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In silico optimization for production of biomass and biofuel feedstocks from microalgae

Optimization of the production rate of biomass rich in N (e.g. for protein) or C (e.g. for biofuels) is key to making algae-based technology commercially viable. Creating the appropriate conditions to achieve this is a challenge; operational permutations are extensive, while geographical variations...

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Detalles Bibliográficos
Autores principales: Kenny, Philip, Flynn, Kevin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4297880/
https://www.ncbi.nlm.nih.gov/pubmed/25620851
http://dx.doi.org/10.1007/s10811-014-0342-2
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author Kenny, Philip
Flynn, Kevin J.
author_facet Kenny, Philip
Flynn, Kevin J.
author_sort Kenny, Philip
collection PubMed
description Optimization of the production rate of biomass rich in N (e.g. for protein) or C (e.g. for biofuels) is key to making algae-based technology commercially viable. Creating the appropriate conditions to achieve this is a challenge; operational permutations are extensive, while geographical variations localise effective methods of cultivation when utilising natural illumination. As an aid to identifying suitable operational envelopes, a mechanistic acclimative model of microalgae growth is used for the first time to simulate production in virtual systems over a broad latitudinal range. Optimization of production is achieved through selection of strain characteristics, system optical depth, nutrient supply, and dilution regimes for different geographic and seasonal illumination profiles. Results reveal contrasting requirements for optimising biomass vs biofuels production. Trade-offs between maximising areal and volumetric production while conserving resources, plus hydrodynamic limits on reactor design, lead to quantifiable constraints for optimal operational permutations. Simulations show how selection of strains with a high maximum growth rate, U (m), remains the prime factor enabling high productivity. Use of an f/2 growth medium with a culture dilution rate set at ~25 % of U (m) delivers sufficient nutrition for optimal biomass production. Further, sensitivity to the balance between areal and volumetric productivity leads to a well-defined critical depth at ~0.1 m at which areal biofuel production peaks with use of a low concentration f/4 growth medium combined with a dilution rate ~15 % of U (m). Such analyses, and developments thereof, will aid in developing a decision support tool to enable more productive methods of cultivation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10811-014-0342-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-42978802015-01-22 In silico optimization for production of biomass and biofuel feedstocks from microalgae Kenny, Philip Flynn, Kevin J. J Appl Phycol Article Optimization of the production rate of biomass rich in N (e.g. for protein) or C (e.g. for biofuels) is key to making algae-based technology commercially viable. Creating the appropriate conditions to achieve this is a challenge; operational permutations are extensive, while geographical variations localise effective methods of cultivation when utilising natural illumination. As an aid to identifying suitable operational envelopes, a mechanistic acclimative model of microalgae growth is used for the first time to simulate production in virtual systems over a broad latitudinal range. Optimization of production is achieved through selection of strain characteristics, system optical depth, nutrient supply, and dilution regimes for different geographic and seasonal illumination profiles. Results reveal contrasting requirements for optimising biomass vs biofuels production. Trade-offs between maximising areal and volumetric production while conserving resources, plus hydrodynamic limits on reactor design, lead to quantifiable constraints for optimal operational permutations. Simulations show how selection of strains with a high maximum growth rate, U (m), remains the prime factor enabling high productivity. Use of an f/2 growth medium with a culture dilution rate set at ~25 % of U (m) delivers sufficient nutrition for optimal biomass production. Further, sensitivity to the balance between areal and volumetric productivity leads to a well-defined critical depth at ~0.1 m at which areal biofuel production peaks with use of a low concentration f/4 growth medium combined with a dilution rate ~15 % of U (m). Such analyses, and developments thereof, will aid in developing a decision support tool to enable more productive methods of cultivation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10811-014-0342-2) contains supplementary material, which is available to authorized users. Springer Netherlands 2014-06-01 2015 /pmc/articles/PMC4297880/ /pubmed/25620851 http://dx.doi.org/10.1007/s10811-014-0342-2 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Article
Kenny, Philip
Flynn, Kevin J.
In silico optimization for production of biomass and biofuel feedstocks from microalgae
title In silico optimization for production of biomass and biofuel feedstocks from microalgae
title_full In silico optimization for production of biomass and biofuel feedstocks from microalgae
title_fullStr In silico optimization for production of biomass and biofuel feedstocks from microalgae
title_full_unstemmed In silico optimization for production of biomass and biofuel feedstocks from microalgae
title_short In silico optimization for production of biomass and biofuel feedstocks from microalgae
title_sort in silico optimization for production of biomass and biofuel feedstocks from microalgae
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4297880/
https://www.ncbi.nlm.nih.gov/pubmed/25620851
http://dx.doi.org/10.1007/s10811-014-0342-2
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