Recirculating Aquaculture Systems

About This Project 

Currently 35 countries produce more fish via aquaculture than from fisheries. In order to sustainably meet the growing demand for fish it is projected that aquaculture will need provide the majority of fish beginning in 2030.

Monitoring and tracking water quality dynamics for Recirculating Aquaculture Systems (RAS) is essential for fish production. Using analytical water chemistry and wireless sensors this project focuses on tracking and managing water quality in real-time.  Specific applications including identifying and degrading off-flavors such as Geosmin and MIB, and other constituents such as TAN, disinfection by-products, tannins, phenolics and, flavenoids.

Additionally, Systems modeling work is focused on mapping potential for national and regional locations for new RAS production based on water quality, water chemistry, market demand, energy etc.

Tracking and Degrading Off-flavors

Tracking and degrading Off-flavors 

Off-flavors such as Geosmin and MIB found in recirculating systems imbue an earthy musty flavor to produced fish which renders them less marketable. The quality control for fish production from land-based containment systems is a major risk factor for the growth of the industry.

Identifying Optimal National and Regional locations for placing Land-based Recirculating Aquaculture Systems

Identifying Optimal National and Regional locations for placing Land-based Recirculating Aquaculture Systems

Considering the variables that influence an optimal decision for locating an aquaculture system are challenging. Using models of the latest available data incorporating regional utility data, water availability, water quality and more, can provide essential information for decision making.  This work provides insight into the following information mapped across every US county:

 

  • Water Stress 
    • Groundwater
    • Surface water
    • Seasonal variability
  • Water Quality Parameters
    • Salinity
    • pH
  • Electricity Cost
    • Variability
    • Production Methods
  • Population Proximity

Using Machine Learning and adaptive wireless water quality sensors for real-time profiling and management

Using machine learning and adaptive wireless water quality sensors for real-time profiling and management

Triangulating historical, grab-sample and, real-time water quality data provides the most robust option for profiling and managing water quality in the long term. Using adaptive wireless water sensors and statistical learning techniques helps us to identify water quality trends in real-time and with much greater accuracy than ever before.