Analysis of Static Electricity Generated in Petroleum Pipeline Transportation Based on the Generalized Gray Incidence Model

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1 223 A publcaton CHEMICAL ENGINEERING TRANSACTIONS VOL. 55, 2016 Guest Edtors: Tchun Wang, Hongyang Zhang, Le Tan Copyrght 2016, AIDIC Servz S.r.l., ISBN ; ISSN The Italan Assocaton Chemcal Engneerng Onlne at DOI: /CET Analyss Statc Electrcty Generated n Petroleum Ppelne Transportaton Based on the Generalzed Gray Incdence Model Xaogang Zhao a, Y Zhou* a, Janyu Zhao b, Zhan Guo a, Yang Peng a a Department Petroleum Supply Engneerng, Logstcal Engneerng Unversty, Chongqng , Chna b Department Cvl Engneerng, Aalto Unversty, ESPOO 02150, Fnland zhouyasd@foxmal.com In order to reduce the accdents caused by statc electrcty, smulatve experments were carred out to obtan the relatonshp between electrostatc and sx factors n petroleum transportaton. The electrostatc current generated n smulatve petroleum was measured. After several groups experments, sx factors were determned to be great nfluence on the electrostatc generaton, namely the length petroleum, the dameter, the roughness, the velocty ol flow, the electrcal resstvty and the knematc vscosty ol materal n the. The expermental data are analyzed based on the Generalzed Gray Incdence Model, and then from ts rankng results, the most crtcal factor and lesser mportant ones can be dstngushed. The expermental data and analyss results show that the velocty flud s the most crtcal factor leadng to the generaton statc electrcty. 1. Introducton Recently, wth the rapd development the petroleum ndustry, a lot accdents caused by statc electrcty were occurred (Zhang and Cha, 2010). These electrostatc dscharge accdents were lkely to result n the petroleum rupture and leakage, whch may even lead to larger fre and exploson accdents. The study the key factors electrostatc dscharge becomes very essental, and t wll also mprove the correspondng techncal equpment and safety management n petroleum ndustry (Wang et al., 2016; Zhang et al., 2016). However, those electrostatc accdents n flow were resulted from a varety reasons and mpact factors, and between whch the relatonshp s extremely complex. It s mportant to fnd out the most crtcal factor to avod the occurrence statc electrcty accdents. At present, many researches ten use sngle assessment model n analyss the key factors electrostatc n flow wthout consderaton the relevance and complexty among those factors (Alkhazaleh and Duwar, 2016; Benarab et al., 2016; Cardnale et al., 2016; Yan et al., 2011; Yn and Tan, 2013; Gulyás et al., 2013; Pdoll, 1997). As a result that the loss preventon n petroleum safety caused by electrostatc develops relatvely slowly. The generalzed gray relatonal ncdence model s the mathematcal model whch may obtan the correlaton degree between maternal factors and sub factors, and t can be appled to study the relatonshp between quantty and nfluence factors for electrostatc generaton. Ths model has the advantages three lnearty requrements: (1) The orgnal data s not harsh, whch means there s no strct regulatons on the capacty samples (Zhou et al., 2007). Snce t s serous lack expermental data n electrostatc generaton, such problem wth a hgh gray degree would more lkely to be solved; (2) The calculated results are accurate, as quanttatve results the numercal smulaton and qualtatve analyss results are matchng together (We et al., 2012); (3) Compared to the orgnal gray correlaton method, the generalzed gray ncdence model s more concse, and t s easy to be programmed by many computer languages such as the vsual basc stware, so t would be smple to get the calculaton result from ths model. The smulatve experments s establsh to measure the electrostatc current n flow under dfferent condtons. Then the generalzed gray ncdence model s Please cte ths artcle as: Zhao X.G., Zhou Y., Zhao J.Y., Guo Z., Peng Y., 2016, Analyss statc electrcty generated n petroleum transportaton based on the generalzed gray ncdence model, Chemcal Engneerng Transactons, 55, DOI: /CET

