Skip to main content

Advertisement

SpringerLink
Log in

Process modeling of solvent extraction of oil from Hura crepitans seeds: adaptive neuro-fuzzy inference system versus response surface methodology

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

Abstract

Vegetable oils are a very important feedstock for many industries such as biofuels. There is the need to source for novel and underexploited plant oilseeds to meet the world demand for oils. Thus, the extraction of oil from Hura crepitans (sandbox) seeds was conducted using the solvent extraction method. Modeling of the extraction process was carried out using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The effects of the nature of the solvent (non-polar (n-hexane) and polar (acetone and ethyl acetate)), solid-solvent ratio (0.1–0.3 g/mL), extraction time (2–6 h), and their interactions on the oil yield were investigated using the D-optimal design technique. Performance assessment of the developed models was carried out to check their effectiveness in predicting the H. crepitans seed oil (HCSO) yield using various fit statistics. The coefficient of determination (R2) observed for the RSM and ANFIS models was 0.9720 and 0.9988, respectively, with corresponding mean relative percent deviation (MRPD) of 2.50 and 0.37%. Maximum HCSO yield of 62.95 wt% was achieved by ANFIS coupled with genetic algorithm (GA) using 0.1 g/mL solid-solvent ratio, extraction time of 4.19 h, and acetone, while maximum HCSO yield of 62.50 wt% was observed by RSM with a solid-solvent ratio of 0.1 g/mL, extraction time of 4.04 h, and acetone. Characteristics of the HCSO indicated that it could serve as a good feedstock for the production of oleochemicals such as biodiesel. The results obtained in this study demonstrated that ANFIS is marginally superior to RSM in the modeling of the HCSO extraction process, while GA was slightly better than the numerical tool of RSM in the optimization of the process.

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
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All data used in this work have been made available.

Abbreviations

A :

solid-to-solvent ratio

ANFIS:

adaptive neuro-fuzzy inference system

B :

extraction time

C :

solvent type

CV:

coefficient of variance

DoE:

design of experiments

df:

degree of freedom

GA:

genetic algorithm

FT-IR:

Fourier transform infrared

HCSO:

Hura crepitans seed oil

MAE:

mean absolute error

MRPD:

mean relative percent deviation

MSE:

mean squared error

R :

correlation coefficient

R 2 :

coefficient of determination

RMSE:

root mean square error

RSM:

response surface methodology

SEP:

standard error of prediction

SD:

standard deviation

SS:

sum of squares

References

  1. Gary H (1983) Species accounts: Hura crepitans. Jabillo, Sandbox Tree

    Google Scholar 

  2. Ibrahim AP, Omilakin RO, Betiku E (2019) Optimization of microwave-assisted solvent extraction of non-edible sandbox (Hura crepitans) seed oil: a potential biodiesel feedstock. Renew Energy 141:349–358

    Article  Google Scholar 

  3. Ezeh I, Umoren S, Essien E, Udoh A (2012) Studies on the utilization of Hura crepitans L. seed oil in the preparation of alkyd resins. Ind Crops Prods 36:94–99

    Article  Google Scholar 

  4. Owolabi JB, Alabi KA, Lajide L (2015) Synthesis and characterization of copper metal soaps from Thevetia peruviana and Hura crepitans seed oils. Scientific Research and Essays 10:649–654

    Article  Google Scholar 

  5. Adewuyi A, Göpfert A, Wolff T, Rao B, Prasad R (2012) Synthesis of azidohydrin from Hura crepitans seed oil: a renewable resource for oleochemical industry and sustainable development. ISRN organic chemistry 2012(873046). https://doi.org/10.5402/2012/873046

  6. Adewuyi A, Gennaro A, Durante C (2015) Bioadsorbent Hura crepitans for the removal of phenol from solution. J Water Chem Technol 37:277–282

    Article  Google Scholar 

  7. Adewuyi A, Awolade PO, Oderinde RA (2014) Hura crepitans seed oil: an alternative feedstock for biodiesel production. J Fuels 2014. https://doi.org/10.1155/2014/464590

  8. Oyelade J, Idowu D, Oniya O, Ogunkunle O (2017) Optimization of biodiesel production from sandbox (Hura crepitans L.) seed oil using two different catalysts. Energy Sources Part A 39:1242–1249

    Article  Google Scholar 

  9. Oraegbunam JC, Oladipo B, Falowo OA, Betiku E (2020) Clean sandbox (Hura crepitans) oil methyl esters synthesis: a kinetic and thermodynamic study through pH monitoring approach. Renew Energy 160:526–537

    Article  Google Scholar 

  10. Ogbu IM, Ajiwe VIE, Okoli CP (2018) Performance evaluation of carbon-based heterogeneous acid catalyst derived from Hura crepitans seed pod for esterification of high ffa vegetable oil. BioEnergy Research 11:772–783

