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Department of Physics and Measurement Technology

Master’s Thesis

Optimization of the Liquefaction Process inBioethanol Production & Development of Method

for Quantification of Nonsolubilized Starch in Mash

Anna AldénLITH-IFM-EX--08/1917--SE

Department of Physics and Measurement TechnologyLinköpings universitet

SE-581 83 Linköping, Sweden

Master’s ThesisLITH-IFM-EX--08/1917--SE

Optimization of the Liquefaction Process inBioethanol Production & Development of Method

for Quantification of Nonsolubilized Starch in Mash

Anna Aldén

Supervisor: Helena Stavklint,Lantmännen Agroetanol AB

Examiner: Prof. Carl-Fredrik Mandenius,Department of Physics, Chemistry and Biology,Linköping University, Sweden

Linköping, 20 February, 2008

Avdelning, InstitutionDivision, Department

Division of BiotechnologyDepartment of Physics and Measurement TechnologyLinköpings universitetSE-581 83 Linköping, Sweden

DatumDate

2008-02-20

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� Svenska/Swedish� Engelska/English

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� Licentiatavhandling� Examensarbete� C-uppsats� D-uppsats� Övrig rapport�

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ISRNLITH-IFM-EX--08/1917--SE

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TitelTitle

Optimering av uppströmsprocessen vid bioetanolproduktion samt utveckling avmetod för kvantifiering av olöst stärkelse i mäskOptimization of the Liquefaction Process in Bioethanol Production & Developmentof Method for Quantification of Nonsolubilized Starch in Mash

FörfattareAuthor

Anna Aldén

SammanfattningAbstract

Ethanol production at Lantmännen Agroetanol AB in Norrköping began in De-cember 2000. The objective of this master’s thesis is to find and optimize factorsaffecting the yield of the liquefaction, a part of the upstream process. To measuresuccessfulness of liquefaction it is desired that amount of non-solubilized starchis quantified, and hence a method for determination of non-solubilized starch inmash has to be developed.

Starch is a carbon reserve in plants. Starch granules are polymers of amy-lose and amylopectin which are polysaccharides of glucose. When a starch/watersolution is heated the starch granules start to absorb water and swell, a processtermed gelatinization. The swelling makes the granules susceptible to hydrolysisby enzymes such as α-amylase, this is called liquefaction. Eventually the granularstructure is broken and the slurry contains solubilized starch which can be sac-charified to glucose by glucoamylase. In the bioethanol production process, themilled grain is mixed with water and enzymes. The slurry is heated, gelatinizationand liquefaction occurs. Saccharification occurs simultaneously to fermentation.Ethanol is purified from the fermented mash during downstream processing.

Starch in the form of starch granules cannot be quantified. The adopted prin-ciple for determination of non-solubilized starch in liquefied mash is to wash awaythe solubilized starch, then quantitatively hydrolyze non-solubilized starch to glu-cose and quantify glucose.

To find and optimize factors significant for yield of liquefaction multiple factorexperiments were conducted where eight factors were studied. pH, temperature inmixtank and temperature in liquefaction tank 1 were the most significant factors.The temperature in liquefaction tank 1 should be kept as is is at 74◦C. A small risein pH should shorten the mean length of dextrins which is preferable. An increaseof pH from 5.2 to 5.4 is therefore proposed. The temperature in mixtank shouldalso be increased by a few degrees. The yield of the process should be carefullyevaluated during the modifications.

NyckelordKeywords ethanol, liquefaction, nonsolubilized starch

AbstractEthanol production at Lantmännen Agroetanol AB in Norrköping began in De-cember 2000. The objective of this master’s thesis is to find and optimize factorsaffecting the yield of the liquefaction, a part of the upstream process. To measuresuccessfulness of liquefaction it is desired that amount of non-solubilized starchis quantified, and hence a method for determination of non-solubilized starch inmash has to be developed.

Starch is a carbon reserve in plants. Starch granules are polymers of amy-lose and amylopectin which are polysaccharides of glucose. When a starch/watersolution is heated the starch granules start to absorb water and swell, a processtermed gelatinization. The swelling makes the granules susceptible to hydrolysisby enzymes such as α-amylase, this is called liquefaction. Eventually the granularstructure is broken and the slurry contains solubilized starch which can be sac-charified to glucose by glucoamylase. In the bioethanol production process, themilled grain is mixed with water and enzymes. The slurry is heated, gelatinizationand liquefaction occurs. Saccharification occurs simultaneously to fermentation.Ethanol is purified from the fermented mash during downstream processing.

Starch in the form of starch granules cannot be quantified. The adopted prin-ciple for determination of non-solubilized starch in liquefied mash is to wash awaythe solubilized starch, then quantitatively hydrolyze non-solubilized starch to glu-cose and quantify glucose.

To find and optimize factors significant for yield of liquefaction multiple factorexperiments were conducted where eight factors were studied. pH, temperature inmixtank and temperature in liquefaction tank 1 were the most significant factors.The temperature in liquefaction tank 1 should be kept as is is at 74◦C. A small risein pH should shorten the mean length of dextrins which is preferable. An increaseof pH from 5.2 to 5.4 is therefore proposed. The temperature in mixtank shouldalso be increased by a few degrees. The yield of the process should be carefullyevaluated during the modifications.

SammanfattningEtanolproduktionen på Lantmännen Agroetanol AB i Norrköping började i De-cember 2000. Målet med examensarbetet är att hitta och optimera faktorer sompåverkar utbytet av likvifieringen i etanolproduktionen. För att studera utfalletav likvifieringen är det önskvärt att mäta hur mycket stärkelse som inte har löstsig, och därför måste en metod för att mäta olöst stärkelse i mäsk utvecklas.

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Stärkelse utgör en kolreserv i växter. Stärkelsegranuler är polymerer av amylosoch amylopektin, vilka i sin tur är polysackarider av glukos. När en stärkelse/vatten-blandning värms upp börjar stärkelsegranulerna att absorbera vatten och svälla, enprocess som kallas gelatinisering. Svällningen gör granulerna känsliga mot hydro-lys av till exempel enzymet a-amylas, vilket kallas för likvifiering. Efter tillräckligtmycket gelatinisering och likvifiering förstörs hela den granulära strukturen ochstärkelsen övergår till löst form. Löst stärkelse kan försockras till glukos med enzy-met glukoamylas. I produktionen av bioetanol blandas malet spannmål med vattenoch enzymer. Slurryn värms upp och gelatinisering och likvifiering sker. Försock-ring sker simultant med fermenteringen. Etanol renas fram från den fermenterademäsken i nedströmsprocessen.

Stärkelse i granulform kan inte kvantifieras. Den valda metoden för mätning avolöst stärkelse i likvifierad mäsk innebär att den lösta stärkelsen tvättas bort, sedanhydrolyseras den olösta stärkelsen kvantitativt till glukos, vilken kan kvantifieras.

Flerfaktorförsök gjordes för att hitta och optimera faktorer signifikanta för ut-bytet av likvifiering. Åtta olika faktorer studerades. pH, temperatur i mixtank ochtemperatur i likvifieringstank 1 visade sig vara de tre mest signifikanta faktorer-na. Temperaturen i likvifieringstank 1 ska bibehålla samma temperatur som idag,74◦C. En liten höjning av pH borde förkorta medellängden av dextrinerna, vilketär fördelaktigt. En ökning av pH från 5,2 till 5,4 är föreslås därför. Temperatu-ren i mixtanken ska ökas några få grader. Utbytet av processen måste noggrantutvärderas under modifieringarna.

Acknowledgments

First I would like to thank Lantmännen Agroetanol AB for giving me the op-portunity of doing this master’s thesis. Special thanks to my supervisor HelenaStavklint for interesting discussions, your support and knowledge. Anna-KarinWingren and Caroline Lundell, you were always helpful in the laboratory. Thanksto everyone else at Lantmännen Agroetanol AB who made this time memorable.

I would also like to express my gratitude to Anders Brundin for advice con-cerning statistical analysis.

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Nomenclature

DDGS Dried distillers grain with solublesDF Degrees of freedomDP Degree of polymerization, followed by a number from 1 to n.Fermentable starch Starch that is solubilized during liquefaction and available

as substrate to yeast. Also called solubilized starch.HPLC High performance liquid chromatographyL1 Liquefaction tank 1L2 Liquefaction tank 2MRP Maillard reaction productsMS Mean square (statistical expression)SS Sum of squares (statistical expression)SSF Simultaneous saccharification and fermentationSugar profile Describing the distribution of lengths of solubilized starch in mash,

i.e. content of DPn, DP3, maltose and glucose.TiM Residence time in mixtankTiL1 Residence time in liquefaction tank 1TiL2 Residence time in liquefaction tank 2TL1 Temperature in liquefaction tank 1TL2 Temperature in liquefaction tank 2TMix Temperature in mixtankTS Total solids

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Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Thesis objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Theoretical background 32.1 Starch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Starch granules . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Upstream processing in bioethanol production . . . . . . . . . . . . 6

2.2.1 Milling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.2 Mixing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.3 Gelatinization . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.4 Liquefaction . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.5 Saccharification . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Factors contributing to lower starch yield . . . . . . . . . . . . . . 92.3.1 Retrogradation . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.2 Maillard Reactions . . . . . . . . . . . . . . . . . . . . . . . 92.3.3 Nonsolubilized starch . . . . . . . . . . . . . . . . . . . . . 10

2.4 Quantification of starch . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Bioethanol production at Lantmännen Agroetanol AB 133.1 An overview of the production process at Lantmännen Agroetanol

AB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Properties of enzymes used in the upstream process . . . . . . . . 15

3.2.1 α-amylase . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2.2 β-glucanase . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2.3 β-glucanase/xylanase . . . . . . . . . . . . . . . . . . . . . 15

4 Method for determination of nonsolubilized starch in mash 174.1 General principle of method . . . . . . . . . . . . . . . . . . . . . . 174.2 Sample preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2.1 Saccharification of solubilized starch . . . . . . . . . . . . . 184.2.2 Filtration and drying of saccharified mash . . . . . . . . . . 194.2.3 Washing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

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4.3 Hydrolysis of nonsolubilized starch . . . . . . . . . . . . . . . . . . 204.4 Evaluation of method accuracy . . . . . . . . . . . . . . . . . . . . 20

5 Experimental details 235.1 Multiple factor experiments . . . . . . . . . . . . . . . . . . . . . . 235.2 Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.2.1 Analysis of raw material . . . . . . . . . . . . . . . . . . . . 245.2.2 Sugar profile . . . . . . . . . . . . . . . . . . . . . . . . . . 245.2.3 Maillard reaction products . . . . . . . . . . . . . . . . . . 245.2.4 Quantitative analysis of fermentable and nonsolubilized starch 255.2.5 Statistical analysis of multiple factor experiments . . . . . . 25

6 Results and discussion 276.1 Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276.2 Selection of factors . . . . . . . . . . . . . . . . . . . . . . . . . . . 286.3 Statistical analysis of multiple factor experiment . . . . . . . . . . 29

6.3.1 DPn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296.3.2 DP3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.3.3 Maltose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.3.4 Glucose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366.3.5 Fermentable starch . . . . . . . . . . . . . . . . . . . . . . . 376.3.6 Nonsolubilized starch . . . . . . . . . . . . . . . . . . . . . 406.3.7 Maillard reaction products . . . . . . . . . . . . . . . . . . 406.3.8 Overview of factors and responses . . . . . . . . . . . . . . 436.3.9 Optimization of liquefaction process . . . . . . . . . . . . . 45

7 Conclusions 497.1 Quantification of nonsolubilized starch . . . . . . . . . . . . . . . . 497.2 Optimal parameter settings . . . . . . . . . . . . . . . . . . . . . . 49

8 Recommendations 51

Bibliography 53

A Experimental design 55

B Results 57

C Distribution of starch between solubilized and nonsolubilized 59

Chapter 1

Introduction

1.1 BackgroundBioethanol production at Lantmännen Agroetanol AB began in December 2000.Alcohol production, described briefly, means preparing starch- or sugar-containingraw material for fermentation by yeast. The produced ethanol is then recoveredand purified through distillation. The continuous work to improve the processat Agroetanol AB regarding increased ethanol yield, decreased resource consump-tion, easier management of the process and decreased production costs has beengoing on since the production started. To achieve improvements different processparameters such as temperatures, pH, flow rates and residence times can be var-ied, various combinations of raw materials and pre-treatments, e.g., milling, is amatter of interest, addition of enzymes, nutrients and other necessary additivescan be optimized, among many other things. When improving a process it is agood idea to divide the process into subunits and study how each subunit canbe optimized. However, it is important to remember that changes in one part ofthe process might affect other parts; therefore one must always have the overallpicture in mind.

This thesis focuses on the upstream process part of the ethanol productionprocess where the raw materials; wheat, barley and triticale are prepared for fer-mentation. The upstream process consists of milling, mixing, gelatinization, andliquefaction. During the upstream process, starch in the cereals is made availablefor the yeast. Heat and enzymes convert starch to fermentable carbohydrates. Themore substrate available to yeast the better yield of ethanol. Starch that is notgained during liquefaction cannot be recovered no matter how good and optimizedthe rest of the process is and will eventually get lost. The loss of starch duringgelatinization and liquefaction occurs in several ways:

• nonsolubilized starch,

• retrograded starch, and

• side-reactions between sugars and other components of mash.

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2 Introduction

The different ways in which starch is lost represents different approaches toincrease the yield of liquefaction; enhance solubility of starch, prevent retrograda-tion, and decrease side-reactions. The challenge, increase yield of liquefaction, isnow divided into these three smaller pieces. Each one of these can be studied andconsidered separately even though the total yield is a combination of all of them.

Lab-scale experiments are often performed to study responses when varyingdifferent process parameters. When the optimal conditions are found a pilot-plantexperiment might be done and if it is successful; a full-scale trial follows. Anorganized, money, and time saving approach to study effects of process param-eters is to use experimental design. Several computer based tools, e.g., Modde,Umetrics; Minitab Statistical Software, Minitab, exist that can be helpful both inexperimental design and in evaluation of experiments.

1.2 Thesis objectivesThe objective of this master’s thesis is to find parameters significant for the yieldof the liquefaction process at Lantmännen Agroetanol AB. The significant factorsfound should be optimized to give the best liquefaction yield. A definition of howliquefaction yield is measured should also be stated. Lantmännen Agroetanol ABespecially desires that share of nonsolubilized starch after liquefaction should bemeasured and considered as a measure of successfulness of liquefaction. Since nomethod for measuring nonsolubilized starch in mash exists it has to be developed.Therefore another objective is to develop a method for determination of nonsol-ubilized starch in liquefied mash, to be used in this master’s thesis and also byLantmännen Agroetanol AB as an analysis of the process.

1.3 MethodThis master’s thesis was carried out at Lantmännen Agroetanol AB in Norrköping.Literature and articles concerning bioethanol production, starch, gelatinization,liquefaction, Maillard reactions and relevant enzymes were studied. The methodfor determination of nonsolubilized starch was divided into steps. For each stepseveral experiments were carried out to develop and validate the method. Frac-tional factorial experiments at laboratory scale were conducted to find and opti-mize process parameters significant for the outcome of liquefaction.

1.4 OutlineIn the next chapter the theoretical background for this master’s thesis is presented.It is followed by a separate section for development and evaluation of method fordetermination of nonsolubilized starch. Thereafter materials and methods usedduring the fractional factorial experiment session are presented followed by resultsand discussion; conclusions; recommendations; references and appendices.

Chapter 2

Theoretical background

2.1 Starch

Starch is the major energy storage component in plants and serves as a carbon re-serve. In the photosynthesis, energy from sunlight is used to combine six moleculesof carbon dioxide and six molecules of water to a carbohydrate (D-glucose) andmolecular oxygen. D-glucose (fig. 2.1) is one of the most abundant carbohydrateson earth and comprises 99.9 % of all the carbohydrates. [1] The D designationrefers to the enantiomeric configuration of glucose which is either D or L. Eachcarbon atom is designated a number for a more comprehensive terminology. C1,also known as the anomeric carbon, is the carbon closest to the oxygen atom inclockwise direction. The cyclic glucose molecule is either in α- or β-configurationwhich gives information of the hydroxyl group at the anomeric carbon. The OH-group below the ring yields α-configuration and the OH-group above yields β-configuration. See the α- and β-configuration of D-glucose in figure 2.1. In thisfigure the numbering system for the carbon atoms is also shown. [2]

Figure 2.1. The image to the left illustrates α-D-glucose with the hydroxyl group at theanomeric carbon extending below the ring structure. The right image shows β-D-glucosewhere the corresponding hydroxyl group extends above the ring structure.

3

4 Theoretical background

α-D-glucose is polymerized into the polysaccharides amylose and amylopectin.Amylose and amylopectin are then packed into starch granules in plastids of leavesand storage organs in plants [3]. Hence, native starch is a polymer of amylopectinand amylose. Amylose is a linear polymer whereas amylopectin is branched [1].The linear polymer amylose consists of α(1-4)-linked glucose subunits. α indicatesthat the hydroxyl group is below the apparent plane of the glucose subunit, justlike before, and 1-4 denotes that the glucose subunits are linked between C1 andC4 of neighboring units. [2] This bond is called the glucosidic bond and is stabileat high pH but hydrolyzes at low pH [4]. The linear part of amylopectin alsoconsists of α(1-4)-linked glucose residues. The branches are linked to the linearpart by α(1-6)-linkages. [2] See figure 2.2 for a structural formula of amylose andamylopectin .

Figure 2.2. Structural formula of amylose (top) and amylopectin(bottom).

The branch points in amylopectin yields a large tree-like structure , see figure2.3.

Glycogen, which is the analogue to starch in animals has a structure closelyrelated to the structure of amylopectin. The difference is that the branch pointsoccur approximately twice as often in glycogen as in amylopectin. [1] Celluloseis the major structural component of cell walls of land plants and brown algae[5]. It is made up of 10,000 or more D-glucose units linked together by β(1-4)glucosidic bonds. This minor difference, β-linkages instead of α has a huge impacton the properties of cellulose. The β-linkages leads to aggregation into extremelyinsoluble fibrils. [2] This is why cellulose is suited for production of paper andtextiles [5].

2.1 Starch 5

Figure 2.3. The large bush like structure of amylopectin. [1]

The molecular masses of amylose and amylopectin vary with the source of thestarch but generally amylopectin is the larger one. Amylose contains on average500-2000 glucose residues per molecule. [2] Amylopectin can contain 100 000glucose subunits. [1] The ratio of amylose to amylopectin content also varies withthe botanical origin of the starch. Wheat starch granules contain approximately25% amylose and 75% amylopectin while waxy maize starch contains 1% amyloseand 99% amylopectin [2]. The relative amount of amylopectin and amylose affectthe properties of starch which is further discussed in sec. 2.2.3 on page 7.

2.1.1 Starch granulesNaturally amylopectin and amylose form starch granules. The sizes and shapes ofthe granules depend on the source of starch. Wheat starch granules have a disk-pancake look and are about 10 µm thick and 20-30 µm in diameter. Starch granulesfrom other botanical sources ranges in sizes from 0.5 to 45 mm. [1] The granulesare stabilized of double helices mainly formed by the branches of amylopectin.Amylose is thought to be in a single-chain state in native starch granules sincedouble helices of amylose are resistant to enzyme degradation (termed resistantstarch) and has therefore no natural function. Amylopectin molecules separatethe amylose chains. [6] The helices are stabilized by hydrogen bonds both withinthe same molecule and between adjacent molecules [2]. The double helices areordered in a parallel arrangement and form either A, B or C crystalline patterns.The A-pattern structure consists of a single double helical complex surroundedby six parallel double helices packed in a hexagonal pattern with water moleculesbetween the complexes. A-pattern structure is found in cereal grain starches suchas maize, wheat and rice. The B-patterns is similar to the A-pattern, except forthat the middle helical complex is replaced by water molecules. Starches fromstem, tuber and fruit, like potato, sago, and banana starches show the B-patternsstructure. In C-type starches, both A- and B-pattern are found. This occurs forexample in pea and tapioca starches. [1]

Due to formation of highly ordered double helices by amylopectin and thesingle chained helices of amylose, starch granules consist of semi-crystalline regions


interspersed with amorphous regions. The semi-crystalline regions contain onlyamylopectin while amorphous regions also contain amylose. Consequently, starcheswith a relatively high content of amylose have a lesser degree of crystallinity. [1].The structure of a starch granule is shown in fig. 2.4

Figure 2.4. The image shows the structure of a potato starch tuber. A is an electronmicroscopic image of the granules. B shows the semi-crystalline regions interspersed withamorphous regions and C shows the mixture of amylose and amylopectin in amorphousregions.[4].

Besides from amylopectin and amylose starch granules have, especially thesurface region, a non-carbohydrate content of lipids (1-5% w/w for cereal starches[1]) and proteins [6]. Lipids complex with the hydrophobic interior of single chainamylose helices, sometimes also with branches of amylopectin if they are longenough [7]. Lipids also exist in free form in the granules [6, 7]. Most of theproteins are believed to belong to starch biosynthetic enzymes which are caughtin the granule during synthesis [8].

2.2 Upstream processing in bioethanol produc-tion

During the upstream process, the grains are prepared for fermentation by yeast.The upstream process can be separated into milling, mixing, liquefaction, gela-tinization and saccharification which are all detailed for in the following sections.

