Date: September 28 , 201 5 Memo 6

- 14

To: Ron Jordan , EPA

From: John Rogers , Luke Smith

Subject: Relationship Between ORP and Analytes

Introduction

D iscussion in a Babcock and Wilcox Technical Report 1 referenced by EPA (herein referred to as BW), suggests that oxidation reduction potential ( ORP ) may affect the portion of the selenium that is present ed as selenate versus selenite. BW explored how “swings” in values of ORP

genera lly affected trends in several analytes, including selenate and selenite. BW concluded that

selenate ions increase and selenite ions decrease as ORP increases. This conclusion is underscored by Figure 3 in BW, which plots selenate and selenite against OR P. There are several limitations of BW. BW makes general conclus ions without backing them up using a hypothesis test. BW includes trend lines to support their conclusion without describing the trend lines or the method used to generate them. BW claims t hat selenite decreases with ORP, but shows a trend line that increases with ORP for large values of ORP. Finally, all of these values come from only one plant , so there is no way to assess plant

- to

- plant variability.

In this memo we examine the relationship between ORP and selenate and selenite concentrations in wastewater and how this relationship changes across plants . We first explore the relationship between ORP and the analytes. We then take a model

- based approa ch that allows us to test if ORP affects selenate and selenite. Finally, we examine how these relationships change across plants

.

Data

For this analysis we used data provided by Duke Energy for the Allen, Belews Creek and Cliffside plants , which have 2, 4, and 1 absorbers respectively. Summary statistics by plant and absorber

are in Table 1.

1 Brown, S. R., R. F. DeVault, M. Gadgil, D. B. Johnson, S. P. Ulbricht, and P. J. Williams. Optimization and
Process Control of Air Quality Control Systems for Improved WFGD Oxidation Chemistry and Effluent Composition for Wastewater Treatment . Tech. no. BR

- 1902. Barberton, OH: Babcock & Wilcox Generation Group, 2013. Print.- 2

-

Table 1: Summary statistics of selenate and selenite by plant and absorber. Selenate and selenite are measured in μg/L.

Plant /Absorber N Date Range Selenate

Mean Selenate Range Selenite

Mean Selenite Range

Allen 1 55 05/28/2012

– 09/12/2013 787 ( 23

, 4800 ) 255 ( 1

, 4170 )

Allen 3 10 5 0 5/23 / 2011

– 10 / 2 2/2013 686 ( 6

, 4800 ) 268 ( 0

, 4170 )

Belews Creek 1A 65 0 8/29 /2012

– 11 / 28 /2013 96 ( 1

, 1010 ) 173 ( 1

, 1280 )

Belews Creek 1B 64 0 8/29 /2012

– 11 / 11 /2013 97 ( 1

, 1010 ) 173 ( 1

, 1280 )

Belews Creek 2A 71 08/29/2012

– 11/28/2013 91 ( 1

, 1010 ) 193 ( 1

, 1450 )

Belews Creek 2B 43 08/29/2012

– 06/04/2013 118 ( 29

, 1010 ) 130 ( 9

, 367 )

Cliffside 24 06/21/2012

– 10/16/2012 322 ( 26

, 750 ) 141 ( 1

, 972 )

Table 1 shows that the sample size and date of collection varies considerably by plant

. Selenate

occurs in smaller concentrations at the Belews Creek plants , while selenite occurs in larger

concentrations at the Belews Creek plants

.

Methods We want to see if there is any relationship between ORP and se lenate and selenite, and the percentage of selenium that is selenate . BW claimed that when ORP is above roughly 300 mV,

selenium shifts forms to become selenate . Looking at the percentage of selenate can help examine the joint response of these two analytes to ORP. To investigate this we plotted the raw data and percent selenate vs. OR P, and these plots are shown Figures 1

– 7 in Appendix 1.

Figures 1

– 7 sh ow an uneven or non

- existent relationship between ORP and the analytes. Identifying overall trends is difficult, and plotting the data on the measurement scale obscures the small values. To further investigate this relationship we plot ted the data on the log scale in Figures 8

- 14 in Appendix 2. Figures 8

- 14 show a clearer relationship between the log of the analytes and ORP.

