Prediction of Carbon Steel Corrosion Rate Based on an Alternating Conditional Expectation (Ace) Algorithm
Based on dynamic corrosion experiments, we propose a new model for predicting corrosion rate that is based on an alternating conditional expectation (ACE) algorithm. This model lets us more accurately predict the corrosion rate for a broad range of temperatures, pH, and concentrations of Ca 2+ , HCO...
Saved in:
| Published in | Chemistry and technology of fuels and oils Vol. 51; no. 6; pp. 728 - 739 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2016
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-3092 1573-8310 |
| DOI | 10.1007/s10553-016-0664-7 |
Cover
| Abstract | Based on dynamic corrosion experiments, we propose a new model for predicting corrosion rate that is based on an alternating conditional expectation (ACE) algorithm. This model lets us more accurately predict the corrosion rate for a broad range of temperatures, pH, and concentrations of Ca
2+
,
HCO
3
−
, Mg
2+
, Cl
–
,
SO
4
2 −
ions. Based on tests performed on a testing sample group, we have confirmed the reliability of the model and have also demonstrated its high accuracy. Sensitivity analysis based on a rank correlation coefficient revealed that the major factor influencing the corrosion rate of N80 steel is the pH value. We have also carried out a comparison analysis of the results obtained when using the ACE algorithm and the results obtained when using a backpropagation neural network (BPNN) and the support vector regression (SVR) method. As a result, we found that the model based on the ACE algorithm is more accurate than other currently used models. |
|---|---|
| AbstractList | Based on dynamic corrosion experiments, we propose a new model for predicting corrosion rate that is based on an alternating conditional expectation (ACE) algorithm. This model lets us more accurately predict the corrosion rate for a broad range of temperatures, pH, and concentrations of Ca
2+
,
HCO
3
−
, Mg
2+
, Cl
–
,
SO
4
2 −
ions. Based on tests performed on a testing sample group, we have confirmed the reliability of the model and have also demonstrated its high accuracy. Sensitivity analysis based on a rank correlation coefficient revealed that the major factor influencing the corrosion rate of N80 steel is the pH value. We have also carried out a comparison analysis of the results obtained when using the ACE algorithm and the results obtained when using a backpropagation neural network (BPNN) and the support vector regression (SVR) method. As a result, we found that the model based on the ACE algorithm is more accurate than other currently used models. |
| Author | Zheng, Yun-ping Yuan, Zong-ming Liu, Wei Chen, Xing-yi |
| Author_xml | – sequence: 1 givenname: Xing-yi surname: Chen fullname: Chen, Xing-yi email: cxy55@163.com organization: Oil and gas engineering Institute of Southwest Petroleum University – sequence: 2 givenname: Zong-ming surname: Yuan fullname: Yuan, Zong-ming organization: Oil and gas engineering Institute of Southwest Petroleum University – sequence: 3 givenname: Yun-ping surname: Zheng fullname: Zheng, Yun-ping organization: Oil and gas engineering Institute of Southwest Petroleum University – sequence: 4 givenname: Wei surname: Liu fullname: Liu, Wei organization: School of Energy Resource of Chengdu University of Technology, Post-Doctoral Research Center of Tarim Oilfield |
| BookMark | eNp9kD1PwzAQhi0EEm3hB7BlhMFwjp04HktVPqRKID5my3UuxVWaVLaR4N_jECaGTvbJ97zne6bkuOs7JOSCwTUDkDeBQVFwCqykUJaCyiMyYYXktOIMjskEABTloPJTMg1hO5Qy5xPinj3WzkbXd1nfZAvj1-n2GhHbbNF734fh5cVEzG5NwDpLlemyeRvRdya6bpPautoNAabNll97tNH8xl3OLV6lzk3vXfzYnZGTxrQBz__OGXm_W74tHujq6f5xMV9Ry_MiUoG2VKoSa4VWNsLWqsqVyK1VqizBSIsFF4CiqSAtKm1uEE1T1YVYA3LF-IywMdemzwePjd57tzP-WzPQgys9utLJlR5caZkY-Y-xbtwieuPag2Q-kiFN6Tbo9bb_TGbacAD6Ae4egG4 |
| CitedBy_id | crossref_primary_10_3390_met15010027 crossref_primary_10_3390_s19010034 crossref_primary_10_1002_maco_202012120 crossref_primary_10_32604_iasc_2021_018516 |
| Cites_doi | 10.1016/j.eswa.2012.11.002 10.3390/en5103892 10.1016/j.corsci.2008.10.038 10.1016/j.commatsci.2008.05.010 10.1142/S0129183106009813 10.1023/B:STCO.0000035301.49549.88 10.1016/j.corsci.2003.09.011 10.1016/j.corsci.2010.09.013 10.1016/j.petrol.2008.12.023 10.1007/978-1-4757-2440-0 10.1021/ef401815q 10.1016/j.jhydrol.2006.02.025 10.1016/j.ces.2007.11.030 10.1016/j.jlp.2011.12.007 10.1016/j.matlet.2006.06.053 10.1016/j.autcon.2014.05.003 10.1163/156939308783122788 10.4028/www.scientific.net/AMR.652-654.1088 10.1080/01621459.1985.10478157 10.6339/JDS.2004.02(4).156 10.1179/cmq.2010.49.1.99 10.1179/1743278211Y.0000000001 10.1016/j.corsci.2009.06.004 10.1016/j.petrol.2006.12.001 10.1016/S0167-6911(82)80025-X |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media New York 2016 |
| Copyright_xml | – notice: Springer Science+Business Media New York 2016 |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s10553-016-0664-7 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Chemistry Ecology |
| EISSN | 1573-8310 |
| EndPage | 739 |
| ExternalDocumentID | 10_1007_s10553_016_0664_7 |
| