초록
<P><B>Abstract</B></P> <P>Lignocellulosic materials are good feedstocks for ethanol production. Mathematical models give information about kinetic-metabolic nature of fermentation. The objectives of this study were to determine the chemical composition of non-detoxified tea processing waste hydrolysate (ND-TPWH) and detoxified tea processing waste hydrolysate (D-TPWH), to generate the ethanol from the D-TPWH, and to model the ethanol fermentation in D-TPWH using models including modified Gompertz, re-modified Gompertz, modified logistic, re-modified logistic, modified Richards, re-modified Richards, Stannard, Baranyi, Weibull, and Morgan-Mercer-Flodin. Results indicated that 11.91% of <SMALL>D</SMALL>-glucuronic acid, 7.28% of acetic acid, 98.12% of hydroxymethylfurfural, and 76.88% of phenolics were adsorbed by detoxification process. Ethanol yields by <I>Saccharomyces cerevisiae</I> and <I>Scheffersomyces stipitis</I> (ATCC 58784 and ATCC 58785) were 35.9, 38.98, and 33.87%, respectively. Regarding modeling, depending on the model comparison results including root-mean-square-error, mean-absolute-error, and regression coefficient, the experimental data of ethanol production and sugar consumption were successfully forecasted using Baranyi and Weibull models for <I>S. cerevisiae</I>; using Morgan-Mercer-Flodin model for <I>S. stipitis</I> (ATCC 58784); and using Stannard model for <I>S. stipitis</I> (ATCC 58785), respectively. Consequently, this was the first report on the ethanol production from D-TPWH and its modeling. TPW can be a good feedstock for ethanol production by the xylose-fermenting yeasts. Suitable flexible models could be applied for more progress of ethanol production process in D-TPWH.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Inhibitors were effectively removed by activated charcoal detoxification method. </LI> <LI> The highest ethanol yield was 38.98% with <I>S. stipitis</I> (ATCC 58784). </LI> <LI> BR and W models fit well the data of <I>P</I> and <I>S</I> for <I>S. cerevisiae</I>, respectively. </LI> <LI> MMF model fit well the observed data of <I>P</I> and <I>S</I> for <I>S. stipitis</I> ATCC 58784. </LI> <LI> ST model fit well the observed data of <I>P</I> and <I>S</I> for <I>S. stipitis</I> ATCC 58785. </LI> </UL> </P>