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Research & Development Information

PCA R&D Serial No. 2855

Data Analysis of Electrostatic Charge in a Finish Ball Mill

by Medgar L. Marceau and Ann M. Caffero

© Portland Cement Association 2005 All rights reserved

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TABLE OF CONTENTS

Introduction......................................................................................................................................1 Background......................................................................................................................................1

Summary of Testing.....................................................................................................................1 Phase 1. ....................................................................................................................................1 Phase 2. ....................................................................................................................................2 Phase 3. ....................................................................................................................................2

Data Reduction.................................................................................................................................2 Data Analysis ...................................................................................................................................2

Parameters Related to Level of Grinding Aid..............................................................................3 Power consumption of motors. ................................................................................................3 Total electric demand per ton. .................................................................................................3 Rejection rate. ..........................................................................................................................4 Water flow. ..............................................................................................................................4 Separator speed. .......................................................................................................................4 Mill vibrations..........................................................................................................................4

Parameters Related to Electrostatic Charge.................................................................................4 Power consumption of motors. ................................................................................................4 Total electric demand per ton. .................................................................................................5 Grinding aid. ............................................................................................................................5 Water flow. ..............................................................................................................................5

Parameters Related to Fineness ...................................................................................................5 Separator feed blains................................................................................................................5

Conclusion .......................................................................................................................................6 Acknowledgements..........................................................................................................................6 Appendix A Electro-Tech Systems, Inc., Audit Report (Phase 1)...............................................A1 Appendix B Additional Mill Data (Phase 1)................................................................................B1 Appendix C Electro-Tech Systems, Inc., Test Report (Phase 2) .................................................C1 Appendix D Additional Mill Data (Phase 2) ...............................................................................D1 Appendix E Construction Technology Laboratories, Inc., Data Analysis (Phase 3)................... E1

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Data Analysis of Electrostatic Charge in a Finish Ball Mill

by Medgar L. Marceau and Ann M. Caffero *

INTRODUCTION

This project represents Phase 3 of an ongoing evaluation of the effects of grinding aid and electrostatic charge on grinding finish mills in cement plants. Data reduction and analysis was performed on grinding parameters and grinding aid data collected during Phase 2 of the evaluation. The purpose of this report is to present the reduced data and the results of the analysis.

BACKGROUND

Electrostatic charge is reported to exist in grinding finish mills. It is commonly held that this electrostatic charge reduces the efficiency of the grinding process and lowers productivity. Current industry practice to reduce electrostatic charge is to introduce a grinding aid at the finish mill to facilitate the grinding process. Previously, the Manufacturing Technical Committee’s Task Committee 1, Materials Preparation and Finishing, evaluated a technology† that claimed to reduce electrostatic charge without using grinding aid. The results were inconclusive and a report was never published. However, the results did raise a number of questions regarding electrostatic charge in grinding finish mills, in particular: how should the buildup of electrostatic charge be measured? and what is its polarity and magnitude?

Task Committee 1 initiated a project to investigate electrostatic charge in grinding finish mills. Phase 1 measured and determined the magnitude and polarity of electrostatic charge in a ball mill with and without grinding aid. The purpose of Phase 2 was to optimize the amount of grinding aid needed to reduce electrostatic charge. Although several dozen grinding parameters were monitored and recorded, the relationship between these parameters, electrostatic charge, and optimum level of grinding aid was not obvious. The goal of Phase 3 (this project); therefore, is to reduce and organize the data so that relationships among the parameters can be observed.

Summary of Testing

Phase 1. Electro-Tech Systems, Inc. (ETS) monitored electrostatic charge in the No. 5 Finisher (a ball mill) at Monarch Cement Company, Humboldt, Kansas, on November 6, 2002. Electrostatic charge measurements were taken with ETS Model 222 Electrostatic Fieldmeter equipped with a custom detector probe. Ambient temperature and humidity were monitored

* Building Science Engineer and Associate Microscopist, respectively, Construction Technology Laboratories, Inc., 5400 Old Orchard Road, Skokie, IL, 60077, (847) 965-7500. www.CTLGroup.com † ECOFOR manufactured by ECOFOR Company, 168 Leninskiy Prospekt, Saint Petersburg, 196191, Russia. www.ecofor.com

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http://www.ctlgroup.com/

http://www.ecofor.com/

throughout the test and remained fairly constant. Temperature ranged from 20.4 to 20.8°C (68.7 to 69.4°F) and humidity ranged from 24.3 to 30.2 % over a period of approximately 3 hours.

Grinding aid was turned off approximately 17 hours before the first set of readings were taken to purge the system of grinding aid. While the grinding aid remained off, the first set of measurements was taken in the recovery chute and in the discharge duct of the ball mill. The grinding aid was turned back on and the second set of measurements was taken in the same locations. Grinding aid usage rate was 0.83 lb/ton of cement‡. The results show that (i) electrostatic charge is detected, (ii) once grinding aid is introduced, electrostatic charge is reduced, and (iii) the polarity of the cement particles is negative. The ETS audit report is presented in Appendix A. Additional mill data, which were not part of the ETS report, are presented in Appendix B.

Phase 2. ETS again monitored electrostatic charge and grinding parameters at the same plant but on a different ball mill—one equipped with a high efficiency separator. The magnitude and polarity of electrostatic charge was monitored on June 23 and 24, 2003, as incrementally greater doses of grinding aid were added to the mill. The grinding aid is a mixture of HEA2 and MTDA, both manufactured by W.R. Grace & Co. When sprayed into the mill, it is diluted at a rate of 20% grinding aid to 80% water. The dosage increments were 0, 25, 50, 87.5, and 100% of normal use, where the normal use rate in this mill is 0.74 lb active solids/ton of cement. The duration between increases was approximately 2 hours. Throughout the test, the production rate was held constant and the feed rate was varied. Consequently, the recirculating load (reject rate) varied. The amount of data collected was tremendous and there was no clear indication of an optimum level of grinding aid. The ETS test report is presented in Appendix C; however, it does not include all the data because it is too voluminous. Additional mill data, which were not part of the ETS report, are presented in Appendix D.

Phase 3. The rest of this report is devoted to Phase 3. This phase of the project consists of reduction and analysis of the data collected in Phase 2. No additional testing was conducted.

DATA REDUCTION

Data reduction consists of identifying the data to be used in the subsequent analysis. Much of the data collected in Phase 2 is superfluous because it was measured and recorded with temporal resolution much greater than the dependent variable (electrostatic charge). Figure 1 shows a flow diagram of the finish mill system. The reduced data are shown in Table 1. Abbreviated names for the grinding parameters are shown in Table 2. Figure 2 shows a sample of the reduced data. It shows the variations in electrostatic charge over the course of testing. All other data are show in Appendix E as part of the data analysis.

DATA ANALYSIS

Using statistical analysis, Construction Technology Laboratories, Inc. (CTL) determined which grinding parameters significantly affect performance of the ball mill. The data were analyzed using the method of least squares. In this method, a model is sought that can explain and predict,

‡ The original ETS report incorrectly reported 0.83 lb/ton clinker.

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with a specified level of confidence§, the performance of the grinding mill as it relates to grinding aid and electrostatic charge.

How well a model fits the data can be measured in many ways. For example, one simple measure is the regression coefficient, R-squared (R-sq)**. In the discussion below, the following qualitative terms are used to describe how well a model fits the data:

• A very good fit describes a model with a regression coefficient greater than 85%, satisfying statistical tests of significance, and satisfying all assumptions for least squares regression††.

• A good fit describes a model with a regression coefficient between 75 and 85% satisfying statistical tests of significance, and satisfying all assumptions for least squares regression.

• A somewhat good fit describes a model with a regression coefficient between 75 and 80%, satisfying statistical tests of significance, and satisfying most assumptions for least squares regression.

The following sections discuss the grinding parameters that significantly affect performance of the ball mill. They also discuss parameters that should affect performance but in fact show no statistical correlation. The complete analysis is presented in the Appendix E.

Parameters Related to Level of Grinding Aid

Power consumption of motors. The best model (R-sq = 83.2%) relating the level of grinding aid to power consumption of individual motors (mill power, elevator power, air sweep fan power and separator fan power) is:

Grind.aid = 546 + 0.237 Mill.pow - 7.19 Elev.pow - 11.3 AS.fan.pow - 0.822 Sep.fan.pow

This relation (see page E13) is conditional because elevator power and separator fan power are strongly negatively correlated (-0.79) (see page E57). As the amount of grinding aid increases, the mill power also increases; however, the elevator, air sweep fan, and separator fan power decrease. The amount of grinding aid has no significant effect on the power consumption of the air compressors.

Total electric demand per ton. There is no simple linear relation between total electric demand per ton of material output (kW/ton) and level of grinding aid. Nor does the analysis suggest a polynomial model. However, the raw data show the lowest average total demand (42.78 kW/ton) occurs when the level of grinding aid is at 62.5% of the normal addition rate (see page E19). § The level of confidence is set at 95%, resulting in level of significance for a Type I error of α = 5%. The value of α is the probability of rejecting the null hypothesis when the null hypothesis is in fact true. In other words, it is the probability of finding a significant association when one does not really exist. ** R-squared is the proportion of the variability in the response that is explained by the model. The remainder of the variability is assumed to be due to random error. †† In order for a least squares regression model to be valid, the error component (also called the residual) must be a standard normal random variable with constant variance.

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Rejection rate. The third degree polynomial regression equation relating rejection rate to the level of grind aid is a very good fit (R-sq = 87.2%):

Rej.rate = 151 + 2.74 Grind.aid - 0.0987 Grind.aid^2 + 0.000647 Grind.aid^3

The maximum rejection rate occurs when the level of grinding aid is 16.6% (see page E21). The lowest rejection rate is achieved when the grinding aid is at 85.1%. Above 85.1%, rejection rate increases.

Water flow. Water flow represents the amount of water used to cool the cement. The linear regression equation relating water flow at the inlet of the grinding mill (wat.flo1) to level of grinding aid is a somewhat good fit (R-sq = 78.6%). It shows that 78.6% of the decrease in water flow can be attributed to the increase in level of grinding aid (see page E22):

Wat.flo1 = 2.96 - 0.0122 Grind.aid

Separator speed. The second-degree polynomial regression equation relating separator speed to the level of grind aid squared is a somewhat good fit (R-sq = 85.7%):

Sep.speed = 150 + 0.00143 Grind.aid^2

Separator speed is at a minimum when grinding aid is zero and at a maximum when grinding aid is at 100% (see page E28).

Mill vibrations. The third degree polynomial regression equation relating vibrations at the inlet of the grinding mill (Sound.c1) to the level of grind aid is a very good fit (R-sq = 94.8%):

Sound.c1 = 32.20 + 0.2899 Grind.aid + 0.002343 Grind.aid^2 - 0.000033 Grind.aid^3.

It shows that vibrations increase with increasing levels of grinding aid up to a maximum of 82.7% and then decrease (see page 30D).

However, since the variables Sound.c1 and Sound.c2 are not strongly correlated (30%), they can be combined into a regression model (see pages D37 and D38). The resulting linear regression equation shows a very good fit between the level of grinding aid and the difference in sound readings (R-sq 94.6%):

Grind.aid = 88.0 + 3.33 Sound.c1 - 3.69 Sound.c2

or

Grind.aid = 69.6 + 3.38 (Sound.c1 - Sound.c2)

Parameters Related to Electrostatic Charge

Power consumption of motors. The best model (R-sq = 79.3%) relating the electric charge to the power consumption of individual motors (mill power, elevator power, air sweep fan power and separator fan power) is:

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Elec.charge = 16.2 – 0.00259 Mill.pow – 0.0275 Sep.fan.pow + 0.0468 Elev.pow

This relation (see page E54) is conditional because elevator power and separator fan power are strongly negatively correlated (-0.79) (see page E57). Electric charge increases as elevator power increases; however, electric charge decreases as mill power and separator fan power increase. The power consumption of the air sweep fan and air compressors has no significant correlation to level of electric charge.

Total electric demand per ton. There is no simple linear relation between electric charge and total electric demand per ton (kW/ton) (see page E42). Nor does the analysis suggest a polynomial model. However, the raw data show that when electric charge is low, electric demand per ton is low.

Grinding aid. The third degree polynomial regression equation relating electric charge to the level of grinding aid squared is a somewhat better fit than a linear relation (R-sq = 67.5% versus R-sq = 51.7%) (see pages D63 and D65). However, in both cases the necessary assumptions for least-squares regression are not met. Therefore, neither model is very useful:

Elec.charge = 2.52 + 0.0339 Grind.aid - 0.00146 Grind.aid^2 + 0.000011 Grind.aid^3

and

Elec.charge = 2.57 - 0.0127 Grind.aid

However, the linear model shows that electric charge generally decreases with increasing levels of grinding aid. In addition, the raw data show that the minimum average electric charge (1.21 kV) occurs when grinding aid is 65.2%.

Water flow. Water flow represents the amount of water used to cool the cement. The linear regression equation relating water flow at the inlet of the grinding mill (wat.flo1) to level of electric charge is a somewhat good fit (R-sq = 82.4%). It shows that 82.4% of the increase in water flow can be attributed to the increase in level of electric charge (see page E70):

Wat.flo1 = 0.9770 + 0.7080 Elec.charge

Parameters Related to Fineness

Separator feed blains. The fineness of cement as measured at the separator feed in blains (average cm2/g) is directly related to the level of grinding aid (R-sq = 94.1%). This is a very good fit; however, there are only six data points and the assumptions for least-squares regression are not met:

Sep.feed.bl = 1847 + 6.39 Grind.aid

The fineness of the separator feed increases with increasing levels of grinding aid (see page E76).

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CONCLUSION

This project, which represents Phase 3 of an ongoing evaluation of the effects of grinding aid and electrostatic charge on grinding finish mills in cement plants, consisted of data reduction and data analysis. Using the method of least squares, CTL determined which grinding parameters significantly affect performance of the ball mill. In addition, the grinding parameters that should affect performance but in fact showed no statistical correlation were also discussed. The complete analysis is presented in the Appendix E.

Many conclusions can be drawn from the charts in Appendix E. For example, increasing the amount of grinding aid results in a combination of a greater fineness and lower energy use. The reader is encouraged to study the charts in Appendix E and draw their own conclusions.

ACKNOWLEDGEMENTS

The research reported in this paper (PCA R&D Serial No. 2855) was conducted by Construction Technology Laboratories, Inc., with the sponsorship of the Portland Cement Association (PCA Project Index No. M04-05a). The contents of this report reflect the views of the authors, who are responsible for the facts and accuracy of the data presented. The contents do not necessarily reflect the views of the Portland Cement Association.

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Figure 1. The flow diagram of the finish mill system shows the grinding parameters that were monitored during Phase 2 testing.

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Table 1. Reduced Data

Vairable

Grin

ding

aid

Volta

ge

Cha

rge

Rec

irc

Act

ual f

eed

rate

Mill

mot

or p

ower

Rej

ect r

ate

Elev

ator

pow

er

Com

pt 1

sou

nd

ear

Com

pt 2

sou

nd

ear

Unf

ilter

ed m

ill

scan

vib

ratio

n

Mill

dis

char

ge

airs

lide

tem

pera

ture

Com

pt 1

wat

er

flow

rate

Com

pt 2

wat

er

flow

rate

Air

swee

p D

C fa

n cu

rren

t

Air

swee

p D

C fa

n po

wer

Air

swee

p D

C

inle

t pre

ssur

e

Air

swee

p D

C

diffe

rent

ial

pres

sure

Air

swee

p D

C

inle

t tem

pera

ture

Airf

low

thro

ugh

sepa

rato

r

Sepa

rato

r DC

fan

pow

er

Sepa

rato

r DC

fan

curr

ent

Sepa

rato

r cu

rren

t

Sepa

rato

r DC

in

let p

ress

ure

Sepa

rato

r DC

di

ffere

ntia

l pr

essu

re

Sepa

rato

r DC

ou

tlet p

ress

ure

Sepa

rato

r DC

in

let t

empe

ratu

re

Sepa

rato

r DC

di

scha

rge

tem

pera

ture

Act

ual s

epar

ator

sp

eed

FK p

ump

curr

ent

Air

com

pres

sor 1

po

wer

Air

com

pres

sor

2 po

wer

Cal

cula

ted

kW/to

n

F501

clin

ker

feed

er

F502

clin

ker

feed

er

F505

gyp

sum

fe

eder

Abbreviated name

Grin

d.ai

d

Volta

ge

Cha

rge

Rec

irc.

Ac.

feed

Mill

.pow

Rej

.rate

Elev

.pow

Soun

d.c1

Soun

d.c2

Unf

il.vi

br

Mill

.dis

.tem

p

wat

.flo1

wat

.flo2

AS.

fan.

cur

AS.

fan.

pow

AS.

inl.p

res

AS.

diff.

pres

AS.

inl.t

emp

thr.s

ep

Sep.

fan.

pow

Sep.

fan.

cur

Sep.

cur

Sep.

inl.p

res

Sep.

diff.

pres

Sep.

out.p

res

Sep.

inl.t

emp

Sep.

dis.

tem

p

Sep.

spee

d

FK.p

ump.

cur

Air.

com

p1

Air.

