Dynamic Approach for the Estimation of Olefins in Cracked Fuel

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Fossil Fuels

A Dynamic Approach for the Estimation of Olefins in Cracked Fuel Range Products of Variable Nature and Composition by 1H-NMR Spectroscopy: Part-III Sujit Mondal, KALICHARAN CHATTOPADHYAY, BHAWANA SRIVASTAV, Kirti Garg, Ravindra Kumar, Anju Chopra, Jayaraj Christopher, and Gurpreet Singh Kapur Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b04392 • Publication Date (Web): 29 Jan 2019 Downloaded from http://pubs.acs.org on February 4, 2019

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A Dynamic Approach for the Estimation of Olefins in Cracked Fuel Range Products of Variable Nature and Composition by 1H-NMR Spectroscopy: Part-III Sujit Mondal*1, Kalicharan Chattopadhayay1, Bhawana Srivastav2, Kirti Garg1, Ravindra Kumar1, Anju Chopra1, J. Christopher1 & Gurpreet S. Kapur1 1Indian

Oil Corporation Limited, R&D Center, Sector-13, Faridabad-121007, India

2Indian

Oil Corporation Limited, Panipat Refinery QC Laboratory, Panipat-132140, India

*Corresponding author: [email protected]

TOC

Two required parameters for olefin estimation- (i) average absolute number of unsaturated hydrogen (H) in the olefinic region and (ii) the average alkyl chain length (n) of the olefins were estimated by 1H NMR and SIMDist respectively. The total protons in an average olefins would then be 2n leading to percentage of oelfinic protons (%UH) as H/2n*100. The olefin multiplication factor (fo) was then obtained (100/UH%) by which the weight percentage of olefin is estimated using a normalized 1H NMR spectrum.

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Abstract A dynamic 1H NMR based method for the estimation of olefin content in all cracked fuel range products, in general and in gasoline/naphtha streams in particular, irrespective of types and composition of olefins and boiling range of samples has been developed. This is in continuation of our earlier works where two methods were described for the determination of hydrocarbon types in straight run gasoline (no olefins) and cracked full range gasoline/naphtha (with olefins). The average absolute number of unsaturated hydrogen (H) in the olefinic region (4.4-6.5 ppm) was directly estimated with the help of a 1H NMR spectrum using dynamic variables in terms of differential population of various kinds of olefins. The average alkyl chain length (n) was estimated by various methods including

13C

NMR, carbon number distribution by GC based detailed

hydrocarbon analyzer [DHA, ASTM D6730-01(2016)] and by simulated distillation [ASTM D2887-16a] data. The percentage of unsaturated hydrogen (%UH) in an average olefin was then obtained providing a multiplication factor (fo) by which the weight percentage of olefin is estimated using a normalized 1H NMR spectrum. The dynamic estimation of H and n for each sample removes the possibilities of errors in the estimation. The method has efficiently been extended to coker kero and coker diesel range products where there has been no method available for olefin estimation. The method was validated by the using DHA following ASTM D6730, by reformulyzer based ASTM D6839 method and finally by fluorescent indicator adsorption (FIA) following ASTM D1319. All the methods were compared. Whereas the proposed NMR method is extremely general, free from manual error, the limitations of existing ASTM methods and old NMR method vis. á vis. of new NMR method are also discussed. Key Words: Quantitative 1H NMR, olefins, cracked fuel range products, variable composition. 1. Introduction Estimation of olefin in cracked fuel range refinery streams like gasoline/naphtha, middle distillates like coker kero (CK), cracked diesel (CD) etc. is extremely important in monitoring various processes in refineries, in general and in pilot plants associated with refining technology research, in particular. The feeds of these processes can be of


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different types and of different origins viz. from fluidized catalytic cracking (FCC) units or from thermal cracked (broadly coker, visbreaker etc.) origins containing different types and varied composition of olefins;1 can be of variable boiling range as well.2 For example, in order to selectively remove diolefins, a mild hydro treatment of light coker naphtha (LCN) or light FCC naphtha (LN) with significant amount of diolefins is done as a prior step before desulfurization and eventually putting them into gasoline pool. The process warrants selective reduction of diolefins and overall retention of total olefins. In another case keeping the olefin percentage intact is one of the prime criteria for a hydrodesulphurization process on heavy naphtha (HN) based on adsorption catalysis in which the resultant product is to be used as high octane blender in gasoline pool. In these processes or in similar kind of other processes estimation of olefins is desirable as overall retention of olefins in the products is required to meet high octane potential for their future uses.3 The determination of alkenes in gasoline is also important because high olefin content can plug injection nozzles and valves.4 Estimation of olefins in cracked kero and diesel range products are also of immense importance, especially in process design for their valorization; however there is no method available for doing so. So monitoring the olefin content in feeds and corresponding products are essential during the optimization of process parameters for various refinery processes. In our earlier work on analyses of gasoline/naphtha by Nuclear Magnetic Resonance (NMR) spectroscopy we had discussed estimation of olefin content5 alongside group molecular weight (GMWt)5-8 based method for paraffin, naphthene and aromatic (PNA) estimation in straight run gasoline (Part-I)6 as well as in cracked gasoline (Part-II).5 The olefin estimation method in Part-II5 considers coker and FCC gasoline as distinct entities; former being predominantly populated with alpha olefins and later internal olefins. Accordingly static values for average absolute number of unsaturated hydrogen (H) as 2.2 for coker gasoline and 1.8 for FCC gasoline were broadly used. Moreover the method uses an average alkyl chain length (n) of 8 (static value) for a full range gasoline [boiling range IBP (initial boiling point) to 180 oC] samples and finally end up with fixed olefin multiplication factor (fo) 7.4 for coker range gasoline and 9.0 for FCC gasoline. This method lacks generality for samples of variable boiling range and composition in terms of presences of uneven amount of alpha- and internal/central-


