INVESTIGATION ON DIESEL COLD FLOW PROPERTIES

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1 INVESTIGATION ON DIESEL COLD FLOW PROPERTIES R.Dinkov*, D. Stratiev, D. Penev, G. Cholakov Chief Process Engineer Department., Lukoil Neftochim Bourgas JLC, Bulgaria, University of Chemical Technology and Metallurgy Sofia, Bulgaria, Abstract: Cloud point (CP) and cold filter plugging point (CFPP) of 0 diesel range boiling fractions from different origin (both straight run and conversion effluents) were tested respectively by EN ISO 05 and EN 6. Their values were calculated by using Khan s formula and CP was also calculated by means of a commercial process simulator. The accuracy of the calculations was evaluated via absolute deviation (AD) and average absolute deviation (AAD). The latter was found to surpass the reproducibility of the test methods for the calculated data with the published in the open literature formulae with 0 C for CP and with 0 C for CFPP. This difference between simulator calculated and experimental data AAD is 0 C. This fact encourages us to analyze our data and as a result simple and versatile correlations for CP and CFPP were derived from diesel fractions bulk properties (density and distillation). Key words: Cloud point; Cold filter plug in point; Individual fractions; Blending. Introduction A major problem for both refiners and users of diesel fuel or home heating oil is the behaviour of such fuels in cold weather. These products always contain varying amounts of n-alkanes. As ambient temperature decreases, the solubility of n-alkanes also decreases. Precipitation occurs over a wide temperature range, until solidification occurs. This limits the use of petroleum products. Dilution with kerosene, cutting the end boiling point of diesel fractions or addition of flow improvers is needed to meet the requirements of the various specifications and seasons []. The cold flow properties are important also from economical point of view. Even though there may be more diesel boiling range material in a specific crude oil or crude oil blend, cold flow properties can limit the amount that can be recovered []. The cold flow properties of diesel fuels are controlled by tree parameters: Cloudpoint (CP) is the temperature at which crystals first appear.

2 Pour point (PP). As the temperature gets colder, crystal growth continues and a lattice is obtained leading to solidification at the pour point. Coldfilter plugging point (CFPP). CP and PP cannot be directly correlated to the phenomenon leading to the plugging of diesel vehicle filters by n-alkane crystals, so a third parameter is used, the cold filter plugging point, which corresponds to the plugging of a 5pm filter under standardized conditions. The best way to deal with this problem is to predict its occurrence and act preventively []. Because CP and CFPP aren t additive properties a lot of researchers have tried to derive accurate correlations. Several correlation and graphical methods exists for calculating diesel blend cold flow properties from known components value. These procedures are either specific in their use or valid for only a limited number of blending components [].. Experimental data Twenty diesel fractions from different technological processes (atmospheric and vacuum distillation, MHCU, FCC, HDS and VBU) were used for this study. In order to evaluate the accuracy of Khan s and simulator s formulas for calculation of CP and CFPP for the available diesel fractions, the following physicochemical properties were determined: specific gravity by EN ISO 675; distillation both according EN ISO 05 and ASTM D 887 (presented in table I); n-alkanes distribution data obtained from ASTM D 887 (table II). The fractions were tested for CP according to the procedure described in EN ISO 05. The test precision for distillate oils is: repeatability 0 C and reproducibility 0 C. CFPP was analyzed by EN 6 with the precision: repeatability.76 0 C and reproducibility calculated with the following expression: Reproducibility 0.0*(5- ) ()

3 Table I. SG and distillation of the investigated diesel fractions feed effluent feed effluent DF SRVD HSRD SRVD DF feed effluent DF DF HSRD SRVD effluent effluent DF mixture LCO Tank Property HDS HDS HDS 5 HDS 5 MHCU VDU ADU VDU VBU HDS 5 HDS 5 MHCU VBU ADU VDU HDS HDS 5 7 FCC Specific gravity Distillation ASTM D 887 IBP FBP ASTM D 86 IBP FBP

4 Table II. n-alkanes distribution according to ASTM D feed effluent feed effluent DF SRVD HSRD SRVD DF feed effluent DF DF HSRD SRVD effluent effluent DF mixture LCO Property HDS HDS HDS 5 HDS 5 MHCU VDU ADU VDU VBU HDS 5 HDS 5 MHCU VBU ADU VDU HDS HDS 5 Tank 7 FCC n-alkanes, % (m/m) C C C C C C C C C C C C C C C C C C C C C C C C C C Wax (C+) content, % (m/m) Total n-alkanes, % (m/m) Carbon Chain length

5 . Results and discussion Khan s formulas were published in []. He derived very complex correlations with the following forms: CP -760,76 +, , ,0057-0, ,0085-0,709-0,95 + 9,50 + 0,005-0, , , , ,87 + 0,0007 () Where: CP = cloud point, 0 K CFPP = cold filter plugging point, 0 K CFPP -9,5+ 0, , , ,0555-0,006-5,6-0,58-0,009 -, ,87 +,8608-0, ,69 + 0, ,000 () = mid boiling point (T 50 ), 0 C; = wax (C + ) content, % (m/m); = total n-alkanes content, % (m/m); = carbon chain length The required values for properties from to are presented in tables I and II. The AAD for CP calculated values by Khan s correlation is 8 0 C - table III. This value is with 0 C greater than the reproducibility of the test method. This determines the correlation () inappropriate for calculating CP of the diesel fractions with properties, presented in tables I and II.

6 Table III. Cold flow properties calculated and measured feed effluent feed effluent DF SRVD HSRD SRVD DF feed effluent DF DF HSRD SRVD effluent effluent DF mixture LCO AAD Reproducibility Property HDS HDS HDS 5 HDS 5 MHCU VDU ADU VDU VBU HDS 5 HDS 5 MHCU VBU ADU VDU HDS HDS 5 Tank 7 FCC CP, 0 C (measured) СFPP, 0 C (measured) CP, 0 C (simulator) CP, 0 C (Khan) СFPP, 0 C (Khan) AD/CPsim, 0 C AD/CPKhan, 0 C AD/CFPPKhan, 0 C

7 According to the data in table III and equation (), the reproducibility of EN 6 for our data set is calculated as 0 C.The AAD for CFPP calculated values by Khan s correlation is 7 0 C. This value is with 0 C greater than the reproducibility of the test method. This determines the correlation () inappropriate for calculating CFPP of the shown diesel fractions. Unsatisfactory result gives also the commercial process simulator ChemCad when calculating CP of diesel boiling range fractions from the data set. It uses the following equation: log (CP) = log (MeABP ) (MeABP) - 0.SG 0.5 () Where: CP = Cloud point of petroleum fraction, R; MeABP = mean average boiling point, R; SG = specific gravity, 60F/60F. The AAD for CP is 8 0 C, which is closer to the reproducibility but still isn t within its range. The AAD for CP and CFPP are shown in table III. That s why a decision was taken to evaluate the gathered in Lukoil s refinery information about the diesel boiling fractions both tables I and IV. The cold flow properties data is enlarged by different diesel boiling fractions, obtained from five types of crude oils (Libyan crude oil Sirtica, Kuwaitian crude oil, REBCO, Heavy Iranian crude oil and Light Iranian crude oil). These fractions are produced by separation from the crude, performed according to ASTM D89. The so obtained fractions are studied for distillation profile according to EN ISO 05, specific gravity, CP and CFPP (table IV).

8 Table IV. Cold flow and physical properties of straight run diesel fractions from different crude oils Property 0-70 Libyan crude oil - Sirtica Kuwaitian crude oil REBCO Heavy Iranian crude oil Light Iranian crude oil Specific gravity Distillation ASTM D 86 IBP FBP CP. 0 C (measured) СFPP. 0 C (measured)

9 The data was analyzed by the integrated in Excel (Microsoft office package) correlation analyses tool. The result shows that the following parameters: T 50, T 90, T 90-0, T 50 *T 90, SG and SG* T 90 correlates well with the CP of the studied fractions. After that Excel regression was applied to the above (most appropriate) parameters, the following equation was derived. CP = a bt (5) 50 ct90 dt90-0 et50 *T90 fsg gsg * T90 Where: T 50 = temperature at which boils 50 % (v/v) according to ASTM D 86, 0 C; T 90 = temperature at which boils 90 % (v/v) according to ASTM D 86, 0 C; T 90-0 = difference in temperatures of 90 % (v/v) and 0 % (v/v) boiling from the fraction; SG = specific gravity a, b, c, d, e, f, g, h = regression coefficients with the following values: a = b = c = d = e = f = g = The maximum CP residual value, calculated by Excel s regression analyses tool, among the studied 0 fractions is.9 0 C for the broad fraction C, derived from REBCO. AAD for the whole data is.5 0 C, which is even within the repeatability of EN ISO 05. It should be pointed that equation (5) was derived for conversion processes effluents, boiling in the diesel range and straight run diesel fractions and also from different type of crude oils and with different distillation range.