2 224 used to the analyss the expermental results to fnd out the most crtcal factor among sx factors whch s great nfluence on electrostatc generaton. Fnally, the correspondng control methods are proposed based on the theoretcal analyss to mprove safety management n petroleum. 2. Expermental system electrostatc n the flow petroleum ppe 2.1 Expermental apparatus Test devce manly conssts ol storage tanks, flter, ol pump, control valves and testng sectons. The ol storage tanks are two ol contaners. The flter s an electrostatc generator, and the ol pump gves power to the ol flow. The control valves are manly used to adjust the ol flow rate n the testng sectons accordng to dfferent condtons. Before the test, t s requred that the ambent temperature and humdty reman constant, and the storage tank and petroleum s should be delvered wth nsulaton. When openng the ol pump, ol flow startng from the storage tank travels through the flter nto the pre-layng ol s. Then t reaches testng sectons for whch the entre s used to measure the electrostatc current by the magnetoelectrc mcroampere measurement. In order to ensure the accuracy the test data, the testng s are dvded nto two sectons the same confguraton parallel, and the measurement data are the average the two testng results. The schematc dagram the expermental system s shown n Fgure. 1. Fgure 1: The schematc dagram expermental system 2.2 Procedures electrostatc measurement n ol flow Accordng to the actual workng condtons the ol flow n, the statc electrcty generated n the s manly determned by the characterstcs the tself, the physcal propertes the ol materal and the velocty ol flow. The characterstcs the nclude length, dameter, and roughness, flter sze, flter separators and other factors. The physcal propertes the ol nclude ol conductvty, mosture content, vscosty, temperature and so on. Because the testng equpment and measurng nstruments under objectve condtons, combned wth relevant nformaton (Pu and Wang, 2006; Guo, 2011), durng the electrostatc generaton process, the parent factor X 0 the electrostatc generaton s most relevant to sx sub factors: the length X 1, the dameter X 2, the roughness X 3, the velocty ol flow X 4, electrcal resstvty ol flow X 5 and knematc vscosty X 6. 6 groups experments were carred out wth the same volume ol at the same ambent temperature and humdty. In order to ensure the accuracy the experment, the resdual and the ndependent varables should be consstent n addton than those 6 sub-factors are not the same. The types and characterstcs ol n the 6 expermental groups are selected as follows: (1) kerosene sample (the electrcal resstvty Ω m, knematc vscosty 2.37m 2 /s) travels at a flow rate 2.44m/s through the length 1.07m, dameter 6.35mm, and surface roughness 0.14mm smooth stanless steel tube; (2) gasolne samples (the electrcal resstvty Ω m, knematc vscosty 0.46m 2 /s) travels at a flow rate 1.83m/s through the length 1.22m, dameter 9.53mm, and surface roughness 0.19mm stanless steel tube; (3) avaton kerosene sample (the electrcal resstvty Ω m, knematc vscosty 6.32m 2 /s) travels at a flow rate 3.05m/s through the length 1.37m, dameter 12.7mm, surface roughness 0.11mm clean carbon tube; (4) kerosene sample (the electrcal resstvty Ω m, knematc vscosty 2.37m 2 /s) travels at a flow rate 3.66m/s through the length 1.52m, dameter 6.35mm, and surface roughness 0.29mm rusted carbon steel ppe; (5) gasolne samples (the electrcal resstvty s Ω m, knematc vscosty 0.46m 2 /s) travels at a flow rate 2.13m/s through the length 1.68m, dameter 9.53mm, and surface roughness 0.19mm rough stanless steel tube;

3 (6) avaton kerosene sample (the electrcal resstvty s Ω m, knematc vscosty for 6.32m 2 /s) travels at a flow rate 4.57m/s through the length 1.83 m, dameter 12.7mm, and surface roughness 0.14mm smooth stanless steel tube. These 6 groups tests were conducted accordng to the combnaton the above schematc dagram, and the measurements the electrostatc current were recorded n each group. 2.3 Expermental results Accordng to procedures mentoned above, sx groups experments were carred out, and the measurements were recorded. The testng data are showed n table 1. Table 1: Expermental data Expermental number Length X 1 /m Dameter X 2 /mm Roughness X 3 /mm Velocty ol flow X 4 /m s -1 Electrcal resstvty ol flow X 5 /10 11 Ω m Knematc vscosty ol flow X 6 /m 2 s -1 Electrostatc current X 0 /10-10 A Methodology Generalzed Gray Incdence Model Recently, analyss mpact factors s mostly on a bass the mathematcal statstcs such as the regresson analyss, the varance analyss, the prncpal component analyss, etc. These methods, however, have the followng shortcomngs: (1) Requrement large amounts data. Small amounts data make t dffcult to fnd out the statstcal laws. (2) Requrement typcal dstrbuton samples. Such assessments requre lnear