2.2 Upstream processing in bioethanol production 7

2.2.1 MillingMilling is the first procedure in the bioethanol production process. The purpose ofmilling is to expose the starch granules to water in the subsequent gelatinizationprocess. The larger the ratio of surface area to volume of the particles, the easier itis for water and enzymes to permeate the kernel. Therefore a fine grounded mealgives higher ethanol yield than coarse grounded meal. There are also disadvantageswith small particle sizes. Small particles increase the load of both centrifuges andevaporators in the downstream process. Therefore the particle size has to be acompromise between yield and minimizing problems in downstream processing.[9]

The hammer mill is the most common milling equipment among distillers. Thehammer mill consists of a grinding chamber wherein hammers rotate at high speed.To exit the chamber the particles have to pass a sieve and therefore have to besmaller than a certain size. This allows for setting a maximum particle size in themeal. To inspect the conditions of the mill and the sieve, analysis of particle sizedistributions are made regularly. Roller mill is another type of milling equipmentwhere the grains are nipped between a pair of rollers. It is suitable for small cerealgrains.[9]

2.2.2 MixingDirectly after milling the meal passes on to a mixing tank. In the mixing tank,meal, water and enzymes are mixed together during warming. Backset stillage 1

and nutrients required for fermentation might also be added here. The temperatureis kept slightly above or under gelatinization temperature (see next sec. 2.2.3).Too extended gelatinization leads to high viscosity and should be avoided here dueto problems with moving the slurry, though higher temperature facilitates heatingin the next step. [9]

2.2.3 GelatinizationIn order to make starch available to yeast (Saccharomyces cerevisiae) it has tobe hydrolyzed to fermentable sugars such as glucose, maltose (two α(1-4)-linkedglucose units) and maltotriose (three α(1-4)-linked glucose units).[10] Hydrolysis ofstarch can occur either by acids or by the action of enzymes. Acids in combinationwith high temperature and pressure hydrolyze starch. Enzymes can do the sameunder relatively mild conditions and also give a higher yield. Since the 1960senzymatic hydrolysis is more common and also because it is used in AgroetanolAB’s process acid hydrolysis will not be considered in the current work. [2] Beforethe enzymes can gain access to the starch molecules the granular structure has tobe broken down. This occurs in a process called gelatinization. [9] Starch granulessuspended in water swell to a limited extent and absorb water up to 30% w/w.When the suspension is heated above a certain temperature the hydrogen bondsin the starch granule rupture, the granules starts to swell irreversibly and the

1Backset stillage is recycled thin stillage. Thin stillage is the liquid part of the residual mashafter evaporation of the ethanol soultion in the downstream process.


granular structure is gradually lost. This temperature is called the gelatinizationtemperature and varies with the source of the starch. [1, 2] During swelling,leaching of granule constituents, mainly amylose occurs [11] which increase theviscosity of the surrounding slurry [4]. Further increase of the temperature leadsto extended swelling of the granules and increased viscosity of the slurry andeventually breaking of the granules [4]. Large granules swell more easily thansmall granules due to a lesser extent of molecular bonding and have consequentlya lower gelatinization temperature. High amylose content starches generally havehigher gelatinization temperatures because linear amylose molecules can be moredensely packed than the bush-like amylopectin molecules and hence more hydrogenbonds will form.[2] See table 2.1 for gelatinization temperatures of different cereals.

Table 2.1. Gelatinization temperatures for some cereal starches. [2]

Cereal starch source Gelatinization range [◦C]Barley 52-59Wheat 58-64Rye 57-70Corn (maize) 62-72High amylose corn 67->80Rice 68-77Sorghum 68-77

2.2.4 Liquefaction

After gelatinization the swollen starch granules and the solubilized polysaccharides(leached out of the granules) are susceptible to hydrolysis by enzymes. The enzymeused here is usually a thermotolerant form of the endo-acting enzyme α-amylase [9].α-amylases are usually isolated from bacterial sources. Hydrolysis by a-amylaseconverts the polysaccharides/starch to oligosaccharides of varying length. Theendo-acting α-amylase hydrolyzes α(1-4)-glucosidic bonds in the inner parts ofamylose and amylopectin [4]. α-amylase cannot hydrolyze α(1-6)-glucosidic bondsof amylopectin. The liberated water-soluble oligosaccharide chains are called dex-trins when linear, and α-limit dextrins when branched. The products are releasedin α-configuration. The purpose of liquefaction is to make the mash less viscousand to prepare it for the next step, saccharification [9].

2.2.5 Saccharification

During saccharification individual glucose molecules are released by the action ofan exo-acting enzyme. A commonly used enzyme is glucoamylase/amyloglucosidase.The enzyme successively hydrolyzes the terminal α(1-4)-glucosidic bond from

2.3 Factors contributing to lower starch yield 9

non-reducing2 ends of dextrins. Glucoamylase is also able to hydrolyze α(1-6)-glucosidic bonds, but at a much slower rate, 20 to 30 times slower, than α(1-4)-glucosidic bonds. Hence, both dextrins and α-limit dextrins are degraded byglucoamylase. The product released is α-D-glucose. [9]

The saccharification step can occur both pre and simultaneous to fermenta-tion, the latter called SSF (Simultaneous Saccharification and Fermentation). If aseparate saccharification step is used, the optimal conditions (e.g. pH, tempera-ture) for the saccharification enzyme can be used. Glucoamylases are usually notthermotolerant due to their fungal origin and often do not tolerate temperaturesabove 60◦C. The pH optimum is usually between pH 4.0 and 4.5. A problem withthese pH and temperature conditions is that they also favor growth of bacterialcontaminants like Lactobacillus. [9] The benefit of pre-saccharification is that highglucose concentration is achieved before fermentation which ensure that glucose isnot limiting [2].

Using SSF the yeast will be spoon-fed with glucose. Too high glucose lev-els leads to osmotic stress of the yeast cells [12].The lower glucose concentrationand also the competition from yeast make it harder for bacterial contaminants tosurvive. Low glucose concentration also prevents repolymerization of glucose toisomaltose, which gives a lower glucose yield. [9]

2.3 Factors contributing to lower starch yield2.3.1 RetrogradationRetrogradation of starch is the physical event when starch molecules (amylose oramylopectin) in solution reassociate via intermolecular hydrogen bonds, forminghelixes and double helixes, and precipitate from the solution. [1, 13, 14]. Thestraight amylose chains have a greater possibility to line up in solution and formintermolecular hydrogen bonds than the branched amylopectin. Therefore, amy-lose has a much lower solubility in aqueous solutions than amylopectin.[1] Due todifferent ratios of amylopectin to amylose content the rate of retrogradation de-pends on the botanical origin of the starch. [14] The retrograded starch, especiallyretrograded amylose, is resistant to digestion by α-amylase [6, 13]. Retrograda-tion occurs after heating a starch solution in temperatures above gelatinizationtemperature and then recooling it. Lower cooling temperature and greater differ-ence between heating and cooling temperature increases the rate of retrogradation.Retrogradation has been observed in samples heated to 90◦C and then cooled to30◦C.[14]

2.3.2 Maillard ReactionsThe Maillard reaction, or non-enzymatic browning involves reactions of reducingsugars, such as glucose, maltose, fructose, and ribose, and amino groups foundin amino acids or proteins [15]. Since Maillard reactions can take place between

2Reducing ends are the ends where the anomeric carbon is free, the other ends are non-reducing ends.


different sugars and different amino acids (or proteins) a wide variety of Mail-lard reaction products (MRP) are formed [16]. Many of the MRP have aroma andtaste properties, which make them desirable for instance in bread baking [17]. Theformation of MRP causes browning whose intensity can be measured spectropho-tometrically at 420 nm [16, 18]. MRP are formed in many different ways due toconditions and which sugar and amino acids that are involved. There are thoughsome main reaction ways and the MRP are often grouped according to them, likeHeyns products and Amadori products. In the formation of Amadori products anunstable Shiff base is formed in the first step. The Shiff base then undergoes theso called Amadori rearrangement to form the Amadori compound. The Amadoricompound can then undergo a wide variety of further reactions to form MRP.[15]

Conjugation of starch with amino acids through the Maillard reaction leads tolower swelling and solubility of starch in water compared to original starches. Alsothe susceptibility to hydrolysis by α-amylase is lowered and the thermal stabilityis increased. [19] Therefore Maillard reactions are not desirable in bioethanolproduction. Tauer et. al. [15] also concludes that MRP have an inhibitory effecton yeast, which leads to lower ethanol production. This inhibitory effect is howeverhighly dependent on pH during fermentation and was weakly significant for pH 5and highly significant for pH 7 and 8 in his study.

Factors affecting the occurrence of Maillard reactions are pH, temperature,time, sugar component and amino component among others [15, 16, 18, 19]. Heatstrongly accelerates the Maillard reaction [19] and is probably the most significantfactor. A prolonged heating time form more MRP, but the pH during the reactionhas a significant impact on the amount of MRP formed. According to Taueret. al [15] the same heating time but different pH (pH 5 and 7) showed lesserformation of MRP at lower pH. Of the amino acids lysine seems to be the onecontributing the most to MRP formation [15, 16]. One possible explanation to thisis that lysine contains two amino groups. Tauer et. al [15] who studied inhibitoryeffects of MRP on fermentation found none or weakly significant difference betweensugar components (fructose, glucose, maltose and ribose) involved in the Maillardreaction. Kwak et. al. [16] saw that the degree of contribution to browningintensity was in the order of xylose > arabinose > glucose > maltose > fructose.Xylose and arabinose are pentoses and more reactive than the others who arehexoses. Glucose and maltose contribute more to browning than fructose due todifferent reaction ways. Fructose forms mainly Heyns products while glucose andmaltose favor formation of Amadori products. Browning due to Amadori productsis faster than browning due to Heyns products [16].

2.3.3 Nonsolubilized starchIn processes where large amounts of mash are gelatinized and liquefied some starchgranules might remain intact, termed ghosts, and hence the starch stays unavail-able for fermentation. Debet and Gidley [6] have studied formation of ghosts.Their suggested mechanism for ghost formation includes cross-linking between glu-cans in the granule. During swelling of the granule, as in gelatinization, amylosemolecules previously separated by amylopectin can move freely within the granule

2.4 Quantification of starch 11

and find another amylose molecule to form a double helix with. As previouslymentioned in 2.3.1 on page 9 double helices formed by two amylose molecules aretightly associated. They have a melting point at 130 ◦C. If the swelling is rela-tively slow, as in wheat, and the cross-linking is rapid, enough glucose residueshave time to participate in cross-linking which prevent the granule from bursting.The cross-linking seem to be mostly located near the surface of ghosts, which forma mechanical skin preventing further swelling and rupture of the granule. Surfaceproteins and lipids are involved in preventing swelling and therefore promote for-mation of ghosts. Ghost formation have though been observed when surface lipidsand proteins have been removed, however in a more swollen form.[6]

2.4 Quantification of starchStarch granules can be detected through addition of triiodide. Iodide complexes inthe hydrophobic interior of the helixes formed by amylose and amylopectin. Theiodide-amylose complex results in a deep-blue colored product while the iodide-amylopectin complex gives a wine-red or violet product. [1] However, this methodis only qualitative and not quantitative.

