From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU146843" target="_blank" >RIV/00216305:26210/22:PU146843 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S136403212200747X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S136403212200747X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rser.2022.112865" target="_blank" >10.1016/j.rser.2022.112865</a>
Alternative languages
Result language
angličtina
Original language name
From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate
Original language description
An emerging renewable energy source from living organisms, microalgae are recognized for its remarkable energy content and continuously receiving interest with a great potential in increasing its shares in fuel market. The main challenge for stable biorefinery operation is cultivation, given that the growth of microalgae is highly dependent on climate conditions, especially ambient temperature, and solar exposure. Herein, an advanced forecasting algorithm predicts daily climate conditions a year ahead. The forecast is then used in a dynamic metaheuristic optimization framework to determine optimal microalgae biorefinery process pathways with promising total annual margins and greenhouse gas emissions. In return, the optimal solution is reported with a total annual margin of 815,716 US$/y and greenhouse gas emission of 1.1 x 10(7) kg CO2-eqv/y. The most feasible microalgae species among the selection pool are identified in terms of kinetic growth, which is attributed to the climate behavior of the selected case-study region. A scheduling scheme is then identified for the optimal harvest period of cultivated microalgae. Next, uncertainty analysis for the selected process configuration is conducted using Monte Carlo simulation to investigate how variations in climate conditions will affect its overall performance. Additionally, the process is further enhanced by including renewable electricity sources which allow reducing 50% greenhouse gas emissions with the configuration of biomass energy (1.2%), solar power (0.1%), and wind energy (98.7%). In summary, this study provided a comprehensive guidelines on strategically deploying large scale microalgae biorefineries considering its long-term operational sustainability abiding the possible uncertainties within the system proposed.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20901 - Industrial biotechnology
Result continuities
Project
<a href="/en/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategic Partnership for Environmental Technologies and Energy Production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
ISSN
1364-0321
e-ISSN
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Volume of the periodical
168
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000888896600004
EID of the result in the Scopus database
2-s2.0-85136576822