Research on Determination of Total Acid Number of Petroleum Using

Aug 15, 2012 - Raw mid-infrared spectra of crude oil from different types. ... These results indicate that the MIR spectral data can be effectively us...
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Research on Determination of Total Acid Number of Petroleum Using Mid-infrared Attenuated Total Reflection Spectroscopy Li Jingyan,* Chu Xiaoli, and Tian Songbai Analytical Department, Research Institute of Petroleum Processing, Xueyuan Road 914-1#, Beijing 100083, People’s Republic of China ABSTRACT: Total acid number (TAN) is one of the most important parameters for petroleum. The combined use of multivariate calibration and mid-infrared attenuated total reflection spectroscopy allows it to be estimated accurately. The calibration models of acid number have been established by partial least-squares (PLS). The predictive ability of the IR technique with regard to the TAN have been explored. The results predicted by this method were very close to those determined by standard methods. Compared with Standards, this method is provided with advantages such as high speed, simplicity, no pretreatment, and good-repeatability.

1. INTRODUCTION Total acid number (TAN) is an important parameter for petroleum. In addition to density and sulfur, this assay plays a very important role in petroleum trading and processing. Naphthenic acid corrosion can do great harm to refining plants,1 so getting total acid number of crude oil accurately is crucial for oil refining and trading. Conventional petroleum acid number programs have been well-developed, mainly based on ASTM methods such as potentiometric titration, which can provide accurate results on total acid number. Potentiometric titration can take more than half an hour for results, and the preparation take even longer. Obviously, newer analytical methodologies are required to provide timely data for rapid decision making in the case of crude oil pipeline, oil blending, and so on. Vibrational spectroscopy such as infrared (IR) and nearinfrared (NIR) spectroscopy in combination with multivariate calibration methods are powerful analytical techniques that have received wide use during the past decade.2 IR and NIR offer the possibility of rapid determination of crude oil parameter. Several researchers have studied their application to refinery and petrochemical products’ properties, such as octane number of gasoline3 and diesel properties.4 At the present time, mid-IR (MIR) has been receiving results as good as those of NIR.5,6 The application of attenuated total reflection (ATR) accessory can simplify and expedite the MIR measurement to a considerable degree.7 Moreover, in contrast to NIR, the spectral absorption peak in MIR are fundamental bands that are specific, sharp, and sensitive. The MIR is attractive for crude oil total acid number analysis because most of the useful information can be possibly extracted by chemometric methods such as partial least-squares (PLS)8,9 and ANN. Some reports use MIR technology to determine chemical and physical properties of heavy petrochemical products. Yuan et al. have characterized SARA (saturates, aromatics, resins, and asphaltenes), viscosity, density, carbon residue, and element contents of residual oils, with the combined use of MIR-ATR spectroscopy and multivariate calibration methods.10 Brian K. Wilt et al. have studied using an MIR method to determine asphaltenes content in crude oils.11 Chung H. et al. have used © 2012 American Chemical Society

an MIR spectrometer equipped with ATR probe and partial least-squares (PLS) method to determined atmospheric residue API gravity.12 Parisotto et al. using MIR with ATR in association with chemometric methods to determine TAN in the atmospheric residue (AR) and vacuum residue (VR) of the petroleum distillation process.13 The objective of this study is to concentrate on the determination of acid number of petroleum using MIR-ATR cell coupled with multivariate quantitative and qualitative calibration methods. In this study, a PLS calibration model was developed. This research will be used in fast-evaluation of crude oil.

2. EXPERIMENTAL SECTION 2.1. Samples. 280 crude oil samples were collected by the crude oil evaluation department in the Research Institute of Petroleum Processing (RIPP) of SINOPEC. Those petroleum samples were obtained from more than 100 oil fields distributed within China and around the world, the samples including the diverse variations were widely collected in order to build up a more robust calibration model. The density distribution of 280 crude oil samples is 0.7687−1.009 g cm−3. The detailed total acid number assay data of each crude oil sample were determined according to standard analytical method by crude oil evaluation department. The crude oil TAN database has been constructed based on the samples. By means of the Kernel−Stone method,14 all crude oil samples collected have been separated into calibration and validation sets. The crude oils have a range of acid number value from 0.01 to 11.4 mg KOH g−1. The calibration sets contain 240 samples; the validation sets consist of 40 samples. The detailed information of those data sets is listed in Table 1. 2.2. Acquisition of MIR Spectra. MIR spectra of petroleum were collected using a Nicolet 6700 Fourier transform infrared (FT-IR) spectrometer equipped with deuterated triglycine sulfate detectors (DTGS) and a horizontal attenuated total reflectance (ATR) temperature control zinc selenide (ZnSe) sample cells accessory. Spectra were collected by film petroleum samples into the ATR plate with a ZnSe crystal and maintaining at the temperature 25 ± 0.1 Received: February 12, 2012 Revised: August 14, 2012 Published: August 15, 2012 5633

