Abstract
Among the various renewable energy sources that have been studied, hydrogen has been considered one of the most promising sustainable fuels. Its obtaining by microorganisms has been analyzed in experimental and simulation studies. Cyanothece sp. ATCC 51142, a nitrogen-fixing cyanobacterium, is one microorganism that produces hydrogen and has a remarkable hydrogen production rate. The objective of this work is to conduct an in silico study to maximize the final volume of hydrogen produced by Cyanothece sp. ATCC 51142 and perform a model sensitivity analysis to provide confidence to the deterministic result of the optimization study. The results obtained showed that, despite the uncertainty in the most significant model parameters and initial conditions, hydrogen produced per culture volume can be increased with the implementation of the feeding policy indicated by the deterministic optimization study. When compared to the result from the literature, the increase in biofuel production showed to be 22.9% on average, with a standard deviation of 251 mL/L.
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Data availability
Data sets generated during the current study are available from the corresponding author on reasonable request.
Abbreviations
- \(C\) :
-
Glycerol concentration (mmol/L)
- \({C}_{feed}\) :
-
Influent glycerol concentration (mmol/L)
- \(c\) :
-
Initial glycerol concentration (mmol/L)
- C 0 :
-
Initial glycerol concentration (mmol/L)
- \({c}_{1}\) :
-
Influent glycerol concentration (mmol/L)
- \({c}_{1}\) :
-
Cognitive parameter
- \({c}_{2}\) :
-
Social parameter
- \({F}_{in}\) :
-
Feed flow rate (L/h)
- \(f\left(N\right) \, \mathrm{and} \, f\left(O\right)\) :
-
Switch functions
- \(H\) :
-
Hydrogen production (mL/L)
- \(h\) :
-
Initial hydrogen production (mL/L)
- H 0 :
-
Initial hydrogen production (mL/L)
- \({K}_{C}\) :
-
Glycerol half-velocity coefficient (mmol/L)
- \({K}_{{H}_{2},1}\) :
-
Hydrogen yield coefficient (mL/g)
- \({K}_{{H}_{2},2}\) :
-
Hydrogen yield rate (mL/g h)
- \({K}_{N}\) :
-
Nitrate half-velocity coefficient (mg/L)
- \({k}_{q}\) :
-
Normalized minimum intracellular nitrogen source concentration
- \(N\) :
-
Nitrate concentration (mg/L)
- \({N}_{feed}\) :
-
Influent nitrate concentration (mg/L)
- \(n\) :
-
Initial nitrate concentration (mg/L)
- N 0 :
-
Initial nitrate concentration (mg/L)
- \(O\) :
-
Oxygen concentration (%)
- \({O}_{feed}\) :
-
Influent oxygen concentration (%)
- \(o\) :
-
Initial oxygen concentration (%)
- O 0 :
-
Initial oxygen concentration (%)
- \(pb\) :
-
Best local position
- \(pg\) :
-
Best global position
- \(q\) :
-
Nitrogen quote
- q 0 :
-
Initial nitrogen quote
- \({r}_{1} \, \mathrm{and} \, {r}_{2}\) :
-
Random values uniformly distributed in [0,1]
- \(st\) :
-
Switch time (h)
- \(T\) :
-
Switch time (h)
- \(V\) :
-
Reactor volume (L)
- \(v\) :
-
Initial culture volume (L)
- \({v}_{k}^{i}\) :
-
Velocity of particle \(i\) at iteration \(k\)
- \(X\) :
-
Biomass concentration (g/L)
- \(x\) :
-
Initial biomass concentration (g/L)
- X 0 :
-
Initial biomass concentration (g/L)
- \({x}_{k}^{i}\) :
-
Position of particle \(i\) at iteration \(k\)
- \(w\) :
-
Inertia weight
- \({Y}_{C/X}\) :
-
Glycerol yield coefficient (mmol/g)
- \({Y}_{D}\) :
-
Oxygen consumption coefficient (L/g)
- \({Y}_{H/X}\) :
-
Yield ratio of hydrogen to biomass (mL/g)
- \({Y}_{N/X}\) :
-
Nitrate yield coefficient (mg/g)
- \({Y}_{O/X}\) :
-
Oxygen yield coefficient (L/g)
- \({Y}_{q/X}\) :
-
Nitrogen quota yield coefficient
- \({\mu }_{d}\) :
-
Biomass specific respiration rate (L/g h)
- \({\mu }_{max}\) :
-
Maximum biomass specific growth (1/h)
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Acknowledgements
The authors acknowledge the National Council for Scientific and Technological Development-CNPq (Brazil), process 307958/2021-3, and Coordination for the Improvement of Higher Education Personnel-CAPES (Brazil), process 88887.470162/2019-00 for the financial support.
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da Silva, D.R., Valvassore, M.S., de Freitas, H.F.S. et al. Biological hydrogen production by Cyanothece sp. ATCC 51142 in a variable volume process: in silico optimization and sensitivity analysis. Braz. J. Chem. Eng. (2023). https://doi.org/10.1007/s43153-023-00330-1
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DOI: https://doi.org/10.1007/s43153-023-00330-1