Transcript Statistical Experimental Design Technique to Determine the Most

Statistical Experimental Design Technique to
Determine the Most Effective Process
Control Variables for the Control of Flotation
Deinking of Office Papers
W J Pauck (M.Sc.)
Head of Pulp and Paper Technology
Durban University of Technology
Oct. 2010
PROJECT TEAM
Funding
– Forest Products Research (CSIR)
Project Manager
– Jerome Andrew (CSIR)
Researcher
– J Pauck
Academic supervisors – Dr. Jon Pocock (UKZN)
Prof. Richard Venditti (NCSU)
Technicians
– Hoosain Adam
Zamani Myende
Nomakhosi Sincuba
Elsie Sibande
Paper Recycling Trends
Paper recycling trends have exceeded expectations:
70
% Utilization rate
60
50
40
Sources:
30
1. Goettsching & Pakarinen 2000 7: 12-22
2. European Declaration on Paper Recycling
2006 – 2010. Monitoring Report” 2007)
3. 2006 Recovered Paper Annual Statistics
20
10
0
2010 Expected (1) Europe 2007 (2)
USA 2006 (3)
Grade mix of recovered papers in
South Africa (Hunt 2008)
SA had a recovery rate of 43% in 2008
(Prasa, 2009)
New legislation (Waste Management Act)
will force the use of the remaining
unutilized domestic waste!
600 000 tpa recoverable
New
products
THE PROBLEM
• Increasing variability of waste feed to
deinking plants.
• Traditionally - Deinking plants run to set
parameters and control the output quality by
varying input waste mix.
• Future scenario –
– Less flexibility to use waste mix as a control
variable
– Will have to use other process parameters to
control.
– Greater process flexibility required.
Survey of SA industrial practice
PROCESS
Newsprint
deinking
WASTE USED
ONP - Old
newsprint
OMG - Books and
magazines
Tissue
manufacturing
Mixed office
waste:
HL1 – white
PROCESS CONDITIONS
Alkaline slushing in presence
of H2O2, deinking flotation with
displector system, washing,
dispersion, bleaching
Neutral slushing, multistage
deinking without chemicals,
dispersion, bleaching
HL2 – white &
colors
Linerboard and
cartonboard
manufacture
OCC
Slushing, no deinking,
cleaning (hydrocyclones),
screening, dispersion
TYPICAL HIGH QUALITY OFFICE PAPER DEINKING
PROCESS
Flotation
1st stage
Pulper
HD cleaner
Screen
Ink sludge
Fine cleaners
and screens
Disperser
Washing &
thickening
Flotation
2nd stage
Washing &
thickening
Pulp
storage
tower
6
Objectives
• To identify influential process control
parameters that could assist in the
control of deinking plants.
LABORATORY DEINKING
• Waste paper grades: HL1 and HL2
• Pulping – measured brightness (UVincl.) and
ERIC on 170 gsm pulp pads
• Flotation - measured brightness (UVincl.) and
ERIC on 170 gsm pulp pads
• Washing - Measured brightness (UVincl.) and
ERIC on 60 gsm handsheets, Yield.
• Followed a Statistical Experimental Design
procedure to screen the effect of 11 different
variables.
Screening Experimental design
• To fully investigate 11 different variables at
two levels would require 211 or 2048
experiments.
• Pulping and flotation as per Plackett-Burman
experimental design:
– 11 factors,
– 2 levels,
– 12 runs,
– reflected – 24 runs eliminates the effect of
interactions.
TYPICAL LEVELS
CONTROL PARAMETERS IN OFFICE PAPER
DEINKING
LEVELS IN
LABORATORY
[Low-High]
PULPING
% Consistency
16-18
8
7-8
monitored.
%NaOH
0
0 and 0.67
% Sodium silicate
0
0 and 2
%H2O2
0
0 and 1
0.085-0.1
0.25 and 0.75
Pulping time (tp mins)
16
5 and 15
Temperature (Tp oC)
50
35 and 50
Chelant
0
0.2
pH
% Dispersant (%Surfp & %Surff )
FLOTATION
Temperature (Tf oC)
40
30 And 45
0.8-1.2
7.5 – 8.0
0.8 and 1.3
8 and 10
Hardness (ppm CaCO3)
200
200
Flotation time (tf , mins.)
< 5 mins
5 and 20
% Consistency
pH
A
B
C
RUN NO.
