The first objective
of this study is to assess the predictive capability
of the ALBA (ALgae-BActeria) model for a pilot-scale (3.8 m
2
) high-rate algae-bacteria pond treating agricultural digestate.
The model, previously calibrated and validated on a one-year data
set from a demonstrative-scale raceway (56 m
2
), successfully
predicted data from a six-month monitoring campaign with a different
wastewater (urban wastewater) under different climatic conditions.
Without changing any parameter value from the previous calibration,
the model accurately predicted both online monitored variables (dissolved
oxygen, pH, temperature) and off-line measurements (nitrogen compounds,
algal biomass, total and volatile suspended solids, chemical oxygen
demand). Supported by the universal character of the model, different
scenarios under variable weather conditions were tested, to investigate
the effect of key operating parameters (hydraulic retention time,
pH regulation, k
L
a) on algae biomass productivity and nutrient
removal efficiency. Surprisingly, despite pH regulation, a strong
limitation for inorganic carbon was found to hinder the process efficiency
and to generate conditions that are favorable for N
2
O emission.
The standard operating parameters have a limited effect on this limitation,
and alkalinity turns out to be the main driver of inorganic carbon
availability. This investigation offers new insights in algae-bacteria
processes and paves the way for the identification of optimal operational
strategies.