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Sulfur and Total Carboxylic Acid Number Determination in Vacuum Gas Oil by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Ramachandra Chakravarthy, Manjula Paramati, Anilkumar Savalia, Anurag Verma, Asit Kumar Das, Chandra Saravanan, and Kalagouda B. Gudasi Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b03712 • Publication Date (Web): 10 Jan 2018 Downloaded from http://pubs.acs.org on January 11, 2018

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Graphical Abstract Title: Sulfur and Total Carboxylic Acid Number Determination in Vacuum Gas Oil by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Authors: Ramachandra Chakravarthy, Manjula Paramati, Anilkumar Savalia, Anurag Verma, Asit Kumar Das, Chandra Saravanan and Kalagouda B. Gudasi*

Abstract: A quick and efficient spectroscopic method was developed for the measurement of total sulphur & naphthenic acid number using ATR-FTIR accessory with diamond crystal. The method is quick, accurate, robust, repeatable, reproducible, and applicable to wide range of samples.

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Sulfur and Total Carboxylic Acid Number Determination in Vacuum Gas Oil by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Ramachandra Chakravarthy,

†, ‡

Manjula Paramati, † Anilkumar Savalia, † Anurag Verma, †

Asit Kumar Das, † Chandra Saravanan, † and Kalagouda B. Gudasi*, ‡ †Reliance Research & Development Centre, Reliance Industries Limited, Reliance Corporate Park, Thane - Belapur Road, Ghansoli, Navi Mumbai - 400701, Maharashtra, India ‡ Department of Chemistry, Karnatak University, Pavate Nagar, Dharwad - 580 003, Karnataka, India * Corresponding author E-Mail: [email protected] Phone No. + 91-836-2215377, + 91 9448571368 Abstract Sulfur removal is one of the key functions of vacuum gas oil (VGO) hydrotreating reactors. Knowing feed and product properties real-time or near real-time improves reactor operations. VGO section of crude distillation unit is also prone to severe high-temperature sulfidic and naphthenic acid corrosion. In this article, we evaluate a single reflectance Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy as a possible quick and cost effective methodology to determine Total Carboxylic Acid Number (TCAN) and total sulfur content of VGO. The study shows that single reflectance diamond ATR crystal methodology has the right signal-to-noise ratio to accurately predict TCAN and total sulfur within the primary method’s repeatability. Statistical models have been developed using 64 sample sets of vacuum gas oil and out of which 10 samples were used for cross validation of the model. The range of TCAN in VGO samples used in this study was between 0.37 and 13.8 mg KOH per gram and Sulfur content was between 0.8 to 5.4 percent by mass. Models have been evaluated by determining correlation coefficient (R2), linearity curves obtained by plotting measured versus predicted values, and the errors associated with the


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prediction and cross-validation. The models showed the correlation coefficient of 0.9991for TCAN and 0.9974 for total sulfur between reference and the measured values for calibration set of samples. A root-mean-square error of calibration (RMSEC) and prediction (RMSEP) for TCAN were found to be 0.0903 and 0.0885 mg KOH per gram. Similarly, RMSEC and RMSEP values for sulfur content were 0.0829 and 0.107 percent by mass respectively. The proposed methodology for the prediction of total sulfur and TCAN is fast, efficient, cost effective and has several advantages over the standard methods. Key Words: Naphthenic acid number, Total sulfur, Chemometrics, ATR-FTIR, Partial least squares 1. INTRODUCTION Petroleum crude oil is a complex mixture of hydrocarbon species with hetero atoms such as nitrogen, oxygen, and sulfur

1-4

. Trace amounts of metallic impurities like nickel,

vanadium, iron, sodium etc. are also present in crude oil

3-4

. Properties of crude oil such as

water and salt content, density, viscosity, Total Acid Number (TAN), distillation yields and composition etc. determine its commercial value 2. Opportunity crudes (e.g. typically heavy crudes with high sulfur, TAN etc.) are low cost materials 5. However processing these crudes require the right hardware, and operational expenses are higher. Refining opportunity crudes (referred to as heavy oil for the rest of the article) pose a great challenge as these oils contain hetero atoms such as oxygen (0.05% to 1.5%), nitrogen (0.1 to 2%) and sulfur (0.05 to 6%) 6. Oxygen is usually present in the form of organic acids and phenolic compounds (in general, all carboxylic acid molecules - aliphatic, cyclic or aromatic - are referred to as naphthenic acids) 7. Sulfur is present in the form of thiols, sulfides, and thiophenes 8. In this article, we focus on issues related to sulfur and naphthenic acids in a typical Vacuum Gas Oil (VGO) section of a refinery.


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Sulfur removal is one of the key functions of VGO hydrotreating reactors. Hydrogenolysis reactions such as hydrodenitrogenation (HDN), hydrodeoxygenation (HDO), hydrodemetallization (HDM), and hydrodesulfurization (HDS) occur in a catalytic hydrotreating process

8-18

. Hydrogenation reactions such as olefins saturation, aromatics

saturation, and hydrocracking also occur during hydrotreating. Desulfurization reaction takes place after olefin saturation. VGO samples usually contain sulfur in the form of mercaptans, sulfides, disulfides, cyclic sulfides and thiophenes

17-18

. Mercaptans and sulfides are easily

converted to hydrogen sulfide (H2S). On the other hand, thiophenes are harder to process as compared to sulfide

13-15

. The following reactions takes place in desulfurization process

during hydrotreating.