    Article  Google Scholar 

  11. Oladipo B, Betiku E (2019) Process optimization of solvent extraction of seed oil from Moringa oleifera: an appraisal of quantitative and qualitative process variables on oil quality using D-optimal design. Biocatalysis and Agricultural Biotechnology 20:101187

    Article  Google Scholar 

  12. Jisieike CF, Betiku E (2020) Rubber seed oil extraction: effects of solvent polarity, extraction time and solid-solvent ratio on its yield and quality. Biocatalysis and Agricultural Biotechnology 24:101522

    Article  Google Scholar 

  13. Sorin-Stefan B, Ionescu M, Voicu G, Ungureanu N, Vladut V (2013) Calculus elements for mechanical presses in oil industry. Food Industry 1(8). https://doi.org/10.5772/53167 

  14. Okeleye AA, Betiku E (2019) Kariya (Hildegardia barteri) seed oil extraction: comparative evaluation of solvents, modeling, and optimization techniques. Chemical Eng Communs 206:1181–1198

    Article  Google Scholar 

  15. Mueanmas C, Nikhom R, Petchkaew A, Iewkittayakorn J, Prasertsit K (2019) Extraction and esterification of waste coffee grounds oil as non-edible feedstock for biodiesel production. Renew Energy 133:1414–1425

    Article  Google Scholar 

  16. Devi V, Khanam S (2019) Comparative study of different extraction processes for hemp (Cannabis sativa) seed oil considering physical, chemical and industrial-scale economic aspects. J Clean Prod 207:645–657

    Article  Google Scholar 

  17. Fernandes SS, Tonato D, Mazutti MA, de Abreu BR, da Costa Cabrera D, D’Oca CDRM, Prentice-Hernández C, de las Mercedes Salas-Mellado M (2019) Yield and quality of chia oil extracted via different methods. J Food Eng 262:200–208

    Article  Google Scholar 

  18. Al Juhaimi F, Uslu N, Babiker EE, Ghafoor K, Ahmed IAM, Özcan MM (2019) The effect of different solvent types and extraction methods on oil yields and fatty acid composition of safflower seed. J Oleo Science, ess19131. https://doi.org/10.5650/jos.ess19131

  19. Mohammadpour H, Sadrameli SM, Eslami F, Asoodeh A (2019) Optimization of ultrasound-assisted extraction of Moringa peregrina oil with response surface methodology and comparison with Soxhlet method. Ind Crops Prods 131:106–116

    Article  Google Scholar 

  20. Oniya O, Oyelade J, Ogunkunle O, Idowu D (2017) Optimization of solvent extraction of oil from sandbox kernels (Hura crepitans L.) by a response surface method. Energy and Policy Research 4:36–43

    Article  Google Scholar 

  21. Betiku E, Odude VO, Ishola NB, Bamimore A, Osunleke AS, Okeleye AA (2016) Predictive capability evaluation of RSM, ANFIS and ANN: a case of reduction of high free fatty acid of palm kernel oil via esterification process. Energ Convers Manage 124:219–230

    Article  Google Scholar 

  22. Mostafaei M, Javadikia H, Naderloo L (2016) Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). Energy 115:626–636

    Article  Google Scholar 

  23. Sajjadi B, Raman AAA, Parthasarathy R, Shamshirband S (2016) Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology. J Taiwan Inst Chem E 64:47–58

    Article  Google Scholar 

  24. Aghbashlo M, Hosseinpour S, Tabatabaei M, Dadak A (2017) Fuzzy modeling and optimization of the synthesis of biodiesel from waste cooking oil (WCO) by a low power, high frequency piezo-ultrasonic reactor. Energy 132:65–78

    Article  Google Scholar 

  25. Ishola NB, Adeyemi OO, Adesina AJ, Odude VO, Oyetunde OO, Okeleye AA, Soji-Adekunle AR, Betiku E (2017) Adaptive neuro-fuzzy inference system-genetic algorithm vs. response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil. Process Saf Environ Prot 111:211–220

    Article  Google Scholar 

  26. Ishola NB, Okeleye AA, Osunleke AS, Betiku E (2019) Process modeling and optimization of sorrel biodiesel synthesis usingbarium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology, neural network and neuro-fuzzy system. Neural Comput and Appl 31:4929–4943

    Article  Google Scholar 

  27. Jang J-S, Sun C-T (1995) Neuro-fuzzy modeling and control. Proceedings of the IEEE 83:378–406

    Article  Google Scholar 

  28. Lohani UC, Fallahi P, Muthukumarappan K (2015) Comparison of ethyl acetate with hexane for oil extraction from various oilseeds. J Am Oil Chem Soc 92:743–754

    Article  Google Scholar 

  29. Tir R, Dutta PC, Badjah-Hadj-Ahmed AY (2012) Effect of the extraction solvent polarity on the sesame seeds oil composition. Eur J Lipid Sci Technol 114:1427–1438