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olefins, or substituted and cyclic olefins for that matter. While the nature and composition of olefins do not mimic the sample type considered during the development of the particular method would provide wrong H value, thus give erroneous results. Similarly n varies drastically varying sample types; from narrow boiling LN/LCN to HN. For instance using the static value for H accurate estimation of total olefin is not possible for a LCN feed overwhelmingly populated with linear alpha olefins and the corresponding products the olefins of which are completely different in nature as alpha olefins get isomerized to internal olefins, however incomplete, during mild hydrocatalytic condition (Figure 1). To visualize the basic principle of olefin estimation let us take a representative example as depicted in Figure 1. In the left hand side, LCN feed has 12 olefinic hydrogens (representative) and right hand side, the corresponding product has 8 olefinic hydrogens (representative). The olefin contents for these two samples would apparently be proportional to the integral values corresponding to 12 hydrogens (for feed) and 8 hydrogens (for products) and thus would be more for feed and less for product (R-groups remain same, Figure 1). However there is no change in olefin content in reality during this mere isomerization. So, towards the estimation of H, in order to accommodate larger contribution in the olefinic proton-signals from terminal olefins for feed and then of less contribution from internal olefins in the product an equation representing mol fractions of various kinds of olefinic protons present in different samples (e.g., in feed vis á vis in corresponding product) is proposed (Eq. 1, vide infra). The static n value is also unwarranted when the naphtha samples are of variable boiling ranges, viz. IBP-70, IBP-100, IBP-140, IBP-200, 60-90, 90-140, 70-220 etc. or of cracked kero or diesel range middle distillates. (put Figure 1) Gas chromatography (GC) [ASTM D5443-14], mass spectrometry (MS) [ASTM D2789] and gas chromatography-infrared (GC-FTIR) [ASTM D5986] methods are being routinely used for PNA type analyses of gasoline range products. There have been a significant number of studies, particularly on multi-column GC for complete hydrocarbon type (PIONA) analysis of light petroleum fractions reported in literature.9 Commercial GC instruments are available based on ASTM D6730-01(2016) where the olefin content in mass percentage can be estimated by way of detailed hydrocarbon analysis (DHA)


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upto 30% olefin. The olefin content upto 55 vol percentage can be estimated using elution-chromatography based fluorescent indicator adsorption (FIA) following ASTM D1319-15. The latest of the series of ASTM methods is reformulyzer (column switching) based ASTM D6839 where the PIONA can be estimated by both volume and mass percentages. Supercritical fluid chromatography (SFC), ASTM D6550-15 has long been used for the estimation of olefin content in gasoline upto 25%. SFC with flame-ionization detector (FID) was reported to be used for PONA estimation in gasoline and jet fuels.4 Estimation of olefins in ultra light gasoline streams (30-60 & 30-105 oC) by a modified GC method has been reported by Punetha et al.10 NMR spectroscopy was also explored by several other groups to analyze gasoline range samples. Burri et al. described11 the compositional analysis of commercial gasoline samples using 1H NMR spectroscopy. Sun et al.12 recently used 1H NMR in characterizing gasoline properties obtained from different stages of refinery processes. Petroleomics has enormously been enriched by intelligent application of NMR13-15 and Mass spectrometry techniques,16,17

notably

groups lead by Abdul Jameel and Sarathy13,14 , by Poveda et al.15 and by Marshall.16,17 Prediction of fuel ignition quality by NMR and multiple linear regression13 as well as prediction of octane number by NMR and artificial neural networks14 were reported by AGA Jameel et al. Application of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry was extensively used by Marshall’s group towards development of petroleomics.16,17 However most of above techniques leading to estimation of olefin content lack generality, need essential dilution for higher aromatic and/or higher olefin contents, fail to identify higher olefins (C8 or C10 onwards) and cyclic substituted olefins presence in certain HN, also fail to analyze raw high sulfur naphtha samples. Moreover most of these methods are generally prone towards errors; both operational and manual. Some of the above mentioned methods have also been found to suffer from poor precision and reproducibility besides being time consuming.18,19 As per our continuous effort to develop intelligent NMR methods5-8,20-23 towards petroleum analysis, herein we report a 1H NMR based dynamic, accurate and general method for the estimation of olefin content in all fuel range products ranging from LN/LCN to HN to cracked kero and diesel. The method does not depend on origin of


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samples, whether coker or FCC, whether rich in alpha, internal, substituted or cyclic olefins. The method would also be independent of the boiling range of the samples. The new method eliminates the limitations of our previous method5, which considered coker and FCC gasoline as distinct entities with static multiplication factors. The newly developed method estimates dynamic fo with the help of dynamic H estimated directly from 1H NMR and dynamic n, realized using various methods including

