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How To Monitor Heat Exchanger Performance in Seeq

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  • Seeq Team

When addressing a business problem with analytics, we should always start by asking ourselves 4 key questions:

  1. Why are we talking about this: what is the business problem we are trying to address, and what value will solving this problem generate?
  2. What data do I have available to help with solving this problem?
  3. How can I build an effective analysis to identify the root of my problem (both in the past, and in future)?
  4. How will I visualize the outputs to ensure proactive action to prevent the problem from manifesting? This is where you extract the value.

With that in mind, please read below how we approach the above 4 questions while working in Seeq to deal with heat exchanger performance issues.


What is the business problem?

Issues with heat exchanger performance can lead to downstream operational issues which may lead to lost production and revenue. To effectively monitor the exchanger, a case-specific approach is required depending on the performance driver:

  • Fouling in the exchanger is limiting heat transfer, requiring further heating/cooling downstream
  • Fouling in the exchanger is limiting system hydraulics, causing flow restrictions or other concerns
  • Equipment integrity, identify leaks inside the exchanger


What Data do we have available?


  • Process Sensors – flow rates, temperatures, pressures, control valve positions
  • Design Data – drawings, datasheets
  • Maintenance Data – previous repairs or cleaning, mean-time between cleanings


How can we tackle the business problem with the available data?

There are many ways to monitor a heat exchanger's performance, and the selection of the appropriate indicator depends on a) the main driver for monitoring and b) the available data. The decision tree below is merely meant to guide what indicators can be applied based on your dataset. Generally speaking, the more data available, the more robust an analysis you can create (ie. first principles based calculations). However, in the real world, we are often working with sparse datasets, and therefore may need to rely on data-based approaches to identify subtle trends which indicate changes in performance over time.


Implementing each of the indicators listed above follow a similar process in Seeq Workbench, as outlined in the steps below. In this example, we focus on a data-based approach (middle category above). For an example of a first-principles based approach, check out this Seeq University video.

Step 1 - Gather Data

  • In a new Workbench, search in the Data Tab for relevant process signals
  • Use Formula to add scalars or use the 
    function to convert supplemental data such as boundary limits or design values
  • Use Formula, Value Search or Custom Condition to enter maintenance period(s) and heat exchanger cycle(s) conditions (if these cannot be imported from a datasource)



Step 2 - Identify Periods of Interest

  • Use Value Search, Custom Condition, Composite Condition or Formula to identify downtime periods, periods where exchanger is bypassed, or periods of bad data which should be ignored in the analysis



Step 3 - Cleanse Data

  • Use Formula to remove periods of bad data or downtime from the process signals, using functions such as
    $signal.remove($condition) or $signal.removeOutliers()
  • Use Formula to smooth data as needed, using functions such as
    or the Low Pass Filter tool



Step 4 - Quantify

  • Use Formula to calculate any required equations

In this example, no calculations are required.


Step 5 - Model & Predict

  • Use Prediction and choose a process signal to be the Target Variable, and use other available process signals as Input Variables; choose a Training Period when it is known the exchanger is in good condition
  • Using Boundaries: establish an upper and lower boundary signal based on the predicted (model) signal from previous step (e.g. +/-5% of the modeled signal represents the boundaries)




Step 6 - Monitor

  • Use Deviation Search or Value Search to find periods where the target signal exceeds a boundary(ies)
    • The deviation capsules created represent areas where heat exchanger performance is not as expected
    • Aggregate the Total Duration or Percent Duration statistic using Scorecard or Signal From Condition to assess deteriorating exchanger health over time




How can we visualize the outputs to ensure proactive action in future?

Step 7 - Publish Once the analysis has been built in a Seeq Workbench, it can be published in a monitoring dashboard in Seeq Organizer as seen in the examples below. This dashboard can then be shared among colleagues in the organization, with the ability to monitor the exchanger, and log alerts and take action as necessary as time progresses - this last step is key to implementing a sustainable workflow to ensure full value is extracted from solving your business problem. 


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