The efficiency of hydrotreating process is determined by feed composition and process parameters such as flow rate, temperature, hydrogen pressure, and gas to oil ratio. Downstream capacity of H2S treatment units (which converts H2S to elemental sulfur) also determines the quality (quantity is usually fixed by the hydrotreator capacity) of the reactor


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product that can be processed. If H2S formed during hydrotreating exceeds the capacity of the treatment plant, it may get discharged to the atmosphere causing severe health and environmental pollution since H2S is a very hazardous chemical. VGO section of crude distillation unit is most prone to high-temperature sulfidic and naphthenic acid corrosion. Several refineries use corrosion inhibitors to minimize corrosion 17-21. The addition of corrosion inhibitor dosage depends on the naphthenic acid and sulfur content

19-21

. Therefore sulfur and naphthenic acid levels need to be monitored

carefully. A real-time or near real-time feed forward and feedback information on feed and product compositions can help optimize VGO hydrotreating reactors, and monitor corrosion on the VGO lines. A single technology capable of online measurements, which is portable, and can rapidly measure multiple properties is attractive. In this article we present a fast and reliable analytical methodology to quantify total sulfur (TS) and TCAN. The American Society for Testing Materials (ASTM) method D-664 is a method specified for the determination of acid number of petroleum products by potentiometric titration

22

. This procedure covers the determination of all the acidic constituents present in

the oil including sulfur species, phenolic compounds and inorganic acids that are titratable by base. Therefore this method is not specific to carboxylic acids. Various analytical techniques such as high performance liquid chromatography (HPLC) with photo diode array and mass spectrometer, gas chromatography (GC) with flame ionization and mass spectrometer and Fourier transform ion cyclotron resonance spectrometer (FTICR) have been used to measure naphthenic acids in crude oils and their fractions 23-49. However, these techniques are neither fast nor portable. Vibration spectroscopic techniques (infrared and near-infrared spectroscopy) with partial least squares (PLS) chemometric statistical modelling has been attracting researchers


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for its wide applicability in quantitative measurement 50-66. Recent advances involves the use of Fourier Transform Infrared Spectroscopy (FTIR) with the quantification of carbonyl (C=O) group of organic acids after dissolution of petroleum oil in a suitable solvent with or without pretreatment of samples. Several published articles in literature suggest the use of mid-IR and NIR techniques for the petroleum products evaluation such as density, octane number of gasoline, and complete diesel properties

50,56,63-65

. FTIR and NIR spectroscopic

methods have been used in the past for the measurement of total acid number and total sulfur content of crude oils and their fractions. Currently, the standard reference methods for the determination of total sulfur content in petroleum heavy fractions are X-ray and ultraviolet fluorescence spectroscopy (ASTM D4294 and ASTM D-5453)

67-68

. Several gas chromatography based methods are used in the

literature for sulfur measurement in refinery fractions. Recent advances in sulfur determination involves the use of CNS-Simulated distillation instrument 69-70. These methods are reliable but needs laborious sample preparation and cumbersome calibration procedures. CNS-Simulated distillation cannot be made portable, and is currently not available as online technology. As large number of samples are tested in a typical refinery process quality control (QC) laboratory, it is essential to develop an alternate technique which is quick, cost effective and reliable. In this context, several spectroscopic methods such as FTIR and NIR in association with multivariate chemometric methods have been successfully published in literature for the determination of sulfur in various refinery fractions

50,56,63-65

. These technique use a

horizontal long Zn-Se ATR-FTIR cell with 10 to 12 reflections. While measuring heavy oil samples which are highly viscous and sticky; in order to get accurate data (spectra) for the successive sample measurements; the long ATR-FTIR cell needs a thorough cleaning with solvents.


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The main objective of the present study was to develop a quick and cost effective method to simultaneously determine the TCAN (naphthenic acid number) and total sulfur content in vacuum gas oil samples of petroleum distillation process. In this study, an ATRFTIR method with a single attenuated total reflectance diamond crystal cell and a PLS calibration model was developed that can be successfully used for the fast evaluation of inprocess samples in QC laboratory of a refinery. The diamond crystal cell can be cleaned easily with soft tissue paper (Kim wipes) and requires less than a minute. 2. EXPERIMENTAL SECTION 2.1. Samples 64 Vacuum gas oil samples were collected from Refinery Division, Reliance Industries Limited, Jamnagar, India. The crude oil samples were obtained from various geographic locations and vacuum gas oil fractions (boiling range 370 to 5650C) were collected from laboratory scale true boiling point (TBP) distillation process and some of the samples were directly obtained from crude distillation units (CDU) of refinery. The diverse variations in naphthenic acid number and total sulfur content in vacuum gas oils were selected for this study in order to build up a more robust calibration model. The naphthenic acid number of vacuum gas oil samples ranges from 0.37 to 13.8 and the sulfur content distribution was 0.80 to 5.4 percent by mass. 2.1. Determination of Total Carboxylic Acid Number (TCAN) or Naphthenic Acid Number (NAN) Vacuum gas oil samples received from the refinery complex were collected in a 500 ml aluminium containers. The samples were made homogeneous by heating on a water bath at 50 degree Celsius. Approximately 1-2 g of the homogenized sample was then weighed accurately to the nearest 0.1 mg into a 10 ml “A” grade borosilicate volumetric flask and the


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small quantity of HPLC grade dichloromethane was added to the flask to dissolve the sample. Samples were dissolved by sonication and made up to the mark with dichloromethane. Calibration standards for total carboxylic acid number were then prepared using hexanoic acid purity of 99.9% obtained from Sigma Aldrich. Initially, total acid number of hexanoic acid was measured as per the method specified in ASTM D-664. TAN value of hexanoic acid in terms of mg KOH per gram was found to be 456 and is the average of five replicate measurements. Since all the organic acids present in petroleum are termed as naphthenic acids or carboxylic acids, the total acid number for hexanoic acid was considered as total carboxylic acid number (TCAN) or naphthenic acid number.The standard stock solution was prepared in dichloromethane by weighing accurate quantity of hexanoic acid in 100 ml volumetric flask. The series of calibration standards were then prepared by diluting hexanoic acid stock solution in HPLC grade dichloromethane in the range of 0.02 to 1.986 mg KOH per gram. FTIR spectra of all calibration standards and samples were measured in the region of 4000 to 1000 cm-1 using Perkin Elmer’s FTIR Spectrum 100 spectrometer equipped with deuterated triglycine sulfate (DTGS) detector and liquid cell with calcium fluoride (CaF2) window accessory purchased from Specac Ltd. (Part No.GS07502). Data analysis was carried out using “Spectrum” software provided by Perkin Elmer. Total area under the curve of both the monomeric and dimeric photon absorption bands in the region of 1800 to 1680 cm-1 was used for all analytical measurements. Calibration plot for TCAN was then drawn using concentrations of hexanoic acid (mg KOH/g) versus area of FTIR 7. TCAN for all the VGO samples were measured in triplicate and the average value was considered for ATR-FTIR model development. 2.2. Determination of Total Sulfur (TS) Total Sulfur content of all VGO samples have been performed using HT-CNS SimDis analyzer

69-70

. Approximately 100 – 200 mg of VGO samples were weighed in a glass vial


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and 5 g of HPLC grade cyclohexane was added to the sample to dissolve the sample. Weight of sample and solvent were accurately measured to the nearest 0.1 mg. Homogeneous solution of the sample was then transferred to 2 ml GC vial and sealed the contents with an aluminium crimp cap. 0.5 µL of the resultant solution was then injected onto GC and the chromatogram was recorded using chemstation software. Agilent 7890 GC with FID detector and Antek’s sulfur and nitrogen detector connected in series which was custom made by PAC, Netherlands was used for the purpose. The calibration plot for total sulfur content was drawn using “VGO NS reference” in house material with known quantity of sulfur content. The values of sulfur content obtained by this method for both VGO samples and VGO NS reference standard were validated with the reference standard methods

69-70

. Data analysis

was done by using SimDis software provided by PAC, Netherlands. All the sample analysis was carried out in triplicate and the average value of sulfur content obtained was considered for ATR-FTIR chemometrics model development. 2.3. Acquisition of Mid-FTIR Spectra All the samples were homogenized by heating to 70 degree Celsius and a small amount was transferred to a small glass vial. After the samples were cooled to room temperature, ATR-FTIR spectra were recorded for all the vacuum gas oil samples using a Thermo Nicolet FTIR (model no. iS50) spectrometer equipped with a diamond crystal single attenuated sample accessory and deuterated triglycine sulfate (DTGS) detector. Sample spectra were collected after placing a drop of the sample onto the diamond ATR crystal at room temperature. The room temperature was maintained strictly at 20 + 0.5 0C during the spectral measurement. After acquisition of the sample spectra, the vacuum gas oil samples left on the surface of diamond crystal cell was removed by gently wiping with Kimwipes tissue paper and finally with dichloromethane solvent. ATR with diamond crystal accessory


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is easy to operate and produces reproducible spectra and hence can be used for quantitative analysis. ATR-FTIR spectra of samples were collected over the spectral range of 4000 to 400 cm-1. Each sample spectrum was the accumulation of 64 scans with the resolution of 4.0 cm-1 and resulted in 7469 variables. A background spectrum of air was collected before collection of the VGO sample spectrum. The software used for the sample collection was “Omnic 9.2” provided by Thermo Nicolet along with the spectrometer.

2.4. Data Analysis Data analysis was carried out by using TQ Analyst 9.3.107 software. In the present study, all the sample spectra were imported and the baseline correction has been done to eliminate the offset in the spectra 52. First derivative spectra with Savitz-Golay smoothening has been carried out to eliminate the noise from the spectrum. In the present study, partial least squares (PLS) regression model was employed to develop quantitative calibration model for total sulfur and carboxylic acid number. The performance of the model was evaluated by determining correlation coefficient (R2) for both calibration and prediction samples set, root mean squared error of calibration (RMSEC) and root mean squared error of prediction (RMSEP). R2 value indicates the closeness of the prediction and calibration values to the fitted regression line. RMSEC and RMSEP stands for the root mean square error of calibration and prediction respectively and are the key performance indicators of the measurement of errors associated with the model and the actual observed values. These were calculated by the following equations.

