Presentation to the Center for Biofilm Engineering, July 2009

1. Fouling & Cleaning Science: Direct Detection
of Biofilms and CIP-Related Problems in
Liquid Process Systems
Mark Fornalik
Industrial Biofouling Science, LLC
www.industrialbiofouling.com

2. Introduction:
Process Cleaning Science
There is a science around determining if your industrial process is truly clean,
and the tools for this determination include microscopy as well as FTIR. Both
are complimentary to the traditional microbiology methods.
This talk introduces fouling cell technology and how to understand the
sequence, chemistry and kinetics of fouling events on the interior surfaces of
pipes, tanks and liquid-handling processes.
2

3. Process Contamination:
Impact to The Bottom Line
• Poor product quality
• Random quality incidents
• Time spent sorting good product from bad
• Wasted materials (raw and finished)
• Sub-optimized process cleaning = process
downtime
• Erosion of customer base
3

4. Cost of Process Contamination
• In a Fortune 500 chemicals company, the fouling
cell approach:
– Found and eliminated the root causes for $20M in
product waste (note: most of this was biofilm related)
– Identified manufacturing sites with best cleaning
practices
– Reduced the cost of commercialization, by identifying
cleaning problems – and proper cleaning procedures
- in the product development cycle
– Enabled more robust process health
4

5. Transfer Line Contamination
Manufacturing Problems:
• Cross contamination between
product types
• Physical waste – spots,
streaks, particles, filter
plugging, viscosity changes
• Chemical waste – chemical
contamination of final product
• Increased brand change time
• Loss of product flow
• Increased production runs to
allow for waste
5

6. Insoluble Wall Fouling
• Fouling: The unwanted formation of insoluble
residues on engineering materials in contact with
flowing solutions
• Fouling is what is left on wall surface after even a
proper water flush clean
• Chemical cleaning must be designed to address
water-insoluble wall fouling
6

7. Insoluble Wall Fouling Types*
• Organic
• Inorganic
• Biological (bacteria, fungi, algae - BIOFILMS)
• Particulate (corrosion)
• Crystallization/Scale (boilers, heat exchangers)
• Combination (any two or more of the above)
* T.R. Bott, Fouling of Heat Exchangers, Elsevier (1995)
7

8. Fouling Rate
steady state
fouling mass
physical
problems
chemical
problems secondary fouling
induction period
time
The goal of cleaning is to return the system to the induction period
level of fouling
8

9. Fouling Cell Technology: Direct
Detection of Biofilms & CIP Efficacy
Fouling Cell Technology:
• Analyze fouling film while in place on substrate
• FTIR for non-destructive chemical characterization (organics)
• Epifluorescence microscopy determines if organics are biofilm
9

10. Process Cleaning: A Structured
Approach
Biofilm Control
Chemical Clean Optimization
Water Flush Optimization
System Design
10

11. Water Flush Cleaning
Water Flush Cleaning: A Two-Step Process
1. Product displacement – governed by hydrodynamics
2. Wall cleaning – governed by kinetics
1000
100
Old process water
flush end point
Percent of Dye in the Flush Solution
10
1
Water flush M a g e n ta
Y e llo w
C yan
0 .1
“plateau “
0 .0 1
0 .0 0 1
0 .0 0 0 1
0 5 10 15 20 25 30 35
T im e ( m in u te s )
Insufficient water flush leaves product behind in pipe;
optimized water flush reaches “plateau” more quickly for
faster cleaning times 11

12. Powerflush (Two-Phase Flow)
Cleaning
Efficient flow ratio Water-rich flow ratio
Cleaning efficiency varies as a function of the ratio of air flow to
water flow
12

13. Direct Measure of Powerflush
0.0080
Cleaning Efficiency
0.0075
0.0070
0.0065
0.0060
Before powerflush
0.0055
0.0050
0.0045
Absorbance
0.0040
After powerflush
0.0035
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
3500 3000 2500 2000 1500 1000
Wavenumbers (cm-1)
Peak height data correlate to effectiveness of cleaning: the smaller
the peak, the more effective the cleaning
13

14. Chemical Cleaning Variables
Chemical cleaner formulation
Concentration
Temperature
Order of addition
14

15. Measuring Chemical Cleaning
Efficiency
FTIR peak height
before & after cleaning
provides an estimate of
cleaning efficiency
100%
80%
cleaning efficiency
60%
40%
20%
0%
TSP NaOCl TSP/NaOCl NaOH Citric acid
15

16. Studying Chemical Cleaning
Parameters
Impact of temperature
100%
90%
cleaning efficiency
80%
70%
60%
50%
40%
30%
20%
10% Impact of concentration
0%
25 C 45 C 65 C 100%
5% NaOH 90%
cleaning efficiency
80%
70%
60%
50%
40%
30%
20%
10%
0%
0.2% 1.0% 5.0%
NaOH wt% @ 60 C
16

