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Hem Joshi |
Optimization of Ethanol Production from Peach Waste
Hem Joshi, Biosystems Engineering, Clemson University
Terry Walker, Associate Professor, Biosystems Engineering, Clemson University
John Nghiem, Adjunct Professor, Biosystems Engineering, Clemson University
Abstract
The interest in the production of renewable energy sources like ethanol from agricultural wastes and feed stocks has gained interest in the last decade. One such feed stock produced in large amounts in the southern part of US is peaches. South Carolina is the second largest producer of peaches in US. The large production of peaches is also accompanied by large amounts of peaches (20 million lbs of the 200 million lbs of peaches harvested) that have been damaged in such a way that cannot be sold and are therefore wasted. This peach waste has high organic value with accessible sugars. The sugars present in peach waste can be used by S. cerevesia to produce ethanol. Thus the peach waste, which is currently a disposal problem, can be used to produce ethanol for use as a biofuel. This study will investigate the use of peach waste as an ethanol feedstock by focusing on characterizing and optimizing the utilization of peach waste for production of ethanol at shake flask and fermenter scale.
Introduction
The purpose of this project is to utilize the peach waste (spoiled and blemished whole peaches) from farms to produce ethanol for use as a biofuel. South Carolina is the second largest producer of peaches in the US. There are more than 200 million pounds of peaches harvested in the state in a normal year (1). This large production is accompanied with large amount of rotten and spoiled peaches produced as peach waste (about 20 million pounds). This waste is subject to disposal problems under strict control of EPA. The peach waste produced has a high organic value with a greater percentage of it being the sugars (4.6 %-9.6 %) and important sugars being glucose, fructose, sucrose, xylose, sorbitol, xylitol, inositol (2).
Current work
The current work mainly focuses on optimization of ethanol production from dry peaches using response surface method. Response surface method is the most common statistical method used for optimization. Three factors that were studied for the optimization process are enzyme concentration, temperature and pH. The range for these factors were fixed based on previous research studies. These factors were independent of each other in the working range. The response variable was the amount of ethanol produced per gm of dry peach. Response surface method was used to find the “best” combination of levels for the factors studied to find the optimum value of the response variable.
Materials and methods
The peaches (Titan farm) were dried so as to free them from any moisture content. All the experiments were carried out in shaker flasks. The working volume was 25 ml (5 grams of dried peach in 25 ml of distilled water). The dry peaches were hydrolyzed using an enzyme complex at experimental conditions for 24 hours followed by fermentation with S.Cerevesia at pH of 4 and at 32°C for 2 days. Acid hydrolysis was skipped, as there is hardly any lignin present in the peach pulp. Since 3 treatment factors were studied, a total of 20 experimental runs were conducted. The experimental runs were conducted randomly. Response-surface methodology (RSM) was used, because it allows the simultaneous consideration of many variables at different levels and the interactions between those variables, using a smaller number of experimental runs than conventional procedures. It is a sequential process that usually starts at the current operating conditions and requires 3 stages to reach optimum conditions as rapidly and as efficiently as possible. It is usually accomplished in three stages: 1) The first stage is to conduct runs of the experiment and then to determine the direction to take in order to move towards the optimal value. 2) The second stage is to perform several runs in the direction indicated by the first stage until we approach the optimal value. 3) The third stage fits an equation of the response surface in the area of the optimal value. The results of the experimental runs will be analyzed using the GLM procedure in Statistical Analysis System (SAS), to find out the combination of three factors studied which will maximize the ethanol produced.
Results and Conclusion
The following table on page four lists the resulting percent yield ethanol from experiments conducted under varying conditions.
Table 1. Ethanol yield for various treatments of enzyme, pH and temperature.
Treatment/replicate |
Enzyme
(grams) |
pH |
T (°C) |
%Yield (g ethanol/g dry peach) |
Factorial point 1 |
0.0121 |
3.746 |
31.07 |
19.11 |
Factorial point 2 |
0.0329 |
3.746 |
31.07 |
24.29 |
Factorial point 3 |
0.0121 |
5.054 |
31.07 |
20.23 |
Factorial point 4 |
0.0329 |
5.045 |
31.07 |
24.02 |
Factorial point 5 |
0.0121 |
3.746 |
48.92 |
20.92 |
Factorial point 6 |
0.0329 |
3.746 |
48.92 |
24.88 |
Factorial point 7 |
0.0121 |
5.054 |
48.92 |
21.08 |
Factorial point 8 |
0.0329 |
5.054 |
48.92 |
24.12 |
Axial point 1 |
0.0050 |
4.400 |
40.00 |
16.26 |
Axial point 2 |
0.0400 |
4.400 |
40.00 |
25.69 |
Axial point 3 |
0.0225 |
3.300 |
40.00 |
17.01 |
Axial point 4 |
0.0225 |
5.500 |
40.00 |
25.98 |
Axial point 5 |
0.0225 |
4.400 |
25.00 |
17.96 |
Axial point 6 |
0.0225 |
4.400 |
55.00 |
24.57 |
Center point 1 |
0.0225 |
4.400 |
40.00 |
21.02 |
Center point 2 |
0.0225 |
4.400 |
40.00 |
20.55 |
Center point 3 |
0.0225 |
4.400 |
40.00 |
20.24 |
Center point 4 |
0.0225 |
4.400 |
40.00 |
21.63 |
Center point 5 |
0.0225 |
4.400 |
40.00 |
21.01 |
Center point 6 |
0.0225 |
4.400 |
40.00 |
21.89 |
Reaction temperature, pH and enzyme concentration each affected the ethanol production. The relationship of ethanol yield with all 3 factors studied was found to be curvilinear with a positive linear and negative quadratic coefficient. The significant interaction term between 3 variables indicate that these factors were not affecting percentage ethanol yield independently. Thus, the effect of one factor on percentage ethanol yield depends on the specific level of the other factor. The maximum ethanol production was found to be at 25.98 grams of ethanol per gram of dry peach and this was obtained at an enzyme concentration of 0.0225 grams, pH of 5.5 and reaction temperature of 40°C. These results were based on only one set of experimental runs for the experimental design. |
