Bioethanol Production from Different Biomass Feedstocks

7890 words (32 pages) Essay in Environmental Sciences

08/02/20 Environmental Sciences Reference this

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Abstract:

This experiment used an autoclave pre-treatment at 121°C for 1 hour and an enzymatic hydrolysis to produce glucose from second generation feedstocks of potato, sugarcane bagasse, waste paper, and grass clippings. This process allowed for a significantly greater production of glucose from potato (27 g/L) than the other feedstocks (3.7-5.3 g/L) due to the lack of lignin present in potatoes. HPLC analysis was employed for a full sugar profile after hydrolysis to determine glucose concentration. Fermentation of these biomasses resulted in a high ethanol yield in sugarcane (124.91%) grass (102.51%), and paper (110.64%), and a low yield in potato (18.17%). Limiting fermentation factors caused potatoes values to be so low. Headspace GC analysis was employed to determine the concentration of ethanol within the fermented samples. Simple distillation of the samples showed an ethanol purity between 99.9% (sugarcane) and 84.4% (paper). Overall, it was found that sugarcane (193%) had the highest efficiency of conversion between cellulose and ethanol per gram of biomass, and potato (29%) had the lowest. This leads to the conclusion that sugarcane is the best biomass to use for the production of bioethanol out of sugarcane, potato, paper, and grass clippings using this method.

Introduction:

The depletion of oil stocks and an increasing worldwide energy demand have generated an increase in attention towards biofuel production from biomass feedstocks as a source of energy security. Biomass is an attractive feedstock for three main reasons; it is a renewable resource that has the potential to be sustainably developed, it has positive environmental properties that result in an almost neutral release of CO2 and sulphur content, and that it appears that it will have a significant economic potential with a projected increase of fossil fuel products costs into the future. [1]

Biofuels can be separated into generations, each one characterised by their feedstock source, their limitations as a renewable energy, and their technological progress. First generation biofuels are biofuels whose feedstocks compete directly with human consumption, such as wheat and corn. While the overall use of first generation technologies have been the most significant step taken to move away the reliance on traditional fossil fuels, there is much debate as to whether these biofuels have an actual environmental benefit. This is due to their competition for land and water use with the food and crop industries, their high cost of production and processing, and the ever changing assumptions of their net greenhouse gas reductions when land use during production is taken into account. [2] [3] Second generation biofuels come from non-competitive biomass feedstocks such as municipal and food crop waste. There is an increasing indication that second-generation lignocellulosic bioethanol will become part of the solution in the shifting of the transport sector towards more sustainable energy sources. [4] Third generation biofuels are specially engineered energy crops such as algae. Aglae is cultured to act as low-cost, high-energy, and a readily renewable feedstock having the potential to produce more energy per acre than conventional crops. Fourth generation biofuels are a developing facet of bioethanol production, aiming to be CO2 capturing, allowing for an overall reduction in CO2 emissions.

Lignocellulosic biomass, dry plant matter, is the most readily available raw material on the Earth for the production of biofuels. [5] The major components of lignocellulosic biomass are cellulose, hemicellulose, and lignin. Cellulose is a pure organic polymer, only consisting of anhydroglucose that is held together in a straight chain. This component can be hydrolysed into glucose, and the efficiency of this conversion is based on the extent of pre-treatment that the biomass undergoes. Hemicellulose occurs in much shorter chains than cellulose and is present in deciduous woods. As hemicellulose is majorly chains of pentose sugars that act as a cement to hold together the glucose, hydrolysis of popular biomasses such as sugar cane and corn lead to a pentose sugar by-product that is not fermented into bioethanol. Both of these carbohydrate polymer chains are bound tightly to lignin, which are aromatic polymers that provide structural strength to plants and are the most recalcitrant within the cell wall.

It is the recalcitrance of lignin that is a major barrier in the production of bioethanol as the sugars that are necessary for fermentation are bound and trapped. Due to this, the hydrogen bonds within hemicellulose and cellulose need to be broken via a pre-treatment. [4] An important facet of a pre-treatment process is a supply of heat to help with the breaking of bonds. Different devices have been researched, however, the moist heat and pressure that is supplied via an autoclave has been found to be one of the most effective processes. [6]. It has been found that fermentation without a pre-treatment gives low (about 20%) yields. [7] This step is crucial for a higher sugar efficiency. 

A hydrolysis procedure must then be employed so that the freed cellulose can be converted into simple sugars. The most commonly employed methods of hydrolysis are chemical and enzymatic, both of which requiring a pre-treatment process to increase the susceptibility of cellulose and hemicellulose. Chemical hydrolysis is a two-step process whereby cellulose is rapidly converted into glucose and hemicellulose to its C5 sugars with little degradation over only a few minutes. When using dilute acid, commonly done to make the process more economical, the resulting sugar yield is low due to harsh conditions that are required. [8] However, when using concentrated acids, the conditions are much more mild, giving rise to higher sugar yields. [9] Both of these methods lead to the production of inhibitors that can affect the fermentation process. [10] Furthermore, when using acid hydrolysis there is a need for recovery and recycling of the acids used, leading to a more costly process. Due to the limitations of acid hydrolysis, research within the last two decades has been focused on enzymatic hydrolysis and its associated cellulolytic enzymes. [11] This process is done under mild conditions, with an incubation at 40-50°C, allowing for a high sugar yield with no inhibitory by-products being produced or released during the process. [4] Due to the longer reaction time associated with enzymatic hydrolysis, an efficient pre-treatment process is required to maximus enzyme loading and the overall sugar yield.

These sugars are then fermented into bioethanol. After the fermentation, the bioethanol must be purified. It has been found that second-generation feedstocks (3-6 vol%) produce significantly lower yields compared to first-generation (12-15 vol%). [12] The value of a biomass as a feedstock for bioethanol production is reliant on the ease in which it can be converted into sugars. 

In this study, we converted the four different biomass feedstocks potato, waste paper, grass clipping, and sugarcane bagasse into bioethanol. An autoclave pre-treatment and an enzymatic hydrolysis was employed. Batch fermentation was done for these biomasses at one and four week intervals to see if there was any difference in bioethanol yield over time. Compared to other potential bioethanol production methods, this process used no harsh chemicals and limited potential production costs while providing high yields of glucose and varying levels of bioethanol.

Method:

Pre-treatment: Approximately 10g sugarcane bagasse, 25g potato, 25g grass, and 20g paper were put into separate containers and were autoclaved at 121°C for 1 hour. The samples were then placed into ceramic bowls covered with aluminium foil and then heated at 100°C for 2 hours to dry the biomass.

Glucose standards: Glucose standards of 0, 0.1, 1.0, 2.0, 4.0, 6.0, 8.0, 10.0, 20.0 and 40.0 g/L were made up of anhydrous glucose in 25mL volumetric flasks with distilled water. A small aliquot of each was then placed into HPLC vials. 

Hydrolysis: Around 0.5g of each biomass (weights used listed in appendix 1) were placed into 50mL tubes, 6 tubes for each type of biomass to account for triplicates of the 1 and 4 week fermentation times (24 all together). To each tube, 10mL of 3M citrate buffer with a pH of 4.8, 0.2mL of 30% cycloheximide (antifungal), and 0.03mL of 88% tetracycline (antibiotic) were added using autopipettes. 0.8mL of cellulase and 0.2mL of diazyme X4NP were then also added. Each vial was then shaken and incubated in a water bath at 50°C for one week.

Supernatant from biomass: After the tubes were taken from the water bath, they were centrifuged to separate the supernatant form the biomass. A syringe with a 3mm, 0.45μm polyproplene filter was then used to take a 1mL sample of the supernatant from each tube, which was then placed into a HPLC vial.

HPLC analysis: HPLC analysis was then done for the glucose standards to obtain a calibration curve, and on the supernatant from the biomass to obtain a sugar profile of the oligomers released for each sample. HPLC analysis was then run on a Perkin Elmer Altus A-10 with two Phenomenex Rezex ROA-organic Acid H+ (8%) 150 x 7.8mm columns running in serial at a flow rate of 0.625mL/min at 65°C. The mobile phase was 0.01M HCl. The detector was an Alltech 3300 ELSD running nitrogen gas at a flow rate of 1.0L/min at a temperature of 110°C. A glucose calibration curve was then created as well as the potential ethanol content. A QC sample of 0.5g/L ethanol also run.

