Severity Factor as an Efficient Control Parameter to Predict Biomass Solubilization and Saccharification During Acidic Hydrolysis of Microalgal Biomass

A Correction to this article was published on 16 May 2020

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Abstract

In this paper, the acidic pretreatment of microalgal biomass is investigated, and the solubilized biomass and hydrolyzed sugars were evaluated. The process is analyzed through the severity factor approach (acidic combined severity factor (ACSF)). A suitable kinetic model is developed and applied, and it is shown that the severity factor theory works. A discussion and comparison are presented with respect to the literature methods, which are mainly related to lignocellulosic biomass. In the case of microalgae, reaction orders for biomass and acid are shown to be the main parameters, and no other assumptions are needed. Two regions of the acidic treatment process have to be evaluated: low and high reactivity regions. Furthermore, a suitable experimental design is required in order to provide an appropriate reaction spectrum to obtain a good estimation of the kinetic parameters. A logarithmic severity factor range (ln ACSF) between 5 and 6 is able to solubilize around 80% of biomass and to hydrolyze more than 90% of sugars present in the biomass.

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Change history

  • 16 May 2020

    Reference: de Farias Silva, C.E., Bertucco, A. Severity Factor as an Efficient Control Parameter to Predict Biomass Solubilization and Saccharification During Acidic Hydrolysis of Microalgal Biomass. Bioenerg. Res. 11, 491���504 (2018). https://doi.org/10.1007/s12155-018-9913-4

References

  1. 1.

    Negahdar L, Delidovich I, Palkovits R (2016) Aqueous-phase hydrolysis of cellulose and hemicellulose over molecular acidic catalysis: insights into the kinetics and reaction mechanism. Appl Catal B: Environ 184:285–298

    Article  CAS  Google Scholar 

  2. 2.

    Silva CEF, Bertucco A (2016) Bioethanol from microalgae and cyanobacteria: a review and technological outlook. Process Biochem 51:1833–1842

    Article  CAS  Google Scholar 

  3. 3.

    Mosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, Landisch M (2005) Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresour Technol 96(6):673–686

    Article  PubMed  CAS  Google Scholar 

  4. 4.

    Nguyen MT, Choi SP, Lee J, Lee JH, Sim SJ (2009) Hydrothermal acid pretreatment of Chlamydomonas reinhardtii for ethanol production. J Microbiol Biotechnol 19(2):161–166

    Article  PubMed  CAS  Google Scholar 

  5. 5.

    Zhou N, Zhang Y, Wu X, Gong X, Wang Q (2011) Hydrolysis of Chlorella biomass for fermentable sugars in the presence of HCl and MgCl2. Bioresour Technol 102:10158–10161

    Article  PubMed  CAS  Google Scholar 

  6. 6.

    Zoulikha M, Thierry M, Quiyu ZJ, Nouviaire A, Sid-Ahmed R (2015) Combined steam-explosion toward vacuum and dilute-acid spraying of wheat straw. Impact of severity factor on enzymatic hydrolysis. Renew Energy 78:516–526

    Article  CAS  Google Scholar 

  7. 7.

    Temiz E, Akpinar O (2017) The effect of severity factor on the release of xylose and phenolics from rice husk and rice straw. Waste Biomass Valor 8(2):505–516

    Article  CAS  Google Scholar 

  8. 8.

    Saeman JF (1945) Kinetics of wood saccharification: hydrolysis of cellulose and decomposition of sugars in dilute acid at higher temperature. Ind Eng Chem 37:43–52

    Article  CAS  Google Scholar 

  9. 9.

    Overend RP, Chronet E (1987) Fractionation of lignocellulosics by steam-aqueous pretreatments. Phil Trans R Soc Lond A 321:523–536

    Article  CAS  Google Scholar 

  10. 10.

    Chum HL, Johnson DK, Black SK, Overend RP (1990) Pretreatment-catalyst effects and the combined severity parameter. Appl Biochem Biotechnol 24(25):1–14

    Article  Google Scholar 

  11. 11.

    Belkacemi K, Abatzaglou N, Overend RP, Chornet E (1991) Phenomenological kinetics of complex systems: mechanistic considerations in the solubilization of hemicelluloses following aqueous/steam treatments. Ind Eng Chem Res 3:2416–2425

    Article  Google Scholar 

  12. 12.

    Jacobsen SE, Wyman CE (2000) Cellulose and hemicellulose hydrolysis models for application to current and novel pretreatment processes. Appl Biochem Biotechnol 84-86:81–96

    Article  PubMed  CAS  Google Scholar 

  13. 13.