13C

NMR,

carbon number distribution (CND) by GC based DHA [ASTM D6730-01(2016)] and by simulated boiling range distribution (SIMDist) data as per ASTM D2887-16a. Specific and characteristic values for H and n for each sample eliminates the error quotient associated with static H and n. The method was effectively extended to estimate olefins in CK and CD range products. The olefin thus estimated for various samples were correlated with the data obtained from by our old NMR method,5 DHA¥ [ASTM D6730], by reformulyzer [ASTM D6839-17] and finally by FIA [ASTM D1319] methods. All the methods were compared. Whereas the proposed NMR method is extremely general, free from manual and operational errors, the limitations of existing methods vis á vis of new NMR method are also discussed. 2. Experimental Methods 2.1. Samples Coker and FCC gasoline/naphtha samples with variable nature, composition and boiling range (IBP-240 oC), cracked kero and diesel range samples were received either from the refining technology division of Indian Oil Corporation Limited (IOCL) R&D centre or from different IOCL refineries. The feeds as well as corresponding products from various underdevelopment refining processes have been incorporated in the samples in order to cover all spectrums of gasoline/naphtha streams and middle distillates. 2.2. NMR Method 1H

NMR: All proton NMR spectra were recorded on a Jeol JNM-ECA500 NMR

spectrometer operating at the proton frequency of 500 MHz, spectral width 7512 Hz (2.5-12.5 ppm), 90° pulse = 10.7µs, relaxation delay (d1) = 10s, digital resolution 0.57 Hz/point. 16 repetitions were averaged with 32K data point and 3.15 min experimental time in ECA machine. It has been observed d1 = 10s was enough to get complete


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relaxation of various protons and no of scan 16 was sufficient to get repeatable, high signal to noise ratio (>150:1) in ~10% (v/v) solution in CDCl3 containing 0.03 (v/v) TMS. 13C

NMR.

13C

NMR spectra were acquired on the same instrument equipped with 5 mm

broadband observe probe operating at a spectrometer frequency of 125.7 MHz for

13C,

acquisition time = 0.84 s, relaxation delay = 15 s, 90° pulse = 9.0 μs at 25 dB, line broadening = 2 Hz, spectral width = 31250 Hz (−10 ppm to 225 ppm), and ∼1500-1600 scans. The concentration of the sample used was 1.0−1.1 g in 2.0−2.5 mL CDCl3. Quantitative

13C

appearance was ensured by adding the relaxation agent chromium

triacetylacetonate [Cr(acac)3, 10 mg/mL] under inverse gated conditions. All the 1H and

13C

NMR spectra were integrated after baseline correction, and a mean

of minimum three integral values has been taken for each calculation. 2.3. Gas Chromatography. DHA (Modified¥ ASTM D6730-16). The analysis of the sample was carried as per ASTM D6730-01(2016). A capillary column of 100 m, 0.25 mm ID, coated with 0.5 µm of 100% dimethyl polysiloxane was used as per the ASTM method. DHA characterization of these streams is based on the Kovats Index, and differentiates the composition into five groups in term of PIANO (parraffins, isoparraffins, Aromatics, Naphthenes and Olefins). SIMDist (ASTM D2887-16a): Simulated distillation (SIMDist) analysis of various samples was carried out by ASTM D2887 configured with flame ionization detector, PTV inlet and a 10 m X 530 µm X 2 µm DB-1 capillary column. A qualiitative mixture of normal paraffins C5 to C44 was used to determine the BP vs. retention time. The analysis includes blank analysis and validation as well using reference gas no 1. Reformulyzer ASTM D6839-17. All the samples ranging from LCN/CN to HN were analyzed using ASTM D6839 method, the latest method for PIONA type estimation in gasoline range sample. The method gives the olefin values in both wt% and volume %. 2.4. FIA ASTM D1319-15. All the samples ranging from LCN/CN to HN were analyzed using standard ASTM D1319 method, the most accepted method for olefin estimation in gasoline range sample since long. The method gives the olefin values in volume %.


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3. Results & Discussions 3.1. Principle of Estimation of Olefin by 1H NMR: Principle for the estimation of olefin in a given sample is based on first, calculating fo and secondly, multiply fo with the total integral value of olefinic signals (Io) resonating in the chemical shift region 4.4-6.5 ppm in the same 1H NMR spectrum in which the total integral values (IT, 0.4-8.0 ppm) has been normalized to 100. Proton NMR signals appear in the region 0.4-8.0 ppm for most of the samples considered in the estimation. In order to calculate fo, one need to estimate the H using the 1H NMR spectrum followed by percentage of unsaturated hydrogen (%UH) in an average olefin. Estimation of %UH also requires realization of n. 3.1.1. Estimation of H: Classification and Assignment of Various Olefinic Protons in a 1H NMR Spectrum. 500 MHz quantitative 1H NMR spectra for a representative LCN (A) and its mild hydro-treated products (B and C) have been shown in Figure 2. The expanded olefinic region (4.4-6.5 ppm) for each spectrum has also been shown as onsets a, b and c respectively. If we closely look at the olefinic region of the LCN spectrum (a, Figure 2) the resonating signals in this region represent various kinds of olefins appearing at distinctly different zones O1-O6. The fragmentation of olefinic signals in six different zones culminated with the fact that the gasoline samples generally contain all types of olefin tabulated in Table 1. The assignment of various kind of signals appearing for these broadly six different sets of olefins were done by literature study,5,11 background knowledge of isomerization (vide infra) and finally by comparison with Aldrich NMR library. The uncertainty in the above assignments due the overlapping of different category of signals may be insignificant towards the estimation of H. Signals due to two of the possible 4/5 protons of the conjugated double bond generally appear in the region δ5.9-6.5 ppm (O6) and the rest spread over the regions 5.0-4.5 ppm overlapped with other olefinic protons.24 As most of the samples contain low (feeds) or insignificant (products) amounts of diolefins only integral intensities between 5.9 to 6.5 ppm (O6) has been considered for the estimation of H. Signals due to two (=CH2) of the possible three protons of linear alpha olefins show-up in the region δ4.80-5.05 ppm (O2) and the other methine (–CH=) proton appears at δ5.73-5.90 ppm (O5). Structures, designations and chemical shift assignments for the signals due to various kinds of olefinic protons have been shown in Table 1 and Figure 2. It is worth mentioning that


