Publication detail

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

LIM, J.Y. TENG, S.Y. HOW, B.S. NAM, K. HEO, S. MÁŠA, V.  STEHLÍK, P YOO, C.K.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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 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.

Keywords

Microalgae biorefinery, Artificial intelligence, Meta-heuristic superstructure optimization, Life cycle assessment, Uncertainty analysis, Renewable energy incorporation

Authors

LIM, J.Y.; TENG, S.Y.; HOW, B.S.; NAM, K.; HEO, S.; MÁŠA, V. ; STEHLÍK, P; YOO, C.K.

Released

1. 10. 2022

Publisher

Elsevier

Location

Oxford, England

ISBN

1364-0321

Periodical

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Year of study

168

Number

1

State

United States of America

Pages from

1

Pages to

18

Pages count

18

URL

https://www.sciencedirect.com/science/article/pii/S136403212200747X

BibTex

@article{BUT180793,
  author="LIM, J.Y. and TENG, S.Y. and HOW, B.S. and NAM, K. and HEO, S. and MÁŠA, V.  and  STEHLÍK, P and  YOO, C.K.",
  title="From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate",
  journal="RENEWABLE & SUSTAINABLE ENERGY REVIEWS",
  year="2022",
  volume="168",
  number="1",
  pages="18",
  doi="10.1016/j.rser.2022.112865",
  issn="1364-0321",
  url="https://www.sciencedirect.com/science/article/pii/S136403212200747X"
}