∑ −

, =


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Here, yi and yp,i is the actual and prediction of the sample i, and n is the number of observations of the total set.

3. RESULTS AND DISCUSSION 3.1. Mid-FTIR Spectral Features Fig. 1 depicts a typical spectra obtained in the range of 4000 cm-1 to 400 cm-1 for vacuum gas oil samples having different naphthenic acid number and sulfur content analyzed by ATR-FTIR accessory using diamond crystal

42-43

. Small changes in the spectral regions

can be observed for the several vacuum gas oil samples having different composition and variability in total sulfur and naphthenic acid number as shown in Fig 2. The spectral regions 3300 to 2400 cm-1 corresponds to carbon to hydrogen (C-H) stretching and 1800 to 1100 cm-1 corresponds C-H bending vibrations whereas, a doublet kind of spectral absorption photon bands for carbonyl group (-C=O) of carboxylic acids can be seen in the range of 1800 to 1680 cm-1. The bending vibrations of –CH3 and CH2 are seen in the spectral region of 1350 to 1450 cm-1. Since the spectral absorption is directly proportional to the concentration as per BeerLambert’s law, any changes in the various spectral regions indicates the changes in the composition of various samples. The whole Mid-FTIR spectrum contains lot of information about the changes in the chemical composition of organic components and is complex in nature, a multivariate chemometric statistical model is essential to understand the spectral variations and has been used in the present study to predict sulfur and naphthenic acid number in vacuum gas oil samples 3.2. Calibration and Prediction of sample sets for TCAN To obtain best prediction measurements of naphthenic acid number and total sulfur content of VGO samples, several different spectral regions have been tested. Naphthenic acid number shows good correlation in the spectral region of 1780 to 1650 cm-1 with correlation


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coefficient of 0.9997 for calibration sets with RMSEC and RMSEP values of 0.0903 and 0.0885 respectively. Figure 3. displays the PLS model correlation between the prediction values and the reference values for naphthenic acid number of vacuum gas oil calibration set of samples. The errors associated with the prediction values versus the actual naphthenic acid number measurement by reference method for all the VGO samples used for calibration and prediction sets has been displayed in error plot as shown in Fig. 4. It has been observed from the plot that the error values are in the range of + 0.19 to -0.20 mg KOH/g of naphthenic acid for the calibration set of samples for the range of 1 to 2 mg KOH/g of TCAN. Fig. 5 depicts the PRESS plot with the RMSEC values versus number of factors used for the calibration plot for TCAN measurement. It has been observed from the PRESS plot that the best factors for TCAN measurement using ATR-FTIR PLS model is 5 and beyond which there is no change in the values and hence 5 factors have been used for the calibration model. 3.3. Calibration and Prediction of sample sets for Sulfur To obtain a good prediction of total sulfur measurement of vacuum gas oil samples, several regions were tried and found that combination of multiple region selection 1900 to 620 cm-1 and 3300 to 2400 cm-1 was fitted well with R2 value of 0.9974 and RMSEC value of 0.0829 for calibration sets and R2 value of 0.9968 and RMSEP value of 0.107 for sample prediction sets. Fig. 6. shows the PLS model correlation between the prediction values and the reference values for total sulfur content of vacuum gas oil calibration set of samples. Figure 7. depicts the error plot obtained from the PLS model using two region spectral selection for sulfur content. It has been observed from the error plot that the maximum error for total sulfur content for the PLS prediction model was between 0.21 to + 0.17. Since any of the single spectral range was not producing a good correlation for total sulfur content, combination of two regions selection was used for PLS model development. Fig. 8 depicts the PRESS Plot for the calibration results with the best factors for total sulfur analysis. It has


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been found that minimum number of factors required are 8 to obtain the good calibration model. Table 1. depicts the PLS optimum model parameters for naphthenic acid number and total sulfur measurement of vacuum gas oil samples. After the PLS model for total sulfur and naphthenic acid number is finalized, a set of 10 VGO samples have been analyzed to find out the accuracy of the predictions for blind samples. Table 2 displays the TCAN and total sulfur content obtained by the actual measurement and the prediction values from the PLS models. It has been found that the measured values and the predicted values of TCAN and TS are found to be within the reproducible limits as mentioned in ASTM method. 3.3. Validation of Repeatability The ATR-FTIR measurement for vacuum gas oil sample shows the stable and good repeatable spectra to determine TCAN and total sulfur measurements. The repeatability of TCAN and TS have been established by the analysis of a vacuum gas oil sample 5 times using different portion of the homogenized sample on ATR-FTIR and the values have been predicted using the PLS model. Table 3 depicts the spectral repeatability and PLS model prediction of total sulfur and naphthenic acid number of a vacuum gas oil sample. For this study, a vacuum gas oil having TCAN of 1.14 mg KOH/g and total sulfur content 1.894 % by mass was selected

and spectra were measured 5 times using different portion of the

homogenized sample and the prediction for total sulfur and naphthenic acid was done using PLS models developed for TCAN and total sulfur respectively. As per ASTM D-664, the repeatability of the measurement of naphthenic acid number can be calculated using the formula 0.044 (X + 1) where X is the average of the measured values. It has been found that the standard deviation for 5 repeat spectral predictions for total sulphur and TCAN values are 0.089 and 0.027 with relative standard deviation of 4.679 and 2.410 percent respectively. 3. CONCLUSION


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The present study was aimed to determine the total sulfur and naphthenic acid number of vacuum gas oil samples of petroleum distillation process by ATR-FTIR method with diamond crystal ATR accessory and DTGS detector. Total sulfur and carboxylic acid number of 65 vacuum gas oil samples have been measured by the reference methods and the ATRFTIR spectra were collected. The PLS chemometric statistical model was developed to build a robust calibration model for the prediction of naphthenic acid number and total sulfur content of vacuum gas oil samples. The proposed methodology can be successfully used in QC laboratory of refineries to determine total sulfur and naphthenic acid number, the key parameters for corrosion measurement of VGO lines and to check the efficiency of hydrotreating processes. The ATR-FTIR methodology is fast, robust, accurate and cost effective.