1
%NaOH
0.67
% Sod Sil
0
%H2O2
1
2
0.67
2
3
0
4
5
6
7
PULPING
D
FLOTATION
I
J
E
F
G
H
% Surf p
0.25
tp, min
5
Tp, deg C
35
Tf, deg C
45
% cons
1.3
pH
10
% Surff
0
tf, min
20
0
0.75
5
35
30
1.3
10
0.5
5
2
1
0.25
15
35
30
0.8
10
0.5
20
0.67
0.67
0.67
0
0
2
2
2
1
0
1
1
0.75
0.75
0.25
0.75
5
15
15
5
50
35
50
50
30
45
30
45
0.8
0.8
1.3
0.8
8
8
8
10
0.5
0
0
0
20
20
5
5
8
9
0
0
0
0
1
0
0.75
0.75
15
15
35
50
45
30
1.3
1.3
8
10
0.5
0
5
20
10
0.67
0
0
0.25
15
50
45
0.8
10
0.5
5
11
12
0
0
2
0
0
0
0.25
0.25
5
5
50
35
45
30
1.3
0.8
8
8
0.5
0
20
5
13
14
15
16
0
0
0.67
0
2
0
0
2
0
1
0
0
0.75
0.25
0.75
0.25
15
15
5
15
50
50
50
35
30
45
45
45
0.8
0.8
1.3
1.3
8
8
8
10
0.5
0
0
0
5
20
5
5
17
0
0
1
0.25
5
50
30
1.3
10
0.5
5
18
0
0
0
0.75
5
35
45
0.8
10
0.5
20
19
20
0.67
0.67
0
2
0
0
0.25
0.25
15
5
35
50
30
30
1.3
0.8
8
10
0.5
0
20
20
21
22
23
0.67
0
0.67
2
2
0
1
1
1
0.25
0.75
0.75
5
5
15
35
35
35
45
30
30
0.8
1.3
0.8
8
8
10
0.5
0
0
5
20
5
50
45
1.3
10
0.5
20
24
ΣY+
0.67
2
1
0.75
15
Sum of outputs, for each experimental factor A to K at the HIGH level
ΣYYavg+
Sum of outputs, for each experimental factor A to K at the LOW level
Average of ΣY+, for each factor A to K
Yavg-
Average of ΣY-, for each factor A to K
EFFECT
Net Effect = Yavg+ minus Yavg-, for each factor A to K
K
LABORATORY DEINKING
Laboratory Hydra Pulper model UEC 2020,
Universal Engineering Corporation, India
Flotation Cell model UEC 2026,
Universal Engineering Corporation, India
RESULTS
Ink removal as a function of flotation time.
Brightness
100
ERIC
140
130
95
120
85
HL1
HL2
110
HL1
100
HL2
ERIC
% BRIGHTNESS
90
80
90
80
75
70
70
60
65
50
60
40
0
5
10
15
20
FLOTATION TIME, tf (mins)
25
0
5
10
15
20
FLOTATION TIME, t f (mins)
25
Yield as a function of flotation time
105
100
HL1
95
YIELD %
HL2
90
85
80
75
70
0
5
10
15
FLOTATION TIME tf (mins)
20
25
Effect of processing stage
Brightness
ERIC
100
140
130
95
120
HL1
110
HL2
85
100
HL1
80
HL2
ERIC
% Brightness
90
90
75
80
70
70
60
65
50
60
PULPER
FLOATED
WASHED
40
PULPER
FLOATED
WASHED
CLUSTER PLOTS – BRIGHTNESS VS ERIC
HL1
HL2
100,00
105,00
PULPED
100,00
90,00
FLOATED
80,00
BRIGHTNESS
BRIGHTNESS
WASHED
95,00
90,00
70,00
60,00
PULPED
50,00
WASHED
85,00
FLOATED
40,00
80,00
0,00
50,00
100,00
ERIC
150,00
200,00
30,00
0,00
50,00
100,00
ERIC
150,00
RESULTS OF EXPERIMENTAL SCREENING – HL1
WASHED ERIC
25,00
24 RUN
12 RUN
NET EFFECT
5,00
4,50
4,00
3,50
3,00
2,50
2,00
1,50
1,00
0,50
0,00
20,00
24 RUN
15,00
12 RUN
10,00
5,00
0,00
VARIABLE
VARIABLE
YIELD
NET EFFECT
NET EFFECT
WASHED BRIGHTNESS
8,00
7,00
6,00
5,00
4,00
3,00
2,00
1,00
0,00
24 RUN
12 RUN
VARIABLE
RESULTS OF EXPERIMENTAL SCREENING – HL2
WASHED BRIGHTNESS
WASHED ERIC
8,00
5,00
4,00
3,00
24 RUN
2,00
12 RUN
1,00
0,00
NET EFFECT
6,00
24 RUN
12 RUN
VARIABLE
VARIABLE
YIELD
NET EFFECT
NET EFFECT
7,00
20,00
18,00
16,00
14,00
12,00
10,00
8,00
6,00
4,00
2,00
0,00
18,00
16,00
14,00
12,00
10,00
8,00
6,00
4,00
2,00
0,00
24 RUN
12 RUN
VARIABLE
RANKING OF CONTROL VARIABLES
(by magnitude of net effect)
WASHED BRIGHTNESS
FACTOR
WASHED ERIC
HL1
HL2
MEAN
%H2O2
% Sodium
Silicate
1.0
2.5
1.7
1.6
1.7
Tp, ⁰ C
-0.6
tf, min
%
consistency
FACTOR
YIELD
HL1
HL2
MEAN
HL1
HL2
MEAN
5.0
FACTOR
%
consistency
tp, min
6
4
3
4
3.9
1.6
%H2O2
4
4
3.6
% Surf-p
0
3
1.4
