Cleanliness guarantee

 

Analysis of contamination before design stage and definition of need

 

 

This part aims to highlight a number of issues that need addressing in order to best define which washing procedure should be implemented:

First, it is necessary to know precisely the input data, to clearly know what requires the use of the washing procedure.

Contamination: is it?

  • Known, controlled (chemically, size, source, vector …)
  • Regular or infrequent (What is the frequency of occurrence and kinetics)
  • Multiple or single
  • Chemical, organic or both
  • Dry or liquid
  • Uniformly distributed in quantity or in thin layers on surfaces (problem: corners and pre-cleaning)
  • Amount of tackiness caused by a possible preliminary treatment: embedded deposits / Aerosol, reaction (laboratory equipment), cooking (glassware), ….

 

Next comes the definition of output data:

 

 

Identify and formulate in writing, from the design stage, the objective of cleanliness, which means :

  • The quantifiable results WITH its pass / fail criteria (operational ranges, alarms and shutdowns)
  • The probable Cleanliness Assurance Level (NAP) and not the average

 

Le procédé 

 

To reduce or eliminate? All or part:

  • of particulate contamination? (Which – Size – Threshold …)
  • of microbiological contamination? (disinfection up to sterilisation – endotoxins)
  • of the risk of cross contamination?

Is it necessary to have the same threshold for all items? Guidelines, operators, products

 

 

Which air classification :

  • no traces visible to the naked eye or to the specific sensor
  • <1 particle / 1 µm
  • <2 particles / 2.5-20 µm
  • no particles / 25 µm

 

 

Are the objectives:

  • Technically realistic?
  • Qualitatively quantifiable?
  • Economically profitable?
  • And consistent according to risk / criticality?(CI = GPxFPxND)

 

 

Only after addressing these questions can we focus on the means and their own parametres.

Any investment in knowledge and upstream analysis would remain a latent economy for output and quality in terms of the anticipated risks for process monitoring.