Fermentation: To each of the biomass tube, 10mL of water was added and then 350mg of turbo yeast was added. The tubes were then shaken and fermentation bubblers were attached.

After the fermentation was complete, each biomass was vacuum filtered to separate the solid waste from the filtrate. The filtrate was then put into new 50mL tubes and a 0.5-1mL aliquot was taken a syringe and the same type of filter as in the supernatant from biomass step, and placed in GC vials for analysis of ethanol content. Ethanol standards of 0, 0.5, 1.0, 5.0, 10.0, 15.0, and 20.0 g/L were made up and placed in GC vials for analysis.

GC analysis: GC analysis was then done for the ethanol standards to obtain a calibration curve, as well as the QC samples and the biomass samples. GC analysis was then run on a Perkin Elmer Gas Chromatograph Clarus 580 with a capillary coloum of 25m x 0.2mm and a phase BP 20. A calibration curve was then made and the concentration of ethanol (g/L) was determined as well as RSD and RE.

Distillation: Using the setup in Figure 1, the group of biomasses were distilled. A round bottom flask was used as a distilling pot, and a tube as the receiving flask. The biomass solution was added to the distilling pot, and the heat was increased slowly. Once the ethanol had stopped distilling off, once the temperature reached 100°C signalling the water being boiled, the distillation is stopped. This method is repeated for each triplicate, and then again for the 4 week fermentation batch.

Figure 1. diagram of distillation set up showing glassware and other equipment required. (Sustainable chemistry lab manual 2018, page 15)

For the distillation, all three replicates of the biomass were added together to get an average content. When distillation was complete, a small aliquot was taken and placed into a GC vial for analysis. GC analysis was then done to determine the purity of the bioethanol produced.

Results:

Glucose calibration:

Figure 2. Calibration curve for glucose standards

Glucose concentration:

Form figure 2, the equation was derived of

 

logpeak area=1.1309logglucose concentration+5.85876

to obtain the glucose concentration of the biomasses.

An example of this calculation is as follows,

logpeak area=1.1309logglucose concentrated+5.85876logglucose concentration=logpeak area5.858761.1309For potato 1.1,logglucose concentration=7.4742371775.858761.1309logglucose concentration=1.43glucose concentration=26.89 g/L

Table 1. Summarised data for glucose concentration for biomass samples

Fermentation time (weeks)

Biomass

Glucose concentration (g/L)

%RSD

1

Grass

4.64

31

Sugarcane

3.77

33

Paper

5.21

9

Potato

27.28

9

4

Grass

5.97

9

Sugarcane

3.78

39

Paper

4.90

41

Potato

26.53

6

The full tabulated data can be found in appendix 2.

Figure 3. Concentration of glucose from HPLC analysis after enzymatic hydrolysis.

Ethanol calibration data:

Figure 4. Ethanol calibration curve for ethanol standards

Ethanol concentration and percent yield:

Form this, the equation was derived of

peak area=5147.7ethanol concentration+308.71

to obtain the ethanol concentration of the biomasses.

An example of this calculation is as follows,

peak area=5147.7ethanol concentration+308.71ethanol concentration=peak area308.715147.7

For potato 1.1,

ethanol concentration=27546.39308.715147.7ethanol concentration=5.29 g/L

Using the following equation for the conversion of glucose to ethanol, 

   

C6H12O6 aq2C2H5OH aq +2CO2 (aq)

the percent yield of the conversion of glucose to ethanol was then found.

Table 2. Summarised data for ethanol concentration and percentage yield of glucose to ethanol for biomass sample.

Fermentation time (weeks)

Biomass

Ethanol concentration (g/L)

%RSD

Percentage yield of glucose to ethanol

1

Grass

7.90

40

114

Sugarcane

5.61

29

95

Paper

6.21

9

77

Potato

5.52

5

13

4

Grass

5.38

26

56

Sugarcane

5.88

42

103

Paper

5.34

25

86

Potato

6.95

15

17

From the QC data, the %RSD was 5 and the %RE was 17.

The full tabulated data can be found in appendix 3.

Figure 5. Summary of average concentration of ethanol produced during the one week fermentation and the percentage yield of glucose to ethanol conversion.