com

p2

kW/to

n

Clin

k.fe

ed1

Clin

k.fe

ed2

Gyp

.feed

Unit % kV TPH TPH TPH kW TPH kW % % % °F GPM GPM Amps kW in. WC in. WC °F SCFM kW Amps Amps in. WC in. WC in. WC °F °F RPM Amps kW kW kW/ton TPH TPH TPH6/23/04 17:15 0 2.64 100 145 99.63 3124.5 144.28 34.07 32.34 52.56 86.40 178.17 2.88 20.25 108.13 72.08 6.65 6.43 197.62 58015 267.51 47.59 138.69 11.15 5.91 17.06 152.21 150.41 150.31 117.85 70.02 70.27 42.64 47.70 47.67 3.756/23/04 17:16 0 2.77 100 145 99.56 3124.5 162.10 30.77 33.20 53.23 87.60 178.37 2.87 20.25 108.03 72.31 6.46 6.93 197.76 58171 270.14 47.59 150.35 11.43 5.84 17.27 152.62 150.64 150.31 145.80 79.72 80.30 42.86 47.62 48.21 3.736/23/04 17:17 0 2.94 100 145 100.42 3133.8 136.06 31.67 32.08 52.56 86.21 178.17 2.87 20.25 109.26 72.92 6.59 6.44 197.07 58476 272.03 47.59 138.48 11.00 5.92 16.92 152.62 150.25 151.06 94.40 66.86 66.66 42.35 48.45 48.17 3.806/23/04 17:18 0 2.98 100 145 99.34 3115.5 141.73 33.47 32.30 52.75 85.34 178.37 2.86 20.25 108.37 72.67 6.54 6.85 197.07 58626 273.12 47.59 137.94 11.01 5.76 16.77 152.41 150.25 150.31 175.20 93.81 94.66 43.00 47.70 47.96 3.686/23/04 17:19 0 2.47 100 145 100.16 3183.3 140.48 32.26 31.85 52.97 87.56 178.17 2.90 20.25 107.81 72.19 6.35 7.05 197.21 58775 270.14 47.59 143.35 11.15 5.67 16.82 152.62 150.25 151.06 126.26 77.23 77.03 43.14 48.45 47.96 3.756/23/04 17:20 0 2.48 100 145 99.53 3226.2 169.10 33.31 32.60 52.71 87.33 178.17 2.88 20.25 107.69 72.19 6.41 6.73 197.21 58928 266.39 47.59 150.03 11.43 5.83 17.26 152.62 149.86 151.06 96.18 65.74 66.21 43.62 47.75 47.92 3.866/23/04 17:21 0 2.45 100 145 100.05 3147.1 139.85 35.42 31.92 52.29 87.56 178.51 2.88 20.25 108.81 72.67 6.53 6.86 197.76 58476 269.02 46.47 148.85 11.29 5.90 17.19 152.62 150.64 150.93 111.38 69.68 69.92 42.71 47.75 48.46 3.856/23/04 17:22 0 2.45 100 145 99.91 3075.0 146.37 35.28 31.25 52.18 87.52 178.51 2.87 20.25 107.91 72.43 6.67 6.47 197.76 58476 265.97 46.47 138.69 10.93 5.90 16.83 152.62 150.41 150.93 124.45 73.20 73.08 42.07 48.04 47.92 3.956/23/04 17:23 0 2.41 100 145 100.07 3124.5 165.82 35.13 32.26 52.75 84.55 178.17 2.87 20.25 108.48 72.31 6.37 6.72 197.21 58321 267.86 46.47 148.95 11.20 5.90 17.10 152.41 150.10 150.93 110.18 68.79 68.47 42.66 47.79 48.17 3.626/23/04 17:24 0 2.40 100 145 99.46 3174.2 142.50 33.31 31.55 52.06 84.48 178.51 2.87 20.25 107.81 71.94 6.45 6.65 197.21 58011 265.97 46.47 149.71 11.14 5.68 16.82 152.96 150.41 150.93 248.78 97.19 98.02 43.73 47.54 47.96 3.966/23/04 17:25 0 2.40 100 145 99.24 3203.5 149.26 36.63 31.59 52.82 86.36 178.37 2.87 20.25 108.81 73.03 6.65 6.43 197.21 58015 265.24 46.47 144.96 10.93 5.82 16.75 152.76 150.41 150.93 177.22 89.41 90.57 43.98 47.79 47.67 3.786/23/04 17:26 0 2.40 100 145 98.51 3124.5 153.88 32.42 33.09 52.85 87.19 178.37 2.83 20.25 108.13 71.58 6.52 6.87 197.62 57708 265.24 46.47 144.86 11.00 5.83 16.83 152.76 150.80 150.93 125.80 71.48 72.19 43.21 46.96 47.67 3.896/23/04 17:27 0 2.40 100 145 97.80 3133.8 158.22 34.52 32.64 52.56 86.47 178.37 2.87 20.25 108.37 71.83 6.60 6.43 197.21 57864 265.62 46.47 141.50 10.93 5.83 16.76 152.96 150.64 150.93 143.09 81.97 83.13 43.76 47.17 46.88 3.756/23/04 17:28 0 2.40 100 145 97.27 3079.4 153.53 34.66 32.96 52.22 86.36 178.37 2.87 20.25 107.91 72.67 6.54 6.61 197.62 57664 268.60 46.47 149.50 11.14 5.90 17.03 152.76 150.96 150.93 111.98 68.33 68.91 43.20 46.92 46.55 3.806/23/04 17:29 0 2.40 100 145 97.67 3135.9 147.33 31.36 30.94 52.33 86.36 177.83 2.86 20.25 108.03 72.19 6.30 6.98 197.07 58321 267.51 46.47 147.65 11.07 5.76 16.83 152.41 150.64 150.93 118.90 69.68 70.49 43.59 47.12 46.84 3.716/23/04 18:00 25 2.51 100 190 94.87 3169.8 180.69 35.88 34.97 52.56 86.21 177.26 2.86 20.25 108.03 72.19 6.70 6.95 195.43 57717 257.35 46.47 147.98 10.04 6.11 16.15 152.62 150.41 150.81 169.55 88.40 89.22 45.67 45.43 45.85 3.596/23/04 18:05 25 2.50 100 190 94.74 3142.6 190.61 37.39 32.90 52.29 88.31 176.87 2.88 20.25 107.13 70.75 6.80 7.04 195.43 58025 256.27 46.47 149.38 10.18 6.04 16.22 152.21 150.25 150.81 141.90 83.77 83.35 45.08 45.64 45.56 3.546/23/04 18:10 25 2.46 100 190 95.96 3160.5 175.33 35.13 38.17 53.31 87.41 176.73 2.83 20.25 108.03 71.83 7.00 6.49 194.88 57102 257.00 45.53 149.16 9.76 6.19 15.95 152.21 150.10 150.81 160.55 79.49 79.53 44.72 45.72 46.64 3.626/23/04 18:15 25 2.58 100 175 95.10 3124.5 172.59 36.63 40.43 52.23 87.52 176.52 2.85 20.25 107.01 72.06 7.08 6.56 195.29 55360 257.00 45.53 150.25 9.62 6.39 16.02 151.86 150.10 150.81 146.71 80.96 82.34 44.81 45.68 45.85 3.576/23/04 18:20 25 2.48 100 175 94.81 3190.0 172.68 37.23 40.73 53.38 87.38 176.52 2.85 20.25 107.59 72.06 7.14 6.64 194.75 56795 253.99 45.53 142.47 9.56 6.33 15.88 151.86 150.64 150.81 106.42 67.32 67.79 45.33 45.64 45.68 3.496/23/04 18:25 25 2.56 100 175 94.86 3133.8 191.68 34.66 43.07 53.95 90.31 175.84 2.81 20.25 107.69 72.58 7.45 6.28 194.54 58048 255.88 45.53 142.47 9.49 6.48 15.95 150.97 150.41 150.81 129.87 72.84 73.31 44.72 45.93 45.64 3.546/23/04 18:30 25 2.42 100 175 95.07 3223.7 165.64 36.94 43.67 53.46 89.41 175.63 2.81 20.25 106.80 71.35 7.19 6.70 194.33 56655 255.53 45.53 143.87 9.69 6.25 15.94 150.43 149.86 150.81 135.74 73.85 74.00 45.70 45.68 45.81 3.596/23/04 18:35 25 2.44 100 165 95.44 3133.8 161.45 33.61 46.23 53.23 88.24 174.95 2.81 20.25 107.02 71.10 6.97 7.11 194.20 57741 256.62 45.53 142.15 9.62 6.37 16.00 149.54 149.55 150.06 132.42 77.69 77.49 44.63 45.85 45.89 3.706/23/04 18:40 25 2.55 100 145 94.94 3118.0 148.81 33.31 45.36 53.42 87.49 175.29 2.83 19.26 107.59 71.58 7.14 6.70 194.20 56963 254.72 46.28 149.50 9.82 6.03 15.85 149.54 149.00 150.81 168.50 82.43 83.13 44.79 45.16 46.14 3.626/23/04 18:45 50 2.57 100 145 95.37 3214.9 138.73 34.23 46.49 53.23 90.35 174.95 2.81 19.02 107.81 71.94 7.10 6.62 193.79 58199 254.37 45.35 141.08 9.31 6.25 15.56 148.44 148.61 150.06 149.12 83.09 83.13 45.62 45.68 46.10 3.596/23/04 18:50 50 2.51 100 145 94.84 3192.1 153.00 33.93 46.97 52.59 94.26 175.29 2.75 18.61 108.59 72.79 7.19 6.41 194.20 57289 254.37 45.35 147.23 9.35 6.26 15.61 148.85 146.61 150.81 188.36 79.95 81.09 45.59 45.85 45.39 3.606/23/04 18:55 50 2.57 100 145 94.55 3176.3 151.33 32.42 48.18 52.93 94.26 188.24 2.78 18.44 107.35 71.83 7.10 6.97 207.69 56824 253.25 45.35 145.62 9.29 6.23 15.52 162.14 162.13 150.81 140.55 73.18 74.56 45.53 45.35 45.56 3.646/23/04 19:00 50 2.55 100 145 95.50 3133.8 142.80 28.65 49.38 51.50 94.26 175.36 2.75 18.29 106.68 73.27 7.31 7.08 194.13 56664 255.14 45.16 144.43 9.33 6.21 15.67 148.71 148.38 150.93 112.72 68.56 67.47 44.44 45.88 46.14 3.486/23/04 19:05 50 2.59 100 145 94.77 3135.9 124.47 32.56 47.54 51.77 94.26 175.09 2.79 18.07 107.35 71.10 7.28 6.97 195.43 56962 255.53 45.16 138.81 9.22 6.09 15.24 147.89 148.06 150.19 124.90 71.71 72.29 44.83 45.72 45.64 3.416/23/04 19:10 50 2.54 100 145 94.40 3158.4 147.26 31.36 47.35 52.41 94.26 175.29 2.76 17.55 107.59 72.08 7.11 6.95 195.64 55719 253.99 45.16 141.50 9.35 6.24 15.67 147.89 147.75 150.93 99.51 64.95 65.42 45.08 44.89 45.85 3.686/23/04 19:15 50 2.59 100 129 95.14 3142.6 126.76 31.67 48.37 52.18 94.26 175.63 2.75 17.70 107.47 71.47 7.23 6.93 195.98 56664 251.75 45.16 141.18 9.30 5.75 15.25 148.30 147.75 150.93 134.83 74.29 74.22 44.73 45.64 45.81 3.696/23/04 19:20 50 2.52 100 129 94.93 3160.8 120.87 31.97 49.42 51.73 94.26 175.63 2.79 16.99 108.03 72.31 7.10 6.91 196.87 56669 250.62 45.16 136.87 9.15 6.02 15.17 147.89 147.20 150.31 89.58 61.79 62.49 44.97 45.72 45.60 3.626/23/04 19:25 50 2.56 100 129 94.91 3201.4 124.77 32.26 49.91 52.75 94.26 176.16 2.76 16.93 107.81 71.71 7.15 6.92 197.62 56510 251.36 45.16 129.10 9.01 5.81 14.82 146.10 147.20 151.06 133.02 74.07 75.80 45.47 45.68 45.64 3.596/24/04 8:55 50 1.61 102 95 101.98 3165.4 98.00 26.85 46.97 53.64 90.54 178.85 2.07 20.25 108.13 72.08 6.47 6.92 201.05 55867 279.53 48.90 137.64 11.67 6.15 17.60 149.54 147.20 155.73 218.27 87.27 89.22 42.48 49.11 48.58 4.046/24/04 8:56 50 1.58 102 95 101.98 3183.3 89.76 29.58 47.05 54.33 89.41 176.85 2.03 20.25 108.37 72.19 6.47 6.79 201.05 56655 283.63 48.90 136.76 11.67 5.80 17.60 149.54 147.20 155.73 280.37 87.71 89.91 42.81 49.11 48.58 4.046/24/04 8:57 50 1.47 102 95 101.98 3176.3 98.95 26.56 49.27 55.31 88.28 176.85 2.06 20.25 109.04 71.94 6.47 6.89 201.05 56023 282.90 48.90 134.81 11.67 6.06 17.60 149.54 147.20 155.73 136.63 78.24 80.19 42.40 49.11 48.58 4.046/24/04 8:58 50 1.49 102 95 102.01 3174.2 90.68 28.20 48.14 54.52 91.36 176.85 2.06 20.25 108.26 71.83 6.54 6.97 201.39 56340 281.39 48.90 139.13 11.33 6.15 17.38 149.74 147.36 155.73 211.35 80.50 81.88 42.20 49.11 48.75 4.156/24/04 8:59 50 1.84 102 95 102.50 3190.0 84.11 29.72 48.71 54.85 90.20 178.85 2.03 20.25 108.13 71.83 6.48 6.91 201.18 56164 281.74 48.90 138.91 11.44 6.19 17.50 150.08 146.66 155.73 149.87 77.69 79.29 42.32 49.19 49.16 4.156/24/04 9:05 62.5 1.48 102 90 102.50 3167.5 88.50 29.10 47.81 53.69 88.43 178.85 2.03 20.25 108.48 71.83 6.48 6.74 201.18 56023 284.02 48.90 134.49 11.44 6.22 17.59 150.08 146.66 155.73 122.65 71.60 71.85 42.24 49.19 49.16 4.156/24/04 9:10 62.5 1.21 102 90 102.34 3199.1 100.02 28.06 49.12 54.59 91.36 178.51 2.05 20.25 107.69 71.47 6.49 6.88 200.84 55862 281.01 48.90 137.94 11.59 6.02 17.69 149.74 146.27 155.11 158.29 88.97 90.80 42.93 49.07 48.99 4.286/24/04 9:15 62.5 1.21 102 90 102.34 3219.3 93.96 27.91 49.50 54.14 88.39 178.51 2.02 20.25 108.37 71.71 6.49 6.97 200.84 56184 277.64 48.90 139.13 11.59 6.02 17.69 149.74 146.27 155.11 232.11 85.46 87.41 43.07 49.07 48.99 4.286/24/04 9:20 62.5 1.24 102 90 101.37 3199.1 97.02 27.01 50.25 54.78 89.52 178.37 2.05 20.25 108.48 72.08 6.61 7.10 200.50 55667 281.01 48.90 139.35 11.81 6.16 17.58 149.33 146.66 158.70 142.21 74.64 76.02 42.81 48.86 48.62 3.896/24/04 9:25 62.5 1.16 102 90 101.37 3151.7 99.25 26.56 52.51 54.71 91.17 178.37 2.02 20.25 107.81 71.83 6.61 6.50 200.50 55547 281.01 48.90 138.69 11.81 6.09 17.56 149.33 146.66 158.70 139.05 78.14 80.19 42.30 48.86 48.62 3.896/24/04 9:30 62.5 1.27 102 90 101.88 3219.3 87.64 29.42 51.00 53.83 91.40 178.37 2.01 20.25 108.59 72.31 6.80 7.01 200.64 56179 280.66 49.09 139.03 11.67 6.10 17.70 149.33 146.81 158.08 162.65 82.30 83.81 42.96 48.86 48.99 4.036/24/04 9:35 62.5 1.15 102 90 101.88 3214.9 89.10 28.81 50.89 53.91 89.52 176.37 2.02 20.25 108.03 71.58 6.80 6.97 200.64 58023 281.74 49.09 138.91 11.67 6.09 17.70 149.33 146.81 158.08 144.61 80.50 82.67 42.75 48.86 48.99 4.036/24/04 9:40 62.5 1.19 102 90 101.53 3178.7 89.22 28.67 50.66 52.97 90.20 176.72 2.05 20.25 108.59 71.47 6.55 6.80 200.50 55705 281.74 49.09 134.60 11.66 6.25 17.70 149.54 146.81 158.82 207.30 82.19 83.69 42.71 48.70 48.87 3.976/24/04 9:45 62.5 1.23 102 90 101.53 3169.8 92.82 28.37 52.62 51.54 90.20 178.72 2.00 20.25 108.48 71.83 6.55 7.04 200.50 55705 284.02 49.09 138.48 11.66 6.03 17.70 149.54 146.81 158.82 161.90 79.72 81.09 42.28 48.70 48.87 3.976/24/04 9:50 62.5 1.22 102 90 102.07 3187.7 95.52 26.85 52.92 53.42 91.21 177.83 2.01 20.25 108.13 71.83 6.55 6.52 200.19 55862 279.53 49.09 134.91 11.68 5.94 17.83 149.74 147.36 158.82 203.38 87.71 89.66 42.86 49.19 49.08 3.836/24/04 9:55 62.5 1.24 102 90 101.88 3219.3 86.48 28.37 52.88 51.27 91.55 178.17 1.98 20.25 108.03 71.94 6.72 6.91 200.09 55867 281.39 49.09 134.16 11.61 6.04 17.55 149.74 148.06 158.82 197.68 79.82 81.33 42.81 48.66 49.12 4.10

6/24/04 10:00 62.5 1.10 102 85 101.88 3248.6 91.29 27.46 51.94 53.04 88.54 178.17 1.94 20.25 109.15 72.31 6.72 6.59 200.09 56179 282.51 49.09 136.55 11.61 6.09 17.55 149.74 148.06 158.82 161.28 96.73 97.68 43.70 48.66 49.12 4.106/24/04 10:10 62.5 1.21 102 85 101.53 3176.3 82.97 26.10 52.58 53.99 88.50 176.87 1.97 20.25 108.92 72.67 6.72 6.48 199.20 56184 282.16 49.09 142.69 11.47 6.16 17.63 148.85 146.81 158.02 126.42 70.92 71.28 42.31 48.86 48.79 3.886/24/04 10:20 62.5 1.11 102 85 102.19 3223.7 89.45 27.46 53.83 53.64 90.23 176.52 1.97 20.25 108.13 71.71 6.60 6.78 198.65 58179 280.27 49.09 137.73 11.68 5.95 17.63 149.33 147.05 158.70 264.42 95.05 95.76 42.97 48.91 49.33 3.966/24/04 10:30 62.5 1.14 102 85 101.48 3214.9 90.36 27.75 54.65 52.56 90.20 176.52 1.97 20.25 108.92 72.67 6.50 6.51 198.65 55869 281.01 49.09 139.13 11.68 6.09 17.70 149.33 147.59 158.70 254.51 87.61 88.65 43.03 48.91 48.58 4.006/24/04 10:40 87.5 1.20 102 68 102.53 3214.9 86.48 27.75 54.54 53.04 89.52 175.84 1.95 20.25 108.48 72.92 6.85 6.86 197.96 56184 279.53 49.09 134.15 11.67 6.16 17.70 149.54 147.59 158.70 129.12 73.40 74.22 42.59 49.40 49.08 4.066/24/04 10:50 87.5 1.37 102 68 102.41 3244.0 78.23 26.40 54.20 53.01 91.17 175.98 1.95 20.25 108.03 71.83 6.87 6.57 198.17 56184 278.38 49.09 136.65 11.53 6.03 17.42 149.33 147.75 158.08 199.62 94.03 95.31 42.94 49.36 49.04 4.016/24/04 11:00 87.5 1.30 102 68 102.21 3259.8 70.80 27.46 53.86 52.14 90.35 176.63 1.90 20.25 107.81 71.35 7.00 7.02 198.31 58340 278.38 49.09 130.82 11.59 6.04 17.56 148.99 147.75 158.82 228.06 84.90 86.06 43.18 49.36 48.83 4.026/24/04 11:10 87.5 1.45 102 68 102.40 3248.6 72.45 26.70 54.65 53.01 89.52 175.29 1.93 20.25 108.80 70.87 6.82 7.08 198.31 56023 279.11 49.09 136.65 11.38 6.04 17.49 148.85 147.75 158.82 209.85 82.43 83.35 43.35 49.36 49.04 3.996/24/04 11:20 87.5 1.43 102 68 101.82 3278.1 69.26 25.96 54.35 49.36 91.21 174.74 1.89 20.25 107.24 71.83 6.87 6.91 198.31 56023 279.11 49.26 135.89 11.59 6.24 17.70 148.99 147.75 161.54 139.20 75.65 76.74 43.11 48.57 49.33 3.926/24/04 11:30 87.5 1.48 102 68 102.34 3282.3 69.70 25.96 52.96 48.27 90.35 176.52 1.91 20.25 108.03 72.19 6.82 6.96 200.84 56023 278.76 49.45 127.90 11.40 5.89 17.49 150.08 148.61 161.54 152.12 77.58 78.84 43.14 49.36 48.83 4.156/24/04 11:40 87.5 1.56 102 68 102.62 3239.5 70.63 25.96 53.41 49.36 90.42 177.62 1.87 20.25 107.59 72.08 6.83 6.79 201.53 56023 279.11 48.90 134.60 11.40 5.90 17.49 150.29 148.61 161.54 227.29 86.38 87.07 43.20 49.11 49.28 4.226/24/04 11:50 87.5 1.64 102 68 101.80 3228.2 74.56 28.56 54.92 49.28 91.40 177.62 1.88 20.25 107.24 71.58 6.70 7.02 202.62 56023 276.13 48.90 135.36 11.53 5.96 17.56 151.18 149.71 161.54 176.47 83.88 84.48 43.16 49.11 48.50 4.196/24/04 12:00 87.5 1.66 102 68 103.04 3219.3 75.25 27.15 54.09 48.64 92.30 177.83 1.91 20.25 107.47 71.24 6.76 7.08 202.62 55862 278.03 48.90 133.95 11.46 6.02 17.42 151.18 149.16 161.54 246.38 85.91 88.85 42.93 49.36 49.57 4.106/24/04 12:10 87.5 1.40 102 68 101.96 3190.0 74.87 26.26 53.34 49.51 89.41 177.96 1.89 20.25 107.24 71.47 6.64 6.86 202.26 55705 278.03 48.90 136.65 11.40 5.96 17.42 151.18 149.17 161.54 154.14 91.22 92.15 42.68 49.07 48.87 4.046/24/04 12:20 87.5 1.68 102 68 101.51 3250.9 74.21 26.85 53.86 47.58 89.37 176.72 1.85 20.25 106.80 70.99 6.74 7.08 202.83 55705 278.38 48.72 135.25 11.46 5.83 17.35 150.77 149.39 161.54 218.86 84.90 86.75 43.00 48.33 49.08 4.116/24/04 12:30 87.5 1.54 102 68 101.66 3219.3 72.16 26.56 52.85 48.79 90.35 178.51 1.85 20.25 107.02 71.24 6.86 7.08 203.17 55862 275.75 48.72 135.36 11.45 5.97 17.28 150.77 149.55 161.54 184.73 65.59 86.97 43.26 48.62 48.91 4.136/24/04 12:40 87.5 1.68 102 68 102.34 3190.0 74.03 27.46 52.43 47.17 88.47 180.15 1.85 20.25 106.91 70.99 6.86 6.45 204.47 55547 276.91 48.72 136.55 11.45 5.91 17.49 151.52 149.86 161.54 199.17 84.67 85.96 42.63 49.11 49.25 3.986/24/04 12:50 87.5 1.71 102 68 101.90 3194.5 76.60 27.32 51.87 46.95 88.20 179.95 1.83 20.25 106.80 71.10 6.81 6.95 204.81 55379 277.26 48.90 134.60 11.52 6.17 17.49 151.73 149.86 161.54 132.88 74.07 75.12 42.73 48.37 49.57 3.966/24/04 13:00 87.5 1.75 102 68 102.39 3176.3 77.00 27.61 51.94 46.16 88.54 180.84 1.85 20.25 107.02 71.24 6.74 7.02 204.82 55379 276.13 48.90 133.40 11.53 6.07 17.52 151.73 149.86 161.54 137.85 81.40 83.46 42.19 49.32 49.08 3.996/24/04 13:10 87.5 1.77 102 68 102.14 3233.0 78.13 26.85 52.62 47.21 91.17 181.05 1.84 20.25 107.59 71.83 6.67 6.51 206.05 55862 281.01 48.90 141.29 11.53 6.07 17.53 151.52 149.71 161.65 137.85 74.86 76.02 42.89 48.91 49.28 3.956/24/04 13:20 100 1.66 102 68 102.07 3237.4 80.09 26.85 52.25 46.69 89.33 182.14 1.81 20.25 106.34 70.51 6.69 6.62 206.05 55547 278.03 48.90 140.43 11.67 6.08 17.69 151.73 150.25 164.75 239.33 85.35 86.18 43.13 49.07 49.33 3.686/24/04 13:30 100 1.75 102 68 102.33 3237.4 76.76 26.26 51.87 44.46 89.52 181.59 1.83 20.25 106.46 71.24 6.69 6.86 206.60 55869 277.00 48.90 131.89 11.54 6.13 17.69 151.18 150.96 164.75 152.71 76.34 77.16 43.26 48.91 49.41 4.026/24/04 13:40 100 1.67 102 68 101.25 3248.6 69.29 26.85 50.89 43.97 88.39 182.28 1.83 20.25 107.13 71.47 6.80 6.78 207.35 56184 279.53 48.90 133.85 11.47 6.00 17.54 150.43 150.41 164.75 206.39 86.03 87.30 43.26 48.62 48.58 4.066/24/04 13:50 100 1.86 102 68 101.90 3219.3 68.22 26.56 51.57 44.16 90.20 182.83 1.81 20.25 107.02 71.47 6.63 6.50 208.79 56500 283.28 48.90 130.28 11.54 6.00 17.68 150.29 150.64 164.75 142.50 75.77 76.02 42.50 48.82 48.99 4.08

