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    Bio-oil production via fast pyrolysis of cassava residues combined with ethanol and volcanic rock in a free-fall reactor
    (2023-01-01)
    Rueangsan, Koson
    ;
    Heman, Adcha
    ;
    Kraisoda, Pakkip
    ;
    Tasarod, Homhuan
    ;
    Duanguppama, Keyoon
    Pyrolysis of waste biomass to produce usable energy has the potential to, in part, alleviate the consumption of limited fossil fuel resources. We describe bio-oil production via fast pyrolysis of cassava residues, a mostly wasted byproduct of cassava crops. The waste biomass was combined with two readily available additives—volcanic rock and ethanol—in a free fall reactor to generate bio-oil, char and syngas. Using cassava stems as the raw feed-stock we tested pyrolysis reaction temperatures in the range 450–500 °C in a free fall reactor using a N<inf>2</inf> flow. Analysis of the pyrolysis products should little variation in this range, so analyses for the effects of ethanol and volcanic rock as additives were tested at 500 °C. The bio-oil yield ranged between 58 and 60%, char represented 17–19% and gas 21–24%. With volcanic rock, the higher heating value was significantly higher at 23.6 MJ/kg compared to ~19 MJ/kg for cassava alone or added ethanol. Readily available, inexpensive, naturally occuring zeolites in the volcanic rock led to significant extra degradation of the biomass, under the same experimental conditions, leading to this improvement.
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    Evaluation of acetic acid and ethanol concentration in a rice vinegar internal venturi injector bioreactor using Fourier transform near infrared spectroscopy
    (2019-12-01)
    Phanomsophon, Thitima
    ;
    Sirisomboon, Panmanas
    ;
    Lapcharoensuk, Ravipat
    ;
    Shrestha, Bim
    ;
    Krusong, Warawut
    In the process of fermenting rice vinegar, the concentration of acetic acid and ethanol concentration must be measured for monitoring of the total concentration. Near infrared spectroscopy has been used to rapidly monitor the concentration of acetic acid and ethanol concentration daily during 10 cycles of the fermentation process. The model was developed using partial least squares regression. For predicting concentration of acetic acid with near infrared spectroscopy, the coefficient of determination (R<sup>2</sup>), root mean square error of calibration, root mean square error of cross validation, ratio of standard error of validation to standard deviation, and bias was 0.96, 2.30 g L<sup>−1</sup>, 2.44 g L<sup>−1</sup>, 1.11 g L<sup>−1</sup>, and 5.56, respectively. For ethanol concentration, the value of R<sup>2</sup>, root mean square error of calibration, root mean square error of cross validation, bias and ratio of prediction to deviation were predicted to be 0.94, 3.15 g L<sup>−1</sup>, 2.73 g L<sup>−1</sup>, −0.40 g L<sup>−1</sup>, and 4.04, respectively. However, both models provided fair performance when tested with an external set of samples, indicating that the models could be applied for rough screening.