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The current theoretical research is focused on using flue gas CO 2 as carbon source to cultivate the microalgae Chlorella vulgaris in a bubble column photobioreactor. The tanks-in-series approach is used to model this semi-batch process. A kinetic model is deployed taking account of light irradiance as well as dissolved CO 2 and inorganic nitrogen concentrations in the liquid phase. Model parameters are evaluated based on the experimental data reported in the literature. The developed model allows for detailed evaluation of concentration variations of key components along the photobioreactor over the whole process time. Using this method, variables can be distinguished and categorized into two groups: those with considerable spatial distributions and those that are assumed to be lumped and volume-averaged under certain conditions. Simulation results demonstrate a considerable aqueous CO 2 concentration gradient along the photobioreactor for moderate liquid backflows unlike biomass and dissolved inorganic nitrogen. After performing sensitivity analysis, a multi-objective optimization problem is formulated and solved using parallelized differential evolution to enhance carbon capture rate and biomass productivity. The proposed approach for modeling and optimization improves the carbon capture rate with a reasonable biomass productivity. This method paves the way for developing more efficient and sophisticated bioprocess design procedures. [Display omitted] • Tanks-in-series approach is used to model the semi-batch cultivation process. • Lumped and distributed variables are identified using the procedure. • Process optimization is performed after sensitivity analysis to improve the process. • Both biomass productivity and CO 2 conversion are improved at the optimal condition. [ABSTRACT FROM AUTHOR]

The growing number of disasters in recent years has become a significant threat to hospital buildings' resilience and preparedness. Besides, the stochastic nature of these disasters and the complexity of the hospital building systems exacerbate the difficulty of making appropriate decisions during and after disasters. To address the issue, this research proposes a novel model that utilizes the capabilities of Bayesian Networks (BNs) and Building Information Modeling (BIM). This model helps decision-makers in hospitals and medical centers measure various effects of disasters on utility systems and analyze the consequences of their decisions. The capabilities of the proposed model are tested in the case of a medical gas distribution system in a hospital building. The findings indicate that using this model brings new insights for decision-makers into the effects of an earthquake on the medical gas system of the hospital case. Applying the hybrid BIM and BN model improves the spatial understanding of the utility systems and expedites the hospital team members' response to critical situations. [ABSTRACT FROM AUTHOR]