Quantification of starch usually follows the general principle: quantitative hy-drolysis of starch to glucose and then quantification of glucose. Free glucose canbe measured by different methods, e.g., HPLC, glucose-oxidase and peroxidasereagents, or polarimetrically. The hydrolysis is done either by acids or enzymes[20]. In AACC Method 76.13 (AOAC Method 996.11) starch is completely solubi-lized by cooking the sample in presence of thermostable α-amylase. Then dextrinsare hydrolyzed quantitatively to free glucose by the action of amyloglucosidase.Glucose is measured spectrophotometrically at 510 nm after addition of glucose-oxidase/peroxidase buffer.

Chapter 3

Bioethanol production atLantmännen Agroetanol AB

3.1 An overview of the production process at Lant-männen Agroetanol AB

The continuous production process at Lantmännen Agroetanol AB consists ofseveral steps, for a schematic picture see fig. 3.1.

The grain is stored in four silos. The desired mixture of grain; wheat, barleyand triticale is grinded in two hammer mills. The milled grain proceeds to themixing tank where it is added to water, urea and enzymes. The enzymes addedare α-amylase, β-glucanase and xylanase. The two latter possess activity towardsnon-starch carbohydrates, the structural material in plants [21, 22] and are addedmainly for viscosity reduction. Residence time in mixing tank is approximately onehour and temperature is about 58◦C. pH is kept at 5.2. The slurry carries on toliquefaction tank 1 (L1) where gelatinization and liquefaction start. Temperatureis increased to 73◦C, residence time is two hours. The hot mash then continuesto liquefaction tank 2 (L2) where the temperature is increased to 89◦C. Also hereresidence time is two hours. The warm mash is then cooled in a mash cooler, to31.5◦C and pH is lowered to 3.5. Thin stillage and a protease are also added here.Fermentation is the next step and occurs progressively in five fermenters. The liq-uefied mash is added both to fermenter 1 and 2. Yeast from the yeast propagationtank is added to fermenter 1 and 2. Total fermentation time is approximately 55hours. The ethanol content in the last fermenter is about 9 %(w/w).

Downstream processing begins with vaporizing the ethanol solution from themash in two parallel mash colons. An aldehyde stripper is used to remove heads,i.e., acetaldehyde and ethylacetate, from the ethanol solution. In the subsequentrectification tower higher alcohols, fusels (sw. "finkel") are removed. A molecularsieve absorbs residual water and the purity of the ethanol is now 99.8% (w/w).

The non-vaporized mash continues to a stillage tank. The stillage is separatedinto solids and liquids by decanters. The solids are further treated and eventually

13

14 Bioethanol production at Lantmännen Agroetanol AB

Figure 3.1. Schematic picture of the production process at Lantmännen AgroetanolAB.

3.2 Properties of enzymes used in the upstream process 15

Figure 3.2. Temperature and pH profile of β-glucanase/xylanase [22]

becomes DDGS (Dried Distillers Grain with Solubles) which is sold as animal feed.The liquid part, thin stillage, is both recycled in the process and evaporated tosyrup. Syrup is involved in the production of DDGS and also sold as animal feedas it is.

3.2 Properties of enzymes used in the upstreamprocess

3.2.1 α-amylaseThe α-amylase used is thermostable and the recommended conditions according tothe manufacturer is a temperature of 83-89◦C and pH of 5.4-5.8. The recommendeddosage is 0.04 - 0.06 %(w/w) as a starting point. [23]

3.2.2 β-glucanaseThe β-glucanase used has multiple activity towards cellulose, hemicellulose, arabi-noxylans and β-D-glucans. The pH and temperature profiles are presented in fig.3.2

3.2.3 β-glucanase/xylanaseβ-glucanase/xylanase modify and digest non-starch carbohydrates. It is mainlyadded for viscosity reduction. The pH and temperature profiles are shown in fig.3.3.

16 Bioethanol production at Lantmännen Agroetanol AB

Figure 3.3. Temperature and pH profile of β-glucanase/xylanase [21]

Chapter 4

Method for determination ofnonsolubilized starch inmash

As previously mentioned, loss of starch occurs during the process, for examplenon-solubilization of starch. It is of interest to study how much starch is lost andto what, as an evaluation of the process. To evaluate the yield of liquefaction,mash samples can be taken just after the liquefaction step, before fermentation.

4.1 General principle of methodTo my knowledge, no method for direct quantification of starch in granular form ex-its. Therefore the nonsolubilized starch has to be completely hydrolyzed to glucoseand then the glucose content can be quantified. Liquefied mash, in which we wantto determine nonsolubilized starch content, already contains high concentrationsof solubilized starch. If the nonsolubilized starch in liquefied mash is hydrolyzedto glucose, the solubilized starch will be hydrolyzed too, and the glucose contentwould resemble content of total solubilized starch in mash. There are two possiblesolutions; determine content of fermentable starch (i.e. solubilized starch) first andthen hydrolyze nonsolubilized starch and quantify the total solubilized starch con-tent. The nonsolubilized starch content is calculated by subtracting fermentablestarch from total solubilized starch. The other alternative is to wash away thefermentable starch, then hydrolyze the nonsolubilized starch and quantify onlythe nonsolubilized starch (in solubilized form). Both approaches have advantagesand disadvantages. In general, when subtracting two figures, both with a certaininaccuracy the inaccuracy of the result is even larger. On the other hand the sec-ond alternative must contain a washing step to get rid of the fermentable starch,this extra procedure might also lead to more inaccuracy of the result. Howeverthe second alternative was chosen since it was believed that it would give the bestaccuracy when directly quantifying the glucose-concentration of interest.

17

18 Method for determination of nonsolubilized starch in mash

4.2 Sample preparationThe sample preparation includes washing solubilized starch away from the mashfollowed by drying of the mash. The drying step is necessary for achieving a knownwater content of mash (i.e. 0%). In glucose determination methods the glucosecontent is specified as % (w/w) which of course is dependent of water content.

The sample preparation begins with saccharification of the solubilized starchin the liquefied mash. This procedure was mainly included because it was de-sirable to measure content of fermentable starch in liquefied mash. It was laterdiscovered that the washing procedure used, only was effective when washing awayshorter dextrins which made the saccharification step even more important. Aftersaccharification the mash is filtered and the filtrand is dried.

4.2.1 Saccharification of solubilized starchTo determine fermentable starch, an existing method at Lantmännen AgroetanolAB for determination of remaining fermentable carbohydrates in fermented mashwas used as a starting-point. In this method 20 g mash from fermenter five isdiluted with deionized water to 120 g. The slurry is heated in 60◦C for 10 minbefore 220 µl glucoamylase is added. The slurry is then incubated at 55◦C for3 hours where complete saccharification of solubilized starch occurs. The glucosecontent, corresponding to fermentable starch, is then measured by HPLC. Thechromatogram also yields information about maltose, DP31 and DPn2 content.

To find optimal conditions for saccharification of liquefied mash, which hasa higher content of free sugars than fermented mash, different incubation times,dilutions and enzyme doses were evaluated to reach as low DPn content as possible.When DPn content approaches zero all solubilized starch is hydrolyzed to glucosewhich is the purpose with this procedure.

To compensate for higher concentration of solubilized starch, experiments wereconducted where the enzyme dose was amplified several times. A shorter incubation-time than three hours was desirable which also was an argument for increasing theenzyme dose. HPLC-analysis were run at 1 and 3 hours of incubation time. Inter-estingly the content of DPn seemed to increase from 1 to 3 hours of incubation.Probably other compounds are included in the DPn-peak which affect the result.The difference in enzyme dose did not seem to have any effect on remaining DPn.This was probably because the lowest enzymatic dose in this experiment alreadywas excessive. To lower the DPn content another experiment was conducted wherethe dilution factor was increased from six to ten, i.e. 20 g mash was diluted to200 g. To compensate for any effects of greater volume, E-flasks with a slightlyhigher enzymatic dose, 350 µl, was included in the experiment. HPLC-analysiswere run at 0.5, 1 and 3 hours of incubation. The DPn content seemed to be thesame between 0.5 and 1 hour of incubation time and then increased between 1and 3 hours as in the previous experiment. The DPn content was generally lowerin flasks with dilution factor 10 compared to flask with dilution factor 6. The

1DP: Degree of Polymerization, DP3 is hence maltotriose, three glucose molecules2n is an integer, always >3 in this report.

4.2 Sample preparation 19

glucose content did not seem to be affected much. The enzymatic dosage, 220 or350 µl, did not seem to affect the DPn content. The higher DPn content in thelower dilution factor experiment was probably due to inhibition of the enzymaticactivity by the high glucose concentration.

It was decided that an enzymatic dosage of 220 µl/20 g mash, an incubationtime of 1 hour and a dilution factor of ~20 should be used in the method toeffectively hydrolyze the solubilized starch to glucose.

4.2.2 Filtration and drying of saccharified mash

To facilitate the drying and washing step the liquefied and saccharified mash wasfiltrated and rinsed with ~100 ml of deionized water with vacuum aid using aBüchner funnel together with a Büchner flask. The free glucose is solved in waterand most of it is therefore found in the filtrate. A similar result could have beenachieved by centrifuging the sample, discard the supernatant, resuspend the pelletwith water and centrifuge again. However no centrifuge for such big samples (200g) was available at the laboratory. The filtrand, which contains the nonsolubilizedstarch, and some residual free glucose, was dried at 40-45◦C until complete dryness.

4.2.3 Washing

Total starch analysis kit (Megazyme International, Bray, Ireland)(AOAC Method996.11/AACC Method 76.13/ICC Standard Method No. 168) was used to hy-drolyze and quantify the nonsolubilized starch in the dried filtrand. Since it wasbelieved that some residual free glucose remained in the sample, a washing pro-cedure used for samples containing free glucose and maltosaccharides described inthe method, was decided to be used. In this washing procedure ~100 mg of thesample is suspended in 5 ml aqueous ethanol (80% v/v) and incubated at 80◦C for5 minutes. Another 5 ml aqueous ethanol is added and the sample is centrifugedfor 10 minutes at 3000 rpm. The supernatant is carefully poured off. Thereafterthe pellet is resuspended in aqueous ethanol and centrifuged once again, the su-pernatant is discarded. Centrifugation was in this application changed from 10minutes and 3000 rpm to 3 minutes and 4500 rpm. The much shorter centrifuga-tion time was possible due to absence of light weight particles in the samples. Theresult of the washing procedure was examined by determination of free glucose inthe washed samples using Total Starch analysis kit with the exception that wa-ter was added instead of enzymes, to avoid solubilization of further starch. Theresults for unwashed samples and samples washed once, twice and three times re-spectively are shown in table 4.1. As can be seen from the table, three washes areneeded to reach acceptable low levels of free sugars in both sample A and B. It wasdecided that the washing procedure described in Megazyme Total starch analysiskit should be used and repeated three times in the method. Controls made duringanalysis of the fractional factorial experiments yielded even lower levels of freesugars in washed samples.


Table 4.1. The results for sample A and B washed none, once, twice and three timesrespectively. The unit is % (w/w) starch in the sample.