17. Biofilm Chemistry Over Time
0.020 *Subtraction Result:ir1848, 610 NRX disc #26, 3-month exposure, no clean
*Subtraction Result:ir1896, 610 NRX, 14 batches (4 days), disc #7 (1/30 - 2/2/98)
0.019 *Subtraction Result:ir2288, 610, NRX, #10, 24 hours, 5 batches, 2/26 - 2/27/98
*Subtraction Result:ir1974, disc 10, 610 NRX, 1 batch, 4 hrs, without santoprene gasket
0.018
0.017
0.016
0.015
0.014
0.013
0.012
0.011
0.010
0.009
0.008
0.007
Absorbance
0.006
0.005
0.004
0.003
6 mo
0.002
0.001
0.000
-0.001
24 hrs
-0.002
-0.003
-0.004
8 hrs
-0.005
-0.006
-0.007
2 hrs
-0.008
4000 3800 3600 3400 3200 3000 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 800 600
Wavenumbers (cm-1)
Biofilm exopolymer becomes more cleaning resistant upon aging 17

18. Case Study: Comparing Cleaning
in Two Winery Product Lines
Fermentation cellar line
Cellar & bottling lines
cleaned daily with hot water
& iodophor before & after
each use Bottling line
Fouling cells
Filler line cleaned daily with
140F water, caustic/bleach,
peracetic acid, 190F water
Filler lines
18

19. Winery Line A 10 Weeks
Fermentation
cellar line
Bottling line
Filler line 19

20. Winery Line B 10 Weeks
Fermentation
cellar line
Bottling line
Filler line 20

21. Winery Line A 10 Weeks Cellar A
cellar
carbohydrate
protein
2000 1000
bottling
surge
2000 1000 tank
filler
2000 1000
bottling A
21

22. Winery Line B 10 Weeks
Cellar B
cellar
protein
2000 1000
bottling
surge
2000 1000
tank
filler
2000 1000
22
bottling B

23. Winery FTIR Peak Height
Comparison
Line A 82 Line/Line 4
Line B Cribari Line/Line 5
0.5 0.5
0.45 0.45
0.4 0.4
0.35 0.35
0.3 0.3
peak height
peak height
amide amide
0.25 0.25
carbo carbo
0.2 0.2
0.15 0.15
0.1 0.1
0.05 0.05
0 0
cellar bottling filler 1st cellar bottling filler A side filler B side
fouling cell location fouling cell location
Conclusions:
• Fillers from both lines were clean
• Both lines A and B exhibit biofilms in cellar and bottling lines
• Line A has thicker fouling layer
• Line A exopolymer is carbohydrate & protein; Line B exopolymer is protein
• Both biofilms resist daily chemical cleaning: hot water, caustic, peracetic acid, iodophore
23

24. Case Study: Mapping Process
Cleaning in Bioproducts Plant
Fermentation
reactor
Centrifuge
Fouling cells
Process
filters
Recovery 24

25. Fermentation Fouling Cells
2-day exposure
before CIP
2-day exposure after
CIP
CIP: 5%
NaOH,
65°C, 30 4-week exposure
min daily after CIP
25

26. Recovery Fouling Cells
2-day exposure
before CIP
2-day exposure after
CIP
CIP: 5%
NaOH,
65°C, 30 4-week exposure
min daily after CIP
26

27. Fermentation vs. Recovery
27

28. Conclusions
• Microscopy provides a valuable tool in industrial biofilm detection
and characterization
• Fouling cells provide an ideal way to acquire biofilms in full-scale
manufacturing processes
• Fouling cell technology is complimentary to existing microbiology
methods for biofilm analysis, enabling analysis of exopolymer and
biofilm morphology while still in place on the fouled surface
• FTIR analysis targets exopolymer and residual chemicals fouling
from product
• This approach can be used to “map” the cleaning effectiveness
within a process or compare cleanliness over different production
lines or sites, and determine whether product fouling or biofilms are
the root cause of process and product contamination issues
28

29. Food Gelatin
Winery dye
Brewery
AgNO3
Bioproducts
Industrial salt system
Food dye
Ultrapure water 29

30. With Thanks to Kodak’s
Former Systems Cleaning
Group
M. Giang, M. Grannas,
D. Gruszczynski, J. Hunt,
D. Irwin, Y. Lerat, C. Puccini,
R. Schmanke, J. Steegstra, M. Wallace,
M. Wilcox, G. Wilson,
K. Brockler, J. Fornalik
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