8

Table 2. Grinding Parameters and the corresponding Abbreviated Name

Grinding parameters Unit Abbreviated name Grinding aid (as a percent of normal addition) % Grind.aid Voltage (electrostatic charge) kV Voltage Charge ton/hour Charge Recirculating load ton/hour Recirc Actual feed rate ton/hour Ac.feed Mill motor power kW Mill.pow Reject rate ton/hour Rej.rate Elevator power kW Elev.pow Compt 1 sound ear % Sound.c1 Compt 2 sound ear % Sound.c2 Unfiltered mill scan vibration % Unfil.vibr Mill discharge airslide temperature °F Mill.dis.temp Compt 1 water flow rate gallon/min wat.flo1 Compt 2 water flow rate gallon/min wat.flo2 Air sweep DC fan current Amps AS.fan.cur Air sweep DC fan power kW AS.fan.pow Air sweep DC inlet pressure in. WC AS.inl.pres Air sweep DC differential pressure in. WC AS.diff.pres Air sweep DC inlet temperature °F AS.inl.temp Airflow through separator SCFM thr.sep Separator DC fan power kW Sep.fan.pow Separator DC fan current amps Sep.fan.cur Separator current amps Sep.cur Separator DC inlet pressure in. WC Sep.inl.pres Separator DC differential pressure in. WC Sep.diff.pres Separator DC outlet pressure in. WC Sep.out.pres Separator DC inlet temperature °F Sep.inl.temp Separator DC discharge temperature °F Sep.dis.temp Actual separator speed RPM Sep.speed FK pump current amps FK.pump.cur Air compressor 1 power kW Air.comp1 Air compressor 2 power kW Air.comp2 Calculated kW/ton kW/ton kW/ton F501 clinker feeder ton/hour Clink.feed1 F502 clinker feeder ton/hour Clink.feed2 F505 gypsum feeder ton/hour Gyp.feed Separator tails blains cm2/g Sep.tail.bl Separator fines blains cm2/g Sep.fines.bl Separator feed blains cm2/g Sep.feed.bl Average electric charge kV El.charge.avg

9

0.75

1.25

1.75

2.25

2.75

3.25

06/2

3 17

:00

06/2

3 18

:00

06/2

3 19

:00

06/2

3 20

:00

Ele

ctro

stat

ic c

harg

e, k

V

06/2

4 08

:00

06/2

4 09

:00

06/2

4 10

:00

06/2

4 11

:00

06/2

4 12

:00

06/2

4 13

:00

06/2

4 14

:00

06/2

415

:00

0.75

1.25

1.75

2.25

2.75

3.25

06/2

3 17

:00

06/2

3 18

:00

06/2

3 19

:00

06/2

3 20

:00

Ele

ctro

stat

ic c

harg

e, k

V

06/2

4 08

:00

06/2

4 09

:00

06/2

4 10

:00

06/2

4 11

:00

06/2

4 12

:00

06/2

4 13

:00

06/2

4 14

:00

06/2

415

:00

Figure 2. The electrostatic charge in the ball mill varied during Phase 2 testing.

10

APPENDIX A

ELECTRO-TECH SYSTEMS, INC., AUDIT REPORT (PHASE 1)

A1

APPENDIX B

ADDITIONAL MILL DATA (PHASE 1)

B1

APPENDIX C

ELECTRO-TECH SYSTEMS, INC., TEST REPORT (PHASE 2)

C1

APPENDIX D

ADDITIONAL MILL DATA (PHASE 2)

D1

APPENDIX E

CONSTRUCTION TECHNOLOGY LABORATORIES, INC., DATA ANALYSIS (PHASE 3)

E1

Appendix E Table of Contents

Various Matrix Plots ..................................................................................................................... E4 Best Subsets Regression: Grind.aid versus Mill.pow, Elev.pow, ... ............................................. E8 Regression Analysis: Elev.pow versus Grind.aid ......................................................................... E9 Regression Analysis: Elev.pow versus Grind.aid, Grind.aid^2.................................................. E10 Regression Analysis: Elev.pow versus Grind.aid, Grind.aid^2, Grind.aid^3............................. E11 Regression Analysis: Grind.aid versus Mill.pow, Elev.pow, ... ................................................. E12 Regression Analysis: Grind.aid versus Elev.pow, AS.fan.pow, Sep.fan.pow............................ E13 Regression Analysis: Grind.aid versus Mill.pow, Elev.pow, AS.fan.pow, and Sep.fan.pow .... E14 Regression Analysis: Mill.pow versus Grind.aid ....................................................................... E15 Regression Analysis: AS.fan.pow versus Grind.aid ................................................................... E16 Regression Analysis: Sep.fan.pow versus Grind.aid .................................................................. E17 Regression Analysis: Air.comp1 versus Grind.aid ..................................................................... E18 Regression Analysis: Air.comp2 versus Grind.aid ..................................................................... E18 Regression Analysis: Unfil.vibr versus Grind.aid ...................................................................... E19 Regression Analysis: kW/ton versus Grind.aid .......................................................................... E20 Regression Analysis: Rej.rate versus Grind.aid.......................................................................... E21 Regression Analysis: Rej.rate versus Grind.aid, Grind.aid^2, Grind.aid^3 ............................... E22 Regression Analysis: Wat.flo1 versus Grind.aid ........................................................................ E23 Regression Analysis: Sep.fan.cur versus Grind.aid .................................................................... E24 Regression Analysis: Sep.inl.pres versus Grind.aid ................................................................... E25 Regression Analysis: Sep.out.pres versus Grind.aid .................................................................. E26 Regression Analysis: Sep.speed versus Grind.aid ...................................................................... E27 Regression Analysis: Sep.speed versus Grind.aid, Grind.aid^2 ................................................. E28 Regression Analysis: Sep.speed versus Grind.aid^2 .................................................................. E29 Regression Analysis: Sound.c1 versus Grind.aid ....................................................................... E30 Regression Analysis: Sep.cur versus Grind.aid .......................................................................... E34 Regression Analysis: AS.inl.temp versus Grind.aid................................................................... E35 Polynomial Regression Analysis: AS.inl.temp versus Grind.aid ............................................... E36 Regression Analysis: Thr.sep versus Grind.aid .......................................................................... E37 Best Subsets Regression: Grind.aid versus Sound.c1, Sound.c2................................................ E38 Regression Analysis: Grind.aid versus Sound.c1, Sound.c2 ...................................................... E38 Regression Analysis: Grind.aid versus Sound.dif....................................................................... E39 Best Subsets Regression: Rej.rate versus Sound.c1, Sound.c2................................................... E40 Regression Analysis: Rej.rate versus Sound.c1, Sound.c2......................................................... E40 Best Subsets Regression: kW/ton versus Sound.c1, Sound.c2 ................................................... E41 Best Subsets Regression: Elec.charge versus Sound.c1, Sound.c2 ............................................ E41 Regression Analysis: Elec.charge versus Sound.c1.................................................................... E42 Regression Analysis: kW/ton versus Elec.charge....................................................................... E43 Regression Analysis: Mill.pow versus Elec.charge.................................................................... E45

E2

Regression Analysis: Elev.pow versus Elec.charge ................................................................... E46 Regression Analysis: AS.fan.pow versus Elec.charge................................................................ E47 Regression Analysis: Sep.fan.pow versus Elec.charge............................................................... E48 Regression Analysis: Air.comp1 versus Elec.charge ................................................................. E49 Regression Analysis: Air.comp2 versus Elec.charge ................................................................. E50 Regression Analysis: Elec.charge versus Mill.pow.................................................................... E51 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow.............................................. E52 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... ......................................... E53 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... ......................................... E54 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, Elev.pow............................. E55 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... ......................................... E56 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... ......................................... E57 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... ......................................... E58 Multicollinearity ......................................................................................................................... E58 Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, Elev.pow............................. E59 Regression Analysis: Elec.charge versus Sep.fan.pow, Mill.pow.............................................. E60 Regression Analysis: Voltage versus T.Mill.pow, T.Elev.pow, ... ............................................. E61 Regression Analysis: kW/ton versus Mill.pow, Elev.pow, ... .................................................... E62 Regression Analysis of all power parameters. ............................................................................ E62 Regression Analysis: kW/ton versus Mill.pow, Elev.pow ......................................................... E63 Regression Analysis: Elec.charge versus Grind.aid ................................................................... E64 Polynomial Regression Analysis: Elec.charge versus Grind.aid ................................................ E65 Polynomial Regression Analysis: Elec.charge versus Grind.aid ................................................ E66 Regression Analysis: Elec.charge versus Grind.aid, Grind.aid^2, ... ......................................... E66 Regression Analysis: Elec.charge versus Ac.feed ...................................................................... E67 Polynomial Regression Analysis: Elec.charge versus Ac.feed................................................... E68 Regression Analysis: Rej.rate versus Elec.charge ...................................................................... E69 Regression Analysis: Rej.rate versus kW/ton............................................................................. E70 Regression Analysis: Wat.flo1 versus Elec.charge..................................................................... E71 Regression Analysis: Sep.fan.cur versus Elec.charge ................................................................ E73 Regression Analysis: Sep.inl.pres versus Elec.charge................................................................ E74 Regression Analysis: Sep.out.pres versus Elec.charge............................................................... E75 Regression Analysis: Sep.speed versus Elec.charge................................................................... E76 Regression Analysis: Sep.feed.bl versus Grind.aid .................................................................... E77 Regression Analysis: Sep.tails.bl versus Grind.aid .................................................................... E78 Regression Analysis: Sep.fines.bl versus Grind.aid ................................................................... E79 Regression Analysis: Grind.aid versus Sep.feed.bl, Sep.tails.bl, ... ........................................... E80 Regression Analysis: El.charge.avg versus Grind.aid ................................................................ E81

E3

Grind.aid

3300

3200

3100

Mill.pow

35

30

25

Elev.pow

73

72

71AS.fan.pow

280

265

250

Sep.fan.pow

100

80

60

Air.comp1

100500

100

80

60330032003100 353025 737271 280265250 1008060

Air.comp2

Matrix Plot of Grind.aid, Mill.pow, Elev.pow, AS.fan.pow, ...

Grind.aid

50

40

30

Sound.c1

55

50

45

Sound.c2

95

90

85

Unfil.vibr

100500

185

180

175

504030 555045 959085

Mill.dis.temp

Matrix Plot of Grind.aid, Sound.c1, Sound.c2, Unfil.vibr, Mill.dis.tem

Grind.aid

3

2

1

Elec.charge

104

100

96Ac.feed

200

150

100Rej.rate

3.0

2.5

2.0Wat.flo1

19.5

18.5

17.5Wat.flo2

100500

46

44

42

321 10410096 200150100 3.02.52.0 19.518.517.5

kW/ton

Matrix Plot of Grind.aid, Elec.charge, Ac.feed, Rej.rate, ...

Grind.aid

109

108

107AS.fan.cur

7.5

7.0

6.5

AS.inl.pres

205

200

195

AS.inl.temp

100500

59000

57000

55000109108107 7.57.06.5 205200195

Thr.sep

Matrix Plot of Grind.aid, AS.fan.cur, AS.inl.pres, AS.inl.temp, ...

E4

Grind.aid

50.0

47.5

45.0

Sep.fan.cur

11

10

9

Sep.inl.pres

6.4

6.0

5.6

Sep.diff.pres

18.0

16.5

15.0

Sep.out.pres

160

155

150Sep.inl.temp

160

155

150Sep.dis.temp

100500

162

156

150

50.047.545.0 11109 6.46.05.6 18.016.515.0 160155150 160155150

Sep.speed

Matrix Plot of Grind.aid, Sep.fan.cur, Sep.inl.pres, Sep.diff.pre, ...

E5

kW/ton

100

50

0

Grind.aid

3

2

1

Elec.charge

104

100

96

Ac.feed

200

150

100Rej.rate

464442

150

140

130

100500 321 10410096 200150100

Sep.cur

Matrix Plot of kW/ton, Grind.aid, Elec.charge, Ac.feed, Rej.rate, ...

kW/ton

50

40

30

Sound.c1

55

50

45

Sound.c2

95

90

85

Unfil.vibr

185

180

175

Mill.dis.temp

3.0

2.5

2.0Wat.flo1

464442

19.5

18.5

17.5

504030 555045 959085 185180175 3.02.52.0

Wat.flo2

Matrix Plot of kW/ton, Sound.c1, Sound.c2, Unfil.vibr, ...

kW/ton

3300

3200

3100

Mill.pow

35

30

25

Elev.pow

73

72

71AS.fan.pow

280

265

250

Sep.fan.pow

100

80

60

Air.comp1

464442

100

80

60330032003100 353025 737271 280265250 1008060

Air.comp2

Matrix Plot of kW/ton, Mill.pow, Elev.pow, AS.fan.pow, ...

kW/ton

109

108

107AS.fan.cur

7.5

7.0

6.5

AS.inl.pres

205

200

195

AS.inl.temp

464442

59000

57000

55000109108107 7.57.06.5 205200195

Thr.sep

Matrix Plot of kW/ton, AS.fan.cur, AS.inl.pres, AS.inl.temp, Thr.sep

E6

kW/ton

50.0

47.5

45.0

Sep.fan.cur

11

10

9

Sep.inl.pres

6.4

6.0

5.6

Sep.diff.pres

18.0

16.5

15.0

Sep.out.pres

160

155

150Sep.inl.temp

160

155

150Sep.dis.temp

464442

162

156

150

50.047.545.0 11109 6.46.05.6 18.016.515.0 160155150 160155150

Sep.speed

Matrix Plot of kW/ton, Sep.fan.cur, Sep.inl.pres, Sep.diff.pre, ...

E7

Best Subsets Regression: Grind.aid versus Mill.pow, Elev.pow, ... Response is Grind.aid S A e S p A A M E . . i i i l f f r r l e a a . . l v n n c c . . . . o o p p p p m m Mallows o o o o p p Vars R-Sq R-Sq(adj) C-p S w w w w 1 2 1 67.1 66.6 60.5 18.925 X 1 53.5 52.9 113.7 22.477 X 2 76.1 75.4 27.1 16.245 X X 2 73.9 73.1 35.7 16.975 X X 3 80.4 79.6 11.9 14.790 X X X 3 79.9 79.0 14.2 15.012 X X X 4 83.2 82.2 3.0 13.809 X X X X 4 80.8 79.7 12.5 14.767 X X X X 5 83.2 82.0 5.0 13.909 X X X X X 5 83.2 82.0 5.0 13.909 X X X X X 6 83.2 81.7 7.0 14.014 X X X X X X Best Subsets Regression: Grind.aid versus Mill.pow, Elev.pow, ... Response is Grind.aid S E A e l S p A A M E e . . i i i l v f f r r l e . a a . . l v p n n c c . . o . . o o p p w p p m m Mallows o o ^ o o p p Vars R-Sq R-Sq(adj) C-p S w w 2 w w 1 2 1 67.1 66.6 71.4 18.925 X 1 65.4 64.9 78.4 19.391 X 2 76.1 75.4 35.0 16.245 X X 2 75.5 74.8 37.5 16.443 X X 3 80.4 79.6 18.4 14.790 X X X 3 80.0 79.1 20.3 14.963 X X X 4 83.2 82.2 8.6 13.809 X X X X 4 82.4 81.3 12.2 14.146 X X X X 5 84.7 83.6 4.1 13.264 X X X X X 5 83.2 82.0 10.6 13.909 X X X X X 6 84.7 83.4 6.0 13.355 X X X X X X 6 84.7 83.4 6.1 13.359 X X X X X X 7 84.8 83.1 8.0 13.456 X X X X X X X

E8

Grind.aid

Elev

.pow

100806040200

37.5

35.0

32.5

30.0

27.5

25.0

S 2.05442R-Sq 67.1%R-Sq(adj) 66.6%

Fitted Line PlotElev.pow = 34.72 - 0.08890 Grind.aid

Regression Analysis: Elev.pow versus Grind.aid The regression equation is Elev.pow = 34.7 - 0.0889 Grind.aid Predictor Coef SE Coef T P Constant 34.7186 0.4408 78.77 0.000 Grind.aid -0.088897 0.007395 -12.02 0.000 S = 2.05442 R-Sq = 67.1% R-Sq(adj) = 66.6% Analysis of Variance Source DF SS MS F P Regression 1 610.01 610.01 144.53 0.000 Residual Error 71 299.67 4.22 Total 72 909.67 Unusual Observations Obs Grind.aid Elev.pow Fit SE Fit Residual St Resid 17 25 37.390 32.496 0.303 4.894 2.41R 19 25 36.630 32.496 0.303 4.134 2.03R

20 25 37.230 32.496 0.303 4.734 2.33R

Standardized Residual

Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

35.032.530.027.525.0

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2

Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

Histogram of the Residuals Residuals Versus the Order of the Data

Residual Plots for Elev.pow

22 25 36.940 32.496 0.303 4.444 2.19R R denotes an observation with a large standardized residual.

E9

Grind.aid

Elev

.pow

100806040200

37.5

35.0

32.5

30.0

27.5

25.0

S 2.04825R-Sq 67.7%R-Sq(adj) 66.8%

Fitted Line PlotElev.pow = 34.41 - 0.06120 Grind.aid

- 0.000304 Grind.aid**2

Regression Analysis: Elev.pow versus Grind.aid, Grind.aid^2 The regression equation is Elev.pow = 34.4 - 0.0612 Grind.aid - 0.000304 Grind.aid^2 Predictor Coef SE Coef T P Constant 34.4135 0.5082 67.71 0.000 Grind.aid -0.06120 0.02432 -2.52 0.014 Grind.aid^2 -0.0003036 0.0002540 -1.20 0.236 S = 2.04825 R-Sq = 67.7% R-Sq(adj) = 66.8% Analysis of Variance Source DF SS MS F P Regression 2 616.00 308.00 73.41 0.000 Residual Error 70 293.67 4.20 Total 72 909.67 Source DF SS F P Linear 1 610.006 144.53 0.000 Quadratic 1 5.993 1.43 0.236


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

35.032.530.027.525.0

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Unusual Observations Obs Grind.aid Elev.pow Fit SE Fit Residual St Resid 17 25 37.390 32.694 0.344 4.696 2.33R 20 25 37.230 32.694 0.344 4.536 2.25R 22 25 36.940 32.694 0.344 4.246 2.10R 36 50 26.560 30.595 0.360 -4.035 -2.00R R denotes an observation with a large standardized residual.

E10

Grind.aid

Elev

.pow

100806040200

37.5

35.0

32.5

30.0

27.5

25.0

S 1.63201R-Sq 79.8%R-Sq(adj) 78.9%

Fitted Line PlotElev.pow = 33.73 + 0.2405 Grind.aid

- 0.008984 Grind.aid**2 + 0.000060 Grind.aid**3

Regression Analysis: Elev.pow versus Grind.aid, Grind.aid^2, Grind.aid^3 The regression equation is Elev.pow = 33.7 + 0.240 Grind.aid - 0.00898 Grind.aid^2 + 0.000060 Grind.aid^3 Predictor Coef SE Coef T P Constant 33.7263 0.4188 80.52 0.000 Grind.aid 0.24049 0.05081 4.73 0.000 Grind.aid^2 -0.008984 0.001367 -6.57 0.000 Grind.aid^3 0.00006008 0.00000935 6.42 0.000 S = 1.63201 R-Sq = 79.8% R-Sq(adj) = 78.9% Analysis of Variance Source DF SS MS F P Regression 3 725.89 241.96 90.85 0.000 Residual Error 69 183.78 2.66 Total 72 909.67 Sequential Analysis of Variance Source DF SS F P

Linear 1 610.006 144.53 0.000


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

35.032.530.027.525.0

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Quadratic 1 5.993 1.43 0.236 Cubic 1 109.895 41.26 0.000 Unusual Observations Obs Grind.aid Elev.pow Fit SE Fit Residual St Resid 25 50 34.230 30.799 0.289 3.431 2.14R 34 50 26.850 30.799 0.289 -3.949 -2.46R 36 50 26.560 30.799 0.289 -4.239 -2.64R 70 100 26.850 28.008 0.672 -1.158 -0.78 X 71 100 26.260 28.008 0.672 -1.748 -1.17 X 72 100 26.850 28.008 0.672 -1.158 -0.78 X 73 100 26.560 28.008 0.672 -1.448 -0.97 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E11

Regression Analysis: Grind.aid versus Mill.pow, Elev.pow, ... The regression equation is Grind.aid = 546 + 0.239 Mill.pow - 7.17 Elev.pow - 11.4 AS.fan.pow - 0.809 Sep.fan.pow - 0.009 Air.comp1 - 0.027 Air.comp2 Predictor Coef SE Coef T P Constant 545.9 296.0 1.84 0.070 Mill.pow 0.23854 0.04708 5.07 0.000 <-- negative Elev.pow -7.1711 0.8619 -8.32 0.000 multicollinarity AS.fan.pow -11.355 3.182 -3.57 0.001 w/ Elev.pow Sep.fan.pow -0.8090 0.2627 -3.08 0.003 Air.comp1 -0.0086 0.6970 -0.01 0.990 ∴ conclude β = 0 Air.comp2 -0.0274 0.7014 -0.04 0.969 ∴ conclude β = 0 S = 14.0138 R-Sq = 83.2% R-Sq(adj) = 81.7% Analysis of Variance Source DF SS MS F P Regression 6 64227 10705 54.51 0.000 Residual Error 66 12961 196 Total 72 77189 Source DF Seq SS Mill.pow 1 41317 Elev.pow 1 17398 AS.fan.pow 1 3381 Sep.fan.pow 1 2126 Air.comp1 1 5 Air.comp2 1 0 Unusual Observations Obs Mill.pow Grind.aid Fit SE Fit Residual St Resid 2 3125 0.00 28.05 3.29 -28.05 -2.06R 5 3183 0.00 32.86 2.45 -32.86 -2.38R 6 3226 0.00 38.99 5.18 -38.99 -2.99R 10 3174 0.00 28.64 4.83 -28.64 -2.18R 12 3125 0.00 28.77 3.40 -28.77 -2.12R 15 3136 0.00 30.38 2.93 -30.38 -2.22R 28 3134 50.00 47.14 7.71 2.86 0.24 X 54 3215 87.00 56.97 4.39 30.03 2.26R 65 3219 87.00 88.41 13.61 -1.41 -0.42 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E12


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

1007550250

2

1

0

-1

-2


Freq

uenc

y

210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2



Residual Plots for Grind.aid

Regression Analysis: Grind.aid versus Elev.pow, AS.fan.pow, Sep.fan.pow The regression equation is Grind.aid = 1555 - 8.82 Elev.pow - 14.2 AS.fan.pow - 0.788 Sep.fan.pow Predictor Coef SE Coef T P Constant 1554.9 255.8 6.08 0.000 Elev.pow -8.8188 0.9078 -9.71 0.000 AS.fan.pow -14.247 3.538 -4.03 0.000 Sep.fan.pow -0.7880 0.2894 -2.72 0.008 S = 16.2474 R-Sq = 76.4% R-Sq(adj) = 75.4% Analysis of Variance Source DF SS MS F P Regression 3 58974 19658 74.47 0.000 Residual Error 69 18214 264 Total 72 77189 Source DF Seq SS Elev.pow 1 51761 AS.fan.pow 1 5256

Sep.fan.pow 1 1957 Error distribution is approx. normal, error variance is approx. constant, and time dependance of error terms is due to increasing levels of grinding aid over time.

Unusual Observations Obs Elev.pow Grind.aid Fit SE Fit Residual St Resid 2 30.8 0.00 40.43 2.39 -40.43 -2.52R 12 32.4 0.00 40.14 2.62 -40.14 -2.50R 15 31.4 0.00 39.01 2.24 -39.01 -2.42R 17 37.4 25.00 15.21 6.74 9.79 0.66 X 26 33.9 50.00 18.15 4.67 31.85 2.05R 28 28.7 50.00 57.27 8.54 -7.27 -0.53 X 54 27.8 87.00 50.97 4.64 36.03 2.31R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E13

Residual

Per

cent

50250-25-50

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

1007550250

40

20

0

-20

-40

Residual

Freq

uenc

y

32160-16-32

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

40

20

0

-20

-40




Regression Analysis: Grind.aid versus Mill.pow, Elev.pow, AS.fan.pow, and Sep.fan.pow The regression equation is Grind.aid = 546 + 0.237 Mill.pow - 7.19 Elev.pow - 11.3 AS.fan.pow - 0.822 Sep.fan.pow Predictor Coef SE Coef T P Constant 545.9 290.3 1.88 0.064 Mill.pow 0.23685 0.04515 5.25 0.000 Elev.pow -7.1930 0.8315 -8.65 0.000 AS.fan.pow -11.264 3.060 -3.68 0.000 Sep.fan.pow -0.8217 0.2461 -3.34 0.001 S = 13.8087 R-Sq = 83.2% R-Sq(adj) = 82.2% Analysis of Variance Source DF SS MS F P Regression 4 64223 16056 84.20 0.000 Residual Error 68 12966 191 Total 72 77189 Source DF Seq SS Mill.pow 1 41317 Elev.pow 1 17398

AS.fan.pow 1 3381 Error terms are independent, normally distributed with mean equal zero and constant variance.

Sep.fan.pow 1 2126

Residual

Per

cent

50250-25-50

99.9

99

90

50

10

1

0.1

Grind.aid

506287

100

025

Fitted Value

Res

idua

l

1007550250

40

20

0

-20

-40

Grind.aid

87100

0255062

Residual

Freq

uenc

y

32160-16-32

6.0

4.5

3.0

1.5

0.0

Grind.aid

87100

0255062

Observation Order

Res

idua

l

7065605550454035302520151051

40

20

0

-20

-40

Grind.aid

87100

0255062




Unusual Observations Obs Mill.pow Grind.aid Fit SE Fit Residual St Resid 2 3125 0.00 28.20 3.09 -28.20 -2.10R 5 3183 0.00 32.76 2.24 -32.76 -2.40R 6 3226 0.00 38.44 3.75 -38.44 -2.89R 10 3174 0.00 29.30 2.26 -29.30 -2.15R 12 3125 0.00 28.58 3.13 -28.58 -2.13R 15 3136 0.00 30.16 2.54 -30.16 -2.22R 28 3134 50.00 47.15 7.51 2.85 0.25 X 54 3215 87.00 56.74 4.09 30.26 2.29R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence. Mill.pow Elev.pow AS.fan.pow Sep.fan.pow Max 3282 37 73 284 Min 3075 26 71 250 Avg 3185 30 72 271 Std dev 45.6 3.5 0.6 10.7 This is the best model relating the power consumption of individual motors versus the level of grinding aid. Mill power increased with increasing levels of grinding aid. Elevator, air sweep fan, and separator fan power decrease with increasing levels of grinding aid. The power consumtoin of the air compressors have no significant correlation to the level of grinding aid.

E14

Grind.aid

Mill

.pow

100806040200

3300

3250

3200

3150

3100

S 31.4888R-Sq 53.5%R-Sq(adj) 52.9%

Fitted Line PlotMill.pow = 3134 + 1.025 Grind.aid

Regression Analysis: Mill.pow versus Grind.aid The regression equation is Mill.pow = 3134 + 1.02 Grind.aid Predictor Coef SE Coef T P Constant 3133.85 6.76 463.86 0.000 Grind.aid 1.0249 0.1133 9.04 0.000 S = 31.4888 R-Sq = 53.5% R-Sq(adj) = 52.9% Analysis of Variance Source DF SS MS F P Regression 1 81088 81088 81.78 0.000 Residual Error 71 70400 992 Total 72 151487 Unusual Observations Obs Grind.aid Mill.pow Fit SE Fit Residual St Resid 6 0 3226.16 3133.85 6.76 92.31 3.00R 11 0 3203.51 3133.85 6.76 69.66 2.26R 22 25 3223.74 3159.48 4.65 64.26 2.06R


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

32503225320031753150

2

0

-2


Freq

uenc

y

3210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2



Residual Plots for Mill.pow

R denotes an observation with a large standardized residual.

Error variance is normal, relatively constant and error terms are independent.

E15

Grind.aid

AS.

fan.

pow

100806040200

73.5

73.0

72.5

72.0

71.5

71.0

70.5

S 0.504135R-Sq 22.2%R-Sq(adj) 21.1%

Fitted Line PlotAS.fan.pow = 72.29 - 0.008178 Grind.aid

Regression Analysis: AS.fan.pow versus Grind.aid The regression equation is AS.fan.pow = 72.29 - 0.008178 Grind.aid S = 0.504135 R-Sq = 22.2% R-Sq(adj) = 21.1% Analysis of Variance Source DF SS MS F P Regression 1 5.1618 5.16182 20.31 0.000 Error 71 18.0448 0.25415 Total 72 23.2066


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

72.272.071.871.671.4

3.0

1.5

0.0

-1.5

-3.0


Freq

uenc

y

2.41.20.0-1.2-2.4

24

18

12

6

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3.0

1.5

0.0

-1.5

-3.0



Residual Plots for AS.fan.pow

E16

Grind.aid

Sep.

fan.

pow

100806040200

285

280

275

270

265

260

255

250

S 9.28657R-Sq 27.4%R-Sq(adj) 26.4%

Fitted Line PlotSep.fan.pow = 262.6 + 0.1732 Grind.aid

Regression Analysis: Sep.fan.pow versus Grind.aid The regression equation is Sep.fan.pow = 262.6 + 0.1732 Grind.aid S = 9.28657 R-Sq = 27.4% R-Sq(adj) = 26.4% Analysis of Variance Source DF SS MS F P Regression 1 2314.38 2314.38 26.84 0.000 Error 71 6123.07 86.24 Total 72 8437.45


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

280275270265

1

0

-1

-2


Freq

uenc

y

1.00.50.0-0.5-1.0-1.5-2.0

12

9

6

3

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

1

0

-1

-2



Residual Plots for Sep.fan.pow

E17

Regression Analysis: Air.comp1 versus Grind.aid The regression equation is Air.comp1 = 76.17 + 0.05980 Grind.aid S = 7.98956 R-Sq = 5.7% R-Sq(adj) = 4.4% Analysis of Variance Source DF SS MS F P Regression 1 276.07 276.071 4.32 0.041 Error 71 4532.14 63.833 Total 72 4808.21 Regression Analysis: Air.comp2 versus Grind.aid The regression equation is Air.comp2 = 76.42 + 0.07899 Grind.aid S = 8.03002 R-Sq = 9.5% R-Sq(adj) = 8.2% Analysis of Variance Source DF SS MS F P Regression 1 481.63 481.628 7.47 0.008 Error 71 4578.17 64.481 Total 72 5059.79

E18

Grind.aid

Unf

il.vi

br

100876250250

95.0

92.5

90.0

87.5

85.0

Individual Value Plot of Unfil.vibr vs Grind.aid

Grind.aid

Unf

il.vi

br

100806040200

95.0

92.5

90.0

87.5

85.0

S 2.07185R-Sq 27.9%R-Sq(adj) 26.9%

Fitted Line PlotUnfil.vibr = 87.54 + 0.03911 Grind.aid

Residual

Per

cen

t

5.02.50.0-2.5-5.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idu

al

9291908988

4

2

0

-2

-4

Residual

Freq

uen

cy

4.53.01.50.0-1.5-3.0

12

9

6

3

0

Observation Order

Res

idu

al

7065605550454035302520151051

4

2

0

-2

-4



Residual Plots for Unfil.vibr

Regression Analysis: Unfil.vibr versus Grind.aid The regression equation is Unfil.vibr = 87.54 + 0.03911 Grind.aid S = 2.07185 R-Sq = 27.9% R-Sq(adj) = 26.9% Analysis of Variance Source DF SS MS F P Regression 1 118.062 118.062 27.50 0.000 Error 71 304.772 4.293 Total 72 422.834

E19

Grind.aid

kW/t

on

100876250250

46

45

44

43

42

Individual Value Plot of kW/ton vs Grind.aid

Grind.aid

kW/t

on

100806040200

46

45

44

43

42

41

S 1.02008R-Sq 6.0%R-Sq(adj) 4.7%

Regression95% PI

Fitted Line PlotkW/ton = 43.83 - 0.007843 Grind.aid

Residual

Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

43.843.643.443.243.0

2

1

0

-1

-2

Residual

Freq

uenc

y

1.60.80.0-0.8-1.6

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2



Residual Plots for kW/ton

Regression Analysis: kW/ton versus Grind.aid The regression equation is kW/ton = 43.8 - 0.00784 Grind.aid Predictor Coef SE Coef T P Constant 43.8343 0.2189 200.28 0.000 Grind.aid -0.007843 0.003672 -2.14 0.036 S = 1.02008 R-Sq = 6.0% R-Sq(adj) = 4.7% Analysis of Variance Source DF SS MS F P Regression 1 4.748 4.748 4.56 0.036 Residual Error 71 73.880 1.041 Total 72 78.628 Unusual Observations Obs Grind.aid kW/ton Fit SE Fit Residual St Resid 16 25 45.670 43.638 0.151 2.032 2.01R 22 25 45.700 43.638 0.151 2.062 2.04R 25 50 45.620 43.442 0.119 2.178 2.15R 26 50 45.590 43.442 0.119 2.148 2.12R

27 50 45.530 43.442 0.119 2.088 2.06R Error terms are not normally distributes. Note that the 95% prediction interval is almost as wide as range of values of kW/ton.

33 50 45.470 43.442 0.119 2.028 2.00R R denotes an observation with a large standardized residual.

E20

Grind.aid

Rej

.rat

e

100876250250

200

175

150

125

100

75

50

Individual Value Plot of Rej.rate vs Grind.aid

Grind.aid

Rej

.rat

e

100806040200

200

150

100

50

0

S 19.7016R-Sq 73.0%R-Sq(adj) 72.6%

Regression95% PI

Fitted Line PlotRej.rate = 163.6 - 0.9817 Grind.aid

Residual

Per

cent

50250-25-50

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

1501209060

60

40

20

0

-20

Residual

Freq

uenc

y

40200-20

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

60

40

20

0

-20



Residual Plots for Rej.rate

Regression Analysis: Rej.rate versus Grind.aid The regression equation is Rej.rate = 164 - 0.982 Grind.aid Predictor Coef SE Coef T P Constant 163.617 4.227 38.71 0.000 Grind.aid -0.98170 0.07091 -13.84 0.000 S = 19.7016 R-Sq = 73.0% R-Sq(adj) = 72.6% Analysis of Variance Source DF SS MS F P Regression 1 74389 74389 191.65 0.000 Residual Error 71 27559 388 Total 72 101948 Unusual Observations Obs Grind.aid Rej.rate Fit SE Fit Residual St Resid 16 25 180.69 139.07 2.91 41.62 2.14R 17 25 190.61 139.07 2.91 51.54 2.64R

21 25 191.68 139.07 2.91 52.61 2.70R Error terms are not normally distributes. Nowever, the cubic polynominal is a much better fit.


