Microalgal cultures for the remediation of wastewaters with different nitrogen to phosphorus ratios: Process modelling using artificial neural networks

Abstract

Microalgae have remarkable potential for wastewater bioremediation since they can efficiently uptake nitrogen and phosphorus in a sustainable and environmentally friendly treatment system. However, wastewater composition greatly depends on its source and has a significant seasonal variability. This study aimed to evaluate the impact of different N:P molar ratios on the growth of Chlorella vulgaris and nutrient removal from synthetic wastewater. Furthermore, artificial neural network (ANN) threshold models, optimised by genetic algorithms (GAs), were used to model biomass productivity (BP) and nitrogen/phosphorus removal rates (RR_N/RR_P). The impact of various inputs culture variables on these parameters was evaluated. Microalgal growth was not nutrient limited since the average biomass productivities and specific growth rates were similar between the experiments. Nutrient removal efficiencies/rates reached 92.0 ± 0.6%/6.15 ± 0.01 mg_N L^-1 d^-1 for nitrogen and 98.2 ± 0.2%/0.92 ± 0.03 mg_P L^-1 d^-1 for phosphorus. Low nitrogen concentration limited phosphorus uptake for low N:P ratios (e.g., 2 and 3, yielding 36 ± 2 mg_DW mg_P^-1 and 39 ± 3 mg_DW mg_P^-1, respectively), while low phosphorus concentration limited nitrogen uptake with high ratios (e.g., 66 and 67, yielding 9.0 ± 0.4 mg_DW mg_N^-1 and 8.8 ± 0.3 mg_DW mg_N^-1, respectively). ANN models showed a high fitting performance, with coefficients of determination of 0.951, 0.800, and 0.793 for BP, RR_N, and RR_P, respectively. In summary, this study demonstrated that microalgae could successfully grow and adapt to N:P molar ratios between 2 and 67, but the nutrient uptake was impacted by these variations, especially for the lowest and highest N:P molar ratios. Furthermore, GA-ANN models demonstrated to be relevant tools for microalgal growth modelling and control. Their high fitting performance in characterising this biological system can contribute to reducing the experimental effort for culture monitoring (human resources and consumables), thus decreasing the costs of microalgae production.

Keywords: Artificial neural network; Chlorella vulgaris; Genetic algorithms; Microalgae; Nutrient removal; Wastewater treatment.