Scholar 

  10. Rich JO, Anderson AM, Leathers TD, Bischoff KM, Liu S, Skory CD (2020) Microbial contamination of commercial corn-based fuel ethanol fermentations. Bioresource Technol Rep 11(April):100433. https://doi.org/10.1016/j.biteb.2020.100433

    Article  Google Scholar 

  11. Ruchala J, Kurylenko OO, Dmytruk KV, Sibirny AA (2020) Construction of advanced producers of first- and second-generation ethanol in Saccharomyces cerevisiae and selected species of non-conventional yeasts (Scheffersomyces stipitis, Ogataea polymorpha). J Ind Microbiol Biotechnol 47(1):109–132. https://doi.org/10.1007/s10295-019-02242-x

    Article  CAS  PubMed  Google Scholar 

  12. Gong C, Cao N, Du J, Tsao G (1999) Ethanol production from renewable resources. Adv Biochem Eng Biotechnol 65:207–242

    CAS  PubMed  Google Scholar 

  13. Lei J, Zhao X, Ge X, Bai F (1998) Ethanol tolerance and the variation of plasma membrane composition of yeast floc populations with different size distribution. J Biotechnol 2:35–43

    Google Scholar 

  14. Ortiz-Muñiz B, Carvajal-Zarrabal O, Torrestiana-Sanchez B, Aguilar-Uscanga MG (2010) Kinetic study on ethanol production using Saccharomyces cerevisiae ITV-01 yeast isolated from sugar canemolasses. J Chem Technol Biotechnol 85(10):1361–1367. https://doi.org/10.1002/jctb.2441

    Article  CAS  Google Scholar 

  15. Domingues L, Vicente AA, Lima N, Teixeira JA (2000) Applications of yeast flocculation in biotechnological processes. Biotechnol Bioprocess Eng 5(4):288–305. https://doi.org/10.1007/BF02942185

    Article  CAS  Google Scholar 

  16. Volesky B, Votruba J (1992) Modeling and optimization of fermentation processes. Elsevier, Boca Raton

    Google Scholar 

  17. Saraceno A, Curcio S, Calabrò V, Iorio G (2010) A hybrid neural approach to model batch fermentation of “ricotta cheese whey” to ethanol. Comput Chem Eng 34:1590–1596

    Article  CAS  Google Scholar 

  18. Feyo de Azevedo S, Dahm B, Oliveira FR (1997) Hybrid modelling of biochemical processes: a comparison with the conventional approach. Comput Chem Eng 21:751–756

    Article  Google Scholar 

  19. Thompson ML, Kramer MA (1994) Modeling chemical processes using prior knowledge and neural networks. AIChE J 40(8):1328–1340. https://doi.org/10.1002/aic.690400806

    Article  CAS  Google Scholar 

  20. Costa A, Henrique ASW, Alves T, Filho R, Lima EL (1999) A hybrid neural model for the optimization of fed-batch fermentations. Braz J Chem Eng. https://doi.org/10.1590/S0104-66321999000100006

    Article  Google Scholar 

  21. Silva RG, Cruz AJG, Hokka CO, Giordano RLC, Giordano RC (2000) A hybrid feedforward neural network model for the cephalosporin C production process. Braz J Chem Eng 17:587–598

    Article  CAS  Google Scholar 

  22. Sivapathasekaran C, Mukherjee S, Ray A, Gupta A, Sen R (2010) Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant. Bioresource Technol 101(8):2884–2887. https://doi.org/10.1016/j.biortech.2009.09.093

    Article  CAS  Google Scholar 

  23. Dhanarajan G, Mandal M, Sen R (2014) A combined artificial neural network modeling-particle swarm optimization strategy for improved production of marine bacterial lipopeptide from food waste. Biochem Eng J 84:59–65. https://doi.org/10.1016/j.bej.2014.01.002

    Article  CAS  Google Scholar 

  24. Bhattacharya S, Dineshkumar R, Dhanarajan G, Sen R, Mishra S (2017) Improvement of ε-polylysine production by marine bacterium Bacillus licheniformis using artificial neural network modeling and particle swarm optimization technique. Biochem Eng J 126:8–15. https://doi.org/10.1016/j.bej.2017.06.020

    Article  CAS  Google Scholar 

  25. Huang J, Mei L-H, Xia J (2006) Application of artificial neural network coupling particle swarm optimization algorithm to biocatalytic production of GABA. Biotechnol Bioeng 96(5):924–931. https://doi.org/10.1002/bit

    Article  Google Scholar 

  26. Cockshott AR, Sullivan GR (2001) Improving the fermentation medium for Echinocandin B production part II: particle swarm optimization. Process Biochem 36(7):661–669. https://doi.org/10.1016/S0032-9592(00)00261-2

    Article  CAS  Google Scholar 

  27. Serapião ABS (2009) PID Tuning By Swarm Optimization Strategies. In Proceedings of the 8th Brazilian Conference on Dynamics. Bauru-SP

  28. Dineshkumar R, Dhanarajan G, Dash SK, Sen R (2015) An advanced hybrid medium optimization strategy for the enhanced productivity of lutein in Chlorella minutissima. Algal Res 7:24–32. https://doi.org/10.1016/j.algal.2014.11.010

    Article  Google Scholar 

  29. Pinheiro ÁD, Barros EM, Rocha LA, da Rocha Ponte VM, de Macedo AC, Rocha MV, Gonçalves LR (2020) Optimization and scale-up of ethanol production by a flocculent yeast using cashew apple juice as feedstock. Braz J Chem Eng. https://doi.org/10.1007/s43153-020-00068-0

    Article  Google Scholar 

  30. Wisselink HW, Toirkens MJ, Berriel MDRF, Winkler AA, Van Dijken JP, Pronk JT, Van Maris AJA (2007) Engineering of Saccharomyces cerevisiae for efficient anaerobic alcoholic fermentation of L-arabinose. Appl Environ Microbiol 73(15):4881–4891. https://doi.org/10.1128/AEM.00177-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cleran Y, Thibault J, Cheruy A, Corrieu G (1991) Comparison of prediction performances between models obtained by the group method of data handling and neural networks for the alcoholic fermentation rate in enology. J Ferment Bioeng 71(5):356–362. https://doi.org/10.1016/0922-338X(91)90350-P

    Article  CAS  Google Scholar 

  32. Salehi M, Mohammadpour A, Mohammadi M, Aminghafari M (2018) A modified F-test for hypothesis testing in large-scale data. J Biopharm Stat 28(6):1078–1089. https://doi.org/10.1080/10543406.2018.1436557

    Article  PubMed  Google Scholar 

  33. Jiao B, Lian Z, Gu X (2008) A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos Sol Fract. https://doi.org/10.1016/j.chaos.2006.05.082

    Article  Google Scholar 

  34. Balat M, Balat H (2009) Recent trends in global production and utilization of bio-ethanol fuel. Appl Energy 86(11):2273–2282. https://doi.org/10.1016/j.apenergy.2009.03.015

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank CAPES, CNPq and FUNCAP (from Brazil) for the financial support that made this work possible. The authors also thank Professor Sandra Regina Ceccato Antonini for the yeast Saccharomyces cerevisiae CCA008 and Embrapa Agroindustria Tropical (Ceará, Brazil) for the Cashew Apple Juice.

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Authors and Affiliations

  1. Chemical Engineering Department, Universidade Federal do Ceará, Campus do Pici, Bloco 709, Fortaleza, CE, 60455-76, Brazil

    Andréa da Silva Pereira, Maria Valderez Ponte Rocha, Luciana Rocha B. Gonçalves & Samuel Jorge Marques Cartaxo

  2. Engineering and Technology Department, Universidade Federal Rural do Semiárido, Mossoró, RN, Brazil

    Álvaro Daniel Teles Pinheiro

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  1. Andréa da Silva Pereira
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Correspondence to Luciana Rocha B. Gonçalves.

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da Silva Pereira, A., Pinheiro, Á.D.T., Rocha, M.V.P. et al. Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice. Bioprocess Biosyst Eng 44, 329–342 (2021). https://doi.org/10.1007/s00449-020-02445-y

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