    Lloyd TA, Wyman CE (2005) Combined sugar yields for dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis of the remaining solids. Bioresour Technol 96:1967–1977

    Article  PubMed  CAS  Google Scholar 

  14. 14.

    Jacquet N, Quiévy N, Vanderghem C, Janas S, Blecker C, Wathelet B, Devaux J, Paquat M (2011) Influence of the steam explosion on the thermal stability of cellulose fibers. Polym Degrad Stab 96:1582–1588

    Article  CAS  Google Scholar 

  15. 15.

    Esteghlalian A, Hashimoto AG, Fenske JJ, Penner MH (1997) Modeling and optimization of the dilute-sulphuric-acid pretreatment of corn stover, poplar and switchgrass. Bioresour Technol 59:129–136

    Article  CAS  Google Scholar 

  16. 16.

    Janga KK, Oyaas K, Hertzberg T, Moe ST (2012) Application of a pseudo-kinetic generalized severity model to the concentrated sulfuric acid hydrolysis of pinewood and aspenwood. Bioresurces 7(3):2728–2741

    CAS  Google Scholar 

  17. 17.

    Janga KK, Hagg M, Moe ST (2012) Influence of acid concentration, temperature, and time on decrystallization in two-stage concentrated sulfuric acid hydrolysis of pinewood and aspenwood: a statistical approach. Bioresources 7(1):391–411

    CAS  Google Scholar 

  18. 18.

    Pedersen M, Meyer AS (2010) Lignocellulose pretreatment severity – relating pH to biomatrix opening. New Biotechnol 27(6):739–750

    Article  CAS  Google Scholar 

  19. 19.

    Chum HL, Johnson DK, Black SK (1990) Organosolv pretreatment for enzymatic hydrolysis of poplars. 2. Catalyst effects and the combined severity parameter. Ind Eng Chem Res 29(2):156–162

    Article  CAS  Google Scholar 

  20. 20.

    Chen S, Mowery RA, Chambliss CK, Peter G, Walsum PVL (2007) Pseudo reaction kinetics of organic degradation products in dilute-acid-catalyzed corn stover pretreatment hydrolisates. Biotechnol Bioeng 98(6):1135–1145

    PubMed  CAS  Google Scholar 

  21. 21.

    Rubio M, Tortosa JF, Quesada J, Gomez D (1998) Fractionation of lignocellulosics. Solubilization of corn stalk hemicellulases by autohydrolysis in aqueous medium. Biomass Bioenergy 15(6):483–491

    Article  CAS  Google Scholar 

  22. 22.

    Montané D, Salvadò J, Farriol X, Jollez P, Chornet E (1994) Phenomenological kinetics of wood delignification: application of a time-dependent rate constant and a generalized severity parameter to pulping and correlation of pulp properties. Wood Sci Technol 28:387–402

    Article  Google Scholar 

  23. 23.

    Pedersen M, Vikso-Nielsen A, Meyer AS (2010) Monosaccharide yield and lignin removal from wheat straw in response to catalyst type and pH during mild thermal pretreatment. Process Biochem 45:1181–1186

    Article  CAS  Google Scholar 

  24. 24.

    Bura R, Bothast RJ, Mansfield SD, Saddler JN (2003) Optimization of SO2-caralyzed steam pretreatment of corn fiber for ethanol production. Appl Biochem Biotechnol 105-108:319–335

    Article  PubMed  CAS  Google Scholar 

  25. 25.

    Kim Y, Kreke T, Mosier NS, Landisch MR (2014) Severity factor coefficients for subcritical liquid hot water pretreatment of hardwood chips. Biotechnol Bioeng 11(2):254–263

    Article  CAS  Google Scholar 

  26. 26.

    Silverstei RA, Sharma-shivappa RRS, Boyette MD, Osborne J (2007) A comparison of chemical pretreatment methods for improving saccharification of cotton stalks. Bioresour Technol 98:3000–3011

    Article  CAS  Google Scholar 

  27. 27.

    Pedersen M, Johassen KS, Meyer AS (2011) Low temperature lignocellulose pretreatment effects and interactions of pretreatment Ph are critical for maximizing enzymatic monosaccharide yields from wheat straw. Biotechnol Biofuels 4(11):1–10

    Google Scholar 

  28. 28.

    Temel B, Mekine M, Reuter K, Seheffler M, Metiu H (2007) Does phenomenological kinetics provide an adequate description of heterogenous catalytic reaction? J Chem Phys 126:204711

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    AOAC – Association of Analytical Chemists (2002) Official Methods of Analysis of the Association of Official Analytical Chemists, 17th edn. Ed. William Horwitz, Gaithersburg

    Google Scholar 

  30. 30.

