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

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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 μmax, m, kL, YC/L, YX/L, and YP/L which were globally optimized via a predefined algorithm using some experimental data as 0.0987 day1, 0.1374 day1, 1.5422 mole 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.

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Abbreviations

\({\rm{C}}_{L_{ini}}\) :

initial concentration in liquid phase mole·m−3]

\({\rm{C}}_{g_{ini}}\) :

initial concentration in gas phase [mole·m−3]

D̅ij :

multicomponent Fick diffusivity [m2·s−1]

C L :

time and space dependent concentration in the liquid phase [mole·m−3]

CLave :

space-averaged concentration in liquid phase [mole· m−3]

CLs :

liquid concentration on gas-liquid interface [mole·m−3]

CX :

time and space dependent concentration of biomass [mole·

m−3] Cg :

time and space dependent concentration in gas phase [mole· m−3]

C g.ave :

space-averaged concentration in gas phase [mole·m−3]

C gs :

gas concentration on gas-liquid interface [mole·m−3]

DL,i :

diffusivity coefficient of gases in liquid phase [m2·s−1]

Deff,i :

effective diffusion coefficient of component i in gas mixture [m2 ·s−1]

DTi :

thermal diffusion coefficient

D ij :

diffusion coefficient of pair gases [m2 ·s−1]

NL :

liquid diffusive flux [mole·m2·s−1]

N g :

gas diffusive flux [mole·m−2·s−1]

Y i/L :

theoretical maximal (C)-molar yields on the basis of hydro gen

Y mod :

response value obtained via modelling

Y prd :

response value predicted by RSM

kL :

saturation constant [mole· m−3]

qmaxL :

maximum H2 consumption [mole·m−3]

rH2 :

H2 consumption rate [mole·m-3 ·s−1]

rC :

CO2 consumption rate [mole·m ·s

rP :

CH4 production rate [mole· ·s

rX :

biomass growth rate [mole·m−3 ·s−1]

yj :

mole fraction in gas phase

ω j :

mass fraction

▽VP:

pressure gradient

▽T:

temperature gradient

Hr:

dimensionless Henrys constant

m:

microorganism maintenance coefficient [day−1 ]

u:

velocity [m·s−1]

p :

mixture density [Kg·m−3]

μ max :

maximum growth rate of biomass [day−1]

References

  1. C. Ampelli, S. Perathoner and G. Centi, Philos. Trans. R. Soc. A, 373, 20140177 (2015).

    Article  Google Scholar 

  2. C. Steinlechner and H. Junge, Angew. Chem. Int. Ed., 57, 44 (2018).

    Article  CAS  Google Scholar 

  3. J. G Olivier and J Peters, Trends n global CO2and total green house gas emissions, Report (2018).

  4. B. Dudley, BP statistical review of world energy, Report (2018).

    Google Scholar 

  5. Q. Chai, Z. Xiao, K.-h. Lai and G. Zhou, Int. J. Prod. Econ., 203, 311 (2018).

    Article  Google Scholar 

  6. H. Naims, Environ. Sci. Pollut. Res., 23, 22226 (2016).

    Article  CAS  Google Scholar 

  7. M. Bailera, P. Lisbona, L. M. Romeo and S. Espatolero, Renew. Sust. Energy Rev, 69, 292 (2017).

    Article  CAS  Google Scholar 

  8. G. Iaquaniello, S. Setini, A. Salladini and M. De Falco, Int. J. Hydrogen Energy, 43, 17069 (2018).

    Article  CAS  Google Scholar 

  9. J. Squalli, Energy, 127, 479 (2017).

    Article  CAS  Google Scholar 

  10. H. Blanco, W Nijs, J. Ruf and A. Faaij, Appl. Energy, 232, 323 (2018).

    Article  CAS  Google Scholar 

  11. J. H. Kim, W S. Chang and D. Pak, Korean J. Chem. Eng, 32, 2067 (2015).

    Article  CAS  Google Scholar 

  12. E. Inkeri, T. Tynjälä, A. Laari and T. Hyppänen, Appl. Energy, 209, 95 (2018).

    Article  CAS  Google Scholar 

  13. K. Ghaib and F.-Z. Ben-Fares, Renew. Sust. Energy Rev, 81, 433 (2018).

    Article  CAS  Google Scholar 

  14. R. Tarkowski, Renew. Sust. Energy Rev, 105, 86 (2019).

    Article  CAS  Google Scholar 

  15. S. Savvas, J. Donnelly, T. Patterson, Z. S. Chong and S. R. Esteves, Appl. Energy, 202, 238 (2017).

    Article  CAS  Google Scholar 

  16. J. Biswas, R. Chowdhury and P. Bhattacharya, in Industry interactive innovations in science, engineering and technology, S. Bhattacharyya, S. Sen, M. Dutta, P. Biswas and H. Chattopadhyay Eds., Springer, Singapore (2018).