    Article  Google Scholar 

  30. Hron RJ Sr, Koltun SP, Graci AV Jr (1982) Biorenewable solvents for vegetable oil extraction. J Am Oil Chem Soc 59:674–682

    Article  Google Scholar 

  31. Atkinson A, Donev A, Tobias R (2007) Optimum experimental designs, with SAS. New York

  32. Ighose BO, Adeleke IA, Damos M, Junaid HA, Okpalaeke KE, Betiku E (2017) Optimization of biodiesel production from Thevetia peruviana seed oil by adaptive neuro-fuzzy inference system coupled with genetic algorithm and response surface methodology. Energ Convers Manage 132:231–240

    Article  Google Scholar 

  33. Akintunde AM, Ajala SO, Betiku E (2015) Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: a potential biofuel candidate. Ind Crops Prods 67:387–394

    Article  Google Scholar 

  34. AOAC (1990) Official methods of analysis of the Association of Official Analytical Chemists. Official methods of analysis of the Association of Official Analytical Chemists, 15th edn. AOAC, Arlington, Virginia.

  35. Demirbaş A (2008) Relationships derived from physical properties of vegetable oil and biodiesel fuels. Fuel 87:1743–1748

    Article  Google Scholar 

  36. Krisnangkura K (1986) A simple method for estimation of cetane index of vegetable oil methyl esters. J Am Oil Chem Soc 63:552–553

    Article  Google Scholar 

  37. Knothe G, Dunn RO (2003) Dependence of oil stability index of fatty compounds on their structure and concentration and presence of metals. J Am Oil Chem Soc 80:1021–1026

    Article  Google Scholar 

  38. Joglekar A, May A (1987) Product excellence through design of experiments. Cereal Foods World 32:857–868

    Google Scholar 

  39. Myers RH, Montgomery DC, Anderson-Cook C (2009) Response surface methodology: product and process optimization using designed experiments. John Wiley & Sons, New York

    MATH  Google Scholar 

  40. Grčić I, Vujević D, Koprivanac N (2010) The use of D-optimal design to model the effects of process parameters on mineralization and discoloration kinetics of Fenton-type oxidation. Chem Eng J 157:408–419

    Article  Google Scholar 

  41. Ajala SO, Betiku E (2015) Yellow oleander seed oil extraction modeling and process parameters optimization: performance evaluation of artificial neural network and response surface methodology. J Food Process Preserv 39:1466–1474

    Article  Google Scholar 

  42. Betiku E, Ishola NB (2020) Optimization of sorrel oil biodiesel production by base heterogeneous catalyst from kola nut pod husk: neural intelligence-genetic algorithm versus neuro-fuzzy-genetic algorithm. Environmental Progress & Sustainable Energy 39:e13393

    Article  Google Scholar 

  43. Attah JC, Ibemesi JA (1990) Solvent extraction of the oils of rubber, melon, pumpkin and oilbean Seeds. J Am Oil Chem Soc 67:25–27

    Article  Google Scholar 

  44. Rubin LJ, Diosady LL, Phillips CR (1984) Solvent extraction of oil bearing seeds (United States Patent). Patent Number: 4460504. https://patentimages.storage.googleapis.com/61/62/7b/129ff887964272/US4460504.pdf. Accessed 30 June 2020

  45. Chakrabarty M (2003) Chemistry and technology of oils & fats, vol 1. Allied Publishers

Download references

Acknowledgments

Authors wish to thank Mr. N. B. Ishola for technical assistance.

Author information

Author notes

  1. Eriola Betiku

    Present address: Department of Biological Sciences, Florida Agricultural and Mechanical University, Tallahassee, FL, 32307, USA

Authors and Affiliations

  1. Biochemical Engineering Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Osun State, Ile-Ife, 220005, Nigeria

    Ropo Oluwasesan Omilakin, Ayooluwa Paul Ibrahim, Babajide Sotunde & Eriola Betiku

Authors
  1. Ropo Oluwasesan Omilakin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayooluwa Paul Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Babajide Sotunde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eriola Betiku
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ropo Oluwasesan Omilakin: methodology, investigation, validation. Ayooluwa Paul Ibrahim: methodology, investigation, data curation, Software. Babajide Sotunde: data curation, writing—original draft. Eriola Betiku: conceptualization, supervision, project administration, writing—reviewing and editing.

Corresponding author

Correspondence to Eriola Betiku.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

No code was generated in this work

Additional information

Publisher’s Note

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

Electronic supplementary material

ESM 1

(DOCX 30 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Omilakin, R.O., Ibrahim, A.P., Sotunde, B. et al. Process modeling of solvent extraction of oil from Hura crepitans seeds: adaptive neuro-fuzzy inference system versus response surface methodology. Biomass Conv. Bioref. 13, 247–260 (2023). https://doi.org/10.1007/s13399-020-01080-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13399-020-01080-7

Keywords

Search

Navigation