SampleWashes A B

0 1.658 0.7041 0.198 0.0882 0.110 0.0683 0.049 0.055

4.3 Hydrolysis of nonsolubilized starchMegazyme Total Starch analysis kit was used to quantify the remaining nonsolu-bilized starch in the washed samples. A starch control was included in the kit andsince the method is validated no further controls or validations seemed necessary.Calculations were made that confirmed the plausibility of the results.

4.4 Evaluation of method accuracyTo evaluate the accuracy of the method three samples, A, B and C were with-drawn from the same mash sample (liquefied mash from the process). A, B andC were saccharified, filtered and dried separately. From each of A, B and C twosamples two samples of the dried filtrands were taken and washed and analyzedfor nonsolubilized starch according to the developed method. The values of thetwo samples for each of the three samples are presented in tab. 4.2, as % (w/w)nonsolubilized starch in the sample, together with mean values for each sample A,B and C. The total mean value is calculated together with the sample standarddeviation, S. The sample standard deviation is based on the tree mean values forA, B and C respectively. The dispersion can be used as a measure of accuracy andis calculated as mean

S which in this case yields an accuracy of 3.3% which must beconsidered satisfying. However, for the B sample the two results are quite distinctfrom each other and the sample standard deviation for sample B is large, while itis good for sample A and C. It was decided that double samples should be run forall the reactors in the future experiments to avoid errors.

4.4 Evaluation of method accuracy 21

Table 4.2. Measurement values of nonsolubilized starch (as %(w/w)) for three samplesof the same mash.

SampleA B C

Sample 1: 2.19 2.40 2.43Sample 2: 2.29 2.07 2.30Mean: 2.24 2.24 2.37Total mean: 2.28Sample std deviation: 0.075Dispersion: 3.3%

Chapter 5

Experimental details

5.1 Multiple factor experimentsTo find and optimize factors important for yield of liquefaction multiple factorexperiments were conducted where the mixtank, liquefaction tank 1 and 2 weresimulated. A two-level eight-factor fractional factorial design is adopted in thisstudy. The eight factors being studied are; temperature in mixtank, L1 and L2(abbr. TMix, TL1 resp. TL2); residence times in mixtank, L1 and L2 (abbr.TiM, TiL1 resp. TiL2; pH (abbr. pH) of the process and addition of urea (abbr.U) or not. The eight factors and their levels are presented in table 5.1. Theexperimental design is found in appendix A. Sugar profile (glucose, maltose, DP3and DPn concentrations in mash) fermentable starch, nonsolubilized starch andbrowning intensity are used as response factors.

Table 5.1. The investigated factors and their levels.

Mixtank L1 L2Temp.[◦C] 55-65 74-94 74-94Time [h] 0.5-1 1-3 1-3pH 4.6-5.6Urea [g] 0-1.86

The experiments were conducted in 2.5 l glass reactors placed in temperatewater bath. Water was added to the reactors according to the recipe (table 5.2).Urea (Yara, Köping, Sweden) and meal were added during constant agitation. pHwas adjusted according to the experimental plan using concentrated sulphuric acid(Merck, New Jersey, USA). β-glucanase (Genencor International, Pattensen, Ger-many), β-glucanase/xylanase (Genencor International, Pattensen, Germany) andα-amylase (Genencor International, Pattensen, Germany) were added accordingto the recipe. Immediately after addition of enzymes countdown for incubationtime in mixtank started. Countdown for incubation times in L1 and L2 started

23

24 Experimental details

instantly after the previous incubation time was ended, even though the tempera-ture for the next incubation was not reached. Boiling water was added to speed upwarming of the water bath. When incubation in L2 was finished pH was adjustedto 3,5 with concentrated sulphuric acid to lower the activity of α-amylase. Sampleswere taken for determination of sugar profile, absorbance at 420 nm, fermentableand nonsolubilized starch.

Table 5.2. Mash recipe

Component QtyWater 1035 gMeal 465 gUrea 0, 2, or 4 g/kg meal

β-glucanase/xylanase 60 µl /kg mealβ-glucanase 50 µl/kg meal

α-amylase 150 µl /kg meal

5.2 Analytical methods

5.2.1 Analysis of raw material

Starch content in meal was determined using Total Starch analysis kit (MegazymeInternational, Bray, Ireland) according to the standard assay procedure.

Moisture content in meal was determined using Halogen Moisture analyzersHG63 and HR73 (Mettler Toledo GmbH, Greifensee, Switzerland) at 130 ◦C.

5.2.2 Sugar profile

Sugar profile, i.e., content of DPn, DP3, maltose, and glucose in liquefied mashwas determined using a HPLC 1100 Series (Agilent Technologies, Waldbronn, Ger-many) with a 100*7,8 mm column (BioRad Laboratories Inc., CA, USA) and 0,005M sulphuric acid as mobile phase.

5.2.3 Maillard reaction products

To assess the formation of brown colored products descending from Maillard reac-tions, the absorbance of the supernatant from mash centrifuged at 5000 rpm for 10minutes was measured spectrophotometrically at 420 nm against deionized water.When necessary, appropriate dilutions were made to assure an optical density lessthan 1.5.

5.2 Analytical methods 25

5.2.4 Quantitative analysis of fermentable and nonsolubi-lized starch

The quantitative analysis of fermentable (i.e. solubilized starch) and nonsolubi-lized starch is divided into five steps;

Saccharification 10 g of liquefied mash was added to autoclaved 250 ml Erlenmeyer-flask (E-flask) and diluted with pre-heated, to avoid retrogradation of starch,deionized water to approximately 200 g. The dilution factor was calculated.A blank was also prepared using only deionized water. The E-flasks wereincubated for 10 minutes in water bath at 60◦C. Glucoamylase (NovozymesA/S, Bagsvaerd, Denmark) was added and the E-flasks were incubated atshaking table in water bath for one hour at 55◦C. Samples were analyzedusing HPLC, as described above, for determination of fermentable starch.

Filtration The saccharified sample was filtrated through a quantitative filterpa-per, grade 454 (VWR International AB, Stockholm, Sweden) with vacuumaid using a Büchner funnel together with a Büchner flask. The residue wasrinsed with approximately 100 ml deionized water.

Drying The residue from the filtration was dried until completely dry in 40-45◦C

Washing ~100 mg of the dried sample was weighed into test tubes. 5 ml ofethanol was added and the contents were mixed on a vortex stirrer. Afterincubation in waterbath at 80◦C for 5 minutes and mixing, 5 ml additionalethanol was added and the tubes were centrifuged at 4500 rpm for 3 minutes.The supernatant were carefully poured off. The samples were rinsed with10 ml ethanol by mixing and centrifugation as above. This procedure wasrepeated three times to assure that free sugars were washed off.

Hydrolysis The remaining starch in nonsolubilized condition in the washed sam-ple were hydrolyzed and quantified using Total Starch analysis kit. Thegeneral principle for the kit is described in section 2.4 on page 11.

5.2.5 Statistical analysis of multiple factor experimentsThe results from the multiple factor experiment were analyzed in Modde 7.0.0.1(Umetrics, Umeå, Sweden). At first, non-significant factors were removed fromthe additive regression model:

Yresponse = β0 + β1 TMix + β2TiM + β3 TL1 + β4 TiL1 ++β5 TL2 + β6 TiL2 + β7 U + β8 pH

where the βs are the regression coefficients and TMix, TiM, TL1, TiL1. TL2, TiL2,U and pH represent the coded ((-1) - 1) values of the factors. For some of theresponses the experimental design were complemented with additional experimentsto explore non-linear relations between factors and responses. A regression model

26 Experimental details

containing all the significant main effects, square effects and two factor interactionswere then made. R2 and Q2 were calculated for each model. R2 and Q2 provideinformation about how well the model fits the data respective how well the modelpredicts new data. To check the adequacy of the models, examination of theresiduals was done. The normal probability plot of the residuals was used to checkthe normality assumption. The residuals were also plotted against predicted valueto discover any nonconstant variance. The statistical significance of the modelwas established by comparing the calculated F-value for the model with the listedF-value. As a rule of thumb the calculated F-value for the model should be at least3-5 times greater than the listed F-value to be statistically significant [24]. Contourplots were then helpful in finding optimal settings for the significant factors.

Chapter 6

Results and discussion

6.1 CalculationsThe results gained from the analysis of nonsolubilized starch are expressed in %(w/w) of the washed and dried mash. It is desirable to instead express the resultsin % of total starch. The example below show how this is done for the performedexperiments.

Starch content in meal(as determ. by total starch analysis kit): 66.9 % (d.w.)TS in meal: 88.44%Added meal: 465 gMash (at the end of exp.): 1498.8 gFermentable starch (as glucose): 20.02 %(w/w)Non-sol. starch in sample: 3.25 %(w/w)

At first the amount of solubilized starch, i.e. fermentable starch that are washedoff have to be calculated:

20.02% · 162180

= 18.02%

1498.8g · 18.02100

= 270.1g

18.02%(w/w) of the mash in the reactor at the end of the experiment is fer-mentable starch (as starch). The fraction 162

180 is the weight ratio of starch toglucose. Approximately 270.1 g of the added meal is washed off as fermentablestarch. The equation below calculates how much meal is left as a washed and driedsample:

465g · 88.44100

− 270.1 = 141.2g

This is the meal that are analyzed for nonsolubilized starch. The result was inthis case 3.25 %(w/w) nonsolubilized starch in the sample.

27

28 Results and discussion

141.2g · 3.25100

= 4.6g

i.e. 4.6 g starch existed in nonsolubilized form in the reactor. Of the addedstarch, 4.6 g constitutes

4.6g

465g · 88.44100 · 66.9

100

= 1.7%

of the total amount of starch added to the reactor.The percentage of solubilized/fermentable starch of total starch is 98.2%.

270.1g

465g · 88.44100 · 66.9

100

= 98.2%

Together, solubilized and nonsolubilized starch constitutes 1.7 + 98.2 = 99.9%of the total amount of starch added. However, many of the figures used in thecalculations are measured and have therefore a certain inaccuracy. The morecalculations done, the larger the inaccuracy. These calculations are made for allthe experiments performed, where nonsolubilized starch was determined, and arepresented in appendix C. As can be seen, the two values sometimes add up tomore than 100% which is a result of the approximations.

6.2 Selection of factorsThe most of the chosen factors are believed to have both positive and negativeeffects of the process and are therefore interesting to study.

Temperatures: It was believed that higher temperatures during the upstreamprocess would have positive effects for the solubilization of starch. The moreenergy provided, the more hydrogen bonds interrupted. However, highertemperature should also lead to formation of more MRP which lowers theyield of liquefaction.

Residence times: Residence times should have effects similar to temperatures.Longer times should provide more energy and better solubilization of starch,but also time for formation of more MRP.

pH: The pH of the liquefaction process has not been studied at LantmännenAgroetanol AB before. pH affect the activity of the enzymes. The pHoptimum of the enzymes used is 5.4-5.8 for α-amylase, 5-6 for β-glucanaseand about 4 for β-glucanase/xylanase. pH is also affecting the formation ofMRP. A more acidic pH seems to lead to a lesser extent of MRP formationaccording to literature.

