PERSONAL FLOTATION DEVICES. Personal Flotation Devices FLOTATION KEEPING YOUR HEAD ABOVE WATER.
Statistical Experimental Design Technique to Determine the Most Effective Process Control Variables...
-
Upload
alexandria-merryman -
Category
Documents
-
view
213 -
download
1
Transcript of Statistical Experimental Design Technique to Determine the Most Effective Process Control Variables...
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 TechnologyOct. 2010
PROJECT TEAM
Funding – Forest Products Research (CSIR)Project Manager – Jerome Andrew (CSIR)Researcher – J PauckAcademic supervisors – Dr. Jon Pocock (UKZN) Prof. Richard Venditti (NCSU)Technicians – Hoosain Adam Zamani Myende Nomakhosi Sincuba
Elsie Sibande
Paper Recycling Trends
2010 Expected (1)
Europe 2007 (2) USA 2006 (3)0
10
20
30
40
50
60
70
% U
tiliza
tion
rate
Paper recycling trends have exceeded expectations:
Sources:1. Goettsching & Pakarinen 2000 7: 12-222. European Declaration on Paper Recycling 2006 – 2010. Monitoring Report” 2007)3. 2006 Recovered Paper Annual Statistics
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
• 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.
THE PROBLEM
PROCESS WASTE USED PROCESS CONDITIONS
Newsprint deinking
ONP - Old newsprint
OMG - Books and magazines
Alkaline slushing in presence of H2O2, deinking flotation with displector system, washing, dispersion, bleaching
Tissue manufacturing
Mixed office waste:
HL1 – white
HL2 – white & colors
Neutral slushing, multistage deinking without chemicals, dispersion, bleaching
Linerboard and cartonboard manufacture
OCC Slushing, no deinking, cleaning (hydrocyclones), screening, dispersion
Survey of SA industrial practice
Pulper Flotation
1st stage
Washing & thickening
Pulp storage tower
HD cleaner Screen
Fine cleaners
and screens
Disperser
Ink sludge
6
TYPICAL HIGH QUALITY OFFICE PAPER DEINKING PROCESS
Flotation 2nd stage Washing &
thickening
Objectives
• To identify influential process control parameters that could assist in the control of deinking plants.
• 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.
LABORATORY DEINKING
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.
CONTROL PARAMETERS
TYPICAL LEVELS IN OFFICE PAPER
DEINKING
LEVELS IN LABORATORY
[Low-High]PULPING
% Consistency 16-18 8
pH 7-8 monitored.
%NaOH 0 0 and 0.67
% Sodium silicate 0 0 and 2
%H2O2 0 0 and 1
% Dispersant (%Surfp & %Surff ) 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
FLOTATIONTemperature (Tf
oC) 40 30 And 45
% Consistency 0.8-1.2 0.8 and 1.3pH 7.5 – 8.0 8 and 10
Hardness (ppm CaCO3) 200 200
Flotation time (tf , mins.) < 5 mins 5 and 20
PULPING FLOTATION A B C D E F G H I J K
RUN NO. %NaOH % Sod Sil %H2O2 % Surf p tp, min Tp, deg C Tf, deg C % cons pH % Surff tf, min1 0.67 0 1 0.25 5 35 45 1.3 10 0 20
2 0.67 2 0 0.75 5 35 30 1.3 10 0.5 5
3 0 2 1 0.25 15 35 30 0.8 10 0.5 20
4 0.67 0 1 0.75 5 50 30 0.8 8 0.5 205 0.67 2 0 0.75 15 35 45 0.8 8 0 206 0.67 2 1 0.25 15 50 30 1.3 8 0 57 0 2 1 0.75 5 50 45 0.8 10 0 5
8 0 0 1 0.75 15 35 45 1.3 8 0.5 59 0 0 0 0.75 15 50 30 1.3 10 0 20
10 0.67 0 0 0.25 15 50 45 0.8 10 0.5 5
11 0 2 0 0.25 5 50 45 1.3 8 0.5 2012 0 0 0 0.25 5 35 30 0.8 8 0 5
13 0 2 0 0.75 15 50 30 0.8 8 0.5 514 0 0 1 0.25 15 50 45 0.8 8 0 2015 0.67 0 0 0.75 5 50 45 1.3 8 0 516 0 2 0 0.25 15 35 45 1.3 10 0 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 0.67 0 0 0.25 15 35 30 1.3 8 0.5 2020 0.67 2 0 0.25 5 50 30 0.8 10 0 20
21 0.67 2 1 0.25 5 35 45 0.8 8 0.5 522 0 2 1 0.75 5 35 30 1.3 8 0 2023 0.67 0 1 0.75 15 35 30 0.8 10 0 5