  17. I. Diaz, C. Perez, N. Alfaro and F. Fdz-Polanco, Bioresour. Technol, 185, 246 (2015).

    Article  CAS  Google Scholar 

  18. W Merkle, K. Baer, J. Lindner, S. Zielonka, F. Ortloff, F. Graf, T. Kolb, T. Jungbluth and A Lemmer, Bioresour. Technol, 232, 72 (2017).

    Article  CAS  Google Scholar 

  19. D. Strubing, B. Huber, M. Lebuhn, J. E. Drewes and K. Koch, Biore-sour. Technol., 245, 1176 (2017).

    Article  Google Scholar 

  20. Y. Huang, F. Mahmoodpoor-Dehkordy, Y. Li, S. Emadi, A. Bagtzo-glou and B. Li, Chem. Eng. J., 334, 1383 (2018).

    Article  CAS  Google Scholar 

  21. A. Bensmann, R. Hanke-Rauschenbach, R. Heyer, F. Kohrs, D. Ben-ndorf, U. Reichl and K. Sundmacher, Appl. Energy, 134, 413 (2014).

    Article  CAS  Google Scholar 

  22. G. Leonzio, Chem. Eng. J., 290, 490 (2016).

    Article  CAS  Google Scholar 

  23. J. Y. Leu, Y. H. Lin and F. L. Chang, Chem. Eng. Res. Des., 89, 1879 (2011).

    Article  CAS  Google Scholar 

  24. S. Osfouri and R. Azin, Gas Process., 4, 14 (2016).

    Article  Google Scholar 

  25. W. Hayduk and H. Laudie, AIChE J., 20, 611 (1974).

    Article  CAS  Google Scholar 

  26. D. Fairbanks and C. Wilke, Ind. Eng. Chem., 42, 471 (1950).

    Article  CAS  Google Scholar 

  27. T. R. Marrero and E. A. Mason, J. Phys. Chem. Ref. Data, 1, 3 (1972).

    Article  CAS  Google Scholar 

  28. S. Weissman and G. Dubro, J. Chem. Phys., 54, 1881 (1971).

    Article  CAS  Google Scholar 

  29. T. Chu, P. S. Chappelear and R. Kobayashi, J. Chem. Eng. Data, 19, 299 (1974).

    Article  CAS  Google Scholar 

  30. R. Fernández-Prini, J. L. Alvarez and A. H. Harvey, J. Phy s. Chem. Ref. Data, 32, 903 (2003).

    Article  Google Scholar 

  31. G. Luo and I. Angelidaki, Bi otechn ol. Bioeng. , 109, 2729 (2012).

    Article  CAS  Google Scholar 

  32. J. Zabranska and D. Pokorna, Biotechnol. Adv., 36, 707 (2018).

    Article  CAS  Google Scholar 

  33. J. Owens and J. Legan, FEMS Microbiol. Rev., 3, 419 (1987).

    Article  Google Scholar 

  34. S. P. Cadogan, G. C. Maitland and J. M. Trusler, J. Chem. Eng. Data, 59, 519 (2014).

    Article  CAS  Google Scholar 

  35. J. W. Schmelzer, E. D. Zanotto and V. M. Fokin, J. Chem. Phys., 122, 074511 (2005).

    Article  Google Scholar 

  36. M. D. Rosa, Energy Environ., 31, 60 (2018).

    Article  Google Scholar 

  37. G. Glenk and S. Reichelstein, Nat. Energy, 4, 216 (2019).

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran

    Seyed Ali Jafari & Shahriar Osfouri

  2. Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran

    Reza Azin

Authors
  1. Seyed Ali Jafari
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  2. Shahriar Osfouri
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  3. Reza Azin
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Correspondence to Shahriar Osfouri.

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Jafari, S.A., Osfouri, S. & Azin, R. Modelling of batch biomethanation process for maximizing income based on values of consumed and produced gases. Korean J. Chem. Eng. 37, 815–826 (2020). https://doi.org/10.1007/s11814-020-0501-x

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