Here we outline the statistical models used in our analyses. We begin by regressing the log concentration of analyte (selenate or selenite) on ORP by plant and absorber. Linear regression assumes the magnitude of the variation around the line is relatively constant. By predicting the log

- transformed selenate and selenite concentrations versus ORP using a linear function we

achie ve three objectives: 1) all predicted concentrations are greater than zero; 2) all functions have the same linear form making comparisons between plants easier; and 3) the magnitude of the variation around the regression line is relatively constant.

In ou r second model we look at how the percentage of selenium that is selenate changes as a

function of ORP. We are interested in seeing if the relationship between both analytes and ORP can be simplified further. At least at some plants , the concentrations a re consistent with this hypothesis in that the sele n ate concentration increases and the selenite concentration decreases as- 3

-

ORP increases and the sum of the selenate and selenite concentrations is relatively constant. Thus, EPA may be interested in the fra ction (or percentage) of s elenium that is present as selen a te as a
function of ORP. This is a reduction in information that enables the relationship between both analytes and ORP to be captured through one analysis. T o avoid predicting fractions of selen ium that are less than zero or greater than one we use a logit transformation, which is standard statistical practice. For a percentage p, the logit = log(p / (1

– p)).

Finally, we want to know how ORP affects the analytes across all plants . We fit a regression model with separate regression effects for each plant and each absorber within plant . This mixed model enables us to construct an overall regression effect that is a weighted average of the individual regression effects at each plant

.

Results

We begin with the linear relationship between ORP and the log of selenate (log selenate), the log of selenite (log selenite), and the log of the percentage of selenate in selenium (log it

). Selenate

tends to increase with ORP and selenite tends t o decrease with ORP. Therefore, modeling the logit allows us to focus on one relationship that captures the increase in selenate and the decrease

in selenite.

We performed linear regressions of log selenate, log selenite, and log it on ORP for each abso rber
within the Allen, Belews Creek and Cliffside plants

. T he results of the log selenate regressions are presented in Table 2

.

Table 2: Regression effects of ORP on log selenate by plant and absorber. Statistical significance

at the 0.05 level is indic ated by *

. Plant /Absorber Slope Estimate p

- value R

- squared

Allen 1 0.41 < 0.001 * 0.22

Allen 3 0.53 < 0.001 * 0.52

Belews Creek 1A

- 0.03 0.816 < 0.01

Belews Creek 1B 0.32 < 0.001 * 0.20

Belews Creek 2A 0.34 0.021 * 0.08

Belews Creek 2B

- 0.05 0.830 < 0.01

Cliffside 0.21 0.021 * 0.22

The slope estimate in Table 2 represents the ex pected increase in log selenate due to a 100 mV increase in ORP. The p

- value in Table 2 represents the probability of a slope estimate as extreme or more extreme than our observed data given there is no linear relationship between log selenate and ORP. Low p

- values indicate that if there is no linear relationship between log selenate and ORP, then the probability of the observed data occurring is unlikely. The R

- squared value represents the proportion of information in log selenate explained by ORP. R

- squared values close to one represent high propo rtions of explained information.

Table 2 shows that at 5 of the 7 plant/absorber combinations ORP had a statistically significant effect on log selenate. T he slope estimates for log selenate are positive for all statistically significant regression eff ects across plant and absorber within plant

. In contrast, the slope- 4

-

estimates for log selenite are negative for all statistically significant regression effects across plant

and absorber within plant , as shown in Table 3

. Table 3 shows that at 4 of the 7 plant/absorber combinations ORP had a statistically significant effect on log selenite.