E21

Grind.aid

Rej

.rat

e

100806040200

200

175

150

125

100

75

50

S 13.7606R-Sq 87.2%R-Sq(adj) 86.6%

Regression95% PI

Fitted Line PlotRej.rate = 151.0 + 2.740 Grind.aid


Regression Analysis: Rej.rate versus Grind.aid, Grind.aid^2, Grind.aid^3 The regression equation is Rej.rate = 151 + 2.74 Grind.aid - 0.0987 Grind.aid^2 + 0.000647 Grind.aid^3 Predictor Coef SE Coef T P Constant 150.999 3.532 42.76 0.000 Grind.aid 2.7404 0.4284 6.40 0.000 Grind.aid^2 -0.09866 0.01152 -8.56 0.000 Grind.aid^3 0.00064683 0.00007886 8.20 0.000 S = 13.7606 R-Sq = 87.2% R-Sq(adj) = 86.6% Analysis of Variance Source DF SS MS F P Regression 3 88883 29628 156.47 0.000 Residual Error 69 13065 189 Total 72 101948 Sequential Analysis of Variance Source DF SS F P

Linear 1 74389.5 191.65 0.000

Residual

Per

cent

50250-25-50

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

16014012010080

40

20

0

-20

-40

Residual

Freq

uenc

y

32160-16-32

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

40

20

0

-20

-40




Quadratic 1 1754.2 4.76 0.033 Cubic 1 12739.3 67.28 0.000 Unusual Observations Obs Grind.aid Rej.rate Fit SE Fit Residual St Resid 26 50 153.00 122.23 2.43 30.77 2.27R 27 50 151.33 122.23 2.43 29.10 2.15R 35 50 89.76 122.23 2.43 -32.47 -2.40R 37 50 90.68 122.23 2.43 -31.55 -2.33R 38 50 84.11 122.23 2.43 -38.12 -2.81R 70 100 80.09 85.28 5.66 -5.19 -0.41 X 71 100 76.76 85.28 5.66 -8.52 -0.68 X 72 100 69.29 85.28 5.66 -15.99 -1.28 X 73 100 68.22 85.28 5.66 -17.06 -1.36 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

Error terms are approximately normally distributed.

E22

Grind.aid

Wat

.flo

1

100876250250

3.0

2.8

2.6

2.4

2.2

2.0

1.8

Individual Value Plot of Wat.flo1 vs Grind.aid

Grind.aid

Wat

.flo

1

100806040200

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

S 0.209952R-Sq 78.6%R-Sq(adj) 78.3%

Fitted Line PlotWat.flo1 = 2.957 - 0.01222 Grind.aid

Residual

Per

cent

0.500.250.00-0.25-0.50

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

3.02.52.0

0.4

0.2

0.0

-0.2

-0.4

Residual

Freq

uenc

y

0.40.30.20.10.0-0.1-0.2-0.3

12

9

6

3

0

Observation Order

Res

idua

l

7065605550454035302520151051

0.4

0.2

0.0

-0.2

-0.4



Residual Plots for Wat.flo1Regression Analysis: Wat.flo1 versus Grind.aid The regression equation is Wat.flo1 = 2.96 - 0.0122 Grind.aid Predictor Coef SE Coef T P Constant 2.95683 0.04505 65.64 0.000 Grind.aid -0.0122178 0.0007557 -16.17 0.000 S = 0.209952 R-Sq = 78.6% R-Sq(adj) = 78.3% Analysis of Variance Source DF SS MS F P Regression 1 11.522 11.522 261.40 0.000 Residual Error 71 3.130 0.044 Total 72 14.652 Unusual Observations Obs Grind.aid Wat.flo1 Fit SE Fit Residual St Resid 25 50 2.8100 2.3459 0.0246 0.4641 2.23R 27 50 2.7800 2.3459 0.0246 0.4341 2.08R 29 50 2.7900 2.3459 0.0246 0.4441 2.13R 32 50 2.7900 2.3459 0.0246 0.4441 2.13R R denotes an observation with a large standardized residual.

E23

Grind.aid

Sep.

fan.

cur

100876250250

50

49

48

47

46

45

Individual Value Plot of Sep.fan.cur vs Grind.aid

Grind.aid

Sep.

fan.

cur

100806040200

50

49

48

47

46

45

S 1.20540R-Sq 38.7%R-Sq(adj) 37.8%

Fitted Line PlotSep.fan.cur = 46.25 + 0.02902 Grind.aid

Residual

Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

49484746

1

0

-1

-2

-3

Residual

Freq

uenc

y

1.00.50.0-0.5-1.0-1.5-2.0-2.5

12

9

6

3

0

Observation Order

Res

idua

l

7065605550454035302520151051

1

0

-1

-2

-3



Residual Plots for Sep.fan.cur

Regression Analysis: Sep.fan.cur versus Grind.aid The regression equation is Sep.fan.cur = 46.3 + 0.0290 Grind.aid Predictor Coef SE Coef T P Constant 46.2517 0.2586 178.84 0.000 Grind.aid 0.029020 0.004339 6.69 0.000 S = 1.20540 R-Sq = 38.7% R-Sq(adj) = 37.8% Analysis of Variance Source DF SS MS F P Regression 1 65.005 65.005 44.74 0.000 Residual Error 71 103.163 1.453 Total 72 168.168 Unusual Observations Obs Grind.aid Sep.fan.cur Fit SE Fit Residual St Resid 28 50 45.160 47.703 0.141 -2.543 -2.12R 29 50 45.160 47.703 0.141 -2.543 -2.12R 30 50 45.160 47.703 0.141 -2.543 -2.12R 31 50 45.160 47.703 0.141 -2.543 -2.12R 32 50 45.160 47.703 0.141 -2.543 -2.12R 33 50 45.160 47.703 0.141 -2.543 -2.12R R denotes an observation with a large standardized residual.

E24

Grind.aid

Sep.

inl.p

res

100876250250

12.0

11.5

11.0

10.5

10.0

9.5

9.0

Individual Value Plot of Sep.inl.pres vs Grind.aid

Grind.aid

Sep.

inl.p

res

100806040200

12.0

11.5

11.0

10.5

10.0

9.5

9.0

S 0.831345R-Sq 11.7%R-Sq(adj) 10.5%

Fitted Line PlotSep.inl.pres = 10.51 + 0.009184 Grind.aid

Residual

Per

cent

210-1-2

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

11.5011.2511.0010.7510.50

1

0

-1

-2

Residual

Freq

uenc

y

0.80.40.0-0.4-0.8-1.2-1.6-2.0

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

1

0

-1

-2



Residual Plots for Sep.inl.pres

Regression Analysis: Sep.inl.pres versus Grind.aid The regression equation is Sep.inl.pres = 10.5 + 0.00918 Grind.aid Predictor Coef SE Coef T P Constant 10.5057 0.1784 58.90 0.000 Grind.aid 0.009184 0.002992 3.07 0.003 S = 0.831345 R-Sq = 11.7% R-Sq(adj) = 10.5% Analysis of Variance Source DF SS MS F P Regression 1 6.5102 6.5102 9.42 0.003 Residual Error 71 49.0706 0.6911 Total 72 55.5808 Unusual Observations Obs Grind.aid Sep.inl.pres Fit SE Fit Residual St Resid 25 50 9.3100 10.9649 0.0973 -1.6549 -2.00R 27 50 9.2900 10.9649 0.0973 -1.6749 -2.03R 29 50 9.2200 10.9649 0.0973 -1.7449 -2.11R 31 50 9.3000 10.9649 0.0973 -1.6649 -2.02R 32 50 9.1500 10.9649 0.0973 -1.8149 -2.20R 33 50 9.0100 10.9649 0.0973 -1.9549 -2.37R R denotes an observation with a large standardized residual.

E25

Grind.aid

Sep.

out.

pres

100876250250

18.0

17.5

17.0

16.5

16.0

15.5

15.0

Individual Value Plot of Sep.out.pres vs Grind.aid

Grind.aid

Sep.

out.

pres

100806040200

18.0

17.5

17.0

16.5

16.0

15.5

15.0

S 0.741807R-Sq 17.7%R-Sq(adj) 16.5%

Fitted Line PlotSep.out.pres = 16.46 + 0.01042 Grind.aid

Residual

Per

cent

210-1-2

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

17.5017.2517.0016.7516.50

1

0

-1

-2

Residual

Freq

uenc

y

0.80.40.0-0.4-0.8-1.2-1.6-2.0

16

12

8

4

0

Observation Order

Res

idua

l

7065605550454035302520151051

1

0

-1

-2



Residual Plots for Sep.out.pres

Regression Analysis: Sep.out.pres versus Grind.aid The regression equation is Sep.out.pres = 16.5 + 0.0104 Grind.aid Predictor Coef SE Coef T P Constant 16.4617 0.1592 103.43 0.000 Grind.aid 0.010419 0.002670 3.90 0.000 S = 0.741807 R-Sq = 17.7% R-Sq(adj) = 16.5% Analysis of Variance Source DF SS MS F P Regression 1 8.3790 8.3790 15.23 0.000 Residual Error 71 39.0697 0.5503 Total 72 47.4486 Unusual Observations Obs Grind.aid Sep.out.pres Fit SE Fit Residual St Resid 29 50 15.2400 16.9826 0.0868 -1.7426 -2.37R 31 50 15.2500 16.9826 0.0868 -1.7326 -2.35R 32 50 15.1700 16.9826 0.0868 -1.8126 -2.46R 33 50 14.8200 16.9826 0.0868 -2.1626 -2.94R R denotes an observation with a large standardized residual.

E26

Grind.aid

Sep.

spee

d

100876250250

166

164

162

160

158

156

154

152

150

Individual Value Plot of Sep.speed vs Grind.aid

Grind.aid

Sep.

spee

d

100806040200

166

164

162

160

158

156

154

152

150

S 2.32338R-Sq 77.2%R-Sq(adj) 76.9%

Fitted Line PlotSep.speed = 149.1 + 0.1297 Grind.aid

Residual

Per

cent

210-1-2

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

17.5017.2517.0016.7516.50

1

0

-1

-2

Residual

Freq

uenc

y

0.80.40.0-0.4-0.8-1.2-1.6-2.0

16

12

8

4

0

Observation Order

Res

idua

l

7065605550454035302520151051

1

0

-1

-2




Regression Analysis: Sep.speed versus Grind.aid The regression equation is Sep.speed = 149 + 0.130 Grind.aid Predictor Coef SE Coef T P Constant 149.066 0.498 299.04 0.000 Grind.aid 0.129667 0.008363 15.51 0.000 S = 2.32338 R-Sq = 77.2% R-Sq(adj) = 76.9% Analysis of Variance Source DF SS MS F P Regression 1 1297.8 1297.8 240.42 0.000 Residual Error 71 383.3 5.4 Total 72 1681.1 Unusual Observations Obs Grind.aid Sep.speed Fit SE Fit Residual St Resid 25 50 150.060 155.550 0.272 -5.490 -2.38R 26 50 150.810 155.550 0.272 -4.740 -2.05R 27 50 150.810 155.550 0.272 -4.740 -2.05R 28 50 150.930 155.550 0.272 -4.620 -2.00R 29 50 150.190 155.550 0.272 -5.360 -2.32R 30 50 150.930 155.550 0.272 -4.620 -2.00R

31 50 150.930 155.550 0.272 -4.620 -2.00R 32 50 150.310 155.550 0.272 -5.240 -2.27R R denotes an observation with a large standardized residual.

E27

Grind.aid

Sep.

spee

d

100806040200

166

164

162

160

158

156

154

152

150

S 1.85208R-Sq 85.7%R-Sq(adj) 85.3%

Fitted Line PlotSep.speed = 150.6 - 0.00571 Grind.aid

+ 0.001484 Grind.aid**2

Regression Analysis: Sep.speed versus Grind.aid, Grind.aid^2 The regression equation is Sep.speed = 151 - 0.0057 Grind.aid + 0.00148 Grind.aid^2 Predictor Coef SE Coef T P Constant 150.558 0.460 327.62 0.000 Grind.aid -0.00571 0.02199 -0.26 0.796 Grind.aid^2 0.0014838 0.0002297 6.46 0.000 S = 1.85208 R-Sq = 85.7% R-Sq(adj) = 85.3% Analysis of Variance Source DF SS MS F P Regression 2 1440.97 720.49 210.04 0.000 Residual Error 70 240.11 3.43 Total 72 1681.09 Source DF Seq SS Grind.aid 1 1297.82 Grind.aid^2 1 143.15

Residual

Per

cent

5.02.50.0-2.5-5.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

165160155150

2

0

-2

-4

Residual

Freq

uenc

y

3210-1-2-3-4

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

2

0

-2

-4



Residual Plots for Sep.speed

Unusual Observations Obs Grind.aid Sep.speed Fit SE Fit Residual St Resid 25 50 150.060 153.981 0.325 -3.921 -2.15R 29 50 150.190 153.981 0.325 -3.791 -2.08R 32 50 150.310 153.981 0.325 -3.671 -2.01R R denotes an observation with a large standardized residual.

E28

Grind.aid^2

Sep.

spee

d

1000080006000400020000

166

164

162

160

158

156

154

152

150

S 1.83987R-Sq 85.7%R-Sq(adj) 85.5%

Fitted Line PlotSep.speed = 150.5 + 0.001427 Grind.aid^2

Regression Analysis: Sep.speed versus Grind.aid^2 The regression equation is Sep.speed = 150 + 0.00143 Grind.aid^2 Predictor Coef SE Coef T P Constant 150.474 0.327 460.51 0.000 Grind.aid^2 0.00142690 0.00006917 20.63 0.000 S = 1.83987 R-Sq = 85.7% R-Sq(adj) = 85.5% Analysis of Variance Source DF SS MS F P Regression 1 1440.7 1440.7 425.61 0.000 Residual Error 71 240.3 3.4 Total 72 1681.1 Unusual Observations Obs Grind.aid^2 Sep.speed Fit SE Fit Residual St Resid 25 2500 150.060 154.041 0.227 -3.981 -2.18R 29 2500 150.190 154.041 0.227 -3.851 -2.11R

32 2500 150.310 154.041 0.227 -3.731 -2.04R

Residual

Per

cent

5.02.50.0-2.5-5.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

165160155150

2

0

-2

-4

Residual

Freq

uenc

y

3210-1-2-3-4

24

18

12

6

0

Observation Order

Res

idua

l

7065605550454035302520151051

2

0

-2

-4





E29

Grind.aid

Soun

d.c1

100876250250

55

50

45

40

35

30

Individual Value Plot of Sound.c1 vs Grind.aid

Grind.aid

Soun

d.c1

100806040200

60

55

50

45

40

35

30

S 3.06359R-Sq 86.5%R-Sq(adj) 86.3%

Fitted Line PlotSound.c1 = 34.25 + 0.2351 Grind.aid

Residual

Per

cent

1050-5-10

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

5550454035

8

4

0

-4

-8

Residual

Freq

uenc

y

6420-2-4-6

12

9

6

3

0

Observation Order

Res

idua

l

7065605550454035302520151051

8

4

0

-4

-8



Residual Plots for Sound.c1

Regression Analysis: Sound.c1 versus Grind.aid The regression equation is Sound.c1 = 34.2 + 0.235 Grind.aid Predictor Coef SE Coef T P Constant 34.2482 0.6573 52.10 0.000 Grind.aid 0.23509 0.01103 21.32 0.000 S = 3.06359 R-Sq = 86.5% R-Sq(adj) = 86.3% Analysis of Variance Source DF SS MS F P Regression 1 4265.9 4265.9 454.52 0.000 Residual Error 71 666.4 9.4 Total 72 4932.3 Unusual Observations Obs Grind.aid Sound.c1 Fit SE Fit Residual St Resid 17 25 32.900 40.125 0.452 -7.225 -2.38R 23 25 46.230 40.125 0.452 6.105 2.01R 72 100 50.890 57.757 0.658 -6.867 -2.30R 73 100 51.570 57.757 0.658 -6.187 -2.07R R denotes an observation with a large standardized residual.

E30

Grind.aid

Soun

d.c1

100806040200

55

50

45

40

35

30

S 1.92304R-Sq 94.8%R-Sq(adj) 94.6%

Fitted Line PlotSound.c1 = 32.20 + 0.2899 Grind.aid

+ 0.002343 Grind.aid**2 - 0.000033 Grind.aid**3

Polynomial Regression Analysis: Sound.c1 versus Grind.aid The regression equation is Sound.c1 = 32.20 + 0.2899 Grind.aid + 0.002343 Grind.aid**2 - 0.000033 Grind.aid**3 S = 1.92304 R-Sq = 94.8% R-Sq(adj) = 94.6% Analysis of Variance Source DF SS MS F P Regression 3 4677.11 1559.04 421.58 0.000 Error 69 255.17 3.70 Total 72 4932.28 Sequential Analysis of Variance Source DF SS F P Linear 1 4265.90 454.52 0.000 Quadratic 1 378.24 91.89 0.000 Cubic 1 32.97 8.92 0.004

Residual

Per

cent

630-3-6

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

5045403530

6

3

0

-3

-6

Residual

Freq

uenc

y

6420-2-4-6

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

6

3

0

-3

-6




E31

Grind.aid

Soun

d.c2

100876250250

55.0

52.5

50.0

47.5

45.0

Individual Value Plot of Sound.c2 vs Grind.aid

Grind.aid

Soun

d.c2

100806040200

55.0

52.5

50.0

47.5

45.0

S 2.22840R-Sq 30.2%R-Sq(adj) 29.2%

Fitted Line PlotSound.c2 = 54.02 - 0.04447 Grind.aid

Residual

Per

cent

840-4-8

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

5453525150

3

0

-3

-6

Residual

Freq

uenc

y

420-2-4-6

16

12

8

4

0

Observation Order

Res

idua

l

7065605550454035302520151051

3

0

-3

-6




Regression Analysis: Sound.c2 versus Grind.aid The regression equation is Sound.c2 = 54.0 - 0.0445 Grind.aid Predictor Coef SE Coef T P Constant 54.0196 0.4781 112.99 0.000 Grind.aid -0.044471 0.008021 -5.54 0.000 S = 2.22840 R-Sq = 30.2% R-Sq(adj) = 29.2% Analysis of Variance Source DF SS MS F P Regression 1 152.65 152.65 30.74 0.000 Residual Error 71 352.57 4.97 Total 72 505.22 Unusual Observations Obs Grind.aid Sound.c2 Fit SE Fit Residual St Resid 71 100 44.460 49.573 0.479 -5.113 -2.35R 72 100 43.970 49.573 0.479 -5.603 -2.57R 73 100 44.160 49.573 0.479 -5.413 -2.49R R denotes an observation with a large standardized residual.

E32

Grind.aid

Soun

d.c2

100806040200

55.0

52.5

50.0

47.5

45.0

S 1.35644R-Sq 74.9%R-Sq(adj) 73.8%

Fitted Line PlotSound.c2 = 52.63 - 0.04092 Grind.aid

+ 0.002724 Grind.aid**2 - 0.000031 Grind.aid**3

Polynomial Regression Analysis: Sound.c2 versus Grind.aid The regression equation is Sound.c2 = 52.63 - 0.04092 Grind.aid + 0.002724 Grind.aid**2 - 0.000031 Grind.aid**3 S = 1.35644 R-Sq = 74.9% R-Sq(adj) = 73.8% Analysis of Variance Source DF SS MS F P Regression 3 378.265 126.088 68.53 0.000 Error 69 126.955 1.840 Total 72 505.220 Sequential Analysis of Variance Source DF SS F P Linear 1 152.651 30.74 0.000 Quadratic 1 196.571 88.21 0.000 Cubic 1 29.042 15.78 0.000

Residual

Per

cent

5.02.50.0-2.5-5.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

5452504846

4

2

0

-2

-4

Residual

Freq

uenc

y

3.21.60.0-1.6-3.2

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

4

2

0

-2

-4




E33

Grind.aid

Sep.

cur

100806040200

150

145

140

135

130

S 3.92730R-Sq 52.6%R-Sq(adj) 52.0%

Fitted Line PlotSep.cur = 146.1 - 0.1255 Grind.aid

Grind.aid

Sep.

cur

100876250250

150

145

140

135

130

Individual Value Plot of Sep.cur vs Grind.aid

Residual

Per

cent

1050-5-10

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

145.0142.5140.0137.5135.0

10

5

0

-5

-10

Residual

Freq

uenc

y

840-4-8

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

10

5

0

-5

-10



Residual Plots for Sep.cur

Regression Analysis: Sep.cur versus Grind.aid The regression equation is Sep.cur = 146 - 0.126 Grind.aid Predictor Coef SE Coef T P Constant 146.067 0.843 173.35 0.000 Grind.aid -0.12553 0.01414 -8.88 0.000 S = 3.92730 R-Sq = 52.6% R-Sq(adj) = 52.0% Analysis of Variance Source DF SS MS F P Regression 1 1216.3 1216.3 78.86 0.000 Residual Error 71 1095.1 15.4 Total 72 2311.4 Unusual Observations Obs Grind.aid Sep.cur Fit SE Fit Residual St Resid 4 0 137.940 146.067 0.843 -8.127 -2.12R 33 50 129.100 139.791 0.460 -10.691 -2.74R R denotes an observation with a large standardized residual.

