음식물쓰레기 당화액을 이용한 바이오에탄올 생산 최적화 연구

Abstract

The aim of this study to investigate the optimal conditions for of enzymatic saccharification and bio-ethanol production from food waste. The B. amyloliquefaciens K5 could produce 96 g/L of reducing sugar from 15% soluble starch for 9 day, and 18.6 g/L of reducing sugar from food wastes for 7 day at 35℃, respectively. When B. amyloliquefaciens K5 and S. cerevisiae KA4 were inoculated simultaneously into the two different media, 23 g/L of ethanol was produced from soluble starch medium, and 12.9 g/L of ethanol was produced from food wastes medium, respectively. When Spirizyme Plus FG, a commercially available enzymes, was used for enzymatic saccharication of cooked rice wastes, the obtained optimal saccharification conditions were as follows: substrate concentration, 350 g-tCOD/L; enzyme concentration, 400 AGU/L; reaction time, 4h; and temperature, 55oC. Under the optimal condition, 250 g/L reducing sugar was produced from 350 g-tCOD/L cooked rice waste. This hydrolyzates was subjected to the ethanol fermentation under anaerobic conditions using S. cerevisiae KA4. This strain was able to produce 106g/L of ethanol during 4 days at 35oC. When food wastes treated with Spirizyme Plus FG for enzymatic saccharification, the optimal conditions were obtained as follows: substrate concentration, 83 g-tCOD/L; enzyme concentration, 400 AGU/L; reaction time, 4h; and temperature, 55oC. Under the optimal condition, 105 g/L of reducing sugar was produced from 80 g-tCOD/L of food waste. This hydrolyzates was subjected to the ethanol fermentation under anaerobic conditions using S. cerevisiae KA4. Finally, KA4 was able to produce 52 g/L of ethanol during 24h at 35oC. When hydrolyzates were used as the production medium, ethanol production yield was 0.50 g-ethanol/g-reducing sugar. To produce reducing sugar and bio-ethanol from food waste, enzymatic saccharification and bio-ethanol fermentation conditions were optimized by a response surface methodology (RSM) based on the 23 factorial central composite design (CCD). In case of reducing sugar production, RSM was adopted to derive a statistical model for the individual and interactive effects of pH, enzyme reaction temperature and enzyme concentration. This method was efficient; because only 20 experiments were necessary to assess these conditions. Model adequacy was very satisfactory, because coefficient of determination (R2) of response surface regression equation for reducing sugar production was 0.95. Optimal conditions for maximum reducing sugar production were determined as follows: pH, 5.12; enzyme reaction temperature, 46℃; and enzyme concentration, 0.156% (v/v). The model predicted that maximum reducing sugar production which can be obtained using the above optimal conditions of variables was 1.026 g-reducing sugar/g-tCOD. Similarly, experimental result of reducing sugar production was obtained as 1.050 g-reducing sugar/g-tCOD and this result was in close agreement with the model prediction. In case of bio-ethanol production, RSM was adopted to derive a statistical model for the individual and interactive effects of pH, temperature and reaction time. As a coefficient of determination (R2) of response surface regression equation for bio-ethanol production was 0.94. Optimal conditions for maximum bio-ethanol production were determined as follows: pH, 5.4; temperature, 35.45℃; and reaction time, 12h. The model predicted that maximum bio-ethanol production which can be obtained using the above optimal conditions of variables was 54.9 g/L. Similarly, experimental result of bio-ethanol production was obtained as 56.5 g/L from reducing sugar of 110 g/L and this result was in close agreement with the model prediction.