Table 3: Regression effects of ORP*100 on log selenite by plant and absorber. Statistical significance at the 0.05 level is indicated by *

. Plant/Absorber Slope Estimate p

- value R

- squared

Allen 1

- 0.74 < 0.001 * 0.25

Allen 3

- 0.94 < 0.001 * 0.64

Belews Creek 1A 0.18 0.353 0.01

Belews Creek 1B

- 0.28 0.032 * 0.07

Belews Creek 2A 0.13 0.569 < 0.01

Belews Creek 2B 0.36 0.317 0.02

Cliffside

- 1 .26 < 0.001 * 0.74

This leads us to the effects of ORP on the logit . Table 4 shows that the effect of ORP on the logit is positive for all statistically significant effects. Further, R

- squared is generally highest for the log it in comparison to log sel enate and log selenite, indicating that the log it best captures the

effect of ORP. The 4 statistically significant effects in Table 4 are the same as in Table 3. ORP is statistically significant for the same plants across analyte, excepting Belews Creek 2B. There is a loss of informa tion in using the logit and sele n a te regression is weak to begin with, so it is not surprising that we don’t have significance for selenite or the logit

.

Table 4: Regression effects of ORP*100 on the logit by plant a nd absorber. Statistical significance at the 0.05 level is indicated by *

. Plant/Absorber Slope Estimate p

- value R

- squared

Allen 1 1 .15 < 0.001 * 0.31

Allen 3 1 .48 < 0.001 * 0.72

Belews Creek 1A

- 0.21 0.355 0.01

Belews Creek 1B 0.60 < 0.001 * 0.24

Belews Creek 2A 0.21 0.431 0.01

Belews Creek 2B

- 0.41 0.407 0.02

Cliffside 1 .47 < 0.001 * 0.77

Plots of the regres sions are show n in Appendix 2

. The plots of selenate and selenite are labeled using the observed concentration unit s using a log axis.

An assessment of the consistency of the relationships across plants can be based on a visual inspection of the plots, a comparison of the slope estimates in the tables above or a formal statistical test. However, any conclusions are limited by the small number of plants for which we

have data.

We fit a mixed linear model to estimate the overall (average) ORP slope across all plants assuming

the Al l en, Belews Creek, and Cliffside plants were representative of all plants EPA is interested in and assuming that there may be different slopes and intercepts in different plants and absorbers (this corresponds to treating plant and absorber intercept and slope differ ences as random- 5

-

effects). Table 5 shows the overall slope estimate

, 95% confidence interval

, and p

- value for the

overall effect of ORP

.

Table 5: Overall ORP*100 slopes predicting log selenate, log selenite, and logit . Statistical significance at the 0.05 level is indicated by *

.

Analyte Overall

Estimate 95% Confidence

Interval p

- value Log Selenate 0.3379 ( 0

. 0034, 0. 6723) 0.049 * Log Selenite

- 0.695 (

- 0.02142, 0. 7522) 0.17 2 Logit

. 018 (

- 0.534, 02

. 571) 0.106

Roughly speaking, the overall slope estimate represents the mean of all linear ORP effects across

all plants , including those not in our data. That is, if we were to collect data on a new plant not in our current data, the overall estimate represents our best guess for the linear relationship between ORP and the analyte at the new plant

. This parameter interpretation comes from using a mixed effects regression model.

For selenate the effect of ORP is positive and barely statistically significant (p

- value of 0.049).

The confidence intervals for the overall effects of ORP on selenite and the logit contain zero and are not statistically significant. The overall slope estimates in Table 5 are weighted averages of the slope estimates at each plant. Log selenate is statistically significant while the others are not because the variability in the slope estimates at each plant is smaller for log selenate than it is for log selenite and the logit. We only have 3 plants for con structing this overall estimate, so additional plants could easily push log selenate into not being statistically significant or push log selenite or the logit into statistical significance.

Although t hese data are consistent with the hypothesis that se lenate concentrations increase and selenite concentrations decrease as ORP increases, based on the p

- values, we cannot rule out the alternative hypothesis that there is no relationship between selenate, selenite, and ORP.

Conclusions

In this memo we hav e examined the relationship between ORP and the analytes selenate and

selenite. We found some evidence at a few absorbers that ORP is linearly related to the analytes.