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(7) Chakravarthy, R.; Naik, G. N.; Savalia, A.; Sridharan, U.; Saravanan, C.; Das, A. K.; Gudasi, K. B. Determination of Naphthenic Acid Number in Petroleum Crude Oils and Their Fractions by Mid-Fourier Transform Infrared Spectroscopy, Energy Fuels 2016, 30, 8579-8586. (8) Totten, G.E.; Westbrook, S.R.; Shah, R.J. Fuels and Lubricants Handbook, Technology, Properties, Performance and Testing. ASTM International, 100 Barr Harbor Drive, PO Box. C700, West Conshohocken, PA. 19428-2959, USA. (9) Mcpherrson, L.J.; Olive, M.F. Alkylation, Isomerization, polymerization, Hydro treatment, and Sulfur production, Modern Petroleum Technology, Vol, I and II, 5th Edn, John Wiley and Sons., New York, 1986 (10) Wang, D.; Qian, E. W.; Amano, H.; Okata, K.; Ishihara, A.; Kabe, T. Oxidative desulfurization of fuel oil. Part I. Oxidation of dibenzothiophenes using tert-butyl hydroperoxide. Appl. Catal. A 2003, 253 (1), 91-99. (11) Foroulis, Z. A. Corrosion and corrosion inhibition in the petroleum industry. Mater. Corros. 1982, 33, 121-131. (12) High Temperature Crude Oil Corrosivity Studies; API Publication 943; American Petroleum Institute: Washington, DC, 1974. (13) Babich, I. V.; Moulijn, J. A. Science and technology of novel processes for deep desulfurization of oil refinery streams: a review. Fuel 2003, 82 (6), 607-631. (14) Girgis, M.J.; Gates, B.C. Reactivities, reaction networks, and kinetics in high-pressure catalytic hydroprocessing. Ind. Eng. Chem. Res. 1991, 30, 2021-2058. (15) Breysse, M.; Djega-Mariadassou, G.; Pessayre, S.; Geantet, C.; Vrinat, M.; Perot, G.; Lemaire, M. Deep desulfurization: reactions, catalysts and technological challenges. Catal. Today 2003, 84 (34), 129-138.


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(16) Ferreira, M. M. C.; Antunes, A. M.; Melgo, M. S.; Volpe, P. L.O. Quimiometria I: calibração multivariada, um tutorial. Quim. Nova 1999, 22, 724-731. (17) Dettman, H. D.; Li, N.; Wickramasinghe, D.; Luo, J. The influence of naphthenic acid and sulphur compound structure on global crude corrosivity under vacuum distillation conditions. Presented at NACE Northern Area Western Conference, Calgary, Alberta, Canada, 2010. (18) Slavcheva, E.; Shone, B.; Turnbull, A. Review of naphthenic acid corrosion in oil refining. Br. Corros. J. 1999, 34 (2), 125-131. (19) Fuhr, B.; Banjac, B.; Blackmore, T.; Rahimi, P. Applicability of Total Acid Number Analysis to Heavy Oil and Bitumens. Energy Fuels 2007, 21, 1322-1324. (20) Rocha, J.T.C.; Oliveira, L.M.S.L.; Dias, J. C.M.; Pinto, U. B.; Marques, M. dL. S.P.; Oliveira, B.P.; Filgueiras, P. R.; Castro, E.V.R.; Marcone, A. L.; Oliveira, M.A.L. Sulfur Determination in Brazilian Petroleum Fractions by Mid-infrared and Near-infrared Spectroscopy and Partial Least Squares Associated with Variable Selection Methods, Energy Fuels 2016, 30, 698-705. (21) Piehl, R. L. Correlation of Corrosion in a Crude Distillation Unit with Chemistry of the Crudes. Corrosion 1960, 16 (6), 305-307. (22) Standard Test ‘Method for Acid Number of Petroleum Products by Potentiometric Titration, ASTM D664-11a, ASTM International, 2017. (23) Corilo, Y. E.; Rowland, S. M.; Rodgers, R.P. Calculation of the Total Sulfur Content in Crude Oils by Positive-Ion Atmospheric Pressure Photoionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Energy Fuels 2016, 30 (5), 3962-3966. (24) Lobodin, V.V.; Robbins, W.K.; Lu, J.; Rodgers, R.P. Separation and Characterization of Reactive and Non-Reactive Sulfur in Petroleum and Its Fractions. Energy Fuels 2015, 29, 6177-6186.


