Seeq > Case Studies > Heat Exchanger Monitoring and End of Cycle Prediction

Heat Exchanger Monitoring and End of Cycle Prediction

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 Heat Exchanger Monitoring and End of Cycle Prediction - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Challenge

Predicting end-of-cycle (EOC) for a heat exchanger due to fouling is a constant challenge faced by refineries. Proactively predicting when a heat exchanger needs to be cleaned enables risk-based maintenance planning and optimization of processing rates, operating costs, and maintenance costs. Before using Seeq, the engineer had to manually combine data entries in a spreadsheet and spend hours/days formatting and filtering the content or removing non-relevant data when necessary (for example when equipment was out-of-service).

About The Customer
The Solution

Utilizing the Seeq Formula Tool to monitor heat exchanger performance in the place of time-consuming spreadsheets will eliminate weeks of work for engineers, freeing them up to perform other valuable company tasks. The same formulas can be applied to many other exchangers, so users can focus on other continuous improvement projects instead of continually monitoring many spreadsheets. Millions of dollars could be saved per year as a result of improved turnaround planning and other improvement opportunities.

Data Collected
Process Data Historian, Heat Exchanger Design Data
Operational Impact
  • [Process Optimization - Predictive Maintenance]

    Enables monitoring of heat exchanger performance degradation, which in turn allows for risk-based maintenance planning

  • [Efficiency Improvement - Operation]

    Operational plans can be optimized based on heat exchanger performance.

  • [Cost Reduction - Management]

    Decrease the impact of unplanned rate reductions from heat transfer constraints to avoid losing millions of dollars in opportunities from crude/intermediate processing margins. Reduce unplanned maintenance requirements by thousands by predicting and planning for one failure event. 

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