Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods

Journal Pre-proof Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods

Mahsa Mohammadi, Mohammadreza Khanmohammadi Khorrami, Ali Vatani, Hossein Ghasemzadeh, Hamid Vatanparast, Alireza Bahramian, Afshin Fallah PII:

S1386-1425(20)30135-9

DOI:

https://doi.org/10.1016/j.saa.2020.118157

Reference:

SAA 118157

To appear in:

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Received date:

28 September 2019

Revised date:

8 February 2020

Accepted date:

15 February 2020

Please cite this article as: M. Mohammadi, M.K. Khorrami, A. Vatani, et al., Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy(2020), https://doi.org/10.1016/j.saa.2020.118157

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© 2020 Published by Elsevier.

Journal Pre-proof Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods a

Mahsa Mohammadi , Mohammadreza Khanmohammadi Khorramia*, Ali Vatanib, Hossein Ghasemzadeha, Hamid Vatanparastc, Alireza Bahramiand, Afshin Fallaha, a

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Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran, b Institute of Liquefied Natural Gas (I-LNG), School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran, c Petroleum Engineering Research Division, Research Institute of Petroleum Industry (RIPI), Tehran, Iran d Institute of Petroleum Engineering, College of Engineering, University of Tehran, Tehran, Iran

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Abstract

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Keywords

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Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATRFTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for regression and classification crude oils based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.

ATR-FTIR, Crude oil, Classification, °API gravity, PLS-R, SVM-R, PLS-DA

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Graphical abstract

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Highlights

ATR-FTIR spectroscopy associated with chemometric methods were applied for determination

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and classification of crude oils based on °API gravity. PLS-DA model was used for classification of crude oil samples based on °API gravity.

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PLS-R and SVM-R were used for determination of crude oil samples based on °API gravity.

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SVM-R showed better result than PLS-R for determination °API gravity.

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Journal Pre-proof 1. Introduction Crude oil is the essential source of fossil fuels in the world. Determination of many physicochemical properties of crude oils such as °API gravity, kinematic viscosity, SARA analysis, pour point, and carbon residue are of the most important general trends in the oil industries [1]. Among the various properties of crude oils [2–5], °API gravity is a fundamental properties in the initial production process to estimate parameters related to production, refinement, storage, oil measurement, pricing, and toxicity of crude oils

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[6]. Also, classification based on °API gravity is very important to estimate the parameters related to the

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extraction, purification, toxicity, and pricing of crude oils. Crude oil is classified as “light”, “medium”, and “heavy”, refer to the relative density of oil based on the °API gravity values. Owing to the presence

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of hydrocarbons that can be separated by distillation, light crude oils with high degree of °API gravity are

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suitable in the oil industry. However, the light crude oils are considered as the most toxic and flammable

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oil [7]. In the other hand, heavy crude oils contain high concentrations of sulfur and different metals (such as vanadium and nickel), which can't be purified by conventional methods. It has density near or even

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exceeds the density of water. So, determination of °API gravity is important to understanding the rheological behavior and chemical composition of crude oils. Most of the standard methods for crude oil

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analysis have been reported according to the American Society for Testing and Materials (ASTM), and

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API. However, the standard methods are expensive and not eco-friendly. Therefore, it is important to propose methods for rapid and reliable evaluation of crude oils in which the °API gravity can be obtained without any sample preparation. In this work a new analytical methods was proposed using ATR-FTIR spectroscopy associated with chemometric methods for simultaneously rapid determination and classification of crude oils based on °API gravity. The significance of spectrochemical °API estimation of crude oil samples are very important in crude oil industry. Medina and Guzmán have developed a method for prediction of °API gravity of the crude oils by gas chromatography (GC) with principal component analysis (PCA) and PLS regression [8-9]. Duarte and co-workers [10] used 1HNMR associated with PLS regression to determine °API gravity, and carbon residue (CR) of a great variety of crude oils. Another

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Journal Pre-proof technique that has gained particular importance in the crude oil analysis is the use of chemometric methods associated with attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. This method is not require sample preparation for analysis, rapid, and not expensive [11-15]. Moreover, this methods are reliable and efficient for characterizing and classification of the crude oils [16-25]. Table 1. summarizes crude oil analysis that utilized chemometric methods for investigation of physicochemical properties of crude oils. Chemometric approaches can be used for prediction include the multiple linear