10 equation: CFPP of the studied diesel fractions can be described by the following CFPP = a bt (6) 50 ct90 dt90-0 et50 *T90 fsg gsg* T90 Where: The parameters are the same as these in equation (5) but the regression coefficients have the meanings: a = b = c = d = e = f = g = For the diesel fractions from tables I and IV with measured CFPP, the maximum residual value from the regression derived equation (6) is 5. 0 C for fraction from Sirtica. AAD for the above fractions is.7 0 C, which is within the reproducibility of EN 6. Equations (5) and (6) are simple and versatile. They are derived from diesel fractions bulk properties (SG and distillation). It can be seen from table I that the CP and CFPP of the hydrotreated fraction is equal or with 0 C higher than the value of the parameter for the hydrotreater feed. In such a way the parameters SG and distillation of diesel fractions can be used not only for determining the maximum recovering of diesel boiling material in the atmospheric and vacuum distillation of crude oils, and conversion processes fractionation but can be used also for modelling the cold flow properties of a refinery diesel pool. Diesel yield is determined by the season requirement for cold flow properties, because usually there is great amount of diesel in a specific crude oil but the recovery depends on the required cold flow properties.

11 The derived correlations (5) and (6) were tested for two diesel fuels, which consists of definite quantities of hydrotreated diesel from different HDSUs. The first diesel blend contains: 0 % (v/v) hydrotreated diesel from HDSUs and ; 0 % (v/v) from HDSU and 50 % (v/v) from HDSU. It possesses the following physicochemical properties: distillation (ASTM D 86) 0 % (v/v) = 8 0 C, 50 % (v/v) = 6 0 C, 90 % (v/v) = 0 C, SG = 0.805, CP = C and CFPP = -0 0 C. Applying equations (5) and (6) for the above blend we received the following results: CP = C and CFPP = C. The AD for CP is. 0 C and for CFPP is C or these deviations are within the repeatability of the standards. The second diesel blend contains: 5 % (v/v) hydrotreated diesel from HDSUs and ; 55 % (v/v) from HDSU. Diesel s physicochemical properties are: distillation (ASTM D 86) 0 % (v/v) = 0 C, 50 % (v/v) = 5 0 C, 90 % (v/v) = 9 0 C, SG = 0.875, CP = - 0 C and CFPP = - 0 C. Applying equations (5) and (6) for the above blend we received the following results: CP = -. 0 C and CFPP = -. 0 C. The AD for CP is. 0 C and for CFPP is. 0 C or these deviations are again within the repeatability of the standards. Another task of this study is to evaluate the ability of published in the open literature correlations for determining the CP of blend when knowing only the CP of its constituents [,] and the distillation, and SG of the fraction isn t known. BlendCP index = n i x vi CP index, i (7) CP 0 CP (8) index = BlendCP BlendCP=.8 index 9 (9)

12 Where: CP = cloud point, 0 C; vi = volume part of the fraction in the blend; CP index = cloud point index, used in order to transform the non additive property CP into an indexes which blend linearly on a volume basis equation (7). We apply the above procedure for blends, containing different quantity of hydrotreated diesel from HDSU with CP = C, from with CP = C and a diesel fuel, produced according to EN 590 with CP = C. Blend Components %, (v/v) CP measured, 0 C CP calc. by eq. (7), (8) and (9), 0 C AD, 0 C HDSU HDSU HDSU HDSU HDSU HDSU HDSU HDSU Diesel EN 590 0

13 Calculating the blend s CP, by equations (7), (8) and (9), of our data shows that for these 8 blends the AD is within the repeatability of BDS EN ISO 05. Depending on the available data for a diesel fractions blend, one can use equations (5) and (6) for determining respectively CP and CFPP when SG and distillation of the blend is known. In case only CPs of the blend components are reported, equations (7), (8) and (9) should be used. References [] Claudy, P., Letoffee, J., Interactions between n-alkanes and cloud point-cold filter plugging point depressants in a diesel fuel. A thermodynamic study, Fuel, volume 7, 99 [] Barletta, T., Crude unit revamp increases diesel yield, PETROLEUM TECHNOLOGY QUARTERLY, issue Spring 000. []Coutinho, J.,Pauly, J., A thermodynamic model to npredict wax formation in petroleum fluids, Brazilian journal of chemical engineering, vol. 8, 00 [] Khan, K., U., New correlation predicts predict diesel cold- flow properties accurately, Oil and gas journal, 99 [] RPMS 000. Refinery and Petrochemical Modelling System, User s Manual, 999 [5]Fundamentals of petroleum refining, Elsevier, 00

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