Microalgae-based wastewater systems are highly complex from a biological point of view. In wastewater, microalgae coexist with thousands of different microorganisms in a unique ecosystem that allows the efficient recovery of nutrients (such as nitrogen and phosphorus) naturally present in this type of medium. In these systems, microalgae interact not only with bacteria, but also with fungi, potential pathogens, and viruses. Consequently, one of the key challenges related to the correct management and operation of these systems is handling co-existing populations, as their interactions significantly impact effectiveness and stability.
In this context, mathematical modelling emerges as an invaluable tool for guiding decision-making within the framework of optimal operation. An accurate mathematical model enables the understanding of microbial population interactions, the evaluation of process variables, optimization of the process through the implementation of control strategies, and improvement of the design of production systems, especially for large-scale production.
In the framework of modelling microalgae growth, the literature provides numerous examples where the growth rate is correlated with light, temperature, pH, and nutrient availability. However, to establish validity in a large-scale system, it is necessary to consider the totality of these factors, in the so-called “comprehensive models,” defined as models that consider the effect of multiple process parameters and biological mechanisms. Additionally, in the case of wastewater systems, it is necessary to include the interaction with other microorganisms present in the medium and their competition for nutrient consumption. However, to develop a model applicable to large-scale production, it must strike a balance between accuracy and complexity, focusing only on relevant mechanisms to avoid exceeding computational demands. Therefore, selecting the right microorganisms to model is one of the fundamental steps.
A study carried out in Denmark that uses next-generation sequencing technologies revealed that in a wastewater treatment plant, up to 3000 different microbial species can be present. However, most of them are in very low abundance and, presumably, are not relevant to the treatment processes. This fact resulted in focusing only on a few bacterial groups that are in high abundance and influence nutrient removal, typically nitrifying (AOB, NOB), and heterotrophic bacteria. In fact, the first bacterial group contributes to the consumption of ammonium, in competition with microalgae, and the production of nitrate, while the second contributes to the degradation of the chemical oxidation demand (COD) present in wastewater.
After defining the system to be modelled, a new comprehensive model for microalgae-bacteria systems, named the ABACO-2 model, has recently been developed and validated (Figure 1). This model effectively represents the interaction between bacteria and microalgae in wastewater treatment, including biomass concentration and nutrient evolution in the medium. Moreover, it has successfully been validated over an extended period of 8 months for a pilot-scale raceway reactor of 80 m2.
ABACO-2 is a robust biological model that can be used to assess populations living in wastewater systems. Differentiating experimentally between communities in such systems poses a significant challenge. In fact, the biomass productivity of raceways is evaluated with the dry weight methodology, which includes the contribution of both bacterial and microalgae populations. Separating them remains challenging but significant, as their ratios impact various process outcomes, such as biomass quality and water remediation efficiency. Successive filtrations based on cell size differences have been explored, but they often result in a notable presence of bacteria that attach to microalgae due to cell aggregation. Alternative methods, including flow cytometry techniques (FCM), prove valuable in assessing the relative composition of mixed microorganism populations, which encompass both prokaryotes and eukaryotes. This approach discriminates between groups by analysing the intrinsic characteristics of individual cells, such as size, complexity, and autofluorescence. Additionally, molecular identification techniques, such as the amplification of 16S and 18S rDNA sequences, serve to evaluate the structure of the microbial community. Alongside these methods, photo-respirometry, based on traditional respirometry, has been employed to discern population differences. However, these methods lack a direct correlation in biomass concentration (g·L-1), which is more straightforward to interpret.
In this context, mathematical models offer a useful tool for indirectly studying how the balance between populations evolves. Operational conditions, notably cultivation height, dilution/harvesting strategy, and oxygen removal capacity, have a substantial influence.
Integrating additional aspects into the models, particularly those relevant to the stability of microalgae culture and the disinfection capacity of the system, presents a future challenge. This involves considering factors such as the impact of algae predators (e.g., fungi, viruses) or pathogenic bacteria (e.g., E. coli, Clostridium). In particular, with respect to the last aspect, their reduction is a crucial requirement set by the European Union in the context of water reuse, especially in agriculture. This expansion of the models will contribute to a more comprehensive understanding and effective management of microalgae-based wastewater systems, aligned with regulatory standards and ensuring sustainable practices.