From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20901 - Industrial biotechnology

Projekt

<a href="/cs/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategické partnerství pro environmentální technologie a produkci energie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

ISSN

1364-0321

e-ISSN

—

Svazek periodika

168

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

000888896600004

EID výsledku v databázi Scopus

2-s2.0-85136576822