E34

Grind.aid

AS.

inl.t

emp

100876250250

210.0

207.5

205.0

202.5

200.0

197.5

195.0

Individual Value Plot of AS.inl.temp vs Grind.aid

Grind.aid

AS.

inl.t

emp

100806040200

210.0

207.5

205.0

202.5

200.0

197.5

195.0

S 2.73480R-Sq 43.5%R-Sq(adj) 42.7%

Fitted Line PlotAS.inl.temp = 195.7 + 0.07279 Grind.aid

Residual

Per

cent

1050-5-10

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

204202200198196

10

5

0

-5

Residual

Freq

uenc

y

86420-2-4-6

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

10

5

0

-5



Residual Plots for AS.inl.temp

Regression Analysis: AS.inl.temp versus Grind.aid The regression equation is AS.inl.temp = 195.7 + 0.07279 Grind.aid S = 2.73480 R-Sq = 43.5% R-Sq(adj) = 42.7% Analysis of Variance Source DF SS MS F P Regression 1 409.003 409.003 54.69 0.000 Error 71 531.017 7.479 Total 72 940.020

E35

Grind.aid

AS.

inl.t

emp

100806040200

210.0

207.5

205.0

202.5

200.0

197.5

195.0

S 2.45559R-Sq 55.1%R-Sq(adj) 53.8%

Fitted Line PlotAS.inl.temp = 197.0 - 0.04530 Grind.aid

+ 0.001294 Grind.aid**2

Polynomial Regression Analysis: AS.inl.temp versus Grind.aid The regression equation is AS.inl.temp = 197.0 - 0.04530 Grind.aid + 0.001294 Grind.aid**2 S = 2.45559 R-Sq = 55.1% R-Sq(adj) = 53.8% Analysis of Variance Source DF SS MS F P Regression 2 517.925 258.963 42.95 0.000 Error 70 422.094 6.030 Total 72 940.020 Sequential Analysis of Variance Source DF SS F P Linear 1 409.003 54.69 0.000 Quadratic 1 108.923 18.06 0.000

Residual

Per

cent

1050-5-10

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

206204202200198

10

5

0

-5

Residual

Freq

uenc

y

1086420-2-4

16

12

8

4

0

Observation Order

Res

idua

l

7065605550454035302520151051

10

5

0

-5



Residual Plots for AS.inl.temp

E36

Grind.aid

Thr.

sep

100876250250

59000

58000

57000

56000

55000

Individual Value Plot of Thr.sep vs Grind.aid

Grind.aid

Thr.

sep

100806040200

59000

58000

57000

56000

55000

S 685.815R-Sq 58.5%R-Sq(adj) 57.9%

Fitted Line PlotThr.sep = 58000 - 24.67 Grind.aid

Residual

Per

cent

200010000-1000-2000

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

580005700056000

2000

1000

0

-1000

-2000

Residual

Freq

uenc

y

200010000-1000-2000

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

2000

1000

0

-1000

-2000



Residual Plots for Thr.sep

Regression Analysis: Thr.sep versus Grind.aid The regression equation is Thr.sep = 58000 - 24.67 Grind.aid S = 685.815 R-Sq = 58.5% R-Sq(adj) = 57.9% Analysis of Variance Source DF SS MS F P Regression 1 46978573 46978573 99.88 0.000 Error 71 33394269 470342 Total 72 80372843

E37

Residual

Per

cent

20100-10-20

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

1007550250

20

10

0

-10

-20

Residual

Freq

uenc

y

20100-10-20

30

20

10

0

Observation Order

Res

idua

l

7065605550454035302520151051

20

10

0

-10

-20




Best Subsets Regression: Grind.aid versus Sound.c1, Sound.c2 Response is Grind.aid S S o o u u n n d d . . Mallows c c Vars R-Sq R-Sq(adj) C-p S 1 2 1 86.5 86.3 105.9 12.119 X 1 30.2 29.2 834.5 27.544 X 2 94.6 94.4 3.0 7.7213 X X Regression Analysis: Grind.aid versus Sound.c1, Sound.c2 The regression equation is Grind.aid = 88.0 + 3.33 Sound.c1 - 3.69 Sound.c2 Predictor Coef SE Coef T P Constant 87.97 20.87 4.21 0.000 Sound.c1 3.3262 0.1152 28.87 0.000 Sound.c2 -3.6873 0.3600 -10.24 0.000

Sound.dif

Grin

d.ai

d

1050-5-10-15-20-25

100

80

60

40

20

0

Recirc

129145165175190

68859095

Scatterplot of Grind.aid vs Sound.difS = 7.72135 R-Sq = 94.6% R-Sq(adj) = 94.4% Analysis of Variance Source DF SS MS F P Regression 2 73016 36508 612.35 0.000 Residual Error 70 4173 60 Total 72 77189 Source DF Seq SS Sound.c1 1 66760 Sound.c2 1 6255 Unusual Observations Obs Sound.c1 Grind.aid Fit SE Fit Residual St Resid 17 32.9 25.000 4.594 1.722 20.406 2.71R 23 46.2 25.000 45.467 1.045 -20.467 -2.68R 24 45.4 25.000 41.873 1.067 -16.873 -2.21R 72 50.9 100.000 95.112 2.850 4.888 0.68 X 73 51.6 100.000 96.673 2.781 3.327 0.46 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E38

Sound.dif

Grin

d.ai

d

1050-5-10-15-20-25

100

80

60

40

20

0

S 7.70925R-Sq 94.5%R-Sq(adj) 94.5%

Fitted Line PlotGrind.aid = 69.59 + 3.382 Sound.dif

y = -0.0024x2 + 0.4533x + 31.833R2 = 0.942

y = -0.0017x2 + 0.1141x + 52.267R2 = 0.6869

0

10

20

30

40

50

60

0 12.5 25 37.5 50 62.5 75 87.5 100 112.5

Sound, %

Grin

d.ai

d

Sound.c1Sound.c2

Residual

Per

cent

20100-10-20

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

1007550250

20

10

0

-10

-20

Residual

Freq

uenc

y

20100-10-20

30

20

10

0

Observation Order

Res

idua

l

7065605550454035302520151051

20

10

0

-10

-20




Regression Analysis: Grind.aid versus Sound.dif The regression equation is Grind.aid = 69.6 + 3.38 Sound.dif Predictor Coef SE Coef T P Constant 69.589 1.062 65.52 0.000 Sound.dif 3.38153 0.09651 35.04 0.000 S = 7.70925 R-Sq = 94.5% R-Sq(adj) = 94.5% Analysis of Variance Source DF SS MS F P Regression 1 72969 72969 1227.76 0.000 Residual Error 71 4220 59 Total 72 77189 Unusual Observations Obs Sound.dif Grind.aid Fit SE Fit Residual St Resid 17 -19.4 25.000 4.021 1.592 20.979 2.78R 23 -7.0 25.000 45.918 0.910 -20.918 -2.73R 24 -8.1 25.000 42.334 0.928 -17.334 -2.26R R denotes an observation with a large standardized residual.

E39

Residual

Per

cent

50250-25-50

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

16014012010080

60

40

20

0

-20

Residual

Freq

uenc

y

6040200-20

20

15

10

5

0

Observation Order

Res

idua

l

7065605550454035302520151051

60

40

20

0

-20




Best Subsets Regression: Rej.rate versus Sound.c1, Sound.c2 Response is Rej.rate S S o o u u n n d d . . Mallows c c Vars R-Sq R-Sq(adj) C-p S 1 2 1 64.3 63.8 10.6 22.630 X 1 19.2 18.1 111.4 34.063 X 2 68.7 67.8 3.0 21.367 X X Regression Analysis: Rej.rate versus Sound.c1, Sound.c2 The regression equation is Rej.rate = 108 - 3.35 Sound.c1 + 3.09 Sound.c2 Predictor Coef SE Coef T P Constant 108.45 57.76 1.88 0.065 Sound.c1 -3.3506 0.3188 -10.51 0.000 Sound.c2 3.0933 0.9962 3.11 0.003 S = 21.3671 R-Sq = 68.7% R-Sq(adj) = 67.8% Analysis of Variance Source DF SS MS F P Regression 2 69990 34995 76.65 0.000 Residual Error 70 31959 457 Total 72 101948 Source DF Seq SS Sound.c1 1 65587 Sound.c2 1 4402 Unusual Observations Obs Sound.c1 Rej.rate Fit SE Fit Residual St Resid 21 43.1 191.68 131.02 3.24 60.66 2.87R 23 46.2 161.45 118.21 2.89 43.24 2.04R 72 50.9 69.29 73.95 7.89 -4.66 -0.23 X 73 51.6 68.22 72.26 7.70 -4.04 -0.20 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E40

Best Subsets Regression: kW/ton versus Sound.c1, Sound.c2 Response is kW/ton S S o o u u n n d d . . Mallows c c Vars R-Sq R-Sq(adj) C-p S 1 2 1 3.3 1.9 1.7 1.0349 X 1 2.2 0.8 2.5 1.0408 X 2 4.3 1.5 3.0 1.0371 X X Best Subsets Regression: Elec.charge versus Sound.c1, Sound.c2 Response is Elec.charge S S o o u u n n d d . . Mallows c c Vars R-Sq R-Sq(adj) C-p S 1 2 1 55.9 55.2 4.2 0.38686 X 1 0.8 0.0 95.3 0.57983 X 2 57.8 56.6 3.0 0.38112 X X

E41

Sound.c1

Elec

.cha

rge

555045403530

3.0

2.5

2.0

1.5

1.0

Grind.aid

87100

0255062

Scatterplot of Elec.charge vs Sound.c1

Sound.c1

Elec

.cha

rge

555045403530

3.0

2.5

2.0

1.5

1.0

S 0.386859R-Sq 55.9%R-Sq(adj) 55.2%

Fitted Line PlotElec.charge = 4.336 - 0.05221 Sound.c1

Residual

Per

cent

1.00.50.0-0.5-1.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

2.72.42.11.81.5

1.0

0.5

0.0

-0.5

Residual

Freq

uenc

y

0.80.60.40.20.0-0.2-0.4-0.6

10.0

7.5

5.0

2.5

0.0

Observation Order

Res

idua

l

7065605550454035302520151051

1.0

0.5

0.0

-0.5



Residual Plots for Elec.charge

Regression Analysis: Elec.charge versus Sound.c1 The regression equation is Elec.charge = 4.34 - 0.0522 Sound.c1 Predictor Coef SE Coef T P Constant 4.3358 0.2574 16.85 0.000 Sound.c1 -0.052215 0.005508 -9.48 0.000 S = 0.386859 R-Sq = 55.9% R-Sq(adj) = 55.2% Analysis of Variance Source DF SS MS F P Regression 1 13.447 13.447 89.85 0.000 Residual Error 71 10.626 0.150 Total 72 24.073 Unusual Observations Obs Sound.c1 Elec.charge Fit SE Fit Residual St Resid 28 49.4 2.5500 1.7574 0.0490 0.7926 2.07R 31 48.4 2.5900 1.8101 0.0471 0.7799 2.03R 33 49.9 2.5600 1.7297 0.0502 0.8303 2.16R R denotes an observation with a large standardized residual.

E42

kW/ton

Elec

.cha

rge

45.7045.4744.8144.4443.6243.2043.1142.9642.8142.6642.4842.3042.07

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs kW/ton

Elec.charge

kW/t

on

3.02.52.01.51.0

46

45

44

43

42

S 0.849392R-Sq 34.9%R-Sq(adj) 33.9%

Fitted Line PlotkW/ton = 41.38 + 1.067 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

44.544.043.543.042.5

2

1

0

-1

-2


Freq

uenc

y

210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Regression Analysis: kW/ton versus Elec.charge The regression equation is kW/ton = 41.38 + 1.067 Elec.charge S = 0.849392 R-Sq = 34.9% R-Sq(adj) = 33.9% Analysis of Variance Source DF SS MS F P Regression 1 27.4042 27.4042 37.98 0.000 Error 71 51.2242 0.7215 Total 72 78.6284 Conclusion: At low levels of electric charge, energy use (kW/ton) is low.

E43

Elec.charge

kW/t

on

3.02.52.01.51.0

46

45

44

43

42

Grind.aid

87100

0255062

Scatterplot of kW/ton vs Elec.charge

E44

Mill.pow

Elec

.cha

rge

3278.093243.963228.163214.893194.453183.293169.803160.533142.633124.513075.00

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Mill.pow

Elec.charge

Mill

.pow

3.02.52.01.51.0

3300

3250

3200

3150

3100

S 36.2085R-Sq 38.6%R-Sq(adj) 37.7%

Fitted Line PlotMill.pow = 3280 - 49.25 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

32203200318031603140

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

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1

0

-1

-2



Residual Plots for Mill.pow

Regression Analysis: Mill.pow versus Elec.charge The regression equation is Mill.pow = 3280 - 49.25 Elec.charge S = 36.2085 R-Sq = 38.6% R-Sq(adj) = 37.7% Analysis of Variance Source DF SS MS F P Regression 1 58402 58402.4 44.55 0.000 Error 71 93085 1311.1 Total 72 151487 See also page 96.

E45

Elev.pow

Elec

.cha

rge

36.6335.1334.0733.3131.9729.7228.8128.3727.7527.1526.5625.96

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Elev.pow

Elec.charge

Elev

.pow

3.02.52.01.51.0

37.5

35.0

32.5

30.0

27.5

25.0

S 2.06964R-Sq 66.6%R-Sq(adj) 66.1%

Fitted Line PlotElev.pow = 20.58 + 5.015 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

35.032.530.027.525.0

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Regression Analysis: Elev.pow versus Elec.charge The regression equation is Elev.pow = 20.58 + 5.015 Elec.charge S = 2.06964 R-Sq = 66.6% R-Sq(adj) = 66.1% Analysis of Variance Source DF SS MS F P Regression 1 605.550 605.550 141.37 0.000 Error 71 304.122 4.283 Total 72 909.672

E46

AS.fan.pow

Elec

.cha

rge

73.2772.9272.6772.4372.1972.0671.8371.5871.3571.1070.8770.51

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs AS.fan.pow

Elec.charge

AS.

fan.

pow

3.02.52.01.51.0

73.5

73.0

72.5

72.0

71.5

71.0

70.5

S 0.554407R-Sq 6.0%R-Sq(adj) 4.6%

Fitted Line PlotAS.fan.pow = 71.42 + 0.2397 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

72.172.071.971.871.7

2

1

0

-1

-2


Freq

uenc

y

210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

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1

0

-1

-2



Residual Plots for AS.fan.pow

Regression Analysis: AS.fan.pow versus Elec.charge The regression equation is AS.fan.pow = 71.42 + 0.2397 Elec.charge S = 0.554407 R-Sq = 6.0% R-Sq(adj) = 4.6% Analysis of Variance Source DF SS MS F P Regression 1 1.3835 1.38353 4.50 0.037 ∴ conclude model is not good fit. Error 71 21.8231 0.30737 Total 72 23.2066

E47

Sep.fan.pow

Elec

.cha

rge

283.63281.74279.53277.64275.75268.60265.62256.27254.37250.62

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Sep.fan.pow

Elec.charge

Sep.

fan.

pow

3.02.52.01.51.0

285

280

275

270

265

260

255

250

S 5.95159R-Sq 70.2%R-Sq(adj) 69.8%

Fitted Line PlotSep.fan.pow = 301.6 - 15.69 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

280270260

2

0

-2


Freq

uenc

y

3210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2



Residual Plots for Sep.fan.pow

Regression Analysis: Sep.fan.pow versus Elec.charge The regression equation is Sep.fan.pow = 301.6 - 15.69 Elec.charge S = 5.95159 R-Sq = 70.2% R-Sq(adj) = 69.8% Analysis of Variance Source DF SS MS F P Regression 1 5922.52 5922.52 167.20 0.000 Error 71 2514.92 35.42 Total 72 8437.45

E48

Air.comp1

Elec

.cha

rge

93.8187.7185.9183.8882.1979.9578.1475.7774.0772.8470.0267.3261.79

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Air.comp1

Elec.charge

Air

.com

p1

3.02.52.01.51.0

100

90

80

70

60

S 7.58801R-Sq 15.0%R-Sq(adj) 13.8%

Fitted Line PlotAir.comp1 = 89.74 - 5.470 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

85.082.580.077.575.0

2

0

-2


Freq

uenc

y

3210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2



Residual Plots for Air.comp1

Regression Analysis: Air.comp1 versus Elec.charge The regression equation is Air.comp1 = 89.74 - 5.470 Elec.charge S = 7.58801 R-Sq = 15.0% R-Sq(adj) = 13.8% Analysis of Variance Source DF SS MS F P Regression 1 720.19 720.187 12.51 0.001 Error 71 4088.03 57.578 Total 72 4808.21

E49

Air.comp2

Elec

.cha

rge

95.3189.9187.4186.1883.6982.3480.1977.1675.1273.0870.4967.7962.49

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Air.comp2

Elec.charge

Air

.com

p2

3.02.52.01.51.0

100

90

80

70

60

S 7.53923R-Sq 20.2%R-Sq(adj) 19.1%

Fitted Line PlotAir.comp2 = 92.99 - 6.523 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

85.082.580.077.575.0

2

0

-2


Freq

uenc

y

3210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

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0

-2



Residual Plots for Air.comp2

Regression Analysis: Air.comp2 versus Elec.charge The regression equation is Air.comp2 = 92.99 - 6.523 Elec.charge S = 7.53923 R-Sq = 20.2% R-Sq(adj) = 19.1% Analysis of Variance Source DF SS MS F P Regression 1 1024.15 1024.15 18.02 0.000 Error 71 4035.64 56.84 Total 72 5059.79

E50


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.51.0

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Regression Analysis: Elec.charge versus Mill.pow The regression equation is Elec.charge = 26.9 - 0.00783 Mill.pow Predictor Coef SE Coef T P Constant 26.864 3.736 7.19 0.000 Mill.pow -0.007827 0.001173 -6.67 0.000 S = 0.456446 R-Sq = 38.6% R-Sq(adj) = 37.7% Analysis of Variance Source DF SS MS F P Regression 1 9.2809 9.2809 44.55 0.000 Residual Error 71 14.7923 0.2083 Total 72 24.0732 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 8 3075 2.4500 2.7957 0.1397 -0.3457 -0.80 X 14 3079 2.4000 2.7611 0.1349 -0.3611 -0.83 X 43 3152 1.1600 2.1954 0.0662 -1.0354 -2.29R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence. See also page 84.

E51


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.5

2

0

-2


Freq

uenc

y

3210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow The regression equation is Elec.charge = 23.7 - 0.00366 Mill.pow - 0.0374 Sep.fan.pow Predictor Coef SE Coef T P Constant 23.745 2.333 10.18 0.000 Mill.pow -0.0036596 0.0008240 -4.44 0.000 Sep.fan.pow -0.037437 0.003492 -10.72 0.000 S = 0.282793 R-Sq = 76.7% R-Sq(adj) = 76.1% Analysis of Variance Source DF SS MS F P Regression 2 18.4751 9.2376 115.51 0.000 Residual Error 70 5.5981 0.0800 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1977 0.0584 0.5723 2.07R 3 3134 2.9400 2.0930 0.0547 0.8470 3.05R 4 3115 2.9800 2.1193 0.0691 0.8607 3.14R R denotes an observation with a large standardized residual.

E52


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.5

2

0

-2


Freq

uenc

y

3210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... The regression equation is Elec.charge = 17.4 - 0.00337 Mill.pow - 0.0375 Sep.fan.pow + 0.0757 AS.fan.pow Predictor Coef SE Coef T P Constant 17.389 5.652 3.08 0.003 Mill.pow -0.0033659 0.0008548 -3.94 0.000 Sep.fan.pow -0.037522 0.003479 -10.78 0.000 AS.fan.pow 0.07573 0.06139 1.23 0.222 ∴ conclude β = 0 S = 0.281746 R-Sq = 77.2% R-Sq(adj) = 76.3% Analysis of Variance Source DF SS MS F P Regression 3 18.5959 6.1986 78.09 0.000 Residual Error 69 5.4773 0.0794 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 AS.fan.pow 1 0.1208 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.2122 0.0594 0.5578 2.03R 3 3134 2.9400 2.1563 0.0749 0.7837 2.89R 4 3115 2.9800 2.1582 0.0757 0.8218 3.03R R denotes an observation with a large standardized residual.

E53


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.82.42.01.61.2

3

2

1

0

-1


Freq

uenc

y

3210-1

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3

2

1

0

-1




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... The regression equation is Elec.charge = 12.7 - 0.00246 Mill.pow - 0.0280 Sep.fan.pow + 0.0457 AS.fan.pow + 0.0446 Elev.pow Predictor Coef SE Coef T P Constant 12.746 5.659 2.25 0.028 Mill.pow -0.0024638 0.0008803 -2.80 0.007 Sep.fan.pow -0.028012 0.004798 -5.84 0.000 AS.fan.pow 0.04569 0.05967 0.77 0.447 ∴ conclude β = 0 Elev.pow 0.04457 0.01621 2.75 0.008 S = 0.269238 R-Sq = 79.5% R-Sq(adj) = 78.3% Analysis of Variance Source DF SS MS F P Regression 4 19.1439 4.7860 66.02 0.000 Residual Error 68 4.9293 0.0725 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 AS.fan.pow 1 0.1208 Elev.pow 1 0.5480 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1562 0.0603 0.6138 2.34R 3 3134 2.9400 2.1484 0.0716 0.7916 3.05R 4 3115 2.9800 2.2318 0.0772 0.7482 2.90R 28 3134 2.5500 2.5029 0.1465 0.0471 0.21 X 73 3219 1.8600 1.3285 0.0511 0.5315 2.01R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E54

Residual

Per

cent

1.00.50.0-0.5-1.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

3.02.52.01.5

1.0

0.5

0.0

-0.5

Residual

Freq

uenc

y

0.80.60.40.20.0-0.2-0.4

16

12

8

4

0

Observation Order

Res

idua

l

7065605550454035302520151051

1.0

0.5

0.0

-0.5




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, Elev.pow The regression equation is Elec.charge = 16.2 - 0.00259 Mill.pow - 0.0275 Sep.fan.pow + 0.0468 Elev.pow Predictor Coef SE Coef T P Constant 16.216 3.380 4.80 0.000 Mill.pow -0.0025891 0.0008623 -3.00 0.004 Sep.fan.pow -0.027478 0.004732 -5.81 0.000 Elev.pow 0.04685 0.01589 2.95 0.004 S = 0.268429 R-Sq = 79.3% R-Sq(adj) = 78.4% Analysis of Variance Source DF SS MS F P Regression 3 19.1014 6.3671 88.37 0.000 Residual Error 69 4.9718 0.0721 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 Elev.pow 1 0.6263 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1448 0.0583 0.6252 2.39R 3 3134 2.9400 2.1110 0.0523 0.8290 3.15R 4 3115 2.9800 2.2129 0.0729 0.7671 2.97R 28 3134 2.5500 2.4337 0.1150 0.1163 0.48 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence. This is the best fit for power parameters and electric charge.

E55


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.82.42.01.61.2

3

2

1

0

-1


Freq

uenc

y

3210-1

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3

2

1

0

-1




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... The regression equation is Elec.charge = 12.6 - 0.00224 Mill.pow - 0.0263 Sep.fan.pow + 0.0356 AS.fan.pow + 0.0480 Elev.pow - 0.00484 Air.comp1 Predictor Coef SE Coef T P Constant 12.585 5.651 2.23 0.029 Mill.pow -0.0022431 0.0009009 -2.49 0.015 Sep.fan.pow -0.026305 0.005029 -5.23 0.000 AS.fan.pow 0.03558 0.06026 0.59 0.557 Elev.pow 0.04803 0.01648 2.91 0.005 Air.comp1 -0.004836 0.004351 -1.11 0.270 S = 0.268773 R-Sq = 79.9% R-Sq(adj) = 78.4% Analysis of Variance Source DF SS MS F P Regression 5 19.2332 3.8466 53.25 0.000 Residual Error 67 4.8400 0.0722 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 AS.fan.pow 1 0.1208 Elev.pow 1 0.5480 Air.comp1 1 0.0892 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1356 0.0630 0.6344 2.43R 3 3134 2.9400 2.1922 0.0816 0.7478 2.92R 4 3115 2.9800 2.1518 0.1054 0.8282 3.35R 28 3134 2.5500 2.4957 0.1464 0.0543 0.24 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E56

Residual

Per

cent

1.00.50.0-0.5-1.0

99.9

99

90

50

10

1

0.1

Fitted Value

Res

idua

l

3.02.52.01.5

1.0

0.5

0.0

-0.5

Residual

Freq

uenc

y

0.80.60.40.20.0-0.2-0.4

12

9

6

3

0

Observation Order

Res

idua

l

7065605550454035302520151051

1.0

0.5

0.0

-0.5




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ... The regression equation is Elec.charge = 15.2 - 0.00232 Mill.pow - 0.0258 Sep.fan.pow + 0.0500 Elev.pow - 0.00522 Air.comp1 Predictor Coef SE Coef T P Constant 15.212 3.467 4.39 0.000 Mill.pow -0.0023206 0.0008870 -2.62 0.011 Sep.fan.pow -0.025762 0.004921 -5.24 0.000 Elev.pow 0.05004 0.01605 3.12 0.003 Air.comp1 -0.005224 0.004280 -1.22 0.227 S = 0.267482 R-Sq = 79.8% R-Sq(adj) = 78.6% Analysis of Variance Source DF SS MS F P Regression 4 19.2080 4.8020 67.12 0.000 Residual Error 68 4.8652 0.0715 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 Elev.pow 1 0.6263 Air.comp1 1 0.1066 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1253 0.0603 0.6447 2.47R 3 3134 2.9400 2.1673 0.0696 0.7727 2.99R 4 3115 2.9800 2.1310 0.0989 0.8490 3.42R R denotes an observation with a large standardized residual.

E57

Multicollinearity Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, ...

The regression equation is Mill.pow Elev.pow AS.fan.

pow Sep.fan.

pow Air. comp1

Air. comp2 kW/ton

Mill.pow 1.00

Elev.pow -0.60 1.00

AS.fan.pow -0.30 0.27 1.00

Sep.fan.pow 0.47 -0.79 -0.13 1.00

Air.comp1 0.33 -0.25 -0.20 0.36 1.00

Air.comp2 0.38 -0.32 -0.25 0.41 0.95 1.00

kW/ton -0.11 0.61 0.00 -0.87 -0.13 -0.16 1.00

Elec.charge = 13.0 - 0.00220 Mill.pow - 0.0261 Sep.fan.pow + 0.0290 AS.fan.pow + 0.0471 Elev.pow + 0.0038 Air.comp1 - 0.0092 Air.comp2

Predictor Coef SE Coef T P Constant 12.955 5.700 2.27 0.026

Mill.pow -0.0022020 0.0009065 -2.43 0.018 Sep.fan.pow -0.026098 0.005059 -5.16 0.000

AS.fan.pow 0.02897 0.06127 0.47 0.638 ∴ conclude β = 0 Elev.pow 0.04714 0.01660 2.84 0.006 Air.comp1 0.00383 0.01342 0.29 0.776 ∴ conclude β = 0 Air.comp2 -0.00922 0.01351 -0.68 0.497 ∴ conclude β = 0


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.5

3

2

1

0

-1


Freq

uenc

y3210-1

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3

2

1

0

-1




S = 0.269851 R-Sq = 80.0% R-Sq(adj) = 78.2% Analysis of Variance Source DF SS MS F P Regression 6 19.2671 3.2112 44.10 0.000 Residual Error 66 4.8061 0.0728 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 AS.fan.pow 1 0.1208 Elev.pow 1 0.5480 Air.comp1 1 0.0892 Air.comp2 1 0.0339 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1351 0.0633 0.6349 2.42R 3 3134 2.9400 2.2020 0.0832 0.7380 2.88R 4 3115 2.9800 2.1365 0.1082 0.8435 3.41R 28 3134 2.5500 2.5096 0.1484 0.0404 0.18 X 65 3219 1.5400 1.4349 0.2620 0.1051 1.63 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E58


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.5

3

2

1

0

-1


Freq

uenc

y

3210-1

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3

2

1

0

-1




Regression Analysis: Elec.charge versus Mill.pow, Sep.fan.pow, Elev.pow The regression equation is Elec.charge = 16.2 - 0.00259 Mill.pow - 0.0275 Sep.fan.pow + 0.0468 Elev.pow Predictor Coef SE Coef T P Constant 16.216 3.380 4.80 0.000 Mill.pow -0.0025891 0.0008623 -3.00 0.004 Sep.fan.pow -0.027478 0.004732 -5.81 0.000 Elev.pow 0.04685 0.01589 2.95 0.004 S = 0.268429 R-Sq = 79.3% R-Sq(adj) = 78.4% Analysis of Variance Source DF SS MS F P Regression 3 19.1014 6.3671 88.37 0.000 Residual Error 69 4.9718 0.0721 Total 72 24.0732 Source DF Seq SS Mill.pow 1 9.2809 Sep.fan.pow 1 9.1943 Elev.pow 1 0.6263 Unusual Observations Obs Mill.pow Elec.charge Fit SE Fit Residual St Resid 2 3125 2.7700 2.1448 0.0583 0.6252 2.39R 3 3134 2.9400 2.1110 0.0523 0.8290 3.15R 4 3115 2.9800 2.2129 0.0729 0.7671 2.97R 28 3134 2.5500 2.4337 0.1150 0.1163 0.48 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E59


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.5

2

0

-2


Freq

uenc

y

3210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




Regression Analysis: Elec.charge versus Sep.fan.pow, Mill.pow The regression equation is Elec.charge = 23.7 - 0.0374 Sep.fan.pow - 0.00366 Mill.pow Predictor Coef SE Coef T P Constant 23.745 2.333 10.18 0.000 Sep.fan.pow -0.037437 0.003492 -10.72 0.000 Mill.pow -0.0036596 0.0008240 -4.44 0.000 S = 0.282793 R-Sq = 76.7% R-Sq(adj) = 76.1% Analysis of Variance Source DF SS MS F P Regression 2 18.4751 9.2376 115.51 0.000 Residual Error 70 5.5981 0.0800 Total 72 24.0732 Source DF Seq SS Sep.fan.pow 1 16.8978 Mill.pow 1 1.5774 Unusual Observations Obs Sep.fan.pow Elec.charge Fit SE Fit Residual St Resid 2 270 2.7700 2.1977 0.0584 0.5723 2.07R 3 272 2.9400 2.0930 0.0547 0.8470 3.05R 4 273 2.9800 2.1193 0.0691 0.8607 3.14R R denotes an observation with a large standardized residual.

E60


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.02.52.01.51.0

4

2

0

-2


Freq

uenc

y

3210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

4

2

0

-2



Residual Plots for Voltage

Regression Analysis: Voltage versus T.Mill.pow, T.Elev.pow, ... These “T” parameters are transformation of original parameter by dividing by Ac.feed. The regression equation is Voltage = 14.1 - 0.181 T.Mill.pow + 6.02 T.Elev.pow + 9.55 T.AS.fan.pow - 5.38 T.Sep.fan.pow + 0.30 T.Air.comp1 - 0.82 T.Air.comp2 Predictor Coef SE Coef T P Constant 14.055 3.962 3.55 0.001 T.Mill.pow -0.18148 0.07146 -2.54 0.013 T.Elev.pow 6.021 1.457 4.13 0.000 T.AS.fan.pow 9.553 3.452 2.77 0.007 T.Sep.fan.pow -5.376 1.134 -4.74 0.000 T.Air.comp1 0.295 1.314 0.22 0.823 ∴ conclude β = 0 T.Air.comp2 -0.820 1.307 -0.63 0.532 ∴ conclude β = 0 S = 0.265137 R-Sq = 80.7% R-Sq(adj) = 79.0% Note: There is strong negative multicollinearity between T.Elv.pow and T.Sep.fan.pow. Analysis of Variance Source DF SS MS F P Regression 6 19.4335 3.2389 46.07 0.000 Residual Error 66 4.6396 0.0703 Total 72 24.0732 Source DF Seq SS T.Mill.pow 1 7.7956 T.Elev.pow 1 9.1308 T.AS.fan.pow 1 0.6488 T.Sep.fan.pow 1 1.7441 T.Air.comp1 1 0.0865 T.Air.comp2 1 0.0277 Unusual Observations Obs T.Mill.pow Voltage Fit SE Fit Residual St Resid 2 31.4 2.7700 2.2017 0.0719 0.5683 2.23R 3 31.2 2.9400 2.3774 0.0855 0.5626 2.24R 4 31.4 2.9800 2.0974 0.1049 0.8826 3.62R 28 32.8 2.5500 2.5375 0.1478 0.0125 0.06 X 65 31.7 1.5400 1.4762 0.2536 0.0638 0.82 X 73 31.6 1.8600 1.2311 0.0765 0.6289 2.48R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence.

E61


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

4645444342

2

0

-2


Freq

uenc

y

210-1-2-3

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




Regression Analysis: kW/ton versus Mill.pow, Elev.pow, ... Regression Analysis of all power parameters. The regression equation is kW/ton = 43.5 + 0.00829 Mill.pow + 0.0053 Elev.pow + 0.0072 AS.fan.pow - 0.106 Sep.fan.pow + 0.0000 Air.comp1 + 0.0192 Air.comp2 Predictor Coef SE Coef T P Constant 43.482 7.093 6.13 0.000 Mill.pow 0.008293 0.001128 7.35 0.000 Elev.pow 0.00526 0.02065 0.25 0.800 ∴ conclude β = 0 AS.fan.pow 0.00722 0.07625 0.09 0.925 ∴ conclude β = 0 Sep.fan.pow -0.105719 0.006295 -16.79 0.000 Air.comp1 0.00001 0.01670 0.00 1.000 ∴ conclude β = 0 Air.comp2 0.01921 0.01681 1.14 0.257 S = 0.335809 R-Sq = 90.5% R-Sq(adj) = 89.7% Note: There is strong negative multicollinearity between Elv.pow and AS.fan.pow. Analysis of Variance Source DF SS MS F P Regression 6 71.186 11.864 105.21 0.000 Best Subsets Regression: kW/ton versus Mill.pow, Elev.pow, ... Residual Error 66 7.443 0.113 Total 72 78.628 Response is kW/ton S Source DF Seq SS A e Mill.pow 1 0.872 S p A A Elev.pow 1 37.072 M E . . i i AS.fan.pow 1 1.036 i l f f r r Sep.fan.pow 1 30.813 l e a a . . Air.comp1 1 1.244 l v n n c c Air.comp2 1 0.147 . . . . o o p p p p m m Mallows o o o o p p Unusual Observations Vars R-Sq R-Sq(adj) C-p S w w w w 1 2 1 76.5 76.2 94.9 0.51014 X Obs Mill.pow kW/ton Fit SE Fit Residual St Resid 1 37.5 36.6 366.8 0.83193 X 8 3075 42.0700 42.9790 0.1106 -0.9090 -2.87R 2 88.6 88.3 12.2 0.35719 X X 16 3170 45.6700 44.9881 0.1043 0.6819 2.14R 2 81.1 80.6 64.5 0.46020 X X 28 3134 44.4400 44.4747 0.1847 -0.0347 -0.12 X 3 90.5 90.1 1.1 0.32865 X X X 65 3219 43.2600 43.3542 0.3260 -0.0942 -1.17 X 3 90.3 89.9 2.3 0.33174 X X X 68 3176 42.1900 42.8958 0.0744 -0.7058 -2.16R 4 90.5 90.0 3.0 0.33086 X X X X 4 90.5 90.0 3.1 0.33100 X X X X R denotes an observation with a large standardized residual. 5 90.5 89.8 5.0 0.33329 X X X X X X denotes an observation whose X value gives it large influence. 5 90.5 89.8 5.0 0.33332 X X X X X 6 90.5 89.7 7.0 0.33581 X X X X X X These kW/ton are not justified because they compare kW/ton with kW. The comparison should be between kw/ton and kW/ton.

E62


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

45.044.544.043.543.0

2

0

-2


Freq

uenc

y

210-1-2-3

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




Regression Analysis: kW/ton versus Mill.pow, Elev.pow The regression equation is kW/ton = 6.05 + 0.00934 Mill.pow + 0.252 Elev.pow Predictor Coef SE Coef T P Constant 6.046 8.410 0.72 0.475 Mill.pow 0.009342 0.002449 3.81 0.000 Elev.pow 0.25241 0.03160 7.99 0.000 S = 0.762361 R-Sq = 48.3% R-Sq(adj) = 46.8% Analysis of Variance Source DF SS MS F P Regression 2 37.945 18.972 32.64 0.000 Residual Error 70 40.684 0.581 Total 72 78.628 Source DF Seq SS Mill.pow 1 0.872 Elev.pow 1 37.072 Unusual Observations Obs Mill.pow kW/ton Fit SE Fit Residual St Resid 7 3147 42.7100 44.3855 0.1577 -1.6755 -2.25R 8 3075 42.0700 43.6771 0.2333 -1.6071 -2.21R 22 3224 45.7000 45.4856 0.2919 0.2144 0.30 X 27 3176 45.5300 43.9018 0.1061 1.6282 2.16R 28 3134 44.4400 42.5525 0.1848 1.8875 2.55R 30 3158 45.0800 43.4670 0.1035 1.6130 2.14R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large influence. These kW/ton are not justified because they compare kW/ton with kW. The comparison should be between kw/ton and kW/ton.

E63

Grind.aid

Elec

.cha

rge

100876250250

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Grind.aid

Grind.aid

Elec

.cha

rge

100806040200

3.0

2.5

2.0

1.5

1.0

S 0.404594R-Sq 51.7%R-Sq(adj) 51.0%

Fitted Line PlotElec.charge = 2.569 - 0.01270 Grind.aid


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.72.42.11.81.5

2

1

0

-1

-2


Freq

uenc

y

1.60.80.0-0.8-1.6

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Regression Analysis: Elec.charge versus Grind.aid The regression equation is Elec.charge = 2.57 - 0.0127 Grind.aid Predictor Coef SE Coef T P Constant 2.56875 0.08681 29.59 0.000 Grind.aid -0.012700 0.001456 -8.72 0.000 S = 0.404594 R-Sq = 51.7% R-Sq(adj) = 51.0% Analysis of Variance Source DF SS MS F P Regression 1 12.451 12.451 76.06 0.000 Residual Error 71 11.622 0.164 Total 72 24.073

Error variance appears constant but error distribution is not normal.