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Table 1. Information of Crude Oil Samples type

paraffin base intermediate base naphthenic base

sample no. year of collection

65 2001−2010

122

naphthenic- intermediate base

paraffin- intermediate base

intermediate- paraffin base

15

9

14

55

°C by a temperature control unit. After the spectral acquisition of the sample, the crude oil left on the surface of ATR was removed gently with absorbent cotton and rinsed using petroleum ether. The ATR accessory is easy to manipulate and gives reproducible spectra to employ high precision quantitative analysis. MIR spectra were collected over the 4000 to 650 cm−1 spectral region. The resolution of collected spectra was 8 cm−1, each spectrum corresponded to an accumulation of 64 scans, approximately 55 s. Background spectra of air were collected for each sample instantly before the collection of the crude oil sample spectrum. The spectral files were transformed into ASCII format by Thermo Nicolet OMNIC8.1 software equipped with the spectrometer. 2.3. Reference Methods. Total acid number (TAN) for the crude oils was determined by potentiometric titration according to the ASTM D664 procedure, which was used to obtain the calibration data on the crude oil, and their corresponding repeatability and reproducibility limits are listed in Table 2.

where n and m are the amount of samples included in the calibration and validation sets and yi, actual and yi, predicted are the sample total acid numbers measured by the reference and MIR methods, respectively. Before calibration, the wavelength selection and spectral pretreatments were applied in order to get an optimal calibration model. The wavelength range selection is based on the chemical knowledge and the correlation level between total acid number value and absorbance. The spectral pretreatment methods employed in this research embody mean centering, first and second differential.

3. RESULTS AND DISCUSSION 3.1. Spectral Features. Figure 1 shows the raw MIR spectra of three typical petroleum samples analyzed by the ATR

Table 2. Reference Method and the Repeatability and Reproducibility Limits reference method

property

ASTM D664

acid number (mg KOH g−1) a

repeatability 0.044 (X + 1)a

reproducibility 0.141 (X + 1)

X is the average of results being compared.

2.4. Data Analysis. Using MIR technology associated with multivariate quantitative calibration methods is necessary to quantify physical and chemical properties of crude oil; since these samples are highly complex in terms of composition, spectra are grossly overlapped. In the research, the partial least-squares (PLS) regression algorithm was employed to build quantitative calibration models for TAN of petroleum. There are two steps in the PLS process: calibration and validation. The evaluation of the calibration performance was assessed by the root mean squared error of calibration (RMSECV), R2, and RPD. R2 represents correlation coefficient; RPD represents relative standard deviation; RPD > 5 indicates that the calibration model was robust; RPD < 2 shows that the calibration model was unacceptable. The calibration model for TAN was obtained with the optimum number of latent PLS factors;15 it was selected based on the predicted residual sum of squares (PRESS) by the cross-validation results. On the other hand, the standard root mean squared error of prediction (RMSEP) for validation gives an evaluation of the predicted performance of the quantitative model obtained before. These were calculated by

Figure 1. Raw mid-infrared spectra of crude oil from different types.

cell from calibration set. Subtle changes in the chemical composition can be observed from the part of MIR spectra shown in Figure 2. In the MIR regions of 3100−2500 and 1800−1000 cm−1 that correspond to the C−H stretching and bending is the most useful information, respectively. The peaks of 2918 and 2853 cm−1 are the C−H stretching bands of CH2 groups, and the shoulder at 2962 cm−1 is the stretching band of

n

RMSECV =

∑i = 1 (yi ,actual − yi ,predicted )2 n−1 m

RMSEP =

∑i = 1 (yi ,actual − yi ,predicted )2 m−1

R2 = 1 −

n ∑i = 1 (yi ,actual − yi ,predicted )2 n 2 ∑i = 1 (yi ,actual − yactual ̅ )

RPD =

SD RMSECV

Figure 2. Selected regions (1850−650 cm−1) of the MIR of petroleum from different types. 5634

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correlation coefficients, so this range can be used to build up calibration model. This research chose the wavelength range that best related to the −COOH functional group to build up the calibration model. The range does not have a absorption attribute to the −COOH functional group, and the overlap range can weaken the estimation result. From Table 4, we can see that the best wavelength range is 1516−1806 cm−1.

CH3 groups. The bending vibrations of methyl and methylene groups are 1377 and 1446 cm−1, respectively. The bands at 3450 and 1650 cm−1 correspond to the O−H stretching and bending vibrations, respectively. The peak at 1740 cm−1 is due to CO stretching, and that at 1170 cm−1 is due to C−O stretching and C−H bending. The wavelength range 650−1000 cm−1 is the so-called fingerprint region, in which much detailed information of organic compounds can be found. Because the IR spectra seem to contain much information approximately around 1710 cm−1 regions with regard to the acidic properties of crude oil samples, this work will import such regions into the calibration model. 3.2. Pretreatment of MIR. ITo obtain the best predictability, several different preprocessing methods such as first and second derivatives were tested. Calibration models were initially built from the preprocessed data using the full length of the recorded spectra. Furthermore, different variable sets were tested both manually, selecting spectral intervals based on chemical knowledge about the samples, and using the correlation coefficient between property and absorbance. Calibration models were compared in terms of their RMSECV values. The differential is a common method used in pretreatment of infrared spectroscopy; it can erase the disturbance of baseline and other background, specify the overlap peak, and increase resolution and sensitivity. Table 3 shows the influence of the

Table 4. Calibration and Prediction Results of Sulfur Content in Different Spectral Ranges wavelength range (cm−1)

factors

R2a

RPDb

RMSECV (mg KOH g−1)

4000−650 1150−1850 1516−1806

8 8 8

0.9486 0.9545 0.9611

4.65 4.91 5.07

0.38 0.26 0.22

a 2 R represent correlation coefficient. bRPD represent relative standard deviation.