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regression (MLR), principal component analysis (PCA), principal component regression (PCR), partial least squares regression (PLS-R), and support vector machine regression (SVM-R) with linear and non-

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linear algorithm. From these, SVM-R as a non-linear model is considered to be more efficient than

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another models for prediction of crude oil samples based on °API gravity values. This superiority for

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SVM-R model for determination of °API gravity may be due to the used as non-linear model. The significance of spectrochemical °API estimation of crude oil using SVM-R and PLS-R methods were

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applied for the quantification of the °API gravity values. For °API gravity determination, SVM-R

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outperformed PLS-R for prediction of crude oil samples based on °API gravity values . The PLS algorithm was applied to the data for constructing the model. In the PLS model, the optimum

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number of components can be obtained using a cross-validation procedure. The selection of optimum

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number of factors is important in the PLS algorithm to avoid overfitting. Full cross-validation procedure was employed for selecting the number of factors [26-29]. SVM-R is a powerful multivariate calibrations method for regression. Optimization of parameters is an important step in the SVM-R algorithm led to following regression model Eq. 1. [30] ( ) where,

and

∑

(

)

(

)

(1)

* illustrate the Lagrange multipliers satisfying the subject to 0

constant C should be optimized by the model and the kernel function

4

(

C, and the ), maps the input data to

Journal Pre-proof feature space. The most commonly used kernel function is the Radial Basis Function (RBF). This function is defined by Eq. 2. (

(

)

)

(2)

This regression model can help to solution of the problem of parameters optimization. γ optimization parameter is necessary before the applied of the SVM-R algorithm. For the RBF kernel,

is a tuning

parameter which determines the width of the kernel function, that can be optimized by the analysis.

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Optimization of modeling parameters in the SVM algorithm is determined by minimization of a cost

and

‫)׀‬

(3)

is the predicted value and measured value of the ith observation in the training set,

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where,

‫׀‬

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(∑

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function was the mean absolute error (MAE) which is calculated by Eq. 3.

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respectively. The number of observations is shown by optimize these parameters according to the minimum

. The cross-validation method was used to parameter [31-33].

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Our purpose was also classification of crude oil based on °API gravity values into light, medium, and

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heavy crude oil samples. In classification models were not observed the good results using PCA and soft independent modelling of class analogies (SIMCA) algorithms based °API gravity values. However, the

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application of ATR-FTIR spectroscopy in combination with PLS-DA algorithm for classification of crude oils based on °API gravity values was well performed. The PLS-DA algorithm was utilized in this study for modeling the °API gravity into three classes according to their values. All acquired data were classified into three classes based on °API gravity values. In PLS-DA model the optimum number of variables was selected according to the minimum classification error. [34-35].

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Type of classification

Chemometric methods PLS-R

True boiling point (TBP) °API gravity and Carbon residue (CR) Geochemical origin of crude oils SARA fractions

Quadratic discriminant analysis (QDA) principal component regression (PCR) and partial least squares regression (PLSR) PLS-R, SVM-R PLS-DA

FT-IR

[14]

FT-IR

[19]

GC

[21]

Multivariate calibration

FT-IR

[24]

Classification

GC–MS

[36]

Classification

FT-ICR MS

[37]

------

ATR-FTIR

[38]

Multivariate calibration

ATR-FTIR

[39]

Multivariate calibration and classification

ATR-FTIR

This work

[6]

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Quality passed/failed data Total acid number (TAN) of colombian crude oils

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[10]

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Cow-PCA-LDA

H-NMR

Multivariate calibration Classification

multi-way principal components analysis (MPCA) principal factors analysis (PARAFAC) PLS-R

m/z 256 mass chromatography °API

Ref

Multivariate calibration Multivariate calibration Classification

PCA SIMCA PLS-R

°API gravity and sulfur content

°API gravity

Type of analysis

PLS-R

Certified Reference Materials (CRM)

Instrumental method HT-GC

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Table 1, Comparison of some crude oils analysis by chemometric methods

2. Materials and methods 2.1. Sample preparation

In this study, 20 different crude oils samples were used with °API gravity ranging from 18.5 to 54.07 from different Iranian oil fields. The °API gravity of the samples was determined according to ISO 12185-96 standard. Density was measured using a digital automatic densimeter (DMA M). °API gravity is computed using the specific gravity (density of crude oil into the density of water) at 15.6 °C (60 °F), and 1atm, (Eq. 4). 6

