Multi-scale modelling of biohydrogen production in closed photobioreactors

The synthesis of bio-based products, fuels, and materials in large-scale closed-photobioreactors (PBRs) presents a sustainable option for tackling the ever-increasing mass and energy demands of the world's rapidly growing population. With the scale-up of production comes significant cost reductions and increased commercial viability. However, successful PBR scale-up must overcome key hurdles relating to the local environmental conditions, including uneven light distribution caused by cellular absorption and mutual shading, as well as nutrient and biohydrogen partial pressure gradients. Therefore, this thesis confronts these engineering challenges with advanced mathematical modelling techniques by tackling the biotechnology's multi-scale complexities with minimum simulation cost strategies. The proposed models were thoroughly validated using both literature and experimental data collected from cultivating different microbial species in PBRs of different configurations and scales. In a "journal format" style thesis, Chapters 1 to 2 covers the general introduction and comprehensive literature review whereas Chapters 3 to 5 present the published original contributions. More specifically, Chapter 3 proposes the first-ever mechanistic model to directly integrate the effect of PBR mixing-induced light/dark cycles into the biomass growth kinetics. This enables the manipulation of the PBR mixing rate to alleviate light attenuation challenges and maintain higher biomass growth rates. Chapter 4 extends the mechanistic model's capabilities to account for the effects of temperature and PBR biohydrogen partial pressure, which were previously ununified for any microbial species. To evaluate the biotechnological transfer across two types of PBR, namely the Schott bottle-based and vertical tubular-based PBR, two parameters related to the PBR's local environmental conditions were derived: the effective light coefficient and the biohydrogen enhancement coefficient for recalibration. The successful systematic upscaling approach was recommended for other similar biosystems. Building on these achievements, Chapter 5 focuses on the multi-physics coupling within a Computational Fluid Dynamics (CFD) solver to facilitate optimisation and upscaling of biohydrogen production. For this, accelerated growth kinetics and parallel computing were combined to greatly reduce the simulation cost, enabling uncertainty estimation via Monte Carlo simulation for the first time. Finally, Chapter 6 concludes the thesis and presents two future directions: the exploitation of the models developed in Chapters 3 and 5 for (i) model-based optimal control of PBR mixing, (ii) the optimisation of PBR static mixers to enhance biomass growth and biohydrogen productivity, and (iii) application to other scalable PBR configurations.