Figure 6. Summary of the average mol of ethanol produced during the four week fermentation and the percentage yield of glucose to ethanol conversion.

Efficiencies of production:

Table 3. Percentage yields and purity of bioethanol throughout experiment

Biomass

Percent efficinct of cellulose availability

Percent efficiency of glucose from cellulose production (%)

Percent efficiency of ethanol from glucose production (%)

Percent efficiency from cellulose to ethanol (%)

Potato

129.19

49.26

59.08

29.39

Paper

27.58

7.95

11.99

144.82

Grass

18.87

13.07

10.93

158.83

Sugarcane

11.66

10.59

8.99

193.15

Full data tabulated in appendix 4 and 5.

Purity of distilled bioethanol:

Potato: 96.9%

Paper: 84.4%

Grass: 98.9%

Sugarcane: 99.9%

Discussion:

From the HPLC analysis of the sugar profile of the biomasses as seen in figure 3, it was found that the potato samples had a much higher concentration of glucose than the others, with results of 27 g/L compared to 3.7-6 g/L. This is due to potato not containing any lignin, like the other biomasses, but instead having a starch component that is much more susceptible to a heat pre-treatment, allowing for more cellulose and hemicellulose to become available for hydrolysis. From this, using the dry biomass weights, as seen in appendix 1, determined glucose concentrations, and literature values for carbohydrate content of each biomass, the percentage efficiency of the pre-treatment process was determined. As seen in table 3, the availability of cellulose for hydrolysis is highest in potato at almost 130% of the literature values, while the other biomass samples were between 11-28%. Furthermore, table 3 shows that the conversion efficiency of cellulose to glucose is highest in potato at almost 50%, with the other biomasses being between 8-13%. This further cements the concept that the pre-treatment process that was employed was not harsh enough to break down enough lignin to access all possible glucose. Further experimentation with the use of different pre-treatments may yield higher conversion results.

The sugar analysis data was obtained with reference to the glucose calibration curve as depicted in figure 2. The relationship between concentration and response on an ELDS detector is routinely fit using a log log transformation of the data. However, this relationship has a lowed R2 value than that of a normal linear fit (0.9961). Despite this, the log log transformation calibration data and curve was used as, when actually looking at the data, the normal linear curve does not fit aswell to any of the lower concentration data, which is where a majority of the biomass glucose concentrations lie. Coupled with this, the R2 value of the calibration data used is 0.9873, which indicates a high precision.

For the fermentation aspect of this experiment, we decided to do two sets of fermentation, a one week set and then a four week set to see if there was any relationship between time and ethanol production. Figures 5 and 6 show the ethanol content and percentage yield for the biomass samples fermented for one and four weeks. The concentrations of ethanol amongst the samples are relatively similar, with values of 5.38-7.90 g/L. However, the efficiencies are drastically different. The one week fermentations are quite high for grass (134%), sugarcane (120%) and paper (94%), and quite low for potato (16%). The four week fermentation results rose slightly for sugarcane (7.6% higher) paper (14% higher), and potato (28.6% higher), however there was a drastic decrease in ethanol present in the grass sample (47% lower).

The reasoning behind why the ethanol yields for the potato samples are so low is mostly unknown. However, as this low yield was present for both of the different fermentation batches, the issue seems to arise due to the potato itself. There are two possible reasons that can be found. The first comes from the method of fermentation that was employed.  As we used a simple batch process whereby the yeast was added all at once at the beginning, and then simply left to ferment over time with no outside interaction, the fermentation process may have been repressed. Since conditions in the fermentation process change continuously due to the metabolism of the yeast, a repression can occur through the development of settlement layers which prevent the yeast from have continuous access to all available glucose, thereby leading to lower ethanol production. The second possibility is that the vast amount of glucose that was present allowed for rapid initial ethanol production causing an early inhibition to occur. This means that the ethanol that was produced killed off the yeast too early, halting further ethanol production. That being said, the actually reasoning behind this is unknown and further experimentation would need to be done to isolate the underlying causing.