Journal Pre-proof ( )

Classification of crude oils based on °API gravity is shown in (Table, 2). Table 2, Classification of crude oils based on °API gravity °API scale [*]

Density [g/cm3]

Light

> 31.1

<0.87

Medium

22.3 – 31.1

0.92-0.87

Heavy

10 – 22.3

1.0-0.92

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Crude oil classification

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2.2. FTIR measurements

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The ATR-FTIR spectra of samples were obtained by FTIR spectrometer (Nicolet, Madison, WI, USA)

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using a horizontal zinc-selenide attenuated reflector. The ATR-FTIR spectra of samples were recorded by

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16 scans at 4 cm-1 resolution. However, when the number of samples in calibration set is small, a manner is to repeat the number of spectra for each sample to acquire enough number of data for analysis. Based

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on this description and in conformity based on the work reported by Yang et al [40], each sample was

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recorded 5 time and utilized for chemometrics analysis. 2.3. Data pretreatment and outlier detection

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A preliminary step before applying most chemometric methods is often baseline correction. Linear baseline correction was used to correct the baseline of samples by Omnic software. Then, the data were smoothed using moving average method data. Data pretreatment were performed by standard normal variate (SNV) for regression models and by orthogonal signal correction (OSC) for classification model. Also, Principal component analysis (PCA) was done for outlier detection of samples using Hotelling T2 statistic parameter. Data pretreatment and principal component analysis (PCA) were performed using the Unscrambler V-10.5 (CAMO software AS, Oslo, Norway).

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Journal Pre-proof 2.4. Chemometric procedures At first, all samples were split into calibration and prediction sets by Kennard Stone algorithm [41]. Then chemometric algorithms were used for quantitative determination of the spectra based on °API gravity values. PLS-R and SVM-R were used for °API gravity determination of crude oil samples. The data set in PLS-R and SVM-R was first pretreated. Also, PLS-DA was performed for supervised classification of the spectra. All acquired data were classified into three classes based on °API gravity values. In order to

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make classification model, a set of 100 samples was split into calibration and prediction sets. Then, a set

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of 70 samples were used to construct the calibration model, and 29 samples were used for the prediction model. Chemometric analysis was performed by Unscrambler (version 10.4, Camo ASA Norway) and

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Matlab R2009a (PLS Toolbox 7.8) software for regression and classification model, respectively. A

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detailed flowchart is shown in Fig.1.

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Fig. 1. Flowchart of the chemometric analysis for determination and classification of crude oils based on °API gravity using ATR-FTIR spectroscopy

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Journal Pre-proof 3. Results and discussion 3.1. Spectral characteristics of crude oil The ATR-FTIR spectrum of a sample of crude oil is shown in Fig. 2. Crude oil is composed of different types of hydrocarbons e.g. linear, chained, and cycloaliphatic hydrocarbons with aromatic compounds. Generally, the chemical component of a crude oil influence directly its spectrum characteristics. Differences in chemical composition of crude oils affect their refining procedure, quality parameters, and

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finally marketing. So, crude oil characterization methods are very important in the petroleum industry.

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Table, 3 shows the typical vibrational bands in the M-IR spectra [14]. The band observed at 2865 and 2950 cm−1 correspond to aliphatic C-H symmetric and asymmetric stretching in crude oil sample,

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respectively. The band around 3000–3050 cm−1 correspond to aromatic C-H. The spectral band at 1635-

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1750 cm−1 corresponds to the C=O stretching vibrations. The bands at 1100-1300 cm−1 indicating the

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presence of N- and S-related stretching and bending vibrations. Table 3, Important functional groups present in crude oil in M-IR

Aromatic C-C

Spectral Region (cm-1)

stretching stretching stretching stretching stretching stretching and bending

3050 - 3000 2950 2865 1750 - 1635 1470 - 1590 1300 - 1100

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aromatic C–H aliphatic C–H asymmetric aliphatic C–H symmetric C=O aromatic C=C N- and S-related

Vibration Type

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Functional Group

bending

820 - 880

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Fig. 2. ATR-FTIR spectrum of a sample of crude oil in the range of 800-4000 cm-1

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3.2. Chemometric data processing