E64

Grind.aid

Elec

.cha

rge

100806040200

3.0

2.5

2.0

1.5

1.0

S 0.401166R-Sq 53.2%R-Sq(adj) 51.9%

Fitted Line PlotElec.charge = 2.643 - 0.01946 Grind.aid

+ 0.000074 Grind.aid**2

Polynomial Regression Analysis: Elec.charge versus Grind.aid The regression equation is Elec.charge = 2.643 - 0.01946 Grind.aid + 0.000074 Grind.aid**2 S = 0.401166 R-Sq = 53.2% R-Sq(adj) = 51.9% Analysis of Variance Source DF SS MS F P Regression 2 12.8078 6.40390 39.79 0.000 Error 70 11.2654 0.16093 Total 72 24.0732 Sequential Analysis of Variance Source DF SS F P Linear 1 12.4508 76.06 0.000 Quadratic 1 0.3570 2.22 0.141


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.502.252.001.751.50

2

1

0

-1

-2


Freq

uenc

y

1.60.80.0-0.8-1.6

12

9

6

3

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2





E65

Grind.aid

Elec

.cha

rge

100806040200

3.0

2.5

2.0

1.5

1.0

S 0.336927R-Sq 67.5%R-Sq(adj) 66.0%

Fitted Line PlotElec.charge = 2.522 + 0.03386 Grind.aid


Polynomial Regression Analysis: Elec.charge versus Grind.aid Regression Analysis: Elec.charge versus Grind.aid, Grind.aid^2, ... The regression equation is Elec.charge = 2.52 + 0.0339 Grind.aid - 0.00146 Grind.aid^2 + 0.000011 Grind.aid^3 Predictor Coef SE Coef T P Constant 2.52178 0.08647 29.16 0.000 Grind.aid 0.03386 0.01049 3.23 0.002 Grind.aid^2 -0.0014601 0.0002821 -5.18 0.000 Grind.aid^3 0.00001062 0.00000193 5.50 0.000 S = 0.336927 R-Sq = 67.5% R-Sq(adj) = 66.0% Analysis of Variance Source DF SS MS F P Regression 3 16.2403 5.4134 47.69 0.000 Residual Error 69 7.8329 0.1135 Total 72 24.0732 Source DF Seq SS

Grind.aid 1 12.4508


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.502.252.001.751.50

2

1

0

-1


Freq

uenc

y

2.01.51.00.50.0-0.5-1.0

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1




Grind.aid^2 1 0.3570 Grind.aid^3 1 3.4325 Unusual Observations Obs Grind.aid Elec.charge Fit SE Fit Residual St Resid 25 50 2.5700 1.8916 0.0596 0.6784 2.05R 27 50 2.5700 1.8916 0.0596 0.6784 2.05R 29 50 2.5900 1.8916 0.0596 0.6984 2.11R 31 50 2.5900 1.8916 0.0596 0.6984 2.11R 33 50 2.5600 1.8916 0.0596 0.6684 2.02R 70 100 1.6600 1.9240 0.1387 -0.2640 -0.86 X 71 100 1.7500 1.9240 0.1387 -0.1740 -0.57 X 72 100 1.6700 1.9240 0.1387 -0.2540 -0.83 X 73 100 1.8600 1.9240 0.1387 -0.0640 -0.21 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large i Sequential Analysis of Variance Source DF SS F P Linear 1 12.4508 76.06 0.000 Quadratic 1 0.3570 2.22 0.141 Cubic 1 3.4325 30.24 0.000


E66

Ac.feed

Elec

.cha

rge

102.50102.33102.01101.82101.48100.0799.5397.8095.4494.9494.8494.40

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Ac.feed

Ac.feed

Elec

.cha

rge

103102101100999897969594

3.0

2.5

2.0

1.5

1.0

S 0.356136R-Sq 62.6%R-Sq(adj) 62.1%

Fitted Line PlotElec.charge = 17.28 - 0.1539 Ac.feed


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.72.42.11.81.5

3

2

1

0

-1


Freq

uenc

y

3210-1

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3

2

1

0

-1




Regression Analysis: Elec.charge versus Ac.feed The regression equation is Elec.charge = 17.3 - 0.154 Ac.feed Predictor Coef SE Coef T P Constant 17.281 1.409 12.27 0.000 Ac.feed -0.15389 0.01412 -10.90 0.000 S = 0.356136 R-Sq = 62.6% R-Sq(adj) = 62.1% Analysis of Variance Source DF SS MS F P Regression 1 15.068 15.068 118.80 0.000 Residual Error 71 9.005 0.127 Total 72 24.073 Unusual Observations Obs Ac.feed Elec.charge Fit SE Fit Residual St Resid 2 100 2.7700 1.9591 0.0417 0.8109 2.29R 3 100 2.9400 1.8267 0.0428 1.1133 3.15R 4 99 2.9800 1.9929 0.0420 0.9871 2.79R R denotes an observation with a large standardized residual.

E67

Ac.feed

Elec

.cha

rge

103102101100999897969594

3.0

2.5

2.0

1.5

1.0

S 0.296001R-Sq 74.5%R-Sq(adj) 73.8%

Fitted Line PlotElec.charge = - 365.3 + 7.613 Ac.feed

- 0.03938 Ac.feed**2

Polynomial Regression Analysis: Elec.charge versus Ac.feed The regression equation is Elec.charge = - 365.3 + 7.613 Ac.feed - 0.03938 Ac.feed**2 S = 0.296001 R-Sq = 74.5% R-Sq(adj) = 73.8% Analysis of Variance Source DF SS MS F P Regression 2 17.9400 8.97001 102.38 0.000 Error 70 6.1332 0.08762 Total 72 24.0732 Sequential Analysis of Variance Source DF SS F P Linear 1 15.0681 118.80 0.000 Quadratic 1 2.8720 32.78 0.000


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

2.52.01.51.0

2

0

-2


Freq

uenc

y

3210-1-2

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




E68

Rej.rate

Elec

.cha

rge

172.59158.22148.81142.50126.7698.9591.2989.1080.0975.2572.1668.22

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Rej.rate

Elec.charge

Rej

.rat

e

3.02.52.01.51.0

200

175

150

125

100

75

50

S 21.1828R-Sq 68.8%R-Sq(adj) 68.3%

Fitted Line PlotRej.rate = 10.20 + 53.96 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

16014012010080

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2




Regression Analysis: Rej.rate versus Elec.charge The regression equation is Rej.rate = 10.2 + 54.0 Elec.charge Predictor Coef SE Coef T P Constant 10.203 8.711 1.17 0.245 Elec.charge 53.959 4.317 12.50 0.000 S = 21.1828 R-Sq = 68.8% R-Sq(adj) = 68.3% Analysis of Variance Source DF SS MS F P Regression 1 70090 70090 156.20 0.000 Residual Error 71 31859 449 Total 72 101948 Unusual Observations Obs Elec.charge Rej.rate Fit SE Fit Residual St Resid 17 2.50 190.61 145.10 3.48 45.51 2.18R 21 2.56 191.68 148.34 3.67 43.34 2.08R 73 1.86 68.22 110.57 2.50 -42.35 -2.01R R denotes an observation with a large standardized residual.

E69

kW/ton

Rej

.rat

e

4645444342

200

175

150

125

100

75

50

S 29.5799R-Sq 39.1%R-Sq(adj) 38.2%

Fitted Line PlotRej.rate = - 863.1 + 22.51 kW/ton

Regression Analysis: Rej.rate versus kW/ton The regression equation is Rej.rate = - 863 + 22.5 kW/ton Predictor Coef SE Coef T P Constant -863.1 145.0 -5.95 0.000 kW/ton 22.506 3.336 6.75 0.000 S = 29.5799 R-Sq = 39.1% R-Sq(adj) = 38.2% Analysis of Variance Source DF SS MS F P Regression 1 39825 39825 45.52 0.000 Residual Error 71 62123 875 Total 72 101948 Unusual Observations Obs kW/ton Rej.rate Fit SE Fit Residual St Resid 2 42.9 162.10 101.46 3.97 60.64 2.07R 8 42.1 146.37 83.68 5.74 62.69 2.16R

9 42.7 165.82 96.96 4.34 68.86 2.35R


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

16014012010080

2

1

0

-1


Freq

uenc

y

2.01.51.00.50.0-0.5-1.0-1.5

10.0

7.5

5.0

2.5

0.0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1





E70

Wat.flo1

Elec

.cha

rge

2.882.862.832.792.762.072.052.022.001.971.941.911.891.871.841.81

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Wat.flo1

Elec.charge

Wat

.flo

1

3.02.52.01.51.0

3.2

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

S 0.190807R-Sq 82.4%R-Sq(adj) 82.1%

Fitted Line PlotWat.flo1 = 0.9770 + 0.7080 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

3.22.82.42.0

1

0

-1

-2

-3


Freq

uenc

y

1.00.50.0-0.5-1.0-1.5-2.0-2.5

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

1

0

-1

-2

-3



Residual Plots for Wat.flo1

Regression Analysis: Wat.flo1 versus Elec.charge The regression equation is Wat.flo1 = 0.9770 + 0.7080 Elec.charge S = 0.190807 R-Sq = 82.4% R-Sq(adj) = 82.1% Analysis of Variance Source DF SS MS F P Regression 1 12.0671 12.0671 331.45 0.000 Error 71 2.5849 0.0364 Total 72 14.6521

E71

Elec.charge

Wat

.flo

1

3.02.52.01.51.0

3.0

2.8

2.6

2.4

2.2

2.0

1.8

Grind.aid

87100

0255062

Scatterplot of Wat.flo1 vs Elec.charge

E72

Sep.fan.cur

Elec

.cha

rge

49.4549.2649.0948.9048.7247.5946.4746.2845.5345.3545.16

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Sep.fan.cur

Elec.charge

Sep.

fan.

cur

3.02.52.01.51.0

50

49

48

47

46

45

S 0.777360R-Sq 74.5%R-Sq(adj) 74.1%

Fitted Line PlotSep.fan.cur = 52.11 - 2.281 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

4948474645

3

2

1

0

-1


Freq

uenc

y

3210-1

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3

2

1

0

-1



Residual Plots for Sep.fan.cur

Regression Analysis: Sep.fan.cur versus Elec.charge The regression equation is Sep.fan.cur = 52.11 - 2.281 Elec.charge S = 0.777360 R-Sq = 74.5% R-Sq(adj) = 74.1% Analysis of Variance Source DF SS MS F P Regression 1 125.263 125.263 207.29 0.000 Error 71 42.905 0.604 Total 72 168.168

E73

Sep.inl.pres

Elec

.cha

rge

11.8111.6111.5211.4411.3311.1410.939.769.499.309.01

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Sep.inl.pres

Elec.charge

Sep.

inl.p

res

3.02.52.01.51.0

12.0

11.5

11.0

10.5

10.0

9.5

9.0

S 0.606843R-Sq 53.0%R-Sq(adj) 52.3%

Fitted Line PlotSep.inl.pres = 13.10 - 1.106 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

12.011.511.010.510.0

2

1

0

-1

-2


Freq

uenc

y

210-1-2

16

12

8

4

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

1

0

-1

-2



Residual Plots for Sep.inl.pres

Regression Analysis: Sep.inl.pres versus Elec.charge The regression equation is Sep.inl.pres = 13.10 - 1.106 Elec.charge S = 0.606843 R-Sq = 53.0% R-Sq(adj) = 52.3% Analysis of Variance Source DF SS MS F P Regression 1 29.4345 29.4345 79.93 0.000 Error 71 26.1463 0.3683 Total 72 55.5808

E74

Sep.out.pres

Elec

.cha

rge

17.6917.5917.5417.4917.2817.1016.8316.7516.0015.8515.5214.82

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Sep.out.pres

Elec.charge

Sep.

out.

pres

3.02.52.01.51.0

18.0

17.5

17.0

16.5

16.0

15.5

15.0

S 0.514355R-Sq 60.4%R-Sq(adj) 59.9%

Fitted Line PlotSep.out.pres = 19.09 - 1.091 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

18.017.517.016.516.0

3.0

1.5

0.0

-1.5

-3.0


Freq

uenc

y

2.41.20.0-1.2-2.4

20

15

10

5

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

3.0

1.5

0.0

-1.5

-3.0




Regression Analysis: Sep.out.pres versus Elec.charge The regression equation is Sep.out.pres = 19.09 - 1.091 Elec.charge S = 0.514355 R-Sq = 60.4% R-Sq(adj) = 59.9% Analysis of Variance Source DF SS MS F P Regression 1 28.6648 28.6648 108.35 0.000 Error 71 18.7838 0.2646 Total 72 47.4486

E75

Sep.speed

Elec

.cha

rge

164.75161.54158.70158.02155.11150.93150.31150.06

3.0

2.5

2.0

1.5

1.0

Individual Value Plot of Elec.charge vs Sep.speed

Elec.charge

Sep.

spee

d

3.02.52.01.51.0

165.0

162.5

160.0

157.5

155.0

152.5

150.0

S 2.95623R-Sq 63.1%R-Sq(adj) 62.6%

Fitted Line PlotSep.speed = 168.4 - 6.638 Elec.charge


Per

cent

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Stan

dard

ized

Res

idua

l

160.0157.5155.0152.5150.0

2

0

-2


Freq

uenc

y

3210-1-2

30

20

10

0

Observation Order

Stan

dard

ized

Res

idua

l

7065605550454035302520151051

2

0

-2




Regression Analysis: Sep.speed versus Elec.charge The regression equation is Sep.speed = 168.4 - 6.638 Elec.charge S = 2.95623 R-Sq = 63.1% R-Sq(adj) = 62.6% Analysis of Variance Source DF SS MS F P Regression 1 1060.60 1060.60 121.36 0.000 Error 71 620.49 8.74 Total 72 1681.09

E76

Grind.aid

Sep.

feed

.bl

100806040200

2600

2500

2400

2300

2200

2100

2000

1900

1800

S 67.1871R-Sq 94.1%R-Sq(adj) 92.7%

Fitted Line PlotSep.feed.bl = 1847 + 6.392 Grind.aid

Regression Analysis: Sep.feed.bl versus Grind.aid The regression equation is Sep.feed.bl = 1847 + 6.39 Grind.aid Predictor Coef SE Coef T P Constant 1846.58 51.21 36.06 0.000 Grind.aid 6.3924 0.7983 8.01 0.001 S = 67.1871 R-Sq = 94.1% R-Sq(adj) = 92.7% Analysis of Variance Source DF SS MS F P Regression 1 289440 289440 64.12 0.001 Residual Error 4 18056 4514 Total 5 307497

Residual

Per

cent

1000-100

99

90

50

10

1

Fitted Value

Res

idua

l

24002250210019501800

80

40

0

-40

-80

Residual

Freq

uenc

y

806040200-20-40-60

2.0

1.5

1.0

0.5

0.0

Observation Order

Res

idua

l

654321

80

40

0

-40

-80



Residual Plots for Sep.feed.bl

E77

Grind.aid

Sep.

tails

.bl

100806040200

900

875

850

825

800

775

750

S 55.0799R-Sq 7.2%R-Sq(adj) 0.0%

Fitted Line PlotSep.tails.bl = 821.9 + 0.3647 Grind.aid

Regression Analysis: Sep.tails.bl versus Grind.aid The regression equation is Sep.tails.bl = 822 + 0.365 Grind.aid Predictor Coef SE Coef T P Constant 821.91 41.98 19.58 0.000 Grind.aid 0.3647 0.6544 0.56 0.607 S = 55.0799 R-Sq = 7.2% R-Sq(adj) = 0.0% Analysis of Variance Source DF SS MS F P Regression 1 942 942 0.31 0.607 Residual Error 4 12135 3034 Total 5 13077

Residual

Per

cent

100500-50-100

99

90

50

10

1

Fitted Value

Res

idua

l

860850840830820

50

0

-50

-100

Residual

Freq

uenc

y

50250-25-50-75-100

3

2

1

0

Observation Order

Res

idua

l

654321

50

0

-50

-100



Residual Plots for Sep.tails.bl

E78

Grind.aid

Sep.

fines

.bl

100806040200

3800

3600

3400

3200

3000

S 347.161R-Sq 7.8%R-Sq(adj) 0.0%

Fitted Line PlotSep.fines.bl = 3168 + 2.406 Grind.aid

Regression Analysis: Sep.fines.bl versus Grind.aid The regression equation is Sep.fines.bl = 3168 + 2.41 Grind.aid Predictor Coef SE Coef T P Constant 3168.0 264.6 11.97 0.000 Grind.aid 2.406 4.125 0.58 0.591 S = 347.161 R-Sq = 7.8% R-Sq(adj) = 0.0% Analysis of Variance Source DF SS MS F P Regression 1 41000 41000 0.34 0.591 Residual Error 4 482083 120521 Total 5 523083

Residual

Per

cent

8004000-400-800

99

90

50

10

1

Fitted Value

Res

idua

l

34003350330032503200

500

250

0

-250

-500

Residual

Freq

uenc

y

5002500-250-500

3

2

1

0

Observation Order

Res

idua

l

654321

500

250

0

-250

-500



Residual Plots for Sep.fines.bl

E79

Residual

Per

cent

20100-10-20

99

90

50

10

1

Fitted Value

Res

idua

l

1007550250

10

5

0

-5

-10

Residual

Freq

uenc

y

50-5-10

3

2

1

0

Observation Order

Res

idua

l

654321

10

5

0

-5

-10




Regression Analysis: Grind.aid versus Sep.feed.bl, Sep.tails.bl, ... The regression equation is Grind.aid = - 351 + 0.143 Sep.sp.bl + 0.0426 Sep.tails.bl + 0.0172 Sep.fines.bl Predictor Coef SE Coef T P Constant -351.31 87.26 -4.03 0.057 Sep.feed.bl 0.14269 0.01932 7.39 0.018 Sep.tails.bl 0.04256 0.09489 0.45 0.698 Sep.fines.bl 0.01721 0.01485 1.16 0.366 S = 10.4688 R-Sq = 96.9% R-Sq(adj) = 92.3% Analysis of Variance Source DF SS MS F P Regression 3 6864.1 2288.0 20.88 0.046 Residual Error 2 219.2 109.6 Total 5 7083.3 Source DF Seq SS Sep.feed.bl 1 6667.4

Sep.tails.bl 1 49.5 Sep.fines.bl 1 147.2

E80

Grind.aid

El.c

harg

e.av

g

100806040200

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

S 0.389626R-Sq 58.6%R-Sq(adj) 48.2%

Fitted Line PlotEl.charge.avg = 2.551 - 0.01101 Grind.aid

E81

Residual

Per

cent

1.00.50.0-0.5-1.0

99

90

50

10

1

Fitted Value

Res

idua

l

2.502.252.001.751.50

0.2

0.0

-0.2

-0.4

-0.6

Residual

Freq

uenc

y

0.20.0-0.2-0.4-0.6

3

2

1

0

Observation Order

Res

idua

l

654321

0.2

0.0

-0.2

-0.4

-0.6



Residual Plots for El.charge.avg

Regression Analysis: El.charge.avg versus Grind.aid The regression equation is El.charge.avg = 2.55 - 0.0110 Grind.aid Predictor Coef SE Coef T P Constant 2.5515 0.2970 8.59 0.001 Grind.aid -0.011012 0.004629 -2.38 0.076 S = 0.389626 R-Sq = 58.6% R-Sq(adj) = 48.2% Analysis of Variance Source DF SS MS F P Regression 1 0.8589 0.8589 5.66 0.076 Residual Error 4 0.6072 0.1518 Total 5 1.4661

data analysis of electrostatic charge in a finish ball millcement.org/manufacture/data analysis of...

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