The drastic decrease in ethanol content present in the four week fermentation compared to the one week fermentation appears to be due to the reversible oxidation of the ethanol to an aldehyde by alcohol dehydrogenases (ADHs) that were present in the yeast used. Within a fermentation process, glucose is glycolysised into pyruvate, a process that convers NAD+ into NADH, releasing it into the reaction. The pyruvate intermediate is then converted into acetaldehyde and CO2 with the acetaldehyde being reduced into ethanol by the ADH gene ADH1. This later step regenerates the NAD+ so that the glycolysis and general fermentation can continue. However, the yeast that was used in this experiment contains another ADH gene, ADH2. ADH2 is expressed when the sugar concentration is low, back converting the ethanol to acetaldehyde. In light of this, it can be seen that the fermentation of grass reaches this back conversion point between one and four weeks of fermentation, whereas the other biomass samples do not reach it until after four weeks.

The percent efficiency of the conversion of glucose to ethanol was calculated, as seen in table 3. Despite having the lowest yield of the biomasses, potato (17 and 13%), the efficiency of ethanol production was the highest amongst the biomass sample (60%). This is due to the sheer amount of glucose that was available to the yeast during fermentation. As the other biomass samples all had relatively the same glucose concentrations (3.7 to 6.0 g/L), their ethanol production efficiencies were relatively similar as well (9-12%). 

Due to the cycloheximide that was used in the experiment being 70% ethanol, some retroactive calculations were attempted to try and account for the ethanol that was already present before fermentation occurred. Despite this, some of the percentage yields for glucose to ethanol conversion were over 100%, meaning that this was not fully taken into account and errors may exist in the purity of the cycloheximide that was used.

The ethanol concentrations were obtained in reference to the calibration curve depicted in figure 4. This relationship was linear, with an R2 value of 0.9992, indicating a very high precision. QC data was also used for the ethanol concentrations, with a relative standard deviation of 5% and a relative error of 17%. This indicates that the data that was obtained was very precise, but not particularly accurate.

 After fermentation of ethanol, the biomass samples were distilled to determine the purity of the ethanol that was produced. The resulting purity was highest for sugarcane (99.9%) and lowest for paper (84.4%).

 Overall percentage efficiencies in the conversion of cellulose to ethanol were calculated, as seen in table 3. When using this experimental method, the conversion of carbohydrate content to ethanol was highest in sugarcane (193%) despite the fact it had the lowest available carbohydrates for hydrolysis (11.66%), thereby making it the best biomass feedstock to use for bioethanol production per gram of biomass.

Conclusion:

Due to its environmental benefits as well as the potential for it to become far more economically viable than fossil fuels, biofuel production such as bioethanol will grow rapidly. By employing an autoclave pre-treatment process and an enzymatic hydrolysis, a high concentration of glucose was able to be derived from potato (26.9 g/L) and a low concentration for sugarcane (3.77 g/L), grass (5.30 g/L), and paper (5.06 g/L). This was a result of the autoclave pre-treatment not being particuarly good at breaking down the lignin inside the cell wall of sugarcane, grass, and paper, to access the cellulose and hemicellulose. Fermentation of these biomasses resulted in high ethanol yield in sugarcane (124.91%) grass (102.51%), and paper (110.64%), and a low yield in potato (18.17%). Limiting fermentation factors caused potatoes values to be so low. It was also found that the point at which ADH2 back converts ethanol into acetaldehyde within the fermentation process occurred after four weeks for potato, paper, and sugarcane, whereas this occurs between one and four weeks in grass, leading to a lower ethanol yield. Overall, it was found that sugarcane (193%) had the highest efficiency of conversion between cellulose and ethanol per gram of biomass, and potato (29%) had the lowest. This leads to the conclusion that sugarcane is the best biomass to use for the production of bioethanol out of sugarcane, potato, paper, and grass clippings using this method. Further experimentation can be done by altering the pre-treatment and hydrolysis steps, catering them to the amount of lignin present, to further maximise the amount of bioethanol that can be produced per gram of biomass.

References:

[1] Cadenas, A. and Cabezudo, S. Biofuels as sustainable technologies: Perspectives for less developed countries. Technol. Forecasting Social Change, 58: 83–103

[2] Mohr, A. and Raman, S. 2013. Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels. Energy policy, 63(100), 114–122

[3] Sims, R. Mabee, W. Saddler, J. and Taylor, M. 2010. An overview of second generation biofuel technologies. Bioresource Technology, 101: 1570-1580.

[4] Taherzadeh, M. and Karimi, K. 2007. Enzymatic-based hydrolysis processes for ethanol from lignocellulosic materials: a review. BioResources, 2: 707-738

[5] Yeoman, C. Han, Y. Dodd, D. Schroeder, C. Mackie, R. Cann, I. 2010. Chapter 1- Thermostable Enzymes as biocatalysts in the biofuel industry. Advances in Applied Microbiology, 20: 1-55.

[6] Obeng, A. Premjet, D. Premjet, S. 2018. Combining autoclaving with mild alkaline solution as a pretreatment technique to enhance glucose recovery from the invasive weed Chloris barbata. Biomolecules, 9: 120-133

[7] Hamelinck, C. Hooijdonk, G. and Faaij, A. 2005. Ethanol from lignocellulosic biomass: techno-economic performance in short-, middle-, and long-term. Biomass Bioenergy, 28: 384-410.

[8] Badger, P. 2002. Ethanol from cellulose: a general review. Trends New Crops New Uses, 1: 17-21.

[9] Balat, M. Balat, H. and Öz, C. 2008. Progress in bioethanol processing. Prog. Energy Combust, Sci., 34: 551-573

[10] Klinke, H. Thomsen, B. and Ahring, B. 2004. Inhibition of ethanol-producing yeast and bacteria by degradation products produced during pre-treatment of biomass. Appl. Microbiol. Biotechnol, 66: 10-26.

[11] Yang, B. Dai, Z. Ding, S. and Wyman, C. 2011. Enzymatic hydrolysis of cellulosic biomass. Biofuels, 2(4): 421-450.

12] Kang, Q. Appels, L. Tan, T. and Dewil, R. 2014. Bioethanol from lignocellulosic biomass: current findings determine research priorities. The Scientific World Journal, 2014.

Appendix:

Appendix 1. Complete list of biomass weights used.

 

Tube number

Mass (g)

Tube number

Mass (g)

Sugarcane

1.1

0.5079

2.1

0.5065

1.2

0.5019

2.2

0.4983

1.3

0.5175

2.3

0.5048

Paper

1.1

0.5044

2.1

0.5107

1.2

0.5162

2.2

0.5109

1.3

0.5026

2.3

0.5067

Grass

1.1

0.4980

2.1

0.5009

1.2

0.5003

2.2

0.4981

1.3

0.5187

2.3

0.5022

Potato

1.1

0.5060

2.1

0.4982

1.2

0.5053

2.2

0.4992

1.3

0.5064

2.3

0.5002

Appendix 2. Summarised data for glucose concentration calculations for biomasses

Biomass

Peak area

Log (peak area)

Log (glucose concentration)

Glucose concentration (g/L)

Mol (glucose)