3.2.1. PCA analysis and outlier detection The detection and elimination of outlier is necessary, because outliers can influence on accuracy of the model. Outliers can be detected using application of PCA model and Hotelling‟s T2 statistic plot. The principal components in PCA model were used to find outliers in all data set. Then, the outliers can removed from the all data set. Fig. 3A, shows the scores plot, after performing PCA model on data set. In score plot, about 99% total variance was presented by these components (PC1, PC2). Also, outliers can be detected utilizing Hotelling‟s T2 statistic plot, as illustrated in Fig. 3B. In Hotelling‟s T2 statistic plot, all samples are in the range and only one outlier detected. 11

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Fig. 3. A) Score plot, B) Hotelling's T2 statistic plot, C) residuals plot on ATR-FTIR of crude oil samples

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Journal Pre-proof 3.2.2. Evaluation of regression models using PLS-R and SVM-R algorithms A set of 100 crude oil samples was considered with °API gravities ranging from 17° to 54°, corresponding to the heavy, medium, and light crude oil. The results of standard ASTM method, were used as reference data for calibration models. In regression models, all samples were split into calibration and prediction sets by Kennard Stone algorithm. A set of 70 samples were assigned as calibration set, while 29 samples were considered for prediction set of °API gravity values. For evaluation of the

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proposed calibration model based on °API gravity, the °API gravity of 70 new samples were analyzed by

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FTIR spectroscopy and chemometrics. PLS-R and SVM-R as multivariate methods were applied using ATR-FTIR spectra data. After creating the calibration models by PLS-R and SVM-R algorithms, the

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°API gravity of the prediction samples were predicted by the models. R2, RMSEC, and RMSEP

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parameters for calibration and prediction samples were also calculated (Table 4). The result showed good

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coordination between calibration and prediction models. In PLS-R, using the full cross validation procedure, the optimum number of factors was selected, with efficiency the minimum prediction error

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sum of squares (PRESS). The optimal values of C and gamma parameters in SVM-R were obtained using cross validation procedure. The RMSEC, RMSEP, and the R2 of regression lines were also calculated for

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calibration models. The RMSE was calculated according to Eq.5.

where,

and

√

∑

(

)

(5)

are the predicted and the measured value of the ith observation, respectively. n is the

number of samples in calibration or prediction set. The models were compared by F-test statistics. Based on the obtained result, using the F-test at 95% of confidence it was determined that the SVM-R model showed better results than PLS-R for °API gravity values. The SVM-R prediction error was lower than that of PLS-R, with RMSEP of 2.452 for SVM-R and 2.993 for PLS-R, but this difference is not statistically significant. The calculated F-test (1.093) was smaller than critical F-test (2.307), and there is no evidence to reject the hypothesis of equal variances at 13

Journal Pre-proof 95% confidence level. Fig. 4A and B display the plot of the °API gravity by PLS-R and SVM-R models for calibration and prediction values, respectively. Both methods demonstrated good calibration results, but SVM-R algorithm seems to have a better calibration and prediction data due to the higher R2 as well

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as the lower RMSEC and RMSEP for °API gravity determination.

Fig.4. Predicted °API gravity values versus reference °API gravity values by (A) PLS-R and (B) SVMR,( ) Calibration samples and (*) prediction samples

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Journal Pre-proof Table 4. Statistical outputs of PLS-R and SVM-R method Model

PLS-R

R2Cal.

SVM-R

0.937

R2Pre

0.958

0.929

0.939

RMSEC

2.208

2.006

RMSEP

2.993

2.452

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3.2.3. Supervised classification using PLS-DA algorithm

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In order to make classification model, the set of 100 samples was split into calibration and prediction sets.

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Then, a set of 70 samples were used for calibration and 29 samples for the prediction. Calibration model was created using PLS-DA algorithm. The calibration data consist of the ATR-FTIR data for each

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sample and predefined class of each calibration according to °API gravity values. The PLS-DA algorithm

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was applied to correlate between the ATR-FTIR spectra and the class of the samples. In PLS-DA, calibration model was optimized. Then, the prediction samples were predicted by this model.

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Classification parameters, including sensitivity (Sens), specificity (Spec), and accuracy (Acc) were used

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to determination of classification model efficiency, which are showed as equations 6-9.