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the intensity of diolefinic 1H signals in the region δ5.9-6.5 ppm (0.23, LCN1, A) got completely depleted (0.078 and 0.038 products B & C respectively) during controlled hydrogenation. The H was then directly estimated using Eq. (1). Genesis of the Eq. 1 is simply an elegant representation of relative mol fractions of each type of olefinic protons, thus providing the H for whole of a sample. To this end, six zones O1 to O6 have been designated with corresponding integral intensities also as O1-O6 respectively. Instead of deducing relative mol fractions separately for separate zones a smart approach to deduce the H was adopted while the summation of Ois-multiplied by the number of olefinic protons in the backbone structure was divided by total olefinic intensities (IO). Deduction of H for various samples with maximum possible variation has been shown in Table 2. It is interesting to mention here that H has significant variations ranging from 1.5 (sample with minimum alpha olefins) to 2.6 (with maximum linear alpha olefins). H = (O6*4+O5*3+O4*2 +O3*1+O2*3+O1*2)/IO - - - - - - (1) The necessity of deducing H for each sample could well be understood by looking at the olefinic region of the 1H NMR spectra of feed LCN (a, Fig 2) and the corresponding region in the product-1 (b, Figure 2) and product-2 (c, Figure 2) (also discussed earlier). The Io for the former is 8.70 whereas those of the products are 6.95 and 6.10 respectively. The reduction of intensities for olefinic protons, here, not only due to reduction in the amount of olefins, rather associated with the isomerization of alpha olefins to internal olefins as shown in Figure 1 (vide supra) and evident by reduction in relative signal intensities in the regions O1, O2 and O5, whereas concomitant increments in the regions O3 and O4 during product formation (Figure 2). The change in the nature of olefins without actually changing the percentage can only be accommodated by deducing H for each sample with the help of differential contribution in O1-O6 regions for different samples. 3.1.2. Estimation of n: The n for a given sample can in principle be estimated by three different approaches-(1) by quantitative 13C NMR, (2) by CND form DHA or reformulyzer and (3) by CND from SIMDist data. However, due to severe overlap estimation of n by 13C

NMR is prone towards error, especially when the samples are of higher boiling

range (heavy naphtha, diesel etc.). It was also noticed that presence of aromatics in


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larger amount jeopardizes the n-estimation by acquisition of

13C

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13C

NMR. Moreover quantitative

NMR for this complex mixture is time consuming, generally takes 10-

12 hours of acquisition. DHA (ASTM D6730) and reformulyzer (ASTM D6839-17) providing CND in mass percentage are used to calculate the average alkyl chain length. However it has been observed that DHA sometime gives erroneous results for samples with high percentage olefins, heavier cyclic olefins and aromatics. In one hand the accuracy of DHA depends on the accurate response factor of different components in FID on the other hand it is also time consuming. For rapid estimation of n an alternative method using the SIMDist [ASTM D2887-16a] data is proposed. The method exploits the distribution of boiling points of various kinds of alkanes, alkenes and iso-alkanes present in a sample and assign them into different carbon numbers (Figure 3). The corresponding weight percentage (Xs, Figure 3) obtained from the SIMDist data was then used to derive the n using Eq (2). Estimation of n by various methods for different kind of samples has also been shown in Table 2. n = (4X4+5X5+6X6+7X7+8X8+9X9+10X10)/100 - - - - - - (2) It is worth mentioning that the n, in real meaning, represents the average alkyl chain length for the olefin only frication of a sample. Presence of higher amount of aromatics and/or significant uneven distribution of olefins over the boiling point range may incur errors in olefin estimation. However it has been observed that in most of the naphtha samples the n estimated by

13C

NMR or by SIMDist data (for whole sample) were

somewhat close to the average olefin only chain length obtained by DHA or by Reformulyzer data. The n for olefin only frication generally gave lower (0.2-0.5) values than estimated for whole sample using SIMDist data. For CK and CD samples the H can be easily estimated following the same principle as in case of naphtha. However, estimation of n remains tricky as only SIMDist methods is the only options left. Equation 2 has also been used to deduce n for CK and CD samples using SIMDist data (see SI). Estimation of n by

13C

NMR, application of CND from DHA/reformulyzer and SIMDist

data towards n estimation has been discussed in the supporting information (SI). 3.1.3. Estimation of Olefin Content in Any Fuel Range Sample: As H and n have been realized the wt. percentage olefin can be easily estimated as follows-


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%UH = H/2n*100; 2n = Tolal no. protons in an average olefin molecule - - - (3) %UH corresponds to 100% olefins. Thus wt. % olefins (with Io% of olefinic protons) = fo*Io; IT =100 - - - - - - (4); where fo = 100/UH% - - - - - - (5) Let us now estimate weight percentage of olefins for one representative sample (LCN3). For LCN3, putting O1-O6 values (Figure 2A) in Eq. 1 the H has been estimated to be 2.47 (Row 3, Table 2). The average n estimated by using

13C

NMR, DHA and Simdist

data (using Eq. 2) are found to be 5.83, 5.87 and 5.90 respectively. So, %UH = 2.47/(2*5.90)*100= 20.93 and thus fo = 100/20.93 = 4.78 (Table 2) So, wt. % of olefin= 4.78*8.70= 41.6 (Row 3, Table 3); (Io= 8.70, Figure 2A and Table 2) The olefin data, as obtained using different techniques, for 36 samples with maximum possible variations have been tabulated in Table 3. 3.2. Comparative Discussion/Limitations of Various Methods: It is evident by the olefin data for light naphtha samples (LCN & LN, Table 3) that all the methods except the old-NMR gave comparable results. Olefin data, in wt%, by various methods have been compared in the bar-diagram (Figure 4). It is worth mentioning that the modified DHA also gave comparable results with other methods including new-NMR in LCN/LN with even >45 wt% of olefins for these light samples. However, as DHA is suitable for the determination of olefins upto C7, few heavy naphtha samples for which the DHA data has been used were found to be having 2-5% unknowns in them. Vol% of olefins by ASTM-6839 and ASTM-1319 also shows good correlation. It is noteworthy that while applying the old-NMR method, application of coker factor for LCN3 (feed) and LCN4 (pdt1) and FCC factor for LN1 (pdt2) would have been appropriate considering the nature of olefins (Figure 2) in these samples, however results in irregular olefin data; more olefin in pdt2 (LN1) than in pdt1 (LCN4). This apparent anomaly is absent in newNMR method where no such definition of coker and FCC are required. Coming to middle cut naphtha MCN and MN; they also behave almost similarly like LCN/LN providing mostly erroneous values by old-NMR method wherein the static factors do not consider the compositional variation in the samples.


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However heavier samples HN shows good correlation between old- and new-NMR methods. Here the static factors of old NMR method represents well the nature and compositional variation in the samples. These samples, on other hand, show poor correlation with DHA data. It is interesting to mention that as the sample gets heavier (LCN to MN to HN) the DHA data apparently gave erroneous olefin contents while compared to other methods (Fig 5). These samples (MN & HN) were found to be populated with substituted cyclic olefins in significant amount. It was envisioned that heavy substituted cyclic olefins present in MN and HN might have been eluted with naphthenes (or as unknowns) giving less olefin values for them. It is worth noted that DHA can accurately and separately recognize the low molecular weight cyclic olefin present in LCN/LN. The amount of cyclic olefin as obtained from reformulyzer has also been given in the parenthesis of “Reformulyzer” column of Table 3. The FIA estimation gave good correlation, it generally over estimates olefin at lower concentration and fail to analyze raw colored samples. As FIA method uses open adsorption column, dye and other organic solvents, it is gradually becoming obsolete for a modern laboratory, besides being time consuming. Refromulizer ASTM D6839 method does require lengthy sample preparation in case of higher amount of olefins and aromatics. It requires dilution (generally by tetradecane), sometime by many folds, to achieve accurate and repeatable results. Present of high sulfur is also detrimental to the olefin-trap used in reformulyzer based ASTM D6839 method. None of these methods are suitable for the estimation of olefin in exceptionally light naphtha cuts, HN and also heavy cracked middle distillates frequently available from various pilot plants or refinery units. NMR method with minimum chance of manual error, minimal sample preparation has been found to be most dynamic, rapid and highly repeatable over a concentration range of 1 to 70% olefins in all fuel range products starting from lightest cut (C5-60/70) to narrow cuts (60-90, 90-120) to HN to CD. The olefin data for CK and CD may not be accurate enough considering the fact that they may contain significant amount of aromatics, however can well be used to monitor various developmental refinery processes using them as feeds. The developed NMR method also remains unaffected by presence of sulfur, oxygenates in the samples. The only drawback is that the method uses n of total sample instead of n of olefin only fraction and may lead to error if they vary significantly


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in a given sample. Nevertheless due to its rapidness, highly repeatable results, the newNMR method can be used to monitor various pilot plant studies that require qualitative and quantitative olefin estimation as one of the main parameters to be examined and controlled. 3.3. Validation As described above the developed NMR method has well been validated by independent olefin estimation using various methods including ASTM D6730 (modified DHA) (R2= 0.9617; 26 samples), ASTM D6839 (reformulyzer) (R2= 0.9938; 29 samples) and also by ASTM D1319 (FIA) (R2= 0.9853; 29 samples). The DHA shows relatively poor correlation, probably due the reasons described in the previous section. There is no direct method available to validate the olefin data obtained for CK and CD samples, however as the principle of estimation remains the same and so that the method is well comparable with other standard methods for naphtha range samples, indirectly validate them. The correlation coefficients derived from regression analysis of data sets obtained by NMR and those by other methods have been given in supporting documents. 3.4. Repeatability and Reproducibility: The accuracy of the NMR method is heavily dependent on the correct assignments of olefinic region in 1H NMR spectra and accurate estimation of n. The repeatability of the estimation of olefin has found to be excellent with RSD of repeatability ~0.20-0.50. The reproducibility of the developed proton NMR method has been established and found to be excellent (RSD ~0.4-1.0) when some of the samples were recorded by two different operators

following

same

experimental

condition,

sometime

in

two

different

spectrometers (Jeol ECA-500 and Agilent DD2) as well. 4. Conclusion A general, fast and highly repeatable method for the estimation of olefin for all fuel range products has been developed. The beauty of the present method is that unlike any other standard method it covers all products ranging from C5-60 cut to cracked heavy diesel range products. The method estimates variables- absolute number of unsaturated hydrogen (H) and average alkyl chain length (n) towards olefin content in a dynamic manner for each sample. While the dynamic estimation of H takes into account


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Page 14 of 27

all the possible variations in terms differential contribution of olefinic protons in the 1H NMR spectrum, the unique and original use of SIMDist data towards n estimation removes errors coming from static alkyl chain length. The NMR method is rapid, free from manual error, highly repeatable and best suitable for monitoring pilot plant reactions with high sample throughput. Corresponding Author *[email protected] The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Supporting Information: Estimation of n by various methods, correlation diagrams for olefin estimations by various methods and few representatives 1H NMR spectra and deduction of the corresponding H-values. Abbreviations/Acronyms

Used:

DHA,

Detailed

Hydrocarbon

Analyzer;

FIA,

Fluorescent Indicator Adsorption; FCC, Fluidized Catalytic Cracking; CK, Coker Kero; CD, Cracked Diesel; LCN, Light Coker Naphtha; LN, Light FCC Naphtha; MN, Medium FCC Naphtha; HCN, Medium Coker Naphtha; HN, Heavy Naphtha; HCN, Heavy Coker Naphtha; PNA, Paraffin, Naphthene and Aromatic; PONA, Paraffin, Olefin, Naphthene and Aromatic; PIONA, Paraffin, Isoparaffin, Olefin, Naphthene and Aromatic; IBP, Initial Boiling Point; GMWt, Group Molecular Weight; GC, Gas Chromatography; MS, mass spectrometry; FID, Flame Ionization Detector; SFC, Supercritical Fluid Chromatography; CND, Carbon Number Distribution; NMR, Nuclear Magnetic Resonance; RSD, Relative Standard Deviation. Notes ¥The

DHA, in a modified approach, was found to be efficiently estimate olefin upto 45%

(wt.) in LN/LCN/MCN/MN whereas failed to accurately detect the cyclic substituted olefins present in HN samples. In order to getting resolution between the merged olefin peaks, the standard ASTM D6730 method was modified in terms of low carrier gas pressure along with longer run time. Acknowledgements


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Energy & Fuels

The authors wish to acknowledge the management of IOCL R&D Centre for proving necessary facilities to carry out the work and granting permission to publish this paper. References 1. Fonseca, N.; dos Santos, L. R. M.; Cerqueira, H. S. F.; Ramoá-Ribeiro, L.F.; Lam, Y. L.; de Almeida, M. B. B. Fuel 2012, 95, 183-189. 2. Gary, J. H.; Handwerk, G. E. ; Kaiser M. J. Petroleum Refining Technology and Economics, 5th ed.; CRC Press: 2007. 3. Morales-Valencia, E. M.; Baldovino-Medrano, V. G.; Giraldo, S. A. Fuel 2015, 153, 294-302. 4. Squicciarini, M. P. J. Chrom. Sci. 1996, 34, 7-12. 5. Sarpal, A. S.; Kapur, G. S.; Mukherjee, S. Tiwari, A. K. Fuel 2001, 80, 521-528. 6. Kapur, G. S.; Singh, A. P.; Sarpal, A. S. Fuel 2000, 79, 1023-1029. 7. Bansal, V.; Kapur, G. S.; Sarpal, A. S.; Kagdiyal, V.; Jain, S.K.; Srivastava, S. P.; Bhatnagar, A. K. Energy Fuels 1998, 12, 1223-1227. 8. Mondal, S.; Yadav, A.; Kumar, R.; Bansal, V.; Das, S. K.; Christopher, J.; Kapur, G. S. Energy Fuels 2017, 31, 7682-7692. 9. Luong, J.; Gras, R.; Cortes, H. J.; Shellie, R. A. J. Chromatogr. A 2013, 1271, 185-191. 10. Punetha, A. K.; Narshima, S. K.; Rao, T. S. R. P. J. Chrom. Sci. 2002, 40, 377-382. 11. Burri, J.; Crockett, R.; Hany, R.; Rentsch, D. Fuel 2004, 83, 187-193. 12. Sun, C.; Wang, Z. Concepts Magn Reson Part A. 2017; e21393. 13. Abdul Jameel, A. G.; Elbaz, A. M.; Emwas, A.-H.; Roberts, W. L.; Sarathy, S. M. Energy Fuels 2016, 30 (5), 3894. 14. Abdul Jameel, A. G.; Oudenhoven, V. V.; Emwas, A.-H.; Sarathy, S. M. Energy Fuels 2018, 32 (5), 6309. 15. Poveda, J. C.; Molina, D. R. J. Pet. Sci. Eng. 2012, 84−85, 1. 16. Marshall, A. G.; Rodgers, R. P. Proc. Natl. Acad. Sci. USA 2008, 105(47), 18090. 17. Marshall, A. G.; Rodgers, R. P. Acc. Chem. Res. 2004, 37 (1), pp 53–59 18. Lee, S. W.; Coulombe, S.; Glavinceske, B. Energy Fuels 1990, 4, 20-23. 19. Norish, T. A.; Rawdon, M. G. Anal. Chem. 1984, 56, 1767-1679. 20. Bansal, V.; Kumar, R.; Krishna, G. J.; Patel, M. B.; Sarpal, A. S.; Basu, B. Fuel 2014, 118, 148155. 21. Mukherjee, S.; Kapur, G. S.; Chopra, A.; Sarpal, A. S. Energy Fuels 2004, 18, 30-36. 22. Bansal, V.; Vatsala, S.; Kapur, G. S.; Basu, B.; Sarpal, A. S. Energy Fuels 2004, 18, 1505-1511. 23. Bansal, V.; Sarpal, A. S.; Kapur, G. S.; Sharma, V. K. Energy Fuels 2000, 14, 1028-1031. 24. Altbach, M. I.; Fitzpatrick, C. P. Fuel, 1994, 73 (2), 223-228.


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R R' R'' R'''

Objective: Selective Diolefin Saturation Mild Hydrocatalytic Condition Only isomerization ocurred, no change in amount of olefins

Page 16 of 27

R R' R'' R'''

R'''' R'''' LCN Feed: Rich in -olefins Incomplete Isomerization Total Olefinic Hydrogen = 12 Total Olefinic Hydrogen = 8 (Only representative) (Only representative) R-gropus are constatnt for both feed & product

Figure 1. Partial isomerization of α-olefins in LCN: The H (eqv. to 12 for feed and 8 for product) could not be static (2.2 for coker and 1.8 for FCC gasoline)5 for both feed and product.


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Energy & Fuels

Figure 2. Representative 1H NMR spectra of (A) Feed (LCN3), corresponding (B) product-1 (LCN4) and (C) product-2 (LN1). The onset a, b and c are the magnified olefinic regions. The assignment to olefinic regions in spectrum-A (as onset a) has been tabulated in Table 1.


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Page 18 of 27

Figure 3. Estimation of n by SIMDist Data.

Figure 4. Olefins in LCN/LN by Different Methdods.


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Figure 5. Olefins in MCN/MN and HN by Different Methdods.


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Page 20 of 27

Table 1. Assignments of Olefin Only Region in a 1H NMR Spectrum

Structure of Olefins

Chemical Shift

No. of Olefinic H Backbon

Region &

(δ)

Actual

4.50-4.80

2

2

O1

4.80-5.05

2

3

O2

5.05-5.34

1

1

O3

5.34-5.73

2

2

O4

5.73-5.90

1

3

O5

e

Intensity

Substituted αR H H R=mostly CH3

Linear αH H H

Internal substituted R H R=mostly CH3

Internal/cyclic unsubstituted H H

n

Linear αH H H

Diolefins 6.25

H

H

4 5.90-6.60

2

H H

(average

O6

)

H 6.03


20

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Energy & Fuels

Table 2. Estimation H by 1H NMR and n by Various Methods. Sample Id*1 LCN1

LCN2

LCN3*2

LCN4*2

LCN6

LN1*2

LN2

LN3

n H 2.4 8 2.4 6 2.4 7 2.1 5 2.1 6 1.8 9 1.9 6 2.0 7

SIMDis

f0

I0 (IT=100)

13C

DHA

5.66

5.63

5.70

4.61

9.48

5.75

5.85

5.86

4.79

8.58

5.83

5.87

5.90

4.78

8.70

5.80

5.92

5.98

5.56

6.95

5.36

5.52

5.74

5.31

10.05

5.80

5.96

5.98

6.33

6.10

5.48

5.36

5.35

5.45

7.04

5.75

5.72

6.00

5.80

6.62

t


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Energy & Fuels 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

LN4

MCN1

MCN2

MCN3

MCN4

MN1

MN2

MN3

HN1

HN2

HN3

HN4

1.9 2 2.4 0 1.9 1 2.5 2 2.5 3 1.9 1 2.0 8 2.2 4 1.7 8 1.6 4 1.9 4 1.7 1

Page 22 of 27

6.86

6.76

6.80

7.08

4.48

7.22

7.48

7.45

6.20

6.94

7.39

7.44

7.52

7.89

5.22

7.28

NA

7.75

6.15

8.52

7.46

NA

7.72

6.09

9.56

7.11

7.23

7.25

7.59

4.72

7.15

7.42

7.36

7.08

5.18

7.10

7.18

7.12

6.36

1.54

-

8.28

8.46

9.51

2.09

-

8.25

8.15

9.94

2.03

-

8.47

8.40

8.66

0.51

-

8.10

8.20

9.59

1.03


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Energy & Fuels

HN5

HCN1

HCN2

HCN3

CK1

CK2

CK3

CD1

CD2

CD3

1.5 2 2.4 4 2.5 9 2.3 9

8.42

8.91

-

8.22

8.35

6.84

0.73

-

NA

8.30

6.41

9.48

8.70

7.30

5.21

-

11.20

9.26

3.06

-

10.60

9.30

2.01

10.80

8.40

3.34

8.00*3

2.4

10.20*

2

3

2.2 8 2.5 7 2.3 6 2.3 4 2.4 5

11.7

-

-

-

NA

-

13.3*3

-

15.40

-

-

17.70

-

-

15.86

2

13.0 5 15.1 3 12.9 5

0.12

1.10

1.38

1.08

*1LCN/LN (IBP-70/100 etc.); MCN/MN (Medium coker naphtha/Medium FCC naphtha) (IBP-130/140); HCN (heavy coker naphtha) (IBP/70-240); *2LCN3 is a feed for selective diolefin saturation study whereas LCN4 and LN1 were the corresponding products; *3Error in the estimation of n for these samples is discussed in the SI.


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Page 24 of 27

Table 3. Estimation Olefins by Various Methods. Olefin bySample

Old

New

Id

NMR

NMR

wt%*1

wt%

LCN1

70.2

43.7

45.1

42.8 (2.1) (44.1)

45.8

LCN2

63.5

41.1

43.2

42.9 (4.0) (45.5)

47.3

LCN3*2

64.4

41.6

39.5

39.8 (2.8) (43.1)

44.0

DHA wt%

Reformulyzer: wt% (Cy-Ole) (vol%)

FIA vol%


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Energy & Fuels

LCN4*2

51.5

38.7

38.5

39.2 (2.6) (43.0)

43.5

LCN5

74.4

53.4

48.9

55.2 (4.2) (57.8)

56.8

LCN6

5.9

4.4

3.8

5.2 (1.1) (6.1)

6.4

LN1*2

54.9

38.6

37.9

38.9 (2.6) (42.8)

43.2

LN2

63.4

38.4

41.7

37.5 (4.0) (40.8)

41.3

LN3

59.6

38.4

39.3

37.8 (4.1) (41.1)

45.3

LN4

40.3

31.8

30.2

30.3 (3.3) (32.3)

33.3

LN5

62.3

42.2

45.7

40.2 (3.9) (43.1)

46.9

MCN1

51.4

43.0

43.2

42.2 (5.5) (44.4)

39.8

MCN2

38.6

41.2

41.8

41.2 (7.7) (43.1)

40.1

MCN3

63.1

52.4

-

53.5 (11.9) (54.8)

58.5

MCN4

70.8

58.2

-

58.9 (14.8) (59.9)

60.9

MCN5

51.9

40.8

40.2

42.8 (9.8) (42.6)

40.8

MN1

42.5

35.8

31.5

38.9 (6.7) (40.8)

39.8

MN2

46.6

36.7

31.3

37.8 (6.2) (40.1)

38.6

MN3

13.9

9.8

9.1

8.5 (1.1) (8.9)

10.9

MN4

48.2

38.9

35.3

37.8 (5.8) (38.4)

40.9

MN5

35.5

30.7

31.8

29.8 (5.7) (31.5)

34.3

MN6

40.4

34.8

30.6

32.9 (5.3) (34.9)

38.2

HN1*3

18.8

19.9

9.5

20.7 (7.6) (22.2)

21.8


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Page 26 of 27

HN2*3

18.3

20.2

14.4

18.8 (6.0) (20.2)

23.5

HN3*3

4.6

4.4

4.0

5.4 (1.0) (5.8)

6.8

HN4*3

9.3

9.9

6.4

10.5 (2.6) (11.8)

14.1

HN5*3

1.1

1.3

2.2

1.2 (0.3) (1.2)

4.6

HCN1

5.4

5.0

4.0

5.5 (0.5) (6.1)

7.4

HCN2*4

70.5

61.1

-

62.6 (2.1) (63.0)

64.8

HCN3*4

38.6

38.0

-

-

-

CK1

-

28.3

-

-

-

CK2

-

18.0

-

-

-

CK3

-

28.1

-

-

-

CD1

-

14.4

-

-

-

CD2

-

20.9

-

-

-

CD3

-

14.0

-

-

-

*1Following a static approach where the olefin multiplication factors were set as 7.4 for coker and 9.0 for FCC case naphtha.5 *2LCN3 is a feed for selective diolefin saturation study whereas were

the

corresponding

products;

*3HN1

and

HN2

are

the

feeds

for

LCN4 and LN1 adsorption

based

hydrodesulphurization study whereas HN3-5 were the products; *4HCN2 and HCN3 are heavy coker type naphtha originated from pyrolysis units.


26

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Energy & Fuels

Two required parameters for olefin estimation- (i) average absolute number of unsaturated hydrogen (H) in the olefinic region and (ii) the average alkyl chain length (n) of the olefins were estimated by 1H NMR and SIMDist respectively. The total protons in an average olefins would then be 2n leading to percentage of oelfinic protons (%UH) as H/2n*100. The olefin multiplication factor (fo) was then obtained (100/UH%) by which the weight percentage of olefin is estimated using a normalized 1H NMR spectrum.


Dynamic Approach for the Estimation of Olefins in Cracked Fuel

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