Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively higher fish density and water turbidity in intensive RAS culture produce major challenges in acquiring high-quality underwater image data.

Denitrifying bioreactor clogging potential during wastewater treatment.

Chemoheterotrophic denitrification technologies using woodchips as a solid carbon source (i.e., woodchip bioreactors) have been widely trialed for treatment of diffuse-source agricultural nitrogen pollution.

Optimizing Hydraulic Retention Times in Denitrifying Woodchip Bioreactors Treating Recirculating Aquaculture System Wastewater.

The performance of wood-based denitrifying bioreactors to treat high-nitrate wastewaters from aquaculture systems has not previously been demonstrated. Four pilot-scale woodchip bioreactors (approximately 1:10 scale) were constructed and operated for 268 d to determine the optimal range of design hydraulic retention times (HRTs) for nitrate removal. The bioreactors were operated under HRTs ranging from 6.