Chemical Flow Rate Estimation
Convert tank level changes into flowrates
Operating chemicals often have no flowrate measurement and dosage rates are estimated based on tank fill volumes and average production rates.Accurate chemical flow rate measurements are important to ensure the chemical dosage is as prescribed, thereby avoiding equipment damage or downtime, due to low concentrations or excessive operating cost, due to over injection.
Using the Eigen Analytics Framework a customer was able to create a virtual flow rate for each of the chemicals using the changes in tank levels.
The result is a live dashboard showing which tank is in use and the flowrate of each chemical.
The challenge
There were two main challenges to deal with on the live data; signal noise and tank fill events.
The chemical injection pumps were piston pumps that caused vibration in the structure when running and this in turn affected the tank level indication giving a high degree of variability in the short term. This is smoothed out using the Eigen Data Transformation Engine to apply an online averaging function.
The Eigen Data Transformation Engine
The Eigen Analytics Framework has a built in Data Transformation Engine that lets you create virtual timeseries by applying mathematical or logical transformations to the source data.
This can be anything from a simple units conversion (For example, if you are measuring a pressure in ‘barg’ but want to plot it in ‘psig’ then you need to multiply it by 14.5) to If..Then statements and Totalisers over irregular calendar periods. These virtual timeseries can be treated just the same as any other real timeseries (for example you can set limits & alerts or use as the basis for another virtual timeseries) but they are evaluated at runtime and do not need the values to be written back to a database.
Detecting Tank Fill events
By applying a Standard Deviation function to the averaged tank level we can clearly identify the tank fill events
The virtual flowrate is calculated by applying a series of transformations to the source signal:
Calibrating the result
Now that we can detect Tank Fill events, an If..Then..Else logical function can be used to filter them out and maintain the previous flowrate.
The resulting smoothed signal can then be calibrated for the actual tank volume by multiplying by a constant and checked against manually calculated rates.
A Consistent approach to timeseries data
The Eigen Analytics Framework timeseries data connectors let you deal with any timeseries source in exactly the same way. Whether your data is stored in anything from OSI PI, AspenTech IP.21, Honeywell PHD to Cognite CDF, the user experience is the same. The Eigen Data Transformation Engine can combine data from different sources (including other Virtual Timeseries) into a new Virtual Timeseries.
Click here to learn more about the list of data connectors:
Creating the Visualisation
The Eigen Analytics Platform as an ever expanding library of user configurable visualisations. These were used to create a dashboard showing all the current tank levels and flows
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