    Trevelyan WE, Harrison JS (1952) Studies on yeast metabolism. 1. Fractionation and microdetermination of cell carbohydrates. Biochem J 50(3):298–303

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. 31.

    Miller JG (1959) Use of dinitrosalicyclic acid reagent for determination of reducing sugars. Anal Chem 31(3):426–428

    Article  CAS  Google Scholar 

  32. 32.

    Silva CEF, Sforza E (2016) Carbohydrate productivity in continuous reactor under nitrogen limitation: effect of light and residence time on nutrient uptake in Chlorella vulgaris. Process Biochem 51:2112–2118

    Article  CAS  Google Scholar 

  33. 33.

    Silva CEF, Sforza E, Bertucco A (2017) Effects of pH and carbon source on Synechococcus PCC 7002 cultivation: biomass and carbohydrate production with different strategies for pH control. Appl Biochem Biotechnol 181:682–698

    Article  CAS  Google Scholar 

  34. 34.

    Lee OK, Oh Y, Lee EY (2015) Bioethanol production from carbohydrate-enriched residual biomass obtained after lipid extraction of Chlorella sp. KR-1. Bioresour Technol 196:22–27

    Article  PubMed  CAS  Google Scholar 

  35. 35.

    Wang Y, Guo W, Cheng C, Ho S, Chang J, Ren N (2016) Enhancing bio-butanol production from biomass of Chlorella vulgaris JSC-6 with sequential alkali pretreatment and acid hydrolysis. Bioresour Technol 200:557–564

    Article  PubMed  CAS  Google Scholar 

  36. 36.

    Markovsky I, Huffel SV (2007) Overview of total least squares methods. Signal Process 87:2283–2302

    Article  Google Scholar 

  37. 37.

    Silva CEF, Bertucco A (2017) Dilute acid hydrolysis of microalgal biomass for bioethanol production:n accurate kinetic model of biomass solubilization, sugars hydrolysis and nitrogen/ash balance. Reaction Kinetics, Mechanisms and Catalysis, in Press

  38. 38.

    Vroom KE (1957) The ‘H’ factor: a means of expressing cooking times and temperatures as a single variable. Pulp paper Mag Con 58(3):228–231

    Google Scholar 

  39. 39.

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

    Article  PubMed  CAS  Google Scholar 

  40. 40.

    Ho S, Huang S, Chen C, Hasunuma T, Kondo A, Chang J (2013) Bioethanol production using carbohydrate-rich microalgal biomass as feedstock. Bioresour Technol 135:191–198

    Article  PubMed  CAS  Google Scholar 

  41. 41.

    Wang H, Chunli J, Shenglei B, Zhou P, Chen L, Liu T (2014) Joint production of bioethanol from filamentous oleaginous microalgae Tribonema sp. Bioresour Technol 172:169–173

    Article  PubMed  CAS  Google Scholar 

  42. 42.

    Miranda JR, Passarinho PC, Gouveia L (2012) Pre-treatment optimization of Scenedesmus obliquus microalga for bioethanol production. Bioresour Technol 104:343–348

    Article  CAS  Google Scholar 

  43. 43.

    Ho SH, Li PJ, Liu CC, Chang JS (2013) Bioprocess development on microalgae-based CO2 fixation and bioethanol production using Scenedesmus obliquus CNW-N. Bioresour Technol 145:142–149

    Article  PubMed  CAS  Google Scholar 

  44. 44.

    Viegas MR (2013) Avaliação de métodos de pré-tratamento na gaseificação da biomassa. (Master Thesis). Biotechnology of Natural Products, University Nova de Lisboa, 84 pp.

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Acknowledgements

The authors would like to thank CNPq – Brazil (National Research Council of Brazil) - Process number 249182/2013-0, for the resources and fellowship. C.E.F. Silva designed and performed the experiments; summarized, analyzed, and discussed the results; wrote the article; and approved the final version. A. Bertucco analyzed the results, wrote the article, and approved the final version.

Funding

This study was funded by CNPq—Brazil (National Research Council of Brazil) Process number 249182/2013-0.

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Correspondence to Carlos Eduardo de Farias Silva.

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de Farias Silva, C.E., Bertucco, A. Severity Factor as an Efficient Control Parameter to Predict Biomass Solubilization and Saccharification During Acidic Hydrolysis of Microalgal Biomass. Bioenerg. Res. 11, 491–504 (2018). https://doi.org/10.1007/s12155-018-9913-4

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Keywords

  • Phenomenological kinetics
  • Microalgae
  • Bioethanol
  • Acidic hydrolysis
  • Activation energy