Keeping a service-life history

You can only manage well what you measure. A filter cloth history does not need software: above all, you need to know what to track, and that depends on what the site expects from its filtration.

Nicolas Ract · 8 min read · October 2026

In short

A common base: which cloth, where, fitted when, removed when, why.
A service-life unit suited to the process: cycles, brews, dry tonnes or campaigns, rather than days.
An indicator linked to the site's objective: production continuity, quality, dry solids content or disposal cost.

The base common to all sites

Whatever the sector, one line per fitted cloth is enough to start with:

Field	Why
Filter, plate, position in the pack	Spot the positions that fail more than others
Cloth reference, batch	Compare references and isolate a defective batch
Fitting date, removal date	Calculate the actual service life
Counter reading at fitting and at removal	Express service life in the process unit (below)
Reason for removal	Failure, leak, blinding, preventive replacement
Location of the defect, photo	Link each removal to a cause (see chapter 4.1)
Events during the period	Change of product, of settings, incident, chemical wash

Field experience: a spreadsheet shared between maintenance and production, filled in at the time of the change, is worth more than a complete system that nobody updates.

Choosing the right service-life unit

Counting in days or months hides the essentials: a cloth that has done twice as many cycles in a month has not "lasted less long". Choose the unit that corresponds to actual wear:

the number of filtration cycles, the most universal;
the number of brews in a brewery;
the tonnes of dry matter filtered, when cycles vary widely in load;
the campaign in seasonal industries such as sugar production.

The indicator depends on the site's objective

The same history does not serve the same purpose everywhere. The three examples below are typical cases, built to illustrate the approach; they do not describe a particular site, and their indicators should be adapted to the actual process.

Example 1 · Sugar plant

Objective: get through the whole campaign without an unplanned stop

During the sugar beet campaign, the plant runs continuously; an unplanned cloth change is costly in lost production. The inter-campaign shutdown is the window for replacement, provided the order has been placed in time.

Unit: the campaign, and the cycles within the campaign.
To track in addition to the base: cloths changed during the campaign (number, position, reason), cycle time at the start and end of the campaign.
Key indicator: the number of replacements during the campaign, the target being zero.
Decision it informs: replace the whole pack during the inter-campaign shutdown or only the at-risk positions, and when to place the order.

Example 2 · Brewery

Objective: brew quality and throughput

In a brewery, filtration is judged on wort clarity, on the time it takes for each brew and on how clean the cloths stay between two cleanings. A cloth that clogs slows down the whole brewhouse sequence.

Unit: the number of brews.
To track in addition to the base: filtration time per brew, wort clarity as measured by the site, cleanings carried out and the product used.
Key indicator: the drift in filtration time over successive brews, and clarity falling outside the site's limits.
Decision it informs: cleaning frequency, and the point at which washing is no longer enough (see chapter 4.4).

Example 3 · Minerals or industrial sludge

Objective: the driest possible filter cake

When the filter cake is transported, dried or disposed of by the tonne, every extra point of moisture has a cost. The cloth then counts for the dry solids content it makes achievable and for its resistance to products that are often abrasive.

Unit: tonnes of dry matter filtered.
To track in addition to the base: filter cake moisture or dry solids content, as measured by the site, tonnes removed and their transport or disposal cost.
Key indicator: total cost per dry tonne, cloths included (see chapter 6.3).
Decision it informs: a more expensive cloth may be justified if it maintains better dry solids content for longer.

Reading the history after a few months

Average service life by position: if a few positions fail markedly earlier, look at those plates or at the feed (chapter 4.2).
Dominant reason: many failures and many blinding cases do not call for the same remedies.
Before and after a change: new reference, new setting, new washing method. Change only one thing at a time, otherwise the history no longer allows any conclusion.

Key point. The history is not an end in itself: it is used to decide, at the right time, whether to replace, wash, change cloth type or repair a plate, according to what actually costs the site money.

Sources

This chapter is based on method and operating experience; the examples are typical cases built for illustration, with no data from a real site.

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