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The Challenge
After purchasing and installing new filters, the plant needed to determine whether the new assets fouled more slowly than the previous ones. The objective was to validate the investment through a data-driven performance comparison.
However, comparing filter behavior over more than one year of historical data was not straightforward. Operating conditions varied significantly due to load changes, shutdowns, maintenance periods, and sensor noise. Simply comparing raw pressure signals would lead to misleading conclusions, as filter differential pressure is strongly influenced by plant production levels.
Engineers required a structured methodology to isolate comparable operating periods, remove irrelevant data, and quantify fouling in a consistent and objective way.
The Approach
A systematic comparison workflow was implemented to evaluate filter performance under comparable conditions:
- Applied value-based searches to filter out non-representative periods such as shutdowns and maintenance
- Segmented historical data into timeframes corresponding to old versus new filter operation
- Performed statistical comparisons across equivalent production regimes
- Created a simulated delta P model based on plant load to normalize expected filter behavior
- Calculated the deviation between actual differential pressure and modeled delta P as a fouling indicator
- Benchmarked this fouling metric across filter generations
This approach enabled apples-to-apples comparison by separating production effects from actual filter degradation.
The Results
The Takeaway
The analysis provided objective evidence to assess whether the new filters improved fouling performance. By correlating pressure differential with plant production and modeling expected behavior, engineers established a reliable fouling measurement framework.
This methodology supports predictive maintenance planning, early detection of performance degradation, and informed procurement decisions for future asset replacements.
The Challenge
After purchasing and installing new filters, the plant needed to determine whether the new assets fouled more slowly than the previous ones. The objective was to validate the investment through a data-driven performance comparison.
However, comparing filter behavior over more than one year of historical data was not straightforward. Operating conditions varied significantly due to load changes, shutdowns, maintenance periods, and sensor noise. Simply comparing raw pressure signals would lead to misleading conclusions, as filter differential pressure is strongly influenced by plant production levels.
Engineers required a structured methodology to isolate comparable operating periods, remove irrelevant data, and quantify fouling in a consistent and objective way.
The Approach
A systematic comparison workflow was implemented to evaluate filter performance under comparable conditions:
- Applied value-based searches to filter out non-representative periods such as shutdowns and maintenance
- Segmented historical data into timeframes corresponding to old versus new filter operation
- Performed statistical comparisons across equivalent production regimes
- Created a simulated delta P model based on plant load to normalize expected filter behavior
- Calculated the deviation between actual differential pressure and modeled delta P as a fouling indicator
- Benchmarked this fouling metric across filter generations
This approach enabled apples-to-apples comparison by separating production effects from actual filter degradation.
The Results
The Takeaway
The analysis provided objective evidence to assess whether the new filters improved fouling performance. By correlating pressure differential with plant production and modeling expected behavior, engineers established a reliable fouling measurement framework.
This methodology supports predictive maintenance planning, early detection of performance degradation, and informed procurement decisions for future asset replacements.
Access now
Share with a co-worker
The Challenge
After purchasing and installing new filters, the plant needed to determine whether the new assets fouled more slowly than the previous ones. The objective was to validate the investment through a data-driven performance comparison.
However, comparing filter behavior over more than one year of historical data was not straightforward. Operating conditions varied significantly due to load changes, shutdowns, maintenance periods, and sensor noise. Simply comparing raw pressure signals would lead to misleading conclusions, as filter differential pressure is strongly influenced by plant production levels.
Engineers required a structured methodology to isolate comparable operating periods, remove irrelevant data, and quantify fouling in a consistent and objective way.
The Approach
A systematic comparison workflow was implemented to evaluate filter performance under comparable conditions:
- Applied value-based searches to filter out non-representative periods such as shutdowns and maintenance
- Segmented historical data into timeframes corresponding to old versus new filter operation
- Performed statistical comparisons across equivalent production regimes
- Created a simulated delta P model based on plant load to normalize expected filter behavior
- Calculated the deviation between actual differential pressure and modeled delta P as a fouling indicator
- Benchmarked this fouling metric across filter generations
This approach enabled apples-to-apples comparison by separating production effects from actual filter degradation.
The Results
The Takeaway
The analysis provided objective evidence to assess whether the new filters improved fouling performance. By correlating pressure differential with plant production and modeling expected behavior, engineers established a reliable fouling measurement framework.
This methodology supports predictive maintenance planning, early detection of performance degradation, and informed procurement decisions for future asset replacements.
Access now
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