Algal-bacterial symbiosis for nutrients removal from wastewater: The application of multivariate data analysis for process monitoring and control

The application of algal-bacterial symbiotic interaction as a low-cost and environment-friendly alternative for wastewatertreatment was investigated in this research. In this context, a photobioreactor working under sequencing batch mode (SBR) was used. It consists of three basins of 200 L which were fed with pre-treated domestic wastewater. Following the adaptation period, and according to the obtained results, a synergistic interaction between algal and bacterial cultures was developed, which ultimately lead to enhance the removal of different pollutants. During daylight cycles, the dissolved oxygen concentration was around 6 mg/L, which promoted aerobic microbial activities to degrade organic pollutants such that more than 60% of the COD was removed. Additionally, more than 40% and 90% of the nitrogen (NH4+-N) and phosphorus were removed, respectively. Based on multivariate data analysis, it was noticed that there is a high positive correlation between DO and NO3- (i.e. 0.97), such that, any increase in the DO was associated with an increase in NO3-, whereas, a high negative correlation (i.e. -.092) between DO and COD was observed. Generally, the obtained results hold great promise for future process development regarding pollutants removal and the positive contribution in reducing greenhouse gas emissions as reactor mechanical aeration was totally replaced by algal photosynthesis. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The research investigated the application of algal-bacterial symbiotic interaction as a low-cost and environment-friendly alternative for wastewater treatment using a photobioreactor working under sequencing batch mode. The study showed a synergistic interaction between algal and bacterial cultures which enhanced the removal of pollutants, with a high positive correlation between DO and NO3- and a high negative correlation between DO and COD observed through multivariate data analysis. The results hold promise for future process development and reducing greenhouse gas emissions.