24 0.67 2 1 0.75 15 50 45 1.3 10 0.5 20ΣY+ Sum of outputs, for each experimental factor A to K at the HIGH level
ΣY- Sum of outputs, for each experimental factor A to K at the LOW levelYavg+ 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
LABORATORY DEINKINGLaboratory 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
0 5 10 15 20 2560
65
70
75
80
85
90
95
100
HL1HL2
FLOTATION TIME, tf (mins)
% B
RIG
HTN
ESS
ERIC
0 5 10 15 20 2540
50
60
70
80
90
100
110
120
HL1HL2
FLOTATION TIME, t f (mins)
ERIC
Yield as a function of flotation time
0 5 10 15 20 2570
75
80
85
90
95
100
105
HL1HL2
FLOTATION TIME tf (mins)
YIEL
D %
Effect of processing stage
Brightness ERIC
PULPER FLOATED WASHED 60
65
70
75
80
85
90
95
100
HL1HL2
% B
righ
tnes
s
PULPER FLOATED WASHED 40
50
60
70
80
90
100
110
120
130
140
HL1
HL2
ERIC
CLUSTER PLOTS – BRIGHTNESS VS ERIC
40.0060.00
80.00
100.00
120.00
140.00
160.00
180.0080.00
85.00
90.00
95.00
100.00
105.00
PULPEDFLOATEDWASHED
ERIC
BRIG
HTN
ESS
20.00 40.00 60.00 80.00 100.00 120.00 140.0030.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
PULPEDWASHEDFLOATED
ERIC
BRIG
HTN
ESS
HL1 HL2
RESULTS OF EXPERIMENTAL SCREENING – HL1
tf, min
% Sod Si
l
tp, min
% Surf-
f
% cons
%NaOH
% Surf-
p
%H2O2
Tf, deg
C
[Tp, d
eg C]
[pH]0.00
1.00
2.00
3.00
4.00
5.00
WASHED BRIGHTNESS
24 RUN12 RUN
VARIABLE
NET
EFF
ECT
%NaOH
[Tp, d
eg C]
% cons
tf, min
Tf, deg
C
% Sod Si
l
[tp, m
in]
% Surf-
f
[%H2O2]
[pH]
% Surf-
p0.00
5.00
10.00
15.00
20.00
25.00
WASHED ERIC
24 RUN12 RUN
VARIABLE
NET
EFF
ECT
[%NaO
H]
% cons
% Sod Si
l
[tf, m
in]
[%H2O2]
[pH]
[Tf, d
eg C]
% Surf-
f
[tp, m
in]
% Surf-
p
Tp, d
eg C
0.001.002.003.004.005.006.007.008.00
YIELD
24 RUN12 RUN
VARIABLE
NET
EFF
ECT
RESULTS OF EXPERIMENTAL SCREENING – HL2
Tp, d
eg C
%H2O2
[% Su
rf-p]
[Tf, d
eg C]
% Sod Si
l
[tp, m
in]
% cons
%NaOH
[% Su
rf-f]
[pH]
[tf, m
in]0.001.002.003.004.005.006.007.008.00
WASHED BRIGHTNESS
24 RUN12 RUN
VARIABLE
NET
EFF
ECT
[% co
ns]
tf, min
[% Su
rf-p]
% Sod Si
l
[tp, m
in][pH]
Tp, d
eg C
[%H2O2]
Tf, deg
C
[%NaO
H]
[% Su
rf-f]
0.002.004.006.008.00
10.0012.0014.0016.0018.0020.00
WASHED ERIC
24 RUN12 RUN
VARIABLE
NET
EFF
ECT
[tf, m
in]
% cons pH
% Surf-
p
[%NaO
H]
[Tp, d
eg C]
[% So
d Sil]
tp, min
% Surf-
f
[Tf, d
eg C]
[%H2O2]
0.002.004.006.008.00
10.0012.0014.0016.0018.00
YIELD
24 RUN12 RUN
VARIABLE
NET
EFF
ECT
RANKING OF CONTROL VARIABLES (by magnitude of net effect)
WASHED BRIGHTNESS WASHED ERIC YIELD
FACTOR HL1 HL2 MEAN FACTOR HL1 HL2 MEAN FACTOR HL1 HL2 MEAN
%H2O2 1.0 2.5 1.7 tp, min 6 4 5.0%
consistency 3 4 3.9% Sodium
Silicate 1.6 1.7 1.6 %H2O2 4 4 3.6 % Surf-p 0 3 1.4
Tp, ⁰ C -0.6 3.5 1.5 Tp, ⁰ C 10 -4 3.4 pH -1 4 1.3
tf, min 2.2 -0.2 1.0 pH 3 4 3.3 % Surf-f 0 1 1.0%
consistency 1.1 0.7 0.9 % Surf-p -1 6 2.8 tp, min 0 2 0.7
%NaOH 1.1 0.7 0.9%
consistency -10 9 -0.7% Sodium
Silicate 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 Tf, ⁰ C -7 -3 -4.8 %H2O2 -1 -1 -1.2
Tf, ⁰ C 1.0 -1.9 -0.5% Sodium
Silicate -7 -5 -5.8 %NaOH -4 -2 -3.0
% Surf-p 1.0 -2.4 -0.7 tf, min -8 -8 -7.8 tf, min -2 -8 -5.0Standard Deviation 2.6 2.9
Standard Deviation 10.4 7.7
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
% H2O2Flotation 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
• 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.
VARIABLES UNSUITABLE FOR CONTROL
• 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.
Further work
THANK YOU
Laboratory procedurePULPING 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 & explanationsBrightness - 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.