3.5
1.5
Tp, ⁰ C
10
-4
3.4
pH
-1
4
1.3
2.2
-0.2
1.0
pH
3
4
3.3
% Surf-f
0
1
1.0
1.1
0.7
0.9
-1
6
2.8
2
0.7
-10
9
-0.7
tp, min
% Sodium
Silicate
0
0.9
% Surf-p
%
consistency
%NaOH
1.1
0.7
2
-2
0.3
% Surf-f
1.4
-0.3
0.6
% Surf-f
-6
1
-2.3
Tp, ⁰ C
0
-2
-1.0
tp, min
1.5
-1.0
0.2
%NaOH
-12
2
-4.7
Tf, ⁰ C
-1
-1
-1.2
pH
-0.3
-0.2
-0.3
-7
-3
-4.8
%H2O2
-1
-1
-1.2
Tf, ⁰ C
1.0
-1.9
-0.5
Tf, ⁰ C
% Sodium
Silicate
-7
-5
-5.8
%NaOH
-4
-2
-3.0
% Surf-p
Standard
Deviation
1.0
-2.4
-0.7
-8
-8
-7.8
-8
-5.0
2.9
10.4
7.7
tf, min
Standard
Deviation
-2
2.6
tf, min
Standard
Deviation
8.6
5.9
CONCLUSION:
SELECTION OF EFFECTIVE CONTROL VARIABLES
Brightness
ERIC
Yield
Waste – HL1
Waste – HL1
Waste – HL1
Waste – HL2
Waste – HL2
Waste – HL2
% H2O2
Flotation time
Flotation time
% Sodium silicate/NaOH
% Sodium silicate/NaOH
Flotation consistency
Flotation time
Pulping time
Flotation consistency
Color code:
Favourable effect
Adverse effect
Note: for ERIC a negative correlation is favorable for final properties
VARIABLES UNSUITABLE FOR CONTROL
• Temperatures (pulping and flotation) have some
influence but are not practical control parameters.
• Surfactant addition to float cell has low influence,
but addition to pulper had some influence.
• pH generally had a low influence.
• The above variables still need to be optimised.
Further work
• To generate a database of flotation results
under all possible process conditions and
waste grades.
• To model the deinking process w.r.t. waste
inputs and process parameters (using
Artificial Neural Networks)
• To use this model to enable mills to
proactively react to changing waste
conditions.
THANK YOU
Laboratory procedure
PULPING
Measure
FLOAT
Measure
WASH &
Measure
•Charge water to
pulper.
•Add chemicals.
•Tear waste and
charge to pulper.
•Allow to soak 10
mins.
•Pre-mix for 30
secs.
•Add H2O2 and
pulp for specified
time.
•Test
temperature,
consistency, pH.
•Form 200 gsm
pulp pads (Tappi
218 om-91).
•Measure GE
brightness and
ERIC on
Technidyne
ColorTouch PC
spectrophoto meter.
•Charge : water,
calcium chloride
and surfactant.
•Adjust pH.
•Charge pulp to
required
consistency.
•Agitate and float
for required time
at 1600rpm.
•Transfer
contents
quantitatively to
a bucket.
•Prepare 200 gsm
pulp pads and
measure
brightness, ERIC.
•Determine mass
and calculate
yield.
•Make 60gsm
handsheets
(Tappi Tappi T
205 sp-95) on
Rapid-Koethen
former.
•Measure
brightness, ERIC.
LABORATORY DEINKING: definitions & explanations
Brightness - UVincluded
•The illuminant in the spectrophotometer has a UV
component, induces fluorescence in blue region.
•Results in higher brightness readings.
•Corresponds to what is actually perceived by an
observer.
ERIC
Measures Effective Residual Ink Concentration.
Infrared reluctance at 950nm.
Yield
Dry mass fibre out/dry mass paper in x 100
Plackett-Burman
design
A non-geometric experimental factorial design in
which each main effect is confounded partially with
all interactions that do not contain the main effect.
12 run design
A 12 run design will allow the screening of 11
different factors.
24 run reflected design A 12 run design reflected, will eliminate the effect
of higher order interactions, providing information
on the main effects only.