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(25) Nielsen, K.E.; Dittmer, J.; Malmendal, A.; Nielsen, N. C. Quantitative analysis of constituents in heavy fuel oil by 1H Nuclear Magnetic Resonance (NMR) spectroscopy and multivariate data analysis. Energy Fuels 2008, 22, 4070−4076. (26) Kuptsov, A. K.; Arbuzova, T. V. A study of heavy oil fractions by Fourier-transform near-infrared Raman spectroscopy. Pet. Chem. 2011, 51, 203-211. (27) Speight, J. G. Handbook of Petroleum Product Analysis; Wiley- Interscience: New York, 2002. (28) Jameel, A.G.A.; Naser, N.; Emwas, A.; Dooley, S.; Sarathy, M. Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression, Energy Fuels 2016 30 (11), 9819-9835. (29) Terra, L. A.; Filgueiras, P. R.; Tose, L. V.; Romão, W.; De Souza, D. D.; de Castro, E. V. R.; de Oliveira, L. M.L.; Dias, J. C. M.; Poppi, R.J. Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils. Analyst 2014, 139, 4908-4916. (30) Rowland, S. J.; Scarlett, A. G.; Jones, D.; West, C. E.; Frank, R. A. Diamonds in the Rough: Identification of Individual Naphthenic Acids in Oil Sands Process Water. Environ. Sci. Technol. 2011, 45, 3154-3159. (31) Holowenko, F. M.; MacKinnon, M. D.; Fedorak, P. M. Characterization of Naphthenic Acids in Oil Sands Waste waters by Gas Chromatography-Mass Spectrometry. Water Res. 2002, 36, 2843-2855. (32) Hsu, C. S.; Dechert, G. J.; Robbins, W. K.; Fukuda, E. K. Naphthenic acids in crude oils characterized by mass spectrometry. Energy Fuels 2000, 14, 217-23. (33) Mohamed, M. H.; Wilson, L. D.; Headley, J. V.; Peru, K. M. Screening of Oil Sands Naphthenic

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Fluorescence

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Spectrophotometry. J. Environ. Sci. Health A Tox. Hazard Subst. Environ. Eng. 2008, 43, 1700-1705. (34) Zhao, B.; Currie, R.; Mian, H. Catalogue of Analytical Methods for Naphthenic Acids Related to Oil Sands Operations, Oil Sands Research and Information Network, University of Alberta, School of Energy and the Environment, Edmonton, Alberta, 2012, OSRIN Report No. TR-21. (35) Barrow, M. P.; Headley, J. V.; Peru, K. M.; Derrick, P. J. Data Visualization for the Characterization of Naphthenic Acids within Petroleum Samples. Energy Fuels 2009, 23(5), 2592-2599. (36) Grewer, D. M.; Young, R. F.; Whittal, R. M.; Fedorak, P. M. Naphthenic Acids and Other Acid-Extractables in Water Samples from Alberta: What is Being Measured? Sci. Total Environ. 2010, 408, 5997-6010. (37) Mapolelo, M. M.; Rodgers, R. P.; Blakney, G. T.; Yen, A. T.; Asomaning, S.; Marshall, A. G. Characterization of Naphthenic Acids in Crude Oils and Naphthenates by Electrospray Ionization FT-ICR Mass Spectrometry. Int. J. Mass Spectrom. 2011, 300, 149-157. (38) Smith, D. F.; Schaub, T. M.; Kim, S.; Rodgers, R. P.; Rahimi, P.; Teclemariam, A.; Marshall, A.G. Characterization of Acidic Species in Athabasca Bitumen and Bitumen Heavy Vacuum Gas Oil by Negative- Ion ESI FT-ICR MS with and without Acid-Ion Exchange Resin Prefractionation. Energy Fuels, 2008, 22, 2372-2378. (39) Jivraj, M. N.; MacKinnon, M.; Fung, B. Naphthenic Acids Extraction and Quantitative Analyses with FT-IR Spectroscopy. Syncrude Analytical Methods Manual. 4th ed.; Syncrude Canada Ltd., Research Department: Edmonton, Alberta, 1995.


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(40) Jones, D. M.; Watson, J. S.; Meredith, W.; Chen, M.; Bennett, B. Determination of Naphthenic Acids in Crude Oils Using Non aqueous Ion Exchange Solid-Phase Extraction. Anal. Chem. 2001, 73, 703-707. (41) Scott, A. C.; Young, R. R.; Fedorak, P. M. Comparison of GC-MS and FTIR methods for Quantifying Naphthenic Acids in Water Samples. Chemosphere 2008, 73, 12581264. (42) Jingyan, L.; Xiaoli, L.; Songbai, T. Research on Determination of Total Acid Number of Petroleum Using Mid-infrared Attenuated Total Reflection Spectroscopy. Energy Fuels 2012, 26, 5633-5637. (43) Coates, J. Interpretation of Infrared Spectra, A Practical Approach. In Encyclopedia of Analytical Chemistry; John Wiley & Sons Ltd.: New York, 2000; pp 10815-10837. (44) Merlin, M.; Guigard, S. E.; Fedorak, P. M. Detecting Naphthenic Acids in Waters by Gas Chromatography-Mass Spectrometry. J. Chromatogr. A. 2007, 1140, 225-229. (45) St. John, W. P.; Rughani, J.; Green, S. A.; McGinnis, G. D. Analysis and Characterization of Naphthenic Acids by Gas Chromatography-Electron Impact Mass Spectrometry of tert.- Butyldimethylsilyl Derivatives. J. Chromatogr. A. 1998, 807, 241251. (46) Young, R. F.; Coy, D. L.; Fedorak, P. M. Evaluating MTBSTFA Derivatization Reagents for Measuring Naphthenic Acids by Gas Chromatography-Mass Spectrometry. Anal. Methods 2010, 2, 765-770. (47) Wang, X.; Kasperski, K. L. Analysis of Naphthenic Acids in Aqueous Solution Using HPLC-MS/MS. Anal. Methods 2010, 2, 1715−1722. (48) Yen, T.W.; Marsh, W. P.; MacKinnon, M. D.; Fedorak, P. M. Measuring Naphthenic Acids Concentrations in Aqueous Environmental Samples by Liquid Chromatography. J. Chromatogr. A 2004, 1033, 83-90.