Urea addition Urea contains two amino groups which can react with reducingsugars and form MRP. Urea is added as a nutrient to yeast and does notneed to be added until after the liquefaction if there are obvious negativeeffects.

6.3 Statistical analysis of multiple factor experiment 29

Other possible factors to study are enzymatic doses. However, the enzymeshave probably only positive effects and the larger dose the better result, untila certain point of course. Therefore the optimization potential is low. Anotherfactor being interesting to study is particle sizes in meal. This would possiblyyield positive effects for liquefaction yield. The negative effects however, occurringduring recovery of ethanol would not have been seen in this study. There arecertainly lots of other factors to study, however the time was limiting and morefactors would require more experiments.

6.3 Statistical analysis of multiple factor experi-ment

In the first stage, nineteen experiments were conducted, including three centerpoint experiments, according to the experimental design generated in Modde(Umetrics, Umeå, Sweden). Separate models were constructed for each one of theresponse variables; DPn, DP3, maltose, glucose, fermentable starch, nonsolubilizedstarch and Maillard reaction products. The models were fitted with multiple linearregression (MLR). If nothing else is stated, a confidence level of 0.95 is used. Theoriginal design was in a later stage complemented with face centers for selectedfactors to support a non-linear model in those factors, i.e. screening fractionalfactorial design to responses surface modeling (RSM). Due to time limitations andfor easier visualization of results, the three most important factors were chosen,TMix, TL1 and pH. See section 6.3.8 on page 43 for more information on why thesethree factors were chosen. In the following sections the responses are detailed for,one by one. For some of them, the extended design is also accounted for. The bestmodels, concerning statistical significance, correlation coefficient (R2) and Q2 wererequested. Thereafter an overview of factors and responses follows where differentparameter settings are tried out to achieve an optimal liquefaction process. Theresults for all responses are shown in appendix B.

6.3.1 DPnA problem concerning statistical evaluation of DPn content is that the same mea-surement value might represent different things since dextrins of polymerizationgrade four and higher is included in the value. Some experimental conditionsmight favor formation of shorter dextrins while other conditions favor formationof dextrins of higher polymerization grade, without any change in the total contentof DPn. Therefore the DPn content of two separate experiments might seem equalwhen they really are not. This is probably the reason why an appropriate modelcould not be accomplished for the DPn yield. The only significant effect found waspH. The coefficient was negative which means that a lower pH favors formation ofDPn. A plausible explanation is that the pH optimum of the α-amylase used is5.4-5.8. At more acidic pH, like 4.6 used in this study, α-amylase works slower andless formation of shorter dextrins like maltotriose, maltose and glucose occurs. ADPn content with dextrins of higher polymerization grade represents more work to


amyloglucosidase in the next step of the process while shorter dextrins representless work.

A confidence level of 0.9 (instead of 0.95) revealed temperature in mixtankand temperature in L1 as statistically significant main effects besides from pH.The design was complemented with additional experiments at the face centersfor TMix, TL1 and pH. With the additional experiments the three main effectsTMix, TL1 and pH together with the interaction effect TMix*pH were significantat confidence level 0.95. Experiment C24, which had an abnormally low value wasexcluded from the analysis. The coefficient plot is shown in fig. 6.1.

Figure 6.1. Coefficient plot for DPn.

The regression model of DPn yield on the significant factor is:

YDPn = β0 + β1 TMix + β3 TL1 + β8 pH + β18 TMix∗pH

where the β’s are the regression coefficients with following values

β0 = 9.3686 β1 = −0.5254 β3 = 0.5823 β8 = −0.9756 β18 = 0.4940

and TMix, TL1 and pH represent coded values of the factors.In table 6.1 the analysis of variance (ANOVA) for DPn is shown. According to

the F-test the model cannot be considered statistically significant. The calculatedF-value, 10.27 is only three times the listed F-value, F4,19 = 2.90 (95%). The R2-value is low. The normal probability plot of residuals and residuals plotted against


Table 6.1. Analysis of variance for DPn. SS = Sum of Squares, MS = Mean Square,DF = Degrees of Freedom

Source of variation SS MS DF F-valueRegression 31.12 7.78 4 10.27Residual 14.39 0.76 19Total 45.51 1.98 23R2 0.68Q2 0.42F listed value F4,19 = 2.90(95%)

predicted value (plots not shown) did not imply any violations of the normalityor equal variance assumptions. The model is though poor and not useful forprediction of new data, which can be seen on the poor value of Q2. Though thecoefficients imply that lower temperature in mixtank, higher temperature in L1and lower pH give the highest concentration of DPn in mash.

The interaction plot for temperature in mixtank and pH (fig. 6.2) shows thatat pH 4.6 the temperature in mixtank influence the formation of DPn while theproduction of DPn at pH 5.6 only has slightly variations over the temperaturerange studied. However, at pH 5.6 the DPn content always underpass the DPncontent at pH 4.6.

Figure 6.2. Interaction plot for pH and TMix.


6.3.2 DP3For the DP3 response four main effects were clearly significant; TMix, TiM, TL1,and pH. Coefficients are positive for TMix and TiM and negative for TL1 and pHwhich means that higher temperature and longer time in mixtank together withlower temperature in L1 and more acidic pH give the highest concentration of DP3in liquefied mash. At significance level 0.90 TL2 is also significant.

The experimental design was complemented with additional experiments at theface centers for TMix, TL1 and pH. Except for the four significant main effectspreviously mentioned the main effect TL2 and two two-interaction effects weresignificant with the new data included; TMix*pH and TiM*pH. The coefficientplot is shown in fig. 6.3.

Figure 6.3. Coefficient plot for DP3.

The regression model obtained for yield of DP3 as a function of the significantvariables is as follows:

YDP3 = β0 + β1 TMix + β2TiM + β3 TL1 + β5 TL2 ++β8 pH + β18 TMix∗pH + β28 TiM∗pH

where the β’s are the regression coefficients with following values

β0 = 2.1034 β1 = 0.1318 β2 = −0.1113 β3 = −0.1337

β5 = −0.0794 β8 = −0.1702 β18 = −0.0890 β28 = −0.0791


and TMix, Tim, TL1, TL2 and pH represent the coded values of the factors.Table 6.2 depicts the analysis of variance for DP3. The values of R2 and Q2

are intermediate. According to the F-test the model is statistically significant.

Table 6.2. Analysis of variance for DP3.


In fig. 6.4 the normal probability plot of the residuals is shown. The plotis satisfying. The plot of residuals versus predicted value does not show anyparticular pattern and cause no concerns (plot not shown).

Figure 6.4. Normal probability plot of the residuals for the model of YDP3.

6.3.3 MaltoseThe proceeding for the maltose response was the same as for DP3 and DPn.The insignificant effects in an additive model including all the eight main effectswere excluded. The same factors as in the DP3 response turned out to be sig-nificant, TMix, TiM, TL1 and pH. TiM was just about to be significant withp-value 0.048 (<0.05). This design was also complemented with the additionalface centered points for TMix, TL1 and pH. The main effect TiL2, the interaction


term TMix*pH, the square terms TMix*TMix and pH*pH were revealed as sig-nificant effects with the additional data included as shown in the coefficient plotfor maltose, fig. 6.5

Figure 6.5. Coefficient plot for maltose.

The regression model obtained for yield of maltose as a function of the signifi-cant variables is as follows:

YMaltose = β0 + β1 TMix + β2 TiM + β3 TL1 + β6 TiL2 ++β8 pH + β11 TMix2 + β88 pH2 + β18 TMix∗pH

where

β0 = 10.1107 β1 = 0.4567 β2 = 0.3807 β3 = −0.4282

β6 = 0.2844 β8 = 1.3215 β11 = 0.6384 β88 = −1.0945 β18 = −0.4019

and TMix, Tim, TL1 and pH represent coded values of the factors.The ANOVA table for maltose is shown in table 6.3. According to the F-

test the model can be considered statistically significant, for 95% of confidencesince the calculated F-value, 32.31 is several times greater than the listed value,F8,16 = 2.59. The values of R2 and Q2 are acceptable.

The normal probability plot of the residuals is shown in fig. 6.6. It is satisfac-tory though experiment 1 and 7 is slightly deviating from the rest.


Table 6.3. Analysis of variance for maltose.


Figure 6.6. Normal probability plot of the residuals for the model of Ymaltose.


6.3.4 GlucoseFor the yield of glucose the main effects TMix, TiM, and TL1 were significant.pH were significant at confidence level 0.90. The experimental design was com-plemented with the additional experiments in the face centers for TMix, TL1 andpH. Though, the ultimate complementary design would be face centers for TMix,TiM and TL1. With the extra experiments the interaction and square termsTiM*TL1, TMix*Tim, and TiM*TiM and the four main effects, TMix, TiM, TL1and pH, seen in fig.6.7 were significant. As can be seen TiM is involved in boththe interaction terms and also constitutes the square term.

Figure 6.7. Significant effects for yield of glucose.

The final regression model for glucose yield is as follows:

YGlucose = β0 + β1 TMix + β2 TiM + β3 TL1 + β8 pH ++β22 TiM2 + β12 TMix∗TiM + β23 TiM∗TL1

where

β0 = 0.8598 β1 = −0.0511 β2 = 0.0544 β3 = −0.0555

β8 = 0.0344β22 = −0.0914 β12 = −0.0227 β23 = 0.0306

and TMix, TiM, TL1 and pH represent the coded values of the factors.


Table 6.4. Analysis of variance for glucose.


Table 6.4 presents the analysis of variance for glucose. The calculated F-value isalmost twelve times the listed F-value and the model can be considered statisticallysignificant. Both R2 and Q2 are acceptable.

The residuals versus the predicted values were studied, see fig. 6.8. The patternmight seem funnel like. Though studies of Box Cox plot do not imply that anytransformation is needed. The normal probability plot of the residuals was alsoexamined (not shown). Experiment 11, 16 and 25 are slightly deviating, as also isthe case in fi. 6.8, but on the whole the plot is gratifying.

Figure 6.8. Residuals plotted against predicted value for glucose yield.

6.3.5 Fermentable starchIn fig. 6.9 fermentable starch and the total sugar content ( composed of DPn +DP3 + maltose + glucose) in the mash from the experiment reactors are plotted.These two curves are almost parallel with the exception from experiment N6. Thetotal sugar content in mash has a clear correlation to fermentable starch which


is not surprising. The correlation coefficient , R2 = 0.73, for the two curveswhen experiment N6 is excluded. For a good liquefaction yield we want as muchfermentable starch as possible.

Figure 6.9. Total sugar (DPn + DP3 + maltose + glucose) and fermentable starchplotted against experiment number.

Unfortunately no significant effects could be found for yield of fermentablestarch as can be seen in fig. 6.10, observe that the confidence level is set at 0.90.

Even though not significant the four most important effects seem to be TMix,TL1, TL2 and TiL2, where low temperature in mixtank, high temperature in L1and L2 together with long time in L2 increases the yield of fermentable starch.Since there is merely a 5% difference between the maximum (20.565) and minimum(19.665) yields of fermentable starch (see appendix B) only a very small errormargin is tolerated not to affect the results. The reason why no significant effectscan be revealed might be due to the fact that the integration of the peak areas inthe chromatogram from HPLC had to be done manually which surely increased themeasurement error. This theory is supported by the poor reproducibility which isonly 0.51.

Since the yields of fermentable starch and total sugar were highly correlatedthe total sugar yield was studied in Modde to see if any significant factors could bediscovered. Experiment number 6 had an extremely low value and was excludedfrom the analysis. The coefficient plot for total sugar, fig. 6.11 showed almostthe same pattern for the coefficients as the coefficient plot for fermentable starch,though two significant factors, TL1 and TL2 were significant at a confidence of0.90. Low temperature in L1 respective high temperature in L2 give higher yieldof total sugar.


Figure 6.10. Coefficient plot for fermentable starch. Please note that the confidencelevel is set at 0.90.

Figure 6.11. Coefficient plot for total sugar. The confidence level is set at 0.90.


Earlier studies at Lantmännen Agroetanol AB have revealed that higher tem-perature in mixtank promotes formation of fermentable starch and therefore alsoshould promote formation of total sugar. Unfortunately that can neither be con-firmed or denied by the current study.

6.3.6 Nonsolubilized starchThe coefficient plot for nonsolubilized starch is showed in fig. 6.12. Three effectsturn out to be significant at confidence level 0.90; TiL1, TL2 and pH. A goodmodel is not possible to make from these data. However, long time in L2, hightemperature in L2 and a basic pH seem to give the least nonsolubilized starch.

Figure 6.12. Coefficient plot for nonsolubilized starch.

The normal probability plot of the residuals is very satisfying (fig. 6.13) andresiduals plotted against predicted value does not show any particular pattern (notshown).

6.3.7 Maillard reaction productsThe absorbance of the liquefied mash at 420 nm was used as a measure of Maillardreaction products. By studying scatter plots of the absorbance against any of thevariables experiment N5 seemed to be an outlier, see fig. 6.14. The absorbancefor N5 was abnormally high and therefore the experiment was excluded from fur-ther analysis. No significant factors could be found. Hence a model could not be


Figure 6.13. Normal probability plot of residuals for nonsolubilized starch.

Figure 6.14. Absorbance at 420 nm plotted against experiment label.


accomplished from these experimental results. This might be due to a time de-pendency of the absorbance which was discovered during the experimental session.The absorbance decreased for samples withdrawn from the reactor at a later stage.On the opposite, the absorbance increased if the diluted supernatant was leavedfor a time. This time dependency has probably affected the results, leading to analmost serendipitous variation of the absorbance, even though all the experimentsfollowed the same routines and the stand-by times should have been approximatelythe same for all the reactors. However, the rate of change of absorbance was notstudied. The three center point experiments had closely spaced absorbance, seefig. 6.14, which led to a reproducibility of 0.987 which means that under the sameexperimental conditions the responses are almost the same. This contradicts thetheory about randomness due to time dependency.

The absorbance at 420 nm for each experiment was plotted together withfermentable starch to discover any relations, see fig 6.15. No strong correlationcan be seen.

Figure 6.15. The absorbance at 420 nm and concentration of fermentable starch plot-ted against experiment number. Observe that the absorbance is linearly transformedAbs*=Abs + 20 for an easier comparison.

Studies were also made to see if there was any correlation between absorbanceand total experimental time, however no correlation could be seen.


6.3.8 Overview of factors and responsesIn fig. 6.16 the normalized coefficients for the significant main effects for each ofthe responses are sorted by factor. No significant effects were found for fermentablestarch and Maillard reaction products and thus they are not in the figure. Thethree factors considered having greatest influence of the responses were TMix, TL1and pH, which were significant to more responses than TiM, TiL1, T2 and TiL2.Therefore TMix, TL1 and pH were further studied in the complemented design aspreviously mentioned.

Figure 6.16. Normalized coefficients of significant factors sorted by factor.

In fig. 6.17 the normalized coefficients for the significant main effects for eachof the responses are presented, sorted by response. The relations of different re-sponses can be studied. A higher temperature in mixtank (TMix) has positiveeffects of maltose and DP3 yield while it is on the contrary for glucose and DPn.It was unexpected that the formation of glucose was not favored by higher tem-perature in mixtank. A longer time in mixtank (TiM) leads to higher yields ofglucose, maltose and DP3. The most surprising about this is that time in mix-tank has more significance than time in L1 or L2. It seems natural that longertime favors formation of shorter dextrins. Formation of glucose, maltose and DP3is favored by low temperature in L1 while it is the opposite for DPn. This wasunexpected since it was believed that a higher temperature in L1 would lead toincreased swelling of granular starch and hence more availability of starch to en-zymes. The temperature optimum for the β-glucanase used is approximately 70◦Cand the activity is reduced by half already at 80◦C (see sec. 3.2) The temperatureoptimum for the β-glucanase/xylanase and α-amylase are 80◦C respective 86◦Cwhich is close to the center point of the temperature interval studied. The lower


Figure 6.17. Normalized coefficients of significant factors sorted by responses.

enzymatic activity at higher temperatures seems to have a larger impact on theyields than increased swelling power. Basic pH favors formation of glucose andmaltose and more acidic pH favors formation of DP3 and DPn. This is explainedby a higher enzymatic activity of α-amylase at more basic pH, in the pH-intervalstudied, which leads to formation of shorter dextrins.

In fig. 6.18 the yields of glucose, maltose, DP3, DPn, and total sugar (DPn+ DP3 + maltose + glucose) are graphically shown. From this figure it is clearthat the maltose and DPn curves are mirror images of each other. The correlationfactor of maltose and DPn is 0.90, which is high. When speaking about liquefactionyield this term is interesting since it means that it is not possible to achieve highconcentrations of both maltose and dextrins of higher polymerization grade at thesame time. A comparison of the significant factors for DPn and maltose supportsthe results showing that the main effects TMix, TL1, and pH have opposite effectson maltose and DPn yield. No other strong correlations can be found betweenany of the curves in fig. 6.18. This is supported by the discussion above whereglucose, maltose, DP3 and DPn sometimes have significant factors in common andsometimes not.


Figure 6.18. Sugar profile of the mash from the experiments plotted against experimentnumber.

In fig. 6.18 total sugar is also plotted to see how the concentration of totalsugar is related to the production of maltose, glucose, DP3, and DPn. Calculationsof the correlation coefficients between total sugar and glucose, maltose, DP3 andDPn respectively revealed that no strong correlation existed. Studying fig 6.17it is clear that formation of total sugar is favored by low temperature in L1, thesame as for glucose, maltose and DP3. The highest concentration of total sugarseems to be reached when the concentration of maltose is high. It is importantto point out that maltose have greater mass percent of water than DPn, due tothe increased hydration grade of maltose. How much this effect contributes to ahigher total sugar content at high concentrations of maltose is not clear.

When comparing the responses total sugar and nonsolubilized starch in fig.6.17 it becomes evident that high temperature in L2 prevents formation of non-solubilized starch and also leads to higher yields of total sugar. The negative pHeffect of nonsolubilized starch indicates that the enzymatic activity affect the sol-ubilization of starch. At more acidic pH, where the activity of α-amylase is loweran increased amount of nonsolubilized starch are seen. TiL1 and TL2 affected thesolubilization of starch as expected, though TL1 and TiL2 were believed to havegreater influence.

6.3.9 Optimization of liquefaction processBy studying fig. 6.16 on page 43 a general picture of the factors effects are given.


TMix has both positive and negative effects and are also involved in interactionsand has a quadratic effect on maltose yield. The interaction effects for DPn,DP3, and glucose involving TMix are weak and does not need to be taken intoconsideration. Though the non-linear effect for maltose has larger impact. Acontour plot for yield of maltose is shown in fig. 6.19.

Figure 6.19. Response contour plot for maltose. The linear effects TiM, TL1 and TiMare set at their high, low respective high values.

In the plot, two zones of high yield is visible, one where pH = 5.3 and TMix =65◦C and the other one at pH = 5.5 and TMix = 55◦C. Therefore, low temperaturein mixtank leads to high yields of glucose, maltose and DPn. High temperaturein mixtank leads to high yields of maltose and DP3. The earlier experiments atLantmännen Agroetanol AB also showed that high temperature in mixtank leadsto high yields of fermentable starch.

Longer time in mixtank seems to be positive for formation of shorter dextrins.The interaction effect between TiM and pH for DP3 response is weak, though thesquare effect of TiM for glucose implies that an intermediate time in mixtank,around 1.1 h is optimal for formation of glucose, see the contour plot in fig. 6.20

For the shorter dextrins and for total sugar a low temperature in L1 seemspositive. However when the process at Lantmännen Agroetanol AB has been runat temperatures of 69-70◦C in L1 the ethanol to starch yield has been bad and thiswas probably due to high amounts of nonsolubilized starch. There is probably abreak-even point somewhere between 70-74◦C.


Time in L1 was a significant factor only for nonsolubilized starch where a longertime leads to better solubilization.

A higher temperature in L2 is positive for the yield of total sugar and also forthe solubilization of starch. Formation of DP3 is though negatively affected byhigh temperature in L2. A longer time in L2 is positive for maltose yield but doesnot seem to have any other effects.

pH is the factor with greatest impact. The coefficients are relatively large. Itlooks like a more basic pH favors formation of shorter dextrins and also promotessolubilization of starch. The interaction effects for DPn and DP3 involving pHare negligible. However in fig. 6.19 on page 46 it is seen that pH = 5.6 is not theoptimum choice for maltose production. pH around 5.3 - 5.5 is better dependingon temperature in mixtank.

The optimum values of pH and temperature in mixtank were either 4.6 or 5.6(or something in between) respective 55◦C or 65◦C depending on response. Apredicted value for total sugar composed of predicted values for glucose, maltose,DP3 and DPn using the established models was calculated for different values ofpH and TMix. Have in mind that the model for DPn was poor and the predictionsare vague. The values tested are listed in table 6.5. The option "current" resemblesthe settings of the process today. The values of the other factors were held constantat appropriate levels. For each of the responses glucose, maltose, DP3, and DPnintervals of confidence level 0.95 were created for each of the proposed options in

Figure 6.20. Response contour plot for glucose yield. TMix and pH are hold at theirlow respective high values while TL1 and TiM vary over the interval studied.


table 6.5. The lower values were summarized and constitute the lower values ofthe intervals for total sugar, the upper values were summarized and constitutesthe corresponding upper values. The intervals are shown in figure 6.21. As can beseen they all overlap and no conclusions about an optimal combination of pH andTMix can be drawn concerning maximum yield of total sugar. This also explainswhy it was not possible to accomplish a good model for total sugar.

Table 6.5. Values of pH and TMix used for prediction of total sugar (derived fromglucose + maltose + DP3 + DPn). The other factors were held constant at TiM =1h TL1 = 74◦C TiL1 = 2h TL2 = 89◦C TiL2 = 2h.

