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Starch hydrolysis modeling: application to fuel ethanol production

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Abstract

Efficiency of the starch hydrolysis in the dry grind corn process is a determining factor for overall conversion of starch to ethanol. A model, based on a molecular approach, was developed to simulate structure and hydrolysis of starch. Starch structure was modeled based on a cluster model of amylopectin. Enzymatic hydrolysis of amylose and amylopectin was modeled using a Monte Carlo simulation method. The model included the effects of process variables such as temperature, pH, enzyme activity and enzyme dose. Pure starches from wet milled waxy and high-amylose corn hybrids and ground yellow dent corn were hydrolyzed to validate the model. Standard deviations in the model predictions for glucose concentration and DE values after saccharification were less than ±0.15% (w/v) and ±0.35%, respectively. Correlation coefficients for model predictions and experimental values were 0.60 and 0.91 for liquefaction and 0.84 and 0.71 for saccharification of amylose and amylopectin, respectively. Model predictions for glucose (R 2 = 0.69–0.79) and DP4+ (R 2 = 0.8–0.68) were more accurate than the maltotriose and maltose for hydrolysis of high-amylose and waxy corn starch. For yellow dent corn, simulation predictions for glucose were accurate (R 2 > 0.73) indicating that the model can be used to predict the glucose concentrations during starch hydrolysis.

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Abbreviations

A aa,max :

Maximum activity of α-amylase

A aa,std :

Activity of the α-amylase under standard conditions

A aa :

Activity of the α-amylase under operating conditions

A avg,dp :

Average degree of polymerization of amylose

AMW:

Average molecular weight of molecules in simulated mash

APavg,dp :

Average degree of polymerization of amylopectin

\(C_{{\rm DP}4^{+}}\) :

Concentration of DP4+ (% db) in corn mash

C effect :

Starch composition effect on the enzyme activities

C Glucose :

Concentration of glucose (% db) in corn mash

C Maltose :

Concentration of maltose (% db) in corn mash

C Maltotriose :

Concentration of maltotriose (% db) in corn mash

DE:

Dextrose equivalent of mash (%)

DPsimulated :

Total number of glucose molecules in simulated mash

DP4 +total :

Total number of DP4+ molecules in simulated mash

E m :

Amount of enzyme (mL) added to the corn mash

GA:

Concentration of glucoamylase (g/L)

GAactivity :

Activity of glucoamylase at given pH and temperature

G total :

Total number of glucose molecules in simulated mash

G :

Concentration of glucose (g/L)

G MW :

Molecular weight of glucose (g/mol)

GP:

Concentration of maltodextrins (g/L)

M total :

Total number of maltose molecules in simulated mash

MTtotal :

Total number of maltotriose molecules in simulated mash

MW:

Molecular weight

N h :

Number of bonds hydrolyzed by E m (mL) of enzyme per sec

N a :

Number of amylose molecules in mash

N ap :

Number of amylopectin molecules in mash

N hA :

Number of bonds hydrolyzed per amylose molecule

N hAP :

Number of bonds hydrolyzed per amylopectin molecule

pH:

Mash/fermenter pH

pHeffect :

Effect of pH on enzyme activity

pHstability :

Effect of pH on enzyme stability

PIeffect :

Effect of product inhibition on enzyme activity

R 2 :

Coefficient of determination

R m :

Mass ratio of amylose to amylopectin in starch

R N :

Number ratio of amylose to amylopectin in starch

RN A :

Relative number of amylose molecules in starch

RNAP :

Relative number of amylopectin molecules in starch

S :

Solids concentration (wet basis) in corn mash

Starchdp :

Total degree of polymerization of all starch molecules in mash

t sim :

Simulation time (min)

T effect :

Effect of temperature on enzyme activity

T stability :

Effect of temperature on enzyme stability

T :

Mash/fermenter temperature (°C)

W mash :

Total weight of the mash (g)

X starch :

Starch content (%) of the corn

References

  1. Tester RF, Karkalas J, Qi X (2004) Starch composition, fine structure and architecture. J Cereal Sci 39:151–165

    Article  CAS  Google Scholar 

  2. Manners DJ (1989) Recent developments in our understanding of amylopectin structure. Carbohydr Polym 11:87–112

    Article  CAS  Google Scholar 

  3. Ellis RP, Cochrane MP, Dale MFB, Duffus CM, Lynn A, Morrison IM, Prentice RDM, Swanston JS, Tiller SA (1998) Starch production and industrial use. J Sci Food Agric 77:289–311

    Article  CAS  Google Scholar 

  4. Paulocci-Jeanjean D, Pierre Belleville M, Zhakia N, Rios GM (1999) Kinetics of cassava starch hydrolysis using Termamyl® enzyme. Biotechnol Bioeng 68:72–77

    Google Scholar 

  5. Young JD, Morgan JA, Konopka AE, Ramakrishna D (2008) Integrating cybernetic modeling with pathway analysis provides a dynamic systems level description of metabolic control. Biotechnol Bioeng 3:542–559

    Article  Google Scholar 

  6. Park JT, Rollings JE (1994) Effects of substrate branching characteristics on kinetics of enzymatic depolymerization of mixed linear and branched polysaccharides. Part 1: amylose/amylopectin α-amylolysis. Biotechnol Bioeng 44:792–800

    Article  CAS  Google Scholar 

  7. Jiahua Z (1999) Kinetic model for the co-action of β-amylase and debranching enzymes in production of maltose. Biotechnol Bioeng 62:618–622

