Permeation is the passage of contaminants through porous and non-metallic

materials. Permeation phenomenon is a concern for buried waterlines where the soil is

contaminated with organic compounds, due to spills or leaks of underground storage

tanks. Permeation cases in polyvinyl chloride pipes (PVC) have been already reported in

US.

OBJECTIVES

1) Track solvent permeation in PVC pipes due to organic solvents with NIRS

2) Predict permeation susceptibility of new PVC pipes with NIRS

LABORATORY SET UP

Tracking permeation in PVC pipes with NIRS

Pipes

- Three kinds of pipes (Same diameter, different company) in duplicates- Same length (12.5 cm) and PVC surface exposed to solvent- Pipes sealed with two glasses and epoxy, and soaked in 1 liter Teflon-lid jars

Solvents

- Pure: Gasoline (9 months soaking), benzene (11 days), and toluene (11 days)

- Aqueous solutions of benzene and toluene: 20%, 40%, 80%, and 100% saturated solution

(7 months soaking)

Laboratory reference data

- Moving front location (mm): Pure toluene and benzene- Weight gain (g/cm and %): Pure toluene and benzene

- Time (days): Gasoline, aqueous solutions

Outer wall

Inner wall

Moving front

Prediction of new PVC pipes permeation susceptibility with NIRS

Pipes

- 58 pipes from 5 major manufacturers

- Six commercial diameter sizes: ½” , ¾”, 1”, 1+ ¼”, 1+ ½”, 2” and 3”

- Three 12.5 cm samples from each pipe were cut for the study

- Each sample was scanned six times, covering the whole surface

Laboratory reference data- Slope k (mm/h1/2) of the line from the moving front test using pure toluene. Millimeters versus square root of time provided straight lines with R2 > 99% (linearized permeation rate)

NIRS SPECTROSCOPY

Spectrometer: - Foss NIRsystems ® 6500

(Foss-NIRsystems, Silver Spring, MD)

Chemometrics:

Calibration models

1) Tracking of permeation: Partial Least Squares (PLS) with cross validation

2) Prediction of permeation susceptibility: PLS and Locally Weighted Regression with Principal

Component Regression (LWR-PCR); validation with ¼ of the scans non accounted in the

calibration, combinations of 25, 50 and 75 neighbors or objects and 5 to 15 principal components.

Spectra pretreatments

1) Tracking of permeation: Raw, first and second Savisky-Golay derivatives

2) Prediction of permeation susceptibility: Raw, first and second Savisky-Golay derivatives, and

Standard Normal Variate (SNV) for LWR models

Model evaluation: Statistic parameters- Number of Principal Components (PCs)- Coefficient of determination (R2) in %- Standard Error of Cross Validation (SECV) for permeation tracking, Standard Error of

Prediction (SEP) for prediction of permeation susceptibility- Relative Performance Determinant (RPD) as the ratio of the standard deviation of the reference

data over the SEP/SECV - Standard Error of the Laboratory (SEL)

Software

- The Unscrambler ® v.9.5 for PLS models

- MATLAB ® v.7.0.4 and PLS_toolbox v.3.5.4 for LWR-PCR

- SAS v.9.1 for SEL

RESULTS

Tracking permeation in PVC pipes with NIRS

The best PLS models for pure solvent were the following:

Best PLS models with aqueous solutions:

Toluene: R2 = 94.5 %, SECV = 16.12 days, RPD = 4.12 (raw spectra)

Benzene: R2 = 95.1 %, SECV = 16.12 days, RPD = 3.99 (first derivative)

Both benzene and toluene: R2 = 90.8 %, SECV= 20.21 days, RPD = 3.25 (raw spectra)

Prediction of new PVC pipe permeation susceptibility with NIRS

- Reference data showed low error (SEL = 0.0018 mm/h1/2)

- The best PLS models gave the following statistics

- The poor results of PLS models were due to a strong clustering among pipe types during the principal component analysis (PCA)

- LWR-PCR improved the results substantially. Best models were achieved when SNV was applied, and worse results when spectral data was treated with second derivative. SEP values from SNV models are represented in a surface plot, as combination of number of objects and number of principal components.

ACKNOWLEDGEMENTSIowa State University gratefully acknowledges that the Awwa Research Foundation is the joint owner of the technical

information upon which this manuscript is based. Iowa State University thanks the Foundation for its financial, technical, and

administrative assistance in funding and managing the project through which this information was discovered. The comments

and views detailed herein may not necessarily reflect the views of the Awwa Research Foundation, its officers, directors,

affiliates, or agents.

The findings described here are preliminary in nature and are subject to revision. A final project report will be published

by AwwaRF.

- There was not a best unique solution. Better combinations have SEP values between 0.001 and 0.002 mm/h1/2

CONCLUSIONS

- NIRS tracked permeation by toluene and benzene in PVC pipes with provided reference data (mm, weight

gain (%), and weight gain (g/cm)) with PLS models- Saturated solutions of toluene or benzene provided models with rough screening accuracy when days of

soaking were used as reference- Permeation by gasoline could be modeled with PLS using days of soaking with good accuracy- Permeation susceptibility was poorly predicted by traditional PLS due to pipe type clustering in the PCA- LWR-PCR provided accurate models, with best results when SNV was applied to spectra. Best models gave

a RPD higher than 7 and SEP lower than 0.002 mm/h1/2

Measurement of Organic Solvent Permeation through PVC Pipes with Near Infrared Spectroscopy

L. Esteve Agelet1 (lesteve@iastate.edu), C. R. Hurburgh1 Jr. (tatry@iastate.edu), F. Mao2 (fengmao@iastate.edu), S. K. Ong2 (skong@iastate.edu), and J. A. Gaunt2 (jagaunt@iastate.edu)

Department of Agricultural and Biosystems Engineering1

Department of Civil, Construction and Environmental Engineering2

Iowa State University

© 2006 Iowa State University and AwwaRF

Model Reference data

Pretreatment R2 (%)

RPD SECV PCs SEL

Gasoline Days Raw data 98.8 8.79 9.27 7 n.a.

Moving front (mm)

1st derivative 99.5 13.05 0.075 6 0.022

Sorption test (g/cm pipe)

1st derivative 99.3 11.36 0.12 7 0.110

Pure

toluene Sorption test

(% ) 1st derivative 99.3 10.77 1.44 7 1.077

Moving front (mm)

Raw data 99.1 9.69 0.08 9 0.015

Sorption test (g/cm pipe)

Raw data 98.0 6.87 0.17 9 0.070

Pure benzene

Sorption test (% )

1st derivative 97.8 6.70 2.13 6 0.239

Moving front (mm)

2nd derivative 96.2 4.66 0.184 9 0.022

Sorption test (g/cm pipe)

Raw data 96.5 5.30 2.65 9 0.094

Pure

benzene and

toluene Sorption test (%)

Raw data 98.5 8.06 2.22 9 0.681

PLS Calibration models (3/4 of whole data)

Validation (1/4 of whole data)

Data Treatment PCs R2 (%) RPD SEP Raw data 18 82.2 2.33 0.0059

1st derivative 16 87.5 2.21 0.0062 2nd derivative 13 85.6 1.87 0.0074

56

78

910

1112

1314

1525 objects

50 objects

75 objects

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

PCs

Surface of SEP (mm/√h) for SNV pretreatment

0.007-0.008

0.006-0.007

0.005-0.006

0.004-0.005

0.003-0.004

0.002-0.003

0.001-0.002

0-0.001