Option TMix pHCurrent 58 5.21 58 5.42 63.5 5.23 63.5 5.44 55 5.25 55 5.26 55 5.47 55 5.0

Figure 6.21. Predicted intervals for total sugar at different values of TMix and pH,composed of predicted intervals for glucose, maltose, DP3 and DPn.

Chapter 7

Conclusions

7.1 Quantification of nonsolubilized starchThe method for quantification of nonsolubilized starch follows the principle:

1. saccharification of mash

2. washing of saccharified mash to wash off solubilized starch

3. determination of starch content in the residual mash

The method is detailed in section 5.2.4 on page 25. Since granular starchcannot be quantified it has to be converted to glucose through gelatinization andsaccharification. For measurements of only nonsolubilized starch in liquefied mash,the solubilized starch has to be washed off first. The washing procedure is verytime-consuming. During method validation the error margin was assessed to below3.5% which was satisfying in relation to the time limits. Though, during analysisof the fractional factorial experiments the error margin was for some samples muchlarger than 3.5% and the analysis had to be redone to achieve satisfying results.To improve the method, concerning both accuracy and time requirement, I believethe attention should be addressed to the washing step.

7.2 Optimal parameter settingsOne of the objectives of this work is to optimize the liquefaction yield. To do thisa definition of what liquefaction yield is must be stated. I believe that measuringfermentable starch provides the best information about how the liquefaction cameoff. Fermentable starch is starch that actually serves as substrate for yeast in thesubsequent fermentation. When the yield of fermentable starch is low, it probablydepends on a sugar profile shifted towards longer dextrins, nonsolubilized starchor high extent of Maillard reactions. Therefore, the parameters sugar profile,nonsolubilized starch and Maillard reaction products can be used as parametersto diagnose the process.

49

50 Conclusions

Eight factors were studied in the fractional factorial experiments, temperaturesand times in mixtank, L1 and L2, urea addition and pH. Of these eight factorsthe three who turned out to be the most significant, TMix, TL1 and pH, werefurther studied in additional experiments. For each of the performed experimentthe following responses were measured; sugar profile (divided into glucose, maltose,DP3 and DPn), fermentable starch, Maillard reaction products and nonsolubilizedstarch.

Unfortunately the reproducibility of fermentable starch was to low and a goodmodel for yield of fermentable starch could not be accomplished. Instead the highlycorrelated total sugar content (derived from glucose + maltose + DP3 + DPn)was studied. Neither here could an acceptable model be accomplished, though lowtemperature in L1 and high temperature in L2 seemed preferable.

The lower temperature, 74◦C, in L1 seems to have positive effects on mostof the responses. The temperature in L1 in the process is about 73◦C and myconclusion is to leave this parameter unchanged. I do not recommend to lowerthe temperature since that is outside the studied interval and also because theprocess have been run at temperatures around 69-70◦C with evident negativeeffects. There is probably a break-even point somewhere between 70◦C and 74◦C.

pH and temperature in mixtank have in several cases interaction effects. Morebasic pH seems to shift the mean length of dextrins towards shorter dextrinswhich is preferable. I therefore suggest an increase in pH from 5.2 to 5.4. Eventhough a lowered temperature in mixtank have positive effects I propose a slowrise of temperature in mixtank to begin with. The conclusion is based on the factthat high temperature in mixtank is preferable for maltose and DP3 production,maltose and DP3 constitute a larger share of total sugar than DPn and glucose.Earlier studies at Agroetanol have shown that higher temperature in mixtank leadsto higher yields of fermentable starch.

Concerning TiL1 and TiL2 the trend is that longer liquefaction time is positive.However, the statistical significance is weak and the practical significance is highlyquestionable and therefore I cannot recommend any changes of these parameters.

For TL2 the trend is that higher temperature leads to higher yields of totalsugar and higher solubilization of starch. The statistical significance is thoughweak. The current temperature in L2 is 88-90◦C which belongs to the upper partof the studied interval and for practical reasons I do not recommend an increasedtemperature.

Regarding addition of urea or not in mixtank it does not seem to affect theliquefaction yield. If the result depends on a poor measurement method, or thatconcentration of Maillard reaction products is too low to affect the yield, or ifurea addition really not significantly affects the extent of Maillard reactions is notclear. Therefore no recommendations concerning urea additions are given.

The liquefaction yield as well as the yield for the whole process should carefullybe investigated during the parameter changes. When approaching higher temper-atures in mixtank, close to 65◦C, viscosity problems will probably appear. Thatand increased costs for heating must, except for yield, be taken into account whenevaluating the parameter changes.

Chapter 8

Recommendations

• Use the method for quantification of nonsolubilized starch either regularlyon a weekly basis or as a diagnostic tool when liquefaction yield is poor. Ifthe method is used on a weekly basis I suggest that analysis of starch in mealis done at the same time (analysis of starch content in meal is today done ona weekly basis). This is both time and resource saving. This means that theanalytic method for determination of starch in meal must be changed fromacidic to enzymatic hydrolysis which might have some complications.

• A full scale trial where pH is increased to 5.4. Slow increase of temperaturein mixtank. Evaluate liquefaction yield and yield of the whole process.

51

52 Recommendations

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[21] G. international inc., Optimash BG, 2005.

[22] G. international inc., Optimash TBG, 2006.

[23] G. international inc., Spezyme ethyl, 2004.

[24] A. Costa, D. Atala, F. Maugeri, and R. Maciel, Factorial design and sim-ulation for the optimization and determination of control structures for anextractive alcoholic fermentation, Process Biochemistry, 37:125 – 137, 2001.

Appendix A

Experimental design

The original experimental design generated in Modde. The eight factors variedare, temperatures in mixtank, liquefaction tank 1 and 2 (TMix, TL1 and TL2),residence times in mixtank, liquefaction tank 1 and 2 (TiM, TiL1 and TiL2), pHand urea addition (U).

Exp. nr. TMix[◦C] TiM[h] TL1[◦C] TiL1[h] TL2[◦C] TiL2[h] U[g] pHN1 55 0.5 74 1 74 1 0 4.6N2 65 0.5 74 1 74 3 1.86 5.6N3 55 1.5 74 1 94 1 1.86 5.6N4 65 1.5 74 1 94 3 0 4.6N5 55 0.5 94 1 94 3 1.86 4.6N6 65 0.5 94 1 94 1 0 5.6N7 55 1.5 94 1 74 3 0 5.6N8 65 1.5 94 1 74 1 1.86 4.6N9 55 0.5 74 3 94 3 0 5.6N10 65 0.5 74 3 94 1 1.86 4.6N11 55 1.5 74 3 74 3 1.86 4.6N12 65 1.5 74 3 74 1 0 5.6N13 55 0.5 94 3 74 1 1.86 5.6N14 65 0.5 94 3 74 3 0 4.6N15 55 1.5 94 3 94 1 0 4.6N16 65 1.5 94 3 94 3 1.86 5.6N17 60 1.0 84 2 84 2 0.93 5.1N18 60 1.0 84 2 84 2 0.93 5.1N19 60 1.0 84 2 84 2 0.93 5.1

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56 Experimental design

The extended design with face centers for TMix, TL1 and pH.

Exp. nr. TMix[◦C] TiM[h] TL1[◦C] TiL1[h] TL2[◦C] TiL2[h] U[g] pHC20 55 1 84 2 84 2 0.93 5.1C21 65 1 84 2 84 2 0.93 5.1C22 60 1 74 2 84 2 0.93 5.1C23 60 1 94 2 84 2 0.93 4.6C24 60 1 84 2 84 2 0.93 5.6C25 60 1 84 2 84 2 0.93 5.1

Appendix B

Results

Results for the performed experiments. FS: Fermentable starch, NS: nonsolubilizedstarch, Abs: absorbance at 420 nm. DPn, DP3, maltose, glucose and fermentablestarch is measured as % (w/w) in mash. Nonsolubilized starch is measured as %(w/w) in the filtrated, washed and dried mash.

Exp. nr. DPn[%] DP3[%] Maltose[%] Glucose[%] FS[%] NS[%] AbsN1 11.9011 2.1047 6.4675 0.7762 19.7863 3.35 1.251N2 7.7892 2.1054 11.0972 0.7924 20.1287 3.25 1.110N3 7.9008 1.8932 11.5393 0.9513 20.5616 2.49 1.023N4 8.7579 2.5886 10.0944 0.7091 20.1663 2.38 0.405N5 12.3355 1.5351 7.2006 0.6251 20.14 3.18 1.976N6 7.3362 1.6267 10.3470 0.6257 19.96 1.88 0.873N7 8.7665 1.8029 10.4528 0.8866 20.25 1.68 0.708N8 8.6144 2.6900 9.2864 0.6624 19.99 3.65 0.428N9 7.9877 2.0944 11.5443 0.8909 20.57 1.00 1.029N10 9.7741 2.3988 9.1201 0.7519 20.15 2.15 0.575N11 9.4671 2.4520 9.1996 0.9435 20.31 2.22 0.573N12 7.3260 2.2360 11.4754 0.7975 20.04 2.92 1.203N13 9.2978 1.8044 9.7804 0.6806 19.96 1.39 0.828N14 10.3442 2.2145 8.5608 0.5697 20.10 3.64 1.150N15 12.2645 2.0455 6.8718 0.8084 20.18 2.25 0.873N16 7.5461 1.9569 11.2891 0.8236 19.67 0.94 1.008N17 9.3505 2.2832 9.7071 0.7652 20.33 - 0.518N18 9.2728 2.0286 10.0901 0.8587 20.41 2.24 0.548N19 9.3455 2.2398 9.8560 0.8589 20.10 2.13 0.538

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58 Results

Exp. nr. DPn[%] DP3[%] Maltose[%] Glucose[%] FS[%] NS[%] AbsC20 8.9406 1.8676 10.8196 0.9222 20.58 - 0.561C21 8.5338 2.1552 10.8257 0.8324 20.40 - 0.640C22 8.6142 2.2435 10.4578 0.9050 20.46 - 0.608C23 10.1123 2.0348 9.5000 0.8362 20.54 - 0.461C24 6.1562 2.4974 7.5582 0.8118 20.66 - 0.386C25 8.9097 1.9427 10.6211 0.8285 20.67 - 0.639

Appendix C

Distribution of starchbetween solubilized andnonsolubilized

The measurement values of fermentable starch and nonsolubilized starch found inappendix B recalculated and specified as % of total starch amount added to thereactors.

Exp. nr. Fermentable starch[%] Nonsolubilized starch[%]N1 97,1 1,8N2 98.1 1.7N3 99.6 1.2N4 98.4 1.2N5 98.6 1.6N6 96.9 1.0N7 98.7 0.9N8 97.6 1.9N9 101.6 0.5N10 99.2 1.1N11 99.4 1.1N12 98.5 1.5N13 98.2 0.7N14 98.2 1.9N15 99.1 1.1N16 95.0 0.5N18 99.9 1.1N19 98.4 1.1

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