Formation of a High-Density Algal-Bacterial Flocculent Biomass in a Pilot-Scale Raceway Pond Treating Municipal Wastewater

This study provides novel insights into the gradual development of an algal-bacterial self-flocculent biomass in a 400 L pilot-scale raceway pond for wastewater treatment to enhance sustainability and minimize environmental footprint. The synergetic interaction of algal-bacteria consortia improves nutrient removal while enabling biomass concentration increase. Initially, the microalgae-bacteria biomass was gradually developed by increasing the operating volume from 60 to 400 L. After 80 days, the biomass reached a plateau at a concentration of about 4 g L−1, and exhibited excellent settling characteristics. The initial settling velocity was 14.8 cm min−1 and a settling time of 3 min was required to achieve efficient separation. The reactor achieved high treatment efficiency of about 95% for all nutrients (organic matter, nitrogen and phosphorous) after the 80th day. The kinetic analysis showed that nutrient removal followed first-order kinetics, with soluble chemical oxygen demand and ammonia removal reaching 0.017 and 0.020 h−1, respectively. The results demonstrate high pollutant removal efficiencies and design guidelines for the use of increased concentrations of microalgae–bacteria consortia in urban wastewater treatment practice, an alternative green way for solving present-day wastewater treatment problems.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2026-04-12
DOI
https://doi.org/10.3390/app16083761
Primary Topic
Algal biology and biofuel production
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Formation of a High-Density Algal-Bacterial Flocculent Biomass in a Pilot-Scale Raceway Pond Treating Municipal Wastewater

Ioannis D. Manariotis, Styliani Biliani, Dimitrios Kakavas
Applied Sciences
Algal biology and biofuel production
article

Formation of a High-Density Algal-Bacterial Flocculent Biomass in a Pilot-Scale Raceway Pond Treating Municipal Wastewater

Ioannis D. Manariotis, Styliani Biliani, Dimitrios Kakavas
article en

Abstract

This study provides novel insights into the gradual development of an algal-bacterial self-flocculent biomass in a 400 L pilot-scale raceway pond for wastewater treatment to enhance sustainability and minimize environmental footprint. The synergetic interaction of algal-bacteria consortia improves nutrient removal while enabling biomass concentration increase. Initially, the microalgae-bacteria biomass was gradually developed by increasing the operating volume from 60 to 400 L. After 80 days, the biomass reached a plateau at a concentration of about 4 g L−1, and exhibited excellent settling characteristics. The initial settling velocity was 14.8 cm min−1 and a settling time of 3 min was required to achieve efficient separation. The reactor achieved high treatment efficiency of about 95% for all nutrients (organic matter, nitrogen and phosphorous) after the 80th day. The kinetic analysis showed that nutrient removal followed first-order kinetics, with soluble chemical oxygen demand and ammonia removal reaching 0.017 and 0.020 h−1, respectively. The results demonstrate high pollutant removal efficiencies and design guidelines for the use of increased concentrations of microalgae–bacteria consortia in urban wastewater treatment practice, an alternative green way for solving present-day wastewater treatment problems.

Applied SciencesVol. 16(8)
University of Patras (GR)
Openalex Percentile: Top 32%
Algal biology and biofuel production
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.