Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater

Quantitative image analysis (QIA) is a simple and automated method for process monitoring, complementary to chemical analysis, that when coupled to mathematical modelling allows associating changes in the biomass to several operational parameters. The majority of the research regarding the use of QIA has been carried out using synthetic wastewater and applied to activated sludge systems, while there is still a lack of knowledge regarding the application of QIA in the monitoring of aerobic granular sludge (AGS) systems. In this work, chemical oxygen demand (COD), ammonium (N-NH₄⁺), nitrite (N-NO₂^-), nitrate (N-NO₃^-), salinity (Cl^-), and total suspended solids (TSS) levels present in the effluent of an AGS system treating fish canning wastewater were successfully associated to QIA data, from both suspended and granular biomass fractions by partial least squares models. The correlation between physical-chemical parameters and QIA data allowed obtaining good assessment results for COD (R² of 0.94), N-NH₄⁺ (R² of 0.98), N-NO₂^- (R² of 0.96), N-NO₃^- (R² of 0.95), Cl^- (R² of 0.98), and TSS (R² of 0.94). While the COD and N-NO₂^- assessment models were mostly correlated to the granular fraction QIA data, the suspended fraction was highly relevant for N-NH₄⁺ assessment. The N-NO₃^-, Cl^- and TSS assessment benefited from the use of both biomass fractions (suspended and granular) QIA data, indicating the importance of the balance between the suspended and granular fractions in AGS systems and its analysis. This study provides a complementary approach to assess effluent quality parameters which can improve wastewater treatment plants monitoring and control, with a more cost-effective and environmentally friendly procedure, while avoiding daily physical-chemical analysis.