In the present study, different photoperiods and nutritional conditions were applied to a mixed wastewater-borne cyanobacterial culture in order to enhance the intracellular accumulation of polyhydroxybutyrates (PHBs) and carbohydrates. Two different experimental set-ups were used. In the first, the culture was permanently exposed to illumination, while in the second it was submitted to light/dark alternation (12 h cycles). In both cases, two different nutritional regimes were also evaluated, N-limitation and P-limitation. Results showed that the highest PHB concentration (104 mg L-1) was achieved under P limited conditions and permanent illumination, whereas the highest carbohydrate concentration (838 mg L-1) was obtained under N limited condition and light/dark alternation. With regard to bioplastics and biofuel generation, this study demonstrates that the accumulation of PHBs (bioplastics) and carbohydrates (potential biofuel substrate) is favored in wastewater-borne cyanobacteria under conditions where nutrients are limited.

One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioprocess models are an essential tool for studying the simultaneous effect of complex factors on carbohydrate accumulation, optimizing the process, and reducing operational costs. In this sense, we use a dataset obtained from an empirical model that analyzed the accumulation of carbohydrates in a single process (simultaneous growth and accumulation) from real wastewater. In this experiment, there were no ideal conditions (limiting nutrient conditions), but rather these limitations are guaranteed by the operating conditions (hydraulic retention times/nutrient or carbon loads). Thus, the model integrates 18 variables that are affected and not only carbohydrates. The effect of these variables directly influences the accumulation of carbohydrates. Therefore, this paper analyzes artificial intelligence (AI) algorithms to develop a model to forecast biomass production in wastewater treatment systems. Carbohydrates were modeled using five artificial intelligence methods: (1) Artificial Neural Networks (ANNs), (2) Convolutional Neural Networks (CNN), (3) Long Short-Term Memory Network (LSTMs), (4) K-Nearest Neighbors (kNN), and (5) Random Forest (RF)). The AI methods allow learning how several components interact and if their combinations work faster than building the physical experiments over the same period of time. After comparing the five learning models, the CNN-1D model obtained the best results with an MSE (Mean Squared Error) = 0.0028. This result shows that the model adequately approximates the system’s dynamics.