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Main and interaction effects of process parameters on the ethanol production capacity of food-waste leachate by Saccharomyces Cerevisiae

Abstract

In the city of Ulsan, South Korea, Food-Waste Leachate (FWL) is let off into the ocean, which eventually poses a severe threat to the marine environment. Preliminary results suggest that this leachate, when subjected to an optimized fermentation process can generate ethanol. A 23-factorial design was employed, and the effects of temperature (30-40°C), pH (4–6), and reducing sugar concentration (RSC, 45–75 g L−1) on ethanol yield was investigated through controlled batch experiments using Saccharomyces cerevisiae (KCTC-7904), a species of budding yeast. A maximum yield of 0.31 g ethanol g−1 RSC corresponding to an ethanol concentration of 23.56 g L−1 was achieved under the following test conditions: temperature − 40°C, pH − 4.0, and RSC − 75 g L−1. Among the different parameters studied, both temperature and RSC had a strong synergistic effect on ethanol yield, while the effects due to changes in pH values were minimal. Anew, under the test conditions, the 2-way interaction effect between temperature × pH and the 3-way interaction between temperature × pH × RSC were negative, while all other interactions, were positive and also found to be statistically significant at 5% probability (p value < 0.05). The results were represented by a suitable model equation, which could be easily applied for scale-up to produce value-added ethanol, by sterilizing FWL prior to fermentation. The practical implications of this research have been stated.

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Correspondence to Hung-Suck Park.

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Le Man, H., Rene, E.R., Behera, S.K. et al. Main and interaction effects of process parameters on the ethanol production capacity of food-waste leachate by Saccharomyces Cerevisiae . KSCE J Civ Eng 15, 1015–1022 (2011). https://doi.org/10.1007/s12205-011-1177-x

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  • DOI: https://doi.org/10.1007/s12205-011-1177-x

Keywords

  • ethanol production
  • food-waste leachate
  • Saccharomyces cerevisiae
  • fermentation
  • factorial design