relatonshp between every factor and the system characterstc data, and factors are unrelated to each other. (3) A lot computaton work. There s a large amount mathematcal operatons whch ten need to be reled on computers. (4) Quanttatve results may not match the qualtatve analyss. Such dfference may result n dstorton relatonshps and laws n the system. In the actual analyss mpact factors, nformaton s very lmted due to the constrants statstcal data, and mpact factors are uncertan or ncomplete, whch present n large degree greyness. The dstrbuton data s ten not typcal as a result dsturbance n human factors, so t s dffcult to analyss by those statstcal methods. Gray Incdence Analyss Model s a branch the Gray system theory whch s proposed by Pressor Deng Julong. Then, Pressor Lu Sfeng developed t to the Generalzed Gray Incdence Model (Deng, 1985; Lu et al., 2011). The Generalzed Gray Incdence Model s an analytcal method not based on the statstcal methods but on the grey theory, thus t s applcable to dfferent amount and dstrbuton samples. What s more, t needs a few operatons and the quanttatve results wll match the qualtatve analyss well. It s an analytcal method to fnd out the nternal relatonshp through the grey relatonal grade and to dscover the man connecton and major factor. The basc dea these two models s to judge whether the relatonshp between the two factors s close. The closer to the curve sequence, the greater the degree ncdence. Ths model s carred out by the followng steps (Lu and Xe, 2013): (1) The system characterstcs and the related factors behavors sequence are set up to obtan the calculaton for the Generalzed Gray Incdence model. The rght mappng quantty s selected from the egenvector behavor system, and then the mpact factors are determned from the behavor system. The behavor sequence X s composed No. k observaton data x(k) (k=1, 2,, n) n the system, namely X=(x(1), x(2),, x(n)), =1, 2,, m. (2) All data are normalzed to the ntalzaton processng. The ntal value X ' s to ntalze or to nondmensonal calculate the behavor sequence X. The process s X '= X /x (1)=(x '(1),x '(2),,x '(n)), =1,2,,m. Where x (1) 0; x (1) s called the ntal value operator. After the ntal value s determned, the reference object s selected to compose the reference sequence X 0 '=(x 0 '(1), x 0 '(2),, x 0 '(n)), =1, 2,, m, whle the rest the ntal value composes the relatve sequences. (3) After data ntalzaton, t s carred out data zero mage processng to obtan the startng pont data to obtan the correlaton coeffcent. Dfference sequence shows the dfferences between two sequences. Accordng to the reference sequence and relatve sequence n the ntal value X ', the dfference sequence s 225

4 226 obtaned by subtractng the correspondng tems from those two sequences, that s Δ ( k) = x 0 ( k) x ( k). The dfference sequence s =( (1), (2),, (n)), =1, 2,, m. Accordng to the comparson the dfference sequence, the maxmum dfference and the mnmum dfference are selected among the varous dfferences. The maxmum dfference and the mnmum dfference reflect the maxmum and the mnmum value between two sequences. The maxmum dfference M = max max Δ ( k), whereas the mnmum dfference Brng the obtaned maxmum dfference and mnmum dfference nto equaton (1) to calculate the grey relatonal coeffcent. m + ξm r k) = Δ ( k) + ξm ( (1) Where k =1, 2,, n; =1, 2,, m; ξ for the resoluton factor and ξ (0,1), ξ s used to weaken the anamorphc nfluence caused by large value the maxmum dfference. (4) The results can be obtaned from the Gray correlaton degrees and ther rankng table. The grey relatonal grade r reflects the relaton degree between the reference sequence and the relatve sequences, and t can be obtaned from equaton (2): 1 n r = r ( k) n k = 1 Where =1, 2,, m. The grey relatonal grade between the two sequences can be reflected by r, whle the value r shows the mutual nfluence those two sequences. The bgger the greater. In the system analyss, the sze the sub-factors between the order s the focus research. From the overall study the numercal value a sngle correlaton degree, there s no practcal sgnfcance. 4. Data analyss based on Generalzed Gray Incdence Analyss The data ntalzaton Generalzed Gray Incdence Analyss s showed n table 2. Due to the dfferent expermental data between varous physcal quanttes, the dmensons factors are not dentcal. If t s drectly calculated, the role certan factors wll be exaggerated or reduced. Therefore, the data frst need to non-dmensonal processng to convert data from table 2 to table 3. Step 1: Data processng Table 2: The ntalzaton the data Expermental number Length X 1 /m Dameter X 2 /mm Roughness X 3 /mm Velocty ol flow X 4 /m s -1 Electrcal resstvty ol flow X 5 /10 11 Ω m Knematc vscosty ol flow X 6 /m 2 s -1 k (2) Electrostatc current X 0 /10-10 A Step 2: Data zero mage processng Table 3: Startng pont data Expermental number Length X 1 /m Dameter X 2 /mm Roughness X 3 /mm Velocty ol flow X 4 /m s -1 Electrcal resstvty ol flow X 5 /10 11 Ω m Knematc vscosty ol flow X 6 /m 2 s -1 Electrostatc current X 0 /10-10 A