%RSD

Potato

1.1

29801435

7.47

1.43

26.89

1.49 x 10-4

9

1.2

27573272

7.44

1.40

25.10

1.39 x 10-4

1.3

33553698

7.53

1.48

29.86

1.66 x 10-4

Average for Po1

29.71

1.51 x 10-4

2.1

29715069

7.47

1.43

26.82

1.49 x 10-4

6

2.2

31119319

7.49

1.45

27.93

1.55 x 10-4

2.3

27267042

7.44

1.40

24.85

1.38 x 10-4

Average for Po2

28.89

1.47 x 10-4

Grass

1.1

5422137

6.73

0.78

5.96

3.31 x 10-5

31

1.2

4266297

6.63

0.68

4.82

2.68 x 10-5

1.3

2625852

6.42

0.50

3.14

1.74 x 10-5

Average for G1

5.44

2.57 x 10-5

2.1

6033871

6.78

0.82

6.55

3.64 x 10-5

9

2.2

5336062

6.73

0.77

5.87

3.26 x 10-5

2.3

4933546

6.69

0.74

5.48

3.04 x 10-5

Average for G2

6.95

3.31 x 10-5

Sugarcane

1.1

4319073

6.64

0.69

4.87

2.70 x 10-5

33

1.2

1931826

6.29

0.38

2.39

1.33 x 10-5

1.3

3504009

6.54

0.61

4.05

2.25 x 10-5

Average for S1

4.49

2.09 x 10-5

39

2.1

4465644

6.65

0.70

5.02

2.79 x 10-5

2.2

3630123

6.56

0.62

4.18

2.32 x 10-5

2.3

1694777

6.23

0.33

2.13

1.18 x 10-5

Average for S2

4.48

2.10 x 10-5

Paper

1.1

4118638

6.61

0.67

4.67

2.59 x 10-5

9

1.2

4786227

6.68

0.73

5.34

2.96 x 10-5

1.3

5086305

6.71

0.75

5.63

3.13 x 10-5

Average for Pa1

6.11

2.89 x 10-5

2.1

5746102

6.76

0.80

6.27

3.48 x 10-5

41

2.2

2090842

6.32

0.41

2.57

1.42 x 10-5

2.3

5316587

6.73

0.77

5.86

3.25 x 10-5

Average for Pa2

5.76

2.72 x 10-5

Appendix 3. Summarised data for ethanol concentration calculations for biomasses

Biomass

Peak area

Ethanol concentration (g/L)

Mol(ethanol)

%RSD

% yield of ethanol from glucose

Potato

1.1

27546.39

5.29

1.15 x 10-4

4

15.89

1.2

28940.81

5.56

1.21 x 10-4

1.3

29705.87

5.71

1.24 x 10-4

Average Po1

5.52

1.20 x 10-4

2.1

39064.79

7.53

1.63 x 10-4

12

20.44

2.2

38173.23

7.36

1.60 x 10-4

2.3

31022.85

5.97

1.30 x 10-4

Average Po2

6.95

1.51 x 10-4

Grass

1.1

56852.58

10.98

2.38 x 10-4

34

133.98

1.2

35439.68

6.82

1.48 x 10-4

1.3

30671.36

5.90

1.28 x 10-4

Average G1

7.90

1.72 x 10-4

2.1

29635.62

5.70

1.24 x 10-4

20

71.04

2.2

21725.53

4.16

9.03 x 10-5

2.3

32664.31

6.29

1.36 x 10-4

Average G2

5.38

1.17 x 10-4

Sugarcane

1.1

32279.28

6.21

1.35 x 10-4

23

120.05

1.2

21516.85

4.12

8.94 x 10-4

1.3

33831.26

6.51

1.41 x 10-4

Average S1

5.61

1.22 x 10-4

2.1

32279.28

8.18

1.78 x 10-4

34

129.77

2.2

42436.07

4.74

1.03 x 10-4

2.3

24722.71

4.71

1.02 x 10-4

Average S2

5.88

1.28 x 10-4

Paper

1.1

34110.01

6.57

1.43 x 10-4

7

94.06

1.2

32950.08

6.34

1.38 x 10-4

1.3

29705.87

5.71

1.24 x 10-4

Average Pa1

6.21

1.35 x 10-4

2.1

23523.49

4.51

9.79 x 10-4

20

107.21

2.2

33883.09

6.52

1.42 x 10-4

2.3

25963.4

4.98

1.08 x 10-4

Average Pa2

5.34

1.16 x 10-4

Appendix 4. Efficiency of pre-treatment 

Biomass

Dry mass weight (g)

Glucose concentration (g/L)

Mol (glucose)

Mol (cellulose)

Mass of cellulose (g)

Cellulose content (%)

Reported cellulose content (%)

Efficiency of pre-treatment (%)

Grass

0.50

5.30

3.27 x 10-4

3.63 x 10-4

0.059

11.70

62

18.87

Paper

0.51

5.06

3.13 x 10-4

3.46 x 10-4

0.056

11.03

40

27.58

Sugarcane

0.51

3.77

2.33 x 10-4

2.58 x 10-4

0.042

8.28

71

11.66

Potato

0.50

26.91

1.66 x 10-3

1.84 x 10-3

0.30

59.43

46

129.19

Appendix 5.

Biomass

Mol (ethanol)

Mass of ethanol (g)

Glucose to ethanol conversion yield (%)

Cellulose to ethanol conversion yield (%)

Grass

1.44 x 10-4

0.0066

10.93

158.83

Paper

1.25 x 10-4

0.0058

11.99

144.82

Sugarcane

1.25 x 10-4

0.0057

8.99

193.15

Potato

1.35 x 10-4

0.0062

59.08

29.39

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