    Article  CAS  Google Scholar 

  8. Marchal LM, Zondervan J, Bergsma J, Beeftink HH, Tramper J (2001) Monte Carlo simulation of α-amylolysis of amylopectin of potato starch. Bioprocess Eng 24:163–170

    Article  CAS  Google Scholar 

  9. Wojciechowski PM, Koziol A, Noworyta A (2001) Iteration model of starch hydrolysis by amylolytic enzymes. Biotechnol Bioeng 75:530–539

    Article  CAS  Google Scholar 

  10. Besselink T, Baks T, Janssen AEM, Boom RM (2008) A stochastic model for predicting dextrose equivalent and saccharide composition during hydrolysis of starch by α-amylase. Biotechnol Bioeng 100:684–697

    Article  CAS  Google Scholar 

  11. Wu X, Zhao R, Wang D, Bean SR, Seib PA, Tuinstra MR, Campbell M, Brien AO (2006) Effects of amylose, corn protein and corn fiber contents on production of ethanol from starch rich media. Cereal Chem 83:568–575

    Google Scholar 

  12. Hizukuri S (1986) Polymodal distribution of the chain lengths of amylopectins, and its significance. Carbohydr Res 147:342–347

    Article  CAS  Google Scholar 

  13. Bertoft E (1991) Investigation of the fine structure of α-dextrins derived from amylopectin and their relation to the structure of waxy-maize starch. Carbohydr Res 212:229–244

    Article  CAS  Google Scholar 

  14. Perez LA, Paredes-Lopez O, Roger P, Colonna P (1996) Amylopectin-properties and fine structure. Food Chem 56:171–176

    Article  Google Scholar 

  15. Thompson DB (2000) On the non-random nature of amylopectin branching. Carbohydr Polym 43:223–239

    Article  CAS  Google Scholar 

  16. Bertoft E (2004) On the nature of categories of chains in amylopectin and their connection to the super helix model. Carbohydr Polym 57:211–224

    Article  CAS  Google Scholar 

  17. Ivanova VN, Dobreva EP, Emanuilova EI (1993) Purification and characterization of a thermostable α-amylase from Bacillus lincheniformis. J Biotechnol 28:277–289

    Article  CAS  Google Scholar 

  18. Buckow R, Jankowiak L, Knorr D, Versteeg C (2009) Pressure-temperature phase diagrams of maize starch with different amylose contents. J Agric Food Chem 57:11510–11516

    Article  CAS  Google Scholar 

  19. Power RF (2003) Enzymatic conversion of starch to fermentable sugars. In: Jacques KA, Lyons TP, Kelsall DR (eds) The alcohol textbook: a reference for the beverage, fuel and industrial alcohol industries, 4th edn. Nottingham University Press, Nottingham, p 29

  20. Rathore SSS, Paulsen M, Sharma V, Singh V (2007) Use of near-infrared spectroscopy for monitoring fermentation in a corn dry-grind ethanol process. Trans ASABE 50:2337–2344

    Google Scholar 

  21. Inglett GE (1987) Action pattern of Bacillius lichemiformis alpha-amylase on ordinary, waxy and high-amylose corn starches and their hydroxypropyl derivatives. J Food Biochem 11:249–258

    Article  CAS  Google Scholar 

  22. Eckhoff SR, Rausch KD, Fox EJ, Tso CC, Wu X, Pan Z, Buriak P (1993) A laboratory wet-milling procedure to increase reproducibility and accuracy of product yields. Cereal Chem 70:723–727

    Google Scholar 

  23. AACC (2002) Approved methods of the AACC, 10th edn. Method 44-15A. The Association, St. Paul

  24. Kandra L, Gyemant G, Liptak A (2002) Action pattern of α-amylases on modified maltooligosaccharides. Biologia 57(Suppl. 11):171–180

    Google Scholar 

  25. Bijttebier A, Goesaert H, Delcour JA (2007) Temperature impacts the multiple attack action of amylases. Biomacromol 8:765–772

    Article  CAS  Google Scholar 

  26. Beschkov V, Marc A, Engasser JM (1984) A kinetic model for hydrolysis and synthesis of maltose, isomaltose, and maltotriose by glucoamylase. Biotechnol Bioeng 26:22–26

    Article  CAS  Google Scholar 

  27. Meagher MM, Reilly PJ (1989) Kinetics of hydrolysis of di- and trisaccharides with Aspergillus niger glucoamylases I and II. Biotechnol Bioeng 34:689–693

    Article  CAS  Google Scholar 

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Author notes

  1. Ganti S. Murthy

    Present address: Biological and Ecological Engineering, Oregon State University, 122 Gilmore Hall, Corvallis, OR, 97331, USA

Authors and Affiliations

  1. Department of Agricultural and Biological Engineering, University of Illinois, 360G AESB, 1304 West Pennsylvania Avenue, Urbana, IL, 61801, USA

    Ganti S. Murthy, Kent D. Rausch, M. E. Tumbleson & Vijay Singh

  2. Eastern Regional Research Center, ARS, USDA, Wyndmoor, PA, 19038, USA

    David B. Johnston

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  1. Ganti S. Murthy
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  2. David B. Johnston
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  3. Kent D. Rausch
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  4. M. E. Tumbleson
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  5. Vijay Singh
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Correspondence to Vijay Singh.

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Murthy, G.S., Johnston, D.B., Rausch, K.D. et al. Starch hydrolysis modeling: application to fuel ethanol production. Bioprocess Biosyst Eng 34, 879–890 (2011). https://doi.org/10.1007/s00449-011-0539-6

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