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

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  • Lim, Juin Yau
  • Teng, Sin Yong
  • How, Bing Shen
  • Nam, KiJeon
  • Heo, SungKu
  • Máša, Vítězslav
  • Stehlík, Petr
  • Yoo, Chang Kyoo

Abstract

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 × 107 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.

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  • Lim, Juin Yau & Teng, Sin Yong & How, Bing Shen & Nam, KiJeon & Heo, SungKu & Máša, Vítězslav & Stehlík, Petr & Yoo, Chang Kyoo, 2022. "From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:rensus:v:168:y:2022:i:c:s136403212200747x
    DOI: 10.1016/j.rser.2022.112865
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    Cited by:

    1. Marcin Dębowski & Izabela Świca & Joanna Kazimierowicz & Marcin Zieliński, 2022. "Large Scale Microalgae Biofuel Technology—Development Perspectives in Light of the Barriers and Limitations," Energies, MDPI, vol. 16(1), pages 1-23, December.
    2. He, Xin & Wang, Ning & Zhou, Qiaoqiao & Huang, Jun & Ramakrishna, Seeram & Li, Fanghua, 2024. "Smart aviation biofuel energy system coupling with machine learning technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Fazril Ideris & Mohd Faiz Muaz Ahmad Zamri & Abd Halim Shamsuddin & Saifuddin Nomanbhay & Fitranto Kusumo & Islam Md Rizwanul Fattah & Teuku Meurah Indra Mahlia, 2022. "Progress on Conventional and Advanced Techniques of In Situ Transesterification of Microalgae Lipids for Biodiesel Production," Energies, MDPI, vol. 15(19), pages 1-32, September.

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