Most of the R

- squared values are low. While the mean of the log analytes often appears to change linearly with ORP, there is still considerable variability along the line unexplained by our model. There is a statistically significant relationship only for some absorbers, but not for others. Where we found a statistically sign ificant relationship, there is a substantial amount of unexplained variability in the model. A model that incorporated pH and other important variables could offer considerable improvement.

The mixed linear model provides estimates of the variance of th e random effects. From these, we conclude that differences in the intercept and slope between different absorbers at the same plant are small enough to be ignored. As a result, the analysis might combine the data across absorbers,- 6

-

fitting one slope per pla nt. Thus, for future analys e s, we suggest predicting the log

- transformed

selenate and selenite concentrations (or logit ) as a linear function of ORP at each plant . This analysis suggests that pooling the data from all absorbers at the same plant is reason able, but this assumption would be good to test with future data

.

BW asserts that selenate increases and selenite decreases as ORP increases, stating that at ORP levels below about 300 mV the selenium will predominantly exist as selenite, and when ORP i s above about 300 mV the selenium will shift form to selenate. BW makes general conclusions without backing them up using any form of statistical analysis, such as modeling techniques or hypothesis testing. BW includes trend lines to support their conclu sion without describing the trend lines or the method used to generate them. We did not find convincing evidence of this for the data at Allen, Belews Creek, and Cliffside plants. As noted above, ORP was found to have a statistically significant effect on the log

- transformed analytes at only some of the absorbers, but
not at others. Furthermore, the R

- squared values for most of the regression lines are low, which indicates that the models for the relationships between the analytes and ORP are not particul arly good. In addition, contrary to the BW assertion, we found that selenate can be present as a substantial fraction of the total selenium at both high and low ORPs (i.e., above and below about 300 mV). We also found that selenite can similarly be prese nt as a substantial fraction of the total selenium at both high and low ORPs.

## Appendix 1

Figure 1: Plots of selenate, selenite, and the proportion of selenium that is selenate against ORP at the Allen plant using absorber 1.- 8

-

Figure 2: Plots of selenate, selenite, and the proportion of selenium that is selenate against ORP at the Allen plant using absorber 3.- 9

-

Figure 3: Plots of selenate, selenite, and the proportion of selenium that is selenate against ORP at

the Belews Creek plant using absorber 1A.- 10

-

Figure 4: Plots of selenate, selenite, and the proportion of selenium that is selenate against ORP at

the Belews Creek plant using absorber 1B.- 11

-

Figure 5: Plots of selenate, selenite, and the proportion of selenium that is selenate against ORP at

the Belews Creek p lant using absorber 2A.- 12

-

Figure 6: Plots of selenate, selenite, and the proportion of selenium that is selenate against ORP at

the Belews Creek plant using absorber 2B.- 13

-

Fig ure 7: Plots of selen ate, selenite, and the proportion of selenium that is selenate against ORP at the Cliffside plant.

## Appendix 2

Figure 8: Regressions of log selenate, log selenite, and log it on ORP at the Allen plant using

absorber 1. The logit corresponds to the proportion of selenium that is selenate.- 15

-

Figure 9: Regressions of log selenate, log selenite, and logit on ORP at the Allen plant using

absorber 3. The logit corresponds to the proportion of selenium that is selenate.- 16

-

Figure 10: Regressions of log selenate, log selenite, and logit on ORP at the Belews Creek plant using absorber 1A. The logit corresponds to the proportion of selenium that is selenate.- 17

-

Figure 11: Regressions of log selenate, log selenite, and logit on ORP at the Belews Creek plant using absorber 1B. The logit corresponds to the proportion of selenium that is selenate.- 18

-

Figure 12: Regressions of log selenate, log selenite, and logit on ORP at the Belews Creek plant using a bsorber 2A. The logit corresponds to the proportion of selenium that is selenate.- 19

-

Figure 13: Regressions of log selenate, log selenite, and logit on ORP at the Belews Creek plant using absorber 2B. The logit corresponds to the proportion of selenium that is selenate.- 20

-

Figure 14: Regressions of log selenate, log selenite, and logit on ORP at the Cliffside plant. The logit corresponds to the proportion of sele nium that is selenate.