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Statistical Experimental Design Technique to Determine the Most Effective Process Control Variables for the Control of Flotation Deinking of Office Papers.

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Presentation on theme: "Statistical Experimental Design Technique to Determine the Most Effective Process Control Variables for the Control of Flotation Deinking of Office Papers."— Presentation transcript:

1 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

2 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

3 Paper Recycling Trends Paper recycling trends have exceeded expectations: Sources: 1. Goettsching & Pakarinen 2000 7: 12-22 2. European Declaration on Paper RecyclingEuropean Declaration on Paper Recycling 2006 – 2010. Monitoring Report 2007 2006 – 2010. Monitoring Report 2007) 3. 2006 Recovered Paper Annual StatisticsRecovered Paper Annual Statistics

4 Grade mix of recovered papers in South Africa (Hunt 2008) SA had a recovery rate of 43% in 2008

5 (Prasa, 2009)

6 New legislation (Waste Management Act) will force the use of the remaining unutilized domestic waste! 600 000 tpa recoverable New products

7 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

8 PROCESSWASTE USEDPROCESS CONDITIONS Newsprint deinking ONP - Old newsprint OMG - Books and magazines Alkaline slushing in presence of H 2 O 2, 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 OCCSlushing, no deinking, cleaning (hydrocyclones), screening, dispersion Survey of SA industrial practice

9 Pulper Flotation 1 st stage Washing & thickening Pulp storage tower HD cleanerScreen Fine cleaners and screens Disperser Ink sludge 6 TYPICAL HIGH QUALITY OFFICE PAPER DEINKING PROCESS Flotation 2 nd stage Washing & thickening

10 Objectives To identify influential process control parameters that could assist in the control of deinking plants.

11 Waste paper grades: HL1 and HL2 Pulping – measured brightness (UV incl. ) and ERIC on 170 gsm pulp pads Flotation - measured brightness (UV incl. ) and ERIC on 170 gsm pulp pads Washing - Measured brightness (UV incl. ) and ERIC on 60 gsm handsheets, Yield. Followed a Statistical Experimental Design procedure to screen the effect of 11 different variables. LABORATORY DEINKING

12 Screening Experimental design To fully investigate 11 different variables at two levels would require 2 11 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.

13 CONTROL PARAMETERS TYPICAL LEVELS IN OFFICE PAPER DEINKING LEVELS IN LABORATORY [Low-High] PULPING % Consistency16-188 pH7-8monitored. %NaOH00 and 0.67 % Sodium silicate00 and 2 %H 2 O 2 00 and 1 % Dispersant (%Surf p & %Surf f )0.085-0.10.25 and 0.75 Pulping time (t p mins)165 and 15 Temperature (T p o C)5035 and 50 Chelant00.2 FLOTATION Temperature (T f o C)4030 And 45 % Consistency0.8-1.20.8 and 1.3 pH7.5 – 8.08 and 10 Hardness (ppm CaCO 3) 200 Flotation time (t f, mins.) < 5 mins 5 and 20

14 PULPINGFLOTATION ABCDEFGHIJK RUN NO.%NaOH% Sod Sil%H 2 O 2 % Surf p t p, minT p, deg CT f, deg C% conspH% Surf f t f, min 10.67010.25535451.310020 20.67200.75535301.3100.55 30210.251535300.8100.520 40.67010.75550300.880.520 50.67200.751535450.88020 60.67210.251550301.3805 70210.75550450.81005 80010.751535451.380.55 90000.751550301.310020 100.67000.251550450.8100.55 110200.25550451.380.520 120000.25535300.8805 130200.751550300.880.55 140010.251550450.88020 150.67000.75550451.3805 160200.251535451.31005 170010.25550301.3100.55 180000.75535450.8100.520 190.67000.251535301.380.520 0.67200.25550300.810020 210.67210.25535450.880.55 220210.75535301.38020 230.67010.751535300.81005 240.67210.751550451.3100.520 ΣY+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 level Yavg+Average of ΣY+, for each factor A to K Yavg-Average of ΣY-, for each factor A to K EFFECTNet Effect = Yavg+ minus Yavg-, for each factor A to K

15 LABORATORY DEINKING Laboratory Hydra Pulper model UEC 2020, Universal Engineering Corporation, India Flotation Cell model UEC 2026, Universal Engineering Corporation, India

16 RESULTS Ink removal as a function of flotation time. BrightnessERIC

17 Yield as a function of flotation time

18 Effect of processing stage BrightnessERIC




22 RANKING OF CONTROL VARIABLES (by magnitude of net effect) WASHED BRIGHTNESSWASHED ERICYIELD FACTORHL1HL2MEANFACTORHL1HL2MEANFACTORHL1HL2MEAN %H 2 O 2 p, min645.0 % consistency343.9 % Sodium Silicate1.61.71.6%H 2 O 2 443.6% Surf-p031.4 T p, C- p, C10-43.4pH41.3 t f, min2.2-0.21.0pH343.3% Surf-f011.0 % consistency1.10.70.9% Surf-p62.8t p, min020.7 %NaOH1.10.70.9 % consistency-109-0.7 % Sodium Silicate2-20.3 % Surf-f1.4-0.30.6% Surf-f-61-2.3T p, C0-2 t p, min1.50.2%NaOH-122-4.7T f, C -1.2 pH-0.3-0.2-0.3T f, C-7-3-4.8%H 2 O 2 -1.2 T f, C1.0-1.9-0.5 % Sodium Silicate -7-5-5.8%NaOH-4-2-3.0 % Surf-p1.0-2.4-0.7t f, min-8 -7.8t f, min-2-8-5.0 Standard Deviation2.62.9 Standard Deviation10.47.7 Standard Deviation8.65.9

23 CONCLUSION: SELECTION OF EFFECTIVE CONTROL VARIABLES BrightnessERICYield Waste – HL1 Waste – HL2 % H 2 O 2 Flotation time % Sodium silicate/NaOH Flotation consistency Flotation timePulping time Flotation consistency Color code:Favourable effectAdverse effect Note: for ERIC a negative correlation is favorable for final properties

24 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

25 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


27 Laboratory procedure PULPINGMeasureFLOATMeasureWASH & Measure Charge water to pulper. Add chemicals. Tear waste and charge to pulper. Allow to soak 10 mins. Pre-mix for 30 secs. Add H 2 O 2 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.

28 LABORATORY DEINKING: definitions & explanations Brightness - UV included 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. ERICMeasures Effective Residual Ink Concentration. Infrared reluctance at 950nm. YieldDry 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 designA 12 run design will allow the screening of 11 different factors. 24 run reflected designA 12 run design reflected, will eliminate the effect of higher order interactions, providing information on the main effects only.

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