3.3. Calibration and Validation for TAN Analysis. The spectral regions selected in section 3.2 were used to build the TAN calibration model. The optimum number factors must be determined according to the cross validation. The calibration results in accordance with the best factors for TAN analysis were plotted in Figure 4; these factors are new, and few

Table 3. Comparison between Different Pretreatments of IR factors R2 RMSECV (mg KOH g−1)

1st order differential

2nd order differential

8 0.9611 0.2230

8 0.9482 0.2574

calibration results between different pretreatment; the F (factor) is set to 8 for making comparisons in the same conditions. After the optimization, the first-order differential could give a better result. Figure 3 shows the dependency relation between 280 crude oil total acid numbers and first-order differential infrared spectroscopy, the method used to calculate correlation coefficients of 280 TAN values and the absorbance values in every wavenumber. It can be easily figured out that, in the wavelength range, 1500−1850 cm−1, TAN and IR have good

Figure 4. PRESS plot.

variables present the original spectral data information. It is shown that the best factor is 8. Figure 5 shows the correlations between MIR prediction values and reference values for TAN

Figure 3. Correlation between infrared spectra and the total acid number of crude oil.

Figure 5. Correlations between MIR prediction values and reference values for total acid number in calibration. 5635

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of crude oil calibration samples. The RMSECV value is 0.22 mg KOH g−1. Figure 6 shows the validation results with good

Table 6. Repeatability Results of MIR-ATR Methods for Determination of TAN of Crude Oil

sample A sample B sample C

avg of IR validation (mg KOH g−1)

SD of IR validation (mg KOH g−1)

ASTM D664 (mg KOH g−1)

0.04 0.33 1.65

0.0096 0.0089 0.0079

0.0458 0.0585 0.1166

spectroscopy on an FT-IR spectrophotometer coupled with an attenuated total reflection (ATR) cell. The PLS algorithm was applied to build a robust calibration model for total acid number of crude oil using 240 crude oil samples. The RMSECV value is 0.22 mg KOH g−1; the R2 value is 0.9611. A set of 40 crude oil samples was predicted as unknown samples using the model, and the RMSEP was 0.16 mg KOH g−1. The validation results indicate that there are high consistencies between the MIR prediction and those measured by the reference method (ASTM D664) or even better. The proposed method is much faster than the reference method. This allows the determination of TAN from a single spectrum and can be used for online monitoring of the several petroleum mixture in pipe.

Figure 6. Correlations between MIR prediction values and reference values for total acid number in validation.

correlation and corresponding reference values, and almost all points fall on or close to the unity line. The RMSEP value is 0.16 mg KOH g−1, the RMSEP value indicates that the acid numbers are accurately predicted (Table 5). This RMSEP is quite good compared to the corresponding reproducibility limits of the reference methods listed in Table 2. These results indicate that the MIR spectral data can be effectively used for the total acid number estimation of petroleum. 3.4. Validation of Repeatability. The infrared spectroscopy measurement by ATR has good stability, and the spectral repeatability of the MIR-ATR method for determine crude oil acid number value has been evaluated. The repeatability has been demonstrated by the analysis of three petroleum samples (three concentration levels of TAN) eight times by the quantitative MIR-ATR method. The ASTM requirement for repeatability can be calculated as follows: 0.044(X + 1), where X is the average of the two test results. According to this formula, the larger the TAN, the lower requirement for repeatability. The averages of eight measurements for acid number and their standard deviations were listed in Table 6. It is shown that standard deviations (SD) from MIR-ATR analysis were well below the corresponding ASTM repeatability limits; the repeatability of the method is approved. The SD of the IR validation of every sample is lower than ASTM requirements for the spectral instrument stability to ensure spectral acquisition repeatability. Because the TAN of sample C is larger than that of A and B, the ASTM requirement for repeatability is lower than that of A and B.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■ ■

ACKNOWLEDGMENTS The author would like to acknowledge RIPP for sample collections. REFERENCES

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4. CONCLUSION In this study, the determination of total acid number of petroleum by IR technique was studied. The total acid number values of 280 crude oils were determined using the ASTM D664 method, and then they were collected using infrared

Table 5. PLS Global Calibration and Validation Results for TAN of All the Samples calibration crude oil

−1

validation 2

no.

pretreatment

wavelength range (cm )

factors

R

240

1st derivative

1516−1806

8

0.9611

5636

−1

RMSECV (mg KOH g )

no.

RMSEP (mg KOH g−1)

0.22

40

0.16

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