Table 4.
Parameter | Mean | P10 | P90 | PDF Typeg |
---|---|---|---|---|
GS farming |
|
|
|
|
Energy use, MJ/kilogram of grain[ [27] |
0.68 |
0.40 |
0.97 |
Normal |
N, gram/kilogram of grain [9] |
24 |
19 |
29 |
Weibull |
P2O5, gram/kilogram of grain [9] |
6.4 |
1.3 |
12 |
Logistic |
K2O, gram/kilogram of grain [9] |
0.70 |
0.16 |
1.2 |
Uniform |
Grain yield, tonne/hectare [9] |
3.4 |
2.5 |
4.4 |
Lognormal |
N content of GS stalk, gram/kilogram of grain [28] |
10 |
7.6 |
11 |
Triangular |
N2O conversion rate of N fertilizer:% [20] |
1.5 |
0.41 |
3.0 |
Weibull |
SS farming | ||||
Energy use, MJ/wet tonne of SS [20] |
100 |
90.4 |
110 |
Normal |
N, gram/wet kilogram of SS [29] |
1.5 |
1.1 |
1.8 |
Lognormal |
P2O5, gram/wet kilogram of SS [29] |
0.56 |
0.37 |
0.76 |
Normal |
K2O, gram/wet kilogram of SS [29] |
0.89 |
0.58 |
1.0 |
Weibull |
Herbicide, gram/wet kilogram of SS [29] |
0.069 |
0.058 |
0.080 |
Lognormal |
Biomass yield, wet tonne/hectare [29] |
76 |
58 |
95 |
Uniform |
Grain yield, wet tonne/hectare [30] |
1.8 |
1.0 |
2.6 |
Normal |
Sugar yield, tonne/hectare [29] |
7.0 |
4.9 |
9.4 |
Lognormal |
Bagasse yield, wet tonne/hectare [29] |
12 |
8.7 |
15 |
Gamma |
FS farming | ||||
Energy use, MJ/wet tonne of FSa |
113 |
102 |
124 |
Normal |
N, gram/wet kilogram of FS [29,31] |
2.2 |
1.2 |
3.2 |
Logistic |
P2O5, gram/wet kilogram of FS [29] |
0.41 |
0.34 |
0.49 |
Uniform |
K2O, gram/wet kilogram of FS [29] |
0.82 |
0.67 |
0.96 |
Uniform |
Herbicide, gram/wet kilogram of FS [29] |
0.067 |
0.056 |
0.079 |
Uniform |
FS dry matter yield, tonne/hectare [9] |
23 |
11 |
36 |
Weibull |
Ethanol Production | ||||
Grain-based ethanol production | ||||
Ethanol plant energy use, MJ/liter of ethanolb[20] |
8.1 |
6.7 |
9.5 |
Normal |
Ethanol plant energy use, MJ/liter of ethanolc[19] |
5.1 |
4.2 |
6.0 |
Normal |
Ethanol plant energy use, MJ/liter of ethanold |
8.3 |
6.9 |
9.8 |
Normal |
Ethanol plant energy use, MJ/liter of ethanole |
5.3 |
4.4 |
6.2 |
Normal |
Ethanol production yield, liter/kilogram of grain [31-35] |
0.42 |
0.40 |
0.44 |
Normal |
DDGS yield, kilogram /liter of ethanol [20] |
0.68 |
0.61 |
0.74 |
Triangular |
WDGS yield, kilogram /liter of ethanol [20] |
1.9 |
1.7 |
2.1 |
Triangular |
Enzyme use, kilogram/tonne of grain [20] |
1.0 |
0.94 |
1.2 |
Normal |
Yeast use, kilogram/tonne of grain [20] |
0.36 |
0.32 |
0.40 |
Normal |
Sugar-based ethanol production |
|
|
|
|
Ethanol plant energy use, MJ/liter of ethanol [36] |
9.2 |
9.0 |
9.3 |
Uniform |
Electricity demand of ethanol production, MJ/liter of ethanolf |
1.4 |
1.3 |
1.5 |
Uniform |
Ethanol production yield, liter/kilogram of sugar [29,31,32,35,37-44] |
0.58 |
0.53 |
0.62 |
Lognormal |
Yeast use, kilogram/tonne of sugar [42-45] |
5.2 |
4.2 |
6.2 |
Uniform |
Cellulosic ethanol production |
|
|
|
|
Ethanol production yield, liters/dry kilogram of bagasse [20] |
0.38 |
0.33 |
0.42 |
Normal |
Enzyme use, kilogram/dry tonne of bagasse [46] |
16 |
9.6 |
23 |
Triangular |
Yeast use, kilogram/dry tonne of bagasse [46] | 2.5 | 2.2 | 2.7 | Triangular |
a Scaled based on yield of FS and SS to the SS farming energy use;
b For FNG-fueled ethanol plants, producing DDGS as the co-product;
c For FNG-fueled ethanol plants, producing WDGS as the co-product;
d For RNG-fueled ethanol plants, producing DDGS as the co-product;
e For RNG-fueled ethanol plants, producing WDGS as the co-product;
f Based on correspondence with Prof. Jaoquim Seabra;
g We employed EasyfitTM, a curve-fitting toolbox [47], to find the probability distribution type from a pool of 55 distributions, e.g. Normal distribution, Weibull distribution, Uniform distributions, etc., that best fits each set of the data points we collected for each parameter. For many parameters, we also applied a weighting factor to fit the distribution. The higher the value of the weighting factor corresponding to a sample value of the parameter, the higher possibility the parameter has the sample value in the probability distribution function to be fitted for the parameter. The toolbox uses one of the four well-known methods to estimate distribution parameters based on available sample data: maximum likelihood estimates; least squares estimates; method of moments; and method of L-moments. The toolbox calculates the goodness-of-fit statistics including the Kolmogorov Smirnov statistic, the Anderson Darling Statistic, and the Chi-squared statistic, for each of the fitted distributions. Then, the toolbox ranks the distributions based on the goodness-of-fit statistics. We then selected the distribution with the highest rank primarily based on the Kolmogorov Smirnov statistic. The curve-fitting requires at least five data points for each parameter. We collected sufficient data for the parameters in Table 4 to meet this criterion, except for N content of GS stalk, herbicide use for FS farming, and electricity demand of ethanol production, which we were able to collect only two or three data points. Accordingly, we assumed a uniform or triangular distribution for these parameters.