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Communication Dans Un Congrès Année : 2023

Comparison of static and dynamic LCA modelling of microalgae-based energy production systems to consider inherent process variations

Résumé

In the current global climate and energy crises, bioenergy from microalgae has been studied as an alternative to fossil fuels due to microalgae’s high solar energy conversion into organic matter, among other advantages. However, doubts can arise on the potentially better environmental performances of bioenergy compared to non-renewable energy sources due to nutrients, energy, water and other requirements of the process, among others. Life Cycle Assessment (LCA) is one systemic and widely accepted method to avoid or at least reduce the potential environmental impacts and allows to spot the main contributors. LCA studies on microalgae systems in the literature have identified the cultivation phase as one main contributor to the environmental impacts of microalgae-based systems (Pérez-López et al., 2017; Portner et al., 2021), which motivates endeavor to evaluate this phase. To date, these LCA studies of microalgae-based bioenergy systems are mostly static, that is, they consider average growth rate and operation conditions for long periods. However, the cultivation phase is strongly dependent on weather, as it relies on temperature and solar radiation. This dependency cannot be accurately accounted for with static models, which may, thus, increase the uncertainties and representativeness issues of the LCA results (Beloin-Saint-Pierre et al., 2020) (Collet et al., 2011). To tackle these issues the current work proposes a dynamic LCA of a microalgae-based bioenergy system by considering weather dependencies (temperature, solar radiation) of the biomass growth. First, a static parameterized LCA model is built with respect to the system boundaries and the input and output flows by using parameters with average values. Then, this static model is adapted and transformed into a dynamic model, which allows to include in the LCA results the fluctuation of the biomass growth along the year. This fluctuation is modeled using time-series data of the biomass productivity, temperature, and solar radiation from a pilot-scale system. Afterwards, the effect of this fluctuation on the environmental impacts of the system is evaluated and compared with results obtained from the static model. This comparison highlights the relevance of considering weather dependencies of the cultivation rather than using a common average value of the biomass productivity. All modeling and computations are done with Python programming tools using open source LCA libraries such as Brightway2 (Mutel, 2017) and lca_algebraic (Jolivet, 2020). Although the data come from a pilot scale system, this dynamic approach gives a more accurate view of the effect of on-site conditions of this kind of system on the environmental results. Therefore, more representative LCA results are expected, which may help evaluating different technologies. Better understanding potential factors to improve the efficiency of the cultivation phase is key to evolve towards more stable and competitive industrial microalgae-based bioenergy systems.
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Dates et versions

hal-04336251 , version 1 (11-12-2023)

Identifiants

  • HAL Id : hal-04336251 , version 1

Citer

Andriamahefasoa Rajaonison, Francisco Gabriel Acién Fernandez, Rebecca Nordio, Ana Sánchez-Zurano, Raphaël Jolivet, et al.. Comparison of static and dynamic LCA modelling of microalgae-based energy production systems to consider inherent process variations. The 11th International Conference on Life Cycle Management (LCM 2023), Sep 2023, Lille, France. ⟨hal-04336251⟩
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