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One of the smaller additions I’m working on right now is actually one of the more interesting telemetry ideas so far: load sensors under the skimmer waste container.
At first glance it probably sounds unnecessary, but the goal is not just “know when the bucket is full.” I already have float shutoffs for overflow protection handled through the PLC.
What I’m really after is trend data.
By continuously tracking the weight of the skimmate over time, Jarvise can start learning what “normal” skimmer production looks like for this system.
For example:
Obviously this is prototype-level hardware and probably won’t survive forever in a salty fish room environment, but it’s cheap enough to experiment with and learn from before moving to more industrial-grade sensors later.
That’s honestly a big part of this whole project:
trying ideas, gathering telemetry, and figuring out which signals actually provide useful insight into how the system behaves over time.
At first glance it probably sounds unnecessary, but the goal is not just “know when the bucket is full.” I already have float shutoffs for overflow protection handled through the PLC.
What I’m really after is trend data.
By continuously tracking the weight of the skimmate over time, Jarvise can start learning what “normal” skimmer production looks like for this system.
For example:
- skimmer suddenly producing much wetter skim than normal
- production rate dropping unexpectedly
- unusually rapid increases after feeding or maintenance
- long-term export trend changes over weeks/months
- feeding events
- reactor behavior
- pH
- ORP
- water changes
- fish behavior
- camera monitoring
Obviously this is prototype-level hardware and probably won’t survive forever in a salty fish room environment, but it’s cheap enough to experiment with and learn from before moving to more industrial-grade sensors later.
That’s honestly a big part of this whole project:
trying ideas, gathering telemetry, and figuring out which signals actually provide useful insight into how the system behaves over time.
