Parametric study on CO2 sequestration using cyanobacterial consortium and production of macromolecules: experimentation, modelling and optimization

Sequestration of CO2 using microalgae is one of the imperative, cost effective and environment‐friendly processes for carbon sequestration and for production of biomass. In the present study, a cyanobacterial consortium was used for the sequestration of CO2 at diverse initial CO2 concentrations (5–3...

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Bibliographic Details
Published in:Water and environment journal : WEJ 2021-05, Vol.35 (2), p.500-513
Main Authors: Upendar, Ganta, Singh, Sunita, Chakrabarty, Jitamanyu, Ghanta, Kartik Chandra, Shahnawaz, Md, Lahiri, Sandip Kumar, Dutta, Susmita
Format: Article
Language:English
Subjects:
Algae
Algorithms
Analytical methods
Artificial neural networks
Biomass
Carbon dioxide
Carbon dioxide fixation
Carbon fixation
Carbon sequestration
climate change
Consortia
environment
Experimentation
Fourier transforms
global warming
Infrared spectroscopy
Inoculum
Macromolecules
Neural networks
Optimization
Parametric statistics
Particle swarm optimization
pH effects
Phytoplankton
Sustainability
wastewater treatment
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Summary:Sequestration of CO2 using microalgae is one of the imperative, cost effective and environment‐friendly processes for carbon sequestration and for production of biomass. In the present study, a cyanobacterial consortium was used for the sequestration of CO2 at diverse initial CO2 concentrations (5–30%), pH of the medium (7–11) and inoculum sizes (5–12.5%). The cyanobacterial consortium showed sustainability in growth upto 30% CO2. The overall CO2 fixation capacity was found as 0.0692 and 0.071 mol CO2/g algae at 15% CO2, pH 9 and at 10% and 12.5% inoculum, respectively, after 14 days. Characterization of dry biomass before and after CO2 sequestration was done using Fourier Transform Infrared spectroscopy. Various algorithm of artificial neural network (ANN) model and a number of activation functions were tried to arrive at best ANN model for such process. Finally, Particle swarm optimization was used for optimization of input variables for maximum sequestration of CO2.
ISSN:1747-6585
1747-6593
DOI:10.1111/wej.12646