(

(

)

(

)

( )

( )

( )

)

(

)

( )

where, TP, TN, FP, and FN explain the number of samples, true positives, true negatives, false positives, and false negatives, respectively. 15

Journal Pre-proof In PLS-DA, about 90.65% of X-block and 98.88% of Y-block variances were explained using two latent variables. The number of latent variables in PLS-DA model has been chosen according to the minimum classification error in cross-validation. The samples are assigned to the three classes based on the predicted value. The confusion matrix for calibration and prediction samples was computed and the results are shown in Table 5. Classification parameters acquired from the confusion matrix are demonstrated in Table 6. These characteristics of the PLS-DA algorithm leads to a robustness model with

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high generalization performance that is especially important in petroleum refinery applications in real time analysis of complex mixtures. As can be seen from the tables 6, PLS-DA model showed about 100%

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accuracy and a zero classification error for calibration and prediction samples and also 91% accuracy for

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validation of samples according to RAC analysis. The process of optimization and classification of PLS-

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DA is shown in Fig. 5, and 6, respectively. The PLS-DA classification model was applied to identify crude oils, as “light”, “medium” and “heavy” crude oils, which refers to the oil‟s relative density based

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on °API gravity. Fig. 7, display the ROC curve according to the data presented in Table 5, and 6.

Fig. 5, The optimum number of latent variable selection in PLS-DA modelling

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Journal Pre-proof Table 5. The confusion matrix for calibration and prediction samples in PLS-DA modelling Real/Prediction Class1 Class2 Class3 Class1 Class2 Class3

Method Calibration

Prediction

Class1 39 0 0 11 0 0

Class2 0 17 0 0 13 0

Class3 0 0 14 0 0 6

Table 6. The classification results for calibration and prediction samples in PLS-DA modelling Spec.

Sens.

Calibration

1 2 3 1 2 3

1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00 1.00 1.00 1.00 1.00

Class. Err 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 1.00 1.00 1.00

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Acc. 1.00

1.00

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Method

Fig. 6. Class measured (A), and Score plot on latent variable 2 vs 1 (B) for PLS-DA modelling 17

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Fig. 7, ROC curve for PLS-DA algorithm

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Journal Pre-proof 5. Conclusion The ATR-FTIR spectroscopy associated with chemometrics methods are efficient tools for quantitative determination and classification of the crude oil samples. In this work, a new analytical methods based on ATR-FTIR spectroscopy associated with chemometric methods were applied for rapid determination and classification of crude oils based on American Petroleum Institute (°API) gravity. The applied methods provide rapid analysis of crude oils due to its simplicity for sample preparation. The values of 2.993, and 2.452 were obtained as root mean square error of prediction (RMSEP) using PLS-R and SVM-R

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algorithm, respectively. Using the F-test at 95% of confidence, it was concluded that the SVM-R model

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produced better results than PLS-R for °API gravity determination. The SVM-R algorithm seems to have

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a better result of data for calibration and prediction model due to the higher R 2 as well as the lower

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RMSEC and RMSEP for °API gravity determination. The ATR-FTIR spectral data were also analyzed into three classes based on their °API gravity values. The result of classification parameters were

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evaluated, including sensitivity, specificity, accuracy, and precision. PLS-DA classification model, showed about 100% accuracy and a zero classification error for calibration and prediction samples. These

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methods could be proposed for development of crude oil analysis.

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Journal Pre-proof

Credit Author Statement Mahsa Mohammadi: Conceptualization, Developed the theory and performed the computations, Methodology, Writing- Original draft preparation. Mohammadreza Khanmohammadi Khorrami: Supervision, Data curation, verified the analytical methods, Writing- Reviewing and Editing.

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Ali Vatani: Visualization, Investigation, Sample preparation.

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Hossein Ghasemzadeh: Writing- Original draft preparation, Writing- Reviewing and Editing.

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Hamid Vatanparast: Sample preparation, Writing- Reviewing and Editing.

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Alireza Bahramian: Visualization, Investigation, Sample preparation. Afshin Fallah: Investigation, Software, Developed the theory and performed the computations

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Conceptualization.

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Journal Pre-proof In the Name of God Novelty Our purposes in this work were to develop a new analytical methods based on ATR-FTIR spectroscopy associated with chemometric methods for simultaneously rapid determination and classification of crude oils based on American Petroleum Institute (°API) gravity. Most of the standard methods for crude oil analysis have been reported according to the American Society

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for Testing and Materials (ASTM), and °API. However, the standard methods are expensive and

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environmental non friendly to be analyzed. Therefore, it is important to determine methods for

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evaluating of the crude oils.

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