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(49) Smith, B. E.; Rowland, S. J. A Derivatisation and Liquid Chromatography/Electrospary Ionisation Multistage Mass Spectrometry Method for the Characterisation of Naphthenic Acids. Rapid Commun. Mass Spectrom. 2008, 22, 3909-3927. (50) Soares, I. P.; Rezende, T. F.; Fortes, I. C. P. Determination of sulphur in diesel using ATR/FTIR and multivariate calibration. Ecletica Quim. 2010, 35 (2), 71-78. (51) Kuptsov, H.; Zhizhin, G. N. Handbook of Fourier Transform Raman and Infrared Spectra of Polymers, Elsevier, Amsterdam, 1998. (52) Khanmohammadi, M.; Garmarudi, A. B.; de la Guardia, M. Characterization of petroleum-based products by infrared spectroscopy and chemometrics. TrAC, Trends. Anal. Chem. 2012, 35, 135-149. (53) Sekulic, S.; Seasholtz, M. B.; Wang, Z.; Kowalski, B. R.; Lee, S. E.; Holt, B. R. Nonlinear multivariate calibration methods in analytical chemistry. Anal. Chem. 1993, 65 (19), 835A-845A. (54) Hannisdal, A.; Hemmingsen, P. V.; Sjöblom, J. Group-type analysis of heavy crude oils using vibration spectroscopy in combinations with multivariate analysis. Ind. Eng. Chem. Res. 2005, 44, 1349-57. (55) Brereton, R. G.; Introduction to multivariate calibration in analytical chemistry. Analyst 2000, 125, 2125-2154. (56) Brereton, R. G.; Chemometrics: Data Analysis for the Laboratory and Chemical Plant; John Wiley & Sons, New York, 2003. (57) Aburto, P.; Zuñiga, K.; Campos-Terán, J.; Aburto, J.; Torres, E. Quantitative analysis of sulfur in diesel by enzymatic oxidation, steady state fluorescence, and linear regression analysis. Energy Fuels 2014, 28, 403-408. (58) Soares, I. P.; Rezende, T. F.; Silva, R. C.; Castro, E. V. R.; Fortes, I. C. P. Multivariate Calibration by Variable Selection for Blends of Raw Soybean Oil/Biodiesel from


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Different Sources Using Fourier Transform Infrared Spectroscopy (FTIR) Spectra Data. Energy Fuels 2008, 22, 2079-2083. (59) Geladi, P.; Kowalski, B. R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 1986, 185, 1-17. (60) Terra, L. A.; Filgueiras, P. R.; Paulo, R.; Pereira, Rosana C. L.; Gomes, A. O.; Vasconcelos, G. A.; Tose, L.V.; Castro, E. V. R.; Vaz, B. G.; Romão, W.; Poppi, R.J. Prediction of Total Acid Number in Distillation Cuts of Crude Oil by ESI(-) FT-ICR MS Coupled with Chemometric Tools. J. Braz. Chem. Soc, 2017, 28(9), 1822-1829 (61) Terra, L. A.; Filgueiras, P. R.; Tose, L. V.; Romão, W.; De Souza, D. D.; de Castro, E. V. R.; de Oliveira, L. M.L.; Dias, J. C. M.; Poppi, R.J. Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils. Analyst 2014, 139, 4908-4916. (62) Leardi, R. Application of genetic algorithm−PLS for feature selection in spectral data sets. J. Chemom. 2000, 14, 643-655. (63) Savitzky, A.; Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36 (8), 1627-1639. (64) Zhang, Z.M.; Chen, S.; Liang, Y.-Z. Baseline Correction using adaptive iteratively reweighted penalized least squares. Analyst 2010, 135, 1138-1146. (65) Olivieri, A. C.; Faber, N. K. M.; Ferré, J.; Boqué, R.; Kalivas, J. H.; Mark, H. Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report). Pure. Appl. Chem. 2006, 78 (3), 633-661. (66) Filgueiras, P. R.; Alves, J. C. L.; Sad, C. M. S.; Castro, E. V. R.; Dias, J. C. M.; Poppi, R. J. Evaluation of trends in residuals of multivariate calibration models by permutation test. Chemom. Intell. Lab. Syst. 2014, 133, 33-41.


