Introduction
The development of energy- efficient recirculating aquaculture systems (RAS) for Atlantic salmon is one of the main future goals of aquacultural engineering. RAS facilities are on its way to have more and more automated technologies. However, CO2 degassing units normally are not regulated or automatised. They just running on full power even if they have strip out very little CO2 from the production water. Energy prices increased a lot over the last years and an automation of CO2 Degassing units could help to significantly reduce energy costs in fish production. Degassing performance can be measured through sensors and displayed on the operating computer of the control panel of the RAS unit. This could be used to regulate the pump, and blower performance to transport the high CO2 air more economically (save energy) out of the production facility. With machine learning and reliable robust sensors an automated degassing unit could be developed and used to safe energy and reduce the environmental footprint of close containment systems.
Material and methods
Two single RAS units (1 m3) in Sunndalsøra (Landing MiniRAS) were used (without fish, CO2 was gassed artificially) with different water salinities (0 and 12 ppt), alkalinities (50, 200 mg/L CaCO3), dissolved CO2 concentration (5, 10, 20 mg/L dissolved CO2), water flow rates through the degasser (hydraulic loading rates (HLR) 10, 20, 30 l/m2s (Water flow rate equal 1000 (21.50 hz), 2000 (22.70 hz) and 3000 (24.30 hz) l/h) and 3 different degassing fan speeds were adjusted to compare the degassing performance (water and air phase) and estimated energy usage under these conditions, each.
Results
There was only a tendency that the degassing efficiency was lower (-10-20%) under the high alkalinity treatment (200 mg/L) in combination with lower water flow rates (1000 and 2000 L/h). At 3000 L/h the 12 ppt salinity and 200 mg/L alkalinity test showed the lowest CO2 stripping efficiency. There was no significant difference measurable between the 2 salinities used. A combined approach of Gas : liquid ratio (Fan speed vs. water flow rate) combined with energy logger measurement gives really easy the most efficient CO2 removal in g/h x kWh. 2000 and 3000 l/h remove significant more g CO2 / kWh than 1000 l/h.
Discussion
The different water quality adjustments were done at one day for a specific CO2 concentration. We tested quite a wide range of variables. We could not find differences in degassing efficiency under different salinities. It can be that the accuracy and pression of the used setup was not enough to verify what was needed to find the effect of salinity. We propose how to use a set of robust sensors that process the incoming data (machine learning) and regulates the Gas to liquid (Blower to Pump operation) ratio of the degassing set based on the energy consumption and target CO2 concentration (Figure 1).