Uncovering the optimal structural characteristics of flocs for microalgae flotation using Python-OpenCV

Abstract

Coagulation-based dissolved air flotation is generally recognized as an efficient and economical method for removal of algae from eutrophicated water, whose efficiency is directly affected by the structural characteristics of flocs. However, it is not clear which floc structures are suitable for microalgal floatation, whether large and compact flocs or small and loose flocs have good rising performance. In this study, a novel technology for dynamically tracking the growth and rising behaviors of flocs was developed based on Python-OpenCV to visually uncover the optimal structural characteristics of flocs for Microcysts aeruginosa coagulation-flotation. Different coagulation flow fields (velocity gradient, G) were adopted to artificially create flocs with various structural characteristics and floatation kinetics. Results showed that G value had significant effects on the growth and rising behaviors of flocs, thus achieving intuitively different flotation kinetics. An idea rising velocity of 91.5 m h−1 was achieved by flocs with optimal structural characteristics generated at a suitable G of 34.1 s−1, which was 29–75% higher than that of flocs at other G. The rising velocity of floc was synergistically determined by the size and fractal dimension of floc. The structural characteristics (size and fractal dimension) of flocs were introduced into the calculation model of rising velocity, which guided the principle of optimal floc structure characteristics (large and compact) suitable for Microcysts aeruginosa removal using DAF. These may provide new technology for the studies of the growth and settling/rising behaviors of flocs, and lay a theoretical foundation for the design of DAF system with high load rate and the selection of optimal operating conditions applied to cyanobacteria blooms prevention and control.