Modelling of batch biomethanation process for maximizing income based on values of consumed and produced gases

다국어 초록 (Multilingual Abstract)

Economic estimation of an environmental-friendly biomethanation process based on economic values of consumed and produced gases would be a unique attitude. In this paper, time and space dependent concentration profiles of components involved in a batch process, designed for biomethanation, were predicted through a mass transfer modelling. The reaction terms used in the modeling required bio-kinetic parameters of max, m, kL, YC/L, YX/L, and YP/L which were globally optimized via a predefined algorithm using some experimental data as 0.0987 day1, 0.1374 day1, 1.5422mole m3, 1.3636, 0.0183, 0.0908. Upon model verification, process income was calculated for a long-term scenario under a variety of factors and maximized through response surface methodology. The maximum income achieved was $0.4/m3 bioreactor. A term carbon subsidy was considered in the income equation in order to find a break-even income for subsidy value of $363/ton CO2. Sensitivity analysis revealed that the amount of carbon subsidy directly influenced the selection of low or high levels of some process parameters to make the process profitable. In addition, it was found that pressure and liquid volume were the most important factors to achieve maximum income when $30 and $300/ton CO2 carbon subsidy were allocated to the process, respectively.

참고문헌 (Reference)

1 C. Ampelli, 373 : 20140177-, 2015

2 G. Glenk, 4 : 216-, 2019

3 C. Steinlechner, 57 : 44-, 2018

4 M. D. Rosa, 31 : 60-, 2018

5 J. W. Schmelzer, 122 : 074511-, 2005

6 S. P. Cadogan, 59 : 519-, 2014

7 Q. Chai, 203 : 311-, 2018

8 H. Naims, 23 : 22226-, 2016

9 M. Bailera, 69 : 292-, 2017

10 G. Iaquaniello, 43 : 17069-, 2018

11 J. Squalli, 127 : 479-, 2017

12 H. Blanco, 232 : 323-, 2018

13 E. Inkeri, 209 : 95-, 2018

14 K. Ghaib, 81 : 433-, 2018

15 R. Tarkowski, 105 : 86-, 2019

16 S. Savvas, 202 : 238-, 2017

17 I. Diaz, 185 : 246-, 2015

18 W. Merkle, 232 : 72-, 2017

19 D. Strubing, 245 : 1176-, 2017

20 Y. Huang, 334 : 1383-, 2018

21 A. Bensmann, 134 : 413-, 2014

22 G. Leonzio, 290 : 490-, 2016

23 J. Y. Leu, 89 : 1879-, 2011

24 S. Osfouri, 4 : 14-, 2016

25 W. Hayduk, 20 : 611-, 1974

26 D. Fairbanks, 42 : 471-, 1950

27 T. R. Marrero, 1 : 3-, 1972

28 S. Weissman, 54 : 1881-, 1971

29 T. Chu, 19 : 299-, 1974

30 R. Fernández-Prini, 32 : 903-, 2003

31 G. Luo, 109 : 2729-, 2012

32 J. Zabranska, 36 : 707-, 2018

33 J. Owens, 3 : 419-, 1987

34 J.G. Olivier, "Trends in global CO2 and total greenhouse gas emissions" 2018

35 J. Biswas, "Industry interactive innovations in science, engineering and technology" Springer 2018

36 Jae Hyung Kim, "Factors affecting biological reduction of CO2 into CH4 using a hydrogenotrophic methanogen in a fixed bed reactor" 한국화학공학회 32 (32): 2067-2072, 2015

37 B. Dudley, "BP statistical review of world energy" 2018