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(67) Standard Test Method for Sulfur in Petroleum and Petroleum Products by Energy Dispersive X-ray Fluorescence Spectrometry, ASTM D4294-10, ASTM International, 2010. (68) Standard test method for determination of total sulfur in light hydrocarbons, spark ignition engine fuel, diesel engine fuel and engine oil by ultraviolet fluorescence. ASTM D5453-12, ASTM International, 2012. (69) Chakravarthy, R.; Naik, G. N.; Savalia, A.; Kedia, J.; Saravanan, C.; Das, A. K.; Sreedharan, U.; Gudasi, K. B. Simultaneous Determination of Boiling Range Distribution of Hydrocarbon, Sulfur, and Nitrogen in Petroleum Crude Oil by Gas Chromatography with Flame Ionization and Chemiluminescence Detections, Energy Fuels 2017, 31, 3101-3110. (70) Chakravarthy, R.; Savalia, A.; Kulkarni, S.; Naik, G. N.; Sridharan, U; Saravanan, C.; Das, A. K.; Gudasi, K. B. Simultaneous Determination of Hydrocarbon, Nitrogen, Sulfur, and Their Boiling Range Distribution in Vacuum Gas Oil Using a High Temperature CNS-SimDis Analyzer, Energy Fuels 2016, 30, 4274-4282.


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Figure Captions: Fig. 1. Typical FTIR spectra of vacuum gas oil samples of different composition Fig. 2. Selected regions a) 3300 to 2400 cm-1 and b) 1800 to 650 cm-1 of FTIR spectra of vacuum gas oil samples of different composition shows the differences in absorption at various regions Fig. 3. TCAN values obtained using the measured value versus predicted values for the PLS model using spectral region of 1800 to 1650 cm-1. Fig. 4. Error plot of the actual versus measured values from the calibration model for naphthenic acid measurement of vacuum gas oil of different types Fig. 5. PRESS Plot depicts the RMSEC values of calibration results with the best factors for TCAN analysis


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Fig. 6. Total sulfur content obtained using the measured value versus predicted values for the PLS model using spectral regions of 1900 to 620 cm-1 and 3300 to 2400 cm-1. Fig. 7. Error plot of the actual versus measured values from the calibration model for total sulfur content of vacuum gas oil of different types Fig. 8. PRESS Plot depicts the calibration results with the best factors for total sulfur analysis

Fig. 1.


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(a)

(b) Fig. 2.


Page 26 of 34

Calculated

NAN, mg KOH/ g RMSEC: 0.0903 Corr. Coeff.: 0.9991 RMSEP: 0.0885 Corr. Coeff.: 0.9846 5 factors used

Calibration Validation Correction Cross-correction Ignore

-0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

14

Energy & Fuels

-0

Actual

Fig. 3.


14

Difference -0.20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.19

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-0

Actual

Fig. 4.


14

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Fig. 5.


Page 28 of 34

Calculated

TS, % by Mass RMSEC: 0.0829 Corr. Coeff.: 0.9974 RMSEP: 0.107 Corr. Coeff.: 0.9968 8 factors used

Calibration Validation Correction Cross-correction Ignore

-0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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5

Page 29 of 34

-0

Actual

Fig. 6.


5

Difference

-0.21

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.17

Energy & Fuels

-0

Actual

Fig. 7.


5

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Fig. 8.


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Table 1. Summary of Naphthenic acid number and total sulfur analysis of VGO samples by ATR-FTIR in association with PLS model. Sl. No.

Parameters

TCAN, mg KOH / g

total sulfur, % by mass

1

wavelength range (cm-1)

1650 to1780

620 to 1900 and 2400 to 3300

2

R2 calibration

0.9991

0.9974

3

RMSEC

0.0903

0.0829

4

R2 prediction

0.9846

0.9968

5

RMSEP

0.0885

0.107

6

Factors used

5

8


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Energy & Fuels

Table 2. Comparison of total sulfur and TCAN value of actual measurement and model predicted values for VGO samples TCAN, mg KOH/g Sample ID

Measured

Predicted

value

value

1

0.739

2

Total Sulfur, % by mass Measured

Predicted

Difference

value

value

Difference

0.929

0.190

1.412

1.486

0.073

3.389

3.290

0.099

3.469

3.470

0.001

3

0.860

0.695

0.165

2.753

2.859

0.105

4

0.980

0.986

0.006

2.492

2.509

0.017

5

1.140

1.127

0.013

1.894

2.002

0.108

6

0.990

1.061

0.071

1.968

2.063

0.095

7

1.100

1.183

0.083

1.910

2.064

0.154

8

1.020

1.038

0.018

1.882

2.090

0.208

9

1.030

0.928

0.102

2.220

2.260

0.040

10

0.920

0.706

0.214

2.691

2.821

0.131


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Table 3. ATR-FTIR spectral repeatability and model prediction study for total sulfur and TCAN value of VGO sample (n=5). TCAN, mg KOH/g Sl. No

measured predicted

Total sulfur, % by mass measured predicted

difference value

value

difference value

value

1

1.127

0.013

2.002

0.108

2

1.145

0.005

1.842

0.052

1.085

0.055

1.948

0.054

4

1.099

0.041

1.893

0.001

5

1.145

0.005

1.775

0.119

3

1.14

1.8942

Average

1.120

1.892

SD

0.027

0.089

RSD, %

2.410

4.679

.


Sulfur and Total Carboxylic Acid Number ... - ACS Publications

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