5 227 Step 3: Calculaton correlaton coeffcent s 0 =1.63, s 1 =1.77, s 2 =2.5, s 3 =1.43, s 4 =0.81, s 5 =13.55, s 6 =0.89 s 1 s 0 =3.175, s 2 0 =3.910, s 3 0 =2.840, s 4 0 =2.215, s 5 0 =14.965, s 6 0 =2.295 means the relatve dfference between X 1 and X 0 's s 0 means self characterstc parameter X 0, 1 s0 s own characterstc parameters. The meanngs the other parameters are smlar to that. Step 4: Gray correlaton degree calculaton and rankng γ 01 =0.581, γ 02 =0.567, γ 03 =0.588, γ 04 =0.608, γ 05 =0.519, γ 06 =0.605 so γ 04> γ 06> γ 03> γ 01> γ 02> γ 05 The correlaton coeffcent above expresses the relatonshp between the factor X 1 and factor X 0, and the other parameters are smlar to such meanng. By the above calculaton, t s found that the value γ 04 s the largest, whch shows that the relatonshp between the sub-factor X 4 (ol flow velocty) and the parent factor X 0 s the most closely, and the correlaton degree between the resdual factor and the parent factor s the same as the numercal value. 5. Conclusons (1) From the correlaton degree the rankng, t can be seen that the value factor X 4 s largest, whch means the sub-factor X 4 and the parent factor X 0 s the most closely related factors, that s, the velocty ol flow s the most mportant factor n the electrostatc generaton. Therefore, the ol flow rate should be strct control n the process upload and download. When the velocty ol flow s large, some safety method should be appled to avod producng a statc spark and to prevent exploson. At the same tme, factor X 6 s the second largest one n all factors, whch shows that the knematc vscosty ol s also a key factor contrbuted to the electrostatc generaton. When the transportaton nvolves some hgh vscosty ol such as avaton kerosene, t s requred to the applcaton some electrostatc elmnaton measures to prevent the occurrence statc electrcty accdent. (2) In ths paper, smulatve expermental system s establshed to measure electrostatc n the flow. The Generalzed Gray Incdence Analyss Method s used n the analyss mult factors causng electrostatc current n flow, and combned wth the expermental data, the most crtcal factor s found out from gray relatonal analyss. Ths model s proved feasble, and correlaton between many factors whch produce statc electrcty s obtaned through rgorous quanttatve analyss. Through the analyss the key factors, t s obtaned to the electrostatc nfluence mechansm and the control mode n the process ol transportaton, whch also presents gudance for safety protecton ol. (3) Ths paper only consders sx factors electrostatc generaton n the flow. However, other factors whch are related to electrostatc such as mosture, temperature, flters and flter separator, etc, s beyond the research due to the lack measurement condtons or experment equpment. In the next step, a detaled study those factors n the experment should be consdered, t s also requred to mprove expermental devce to a hgher precson, and the number expermental groups should be ncreased to get more accurate conclusons. Acknowledgments Fnancal supports provded by the Natonal Natural Scence Foundaton Chna (No ) and Innovaton Foundaton for Postgraduate Logstcal Engneerng Unversty s gratefully acknowledged. Reference Alkhazaleh A., Duwar H., 2015, Analyss mechancal system ventlaton performance n an atrum by consoldated model fre and smoke transport smulaton, Internatonal Journal Heat and Technology, 33(3), Do: /jht Benarab F.Z., Medjelled A., Benchatt T., 2016, Physcal Approach for Sand Flux Quantfcaton and Flow Dynamc Propertes Investgaton for Fne Sand Grans Transport, Internatonal Journal Heat and Technology, 34(4), Do: /jht Cardnale T., De Fazo P., Grandzo F., 2016, Numercal and Expermental Computaton Arflow n a Transport Contaner, Internatonal Journal Heat and Technology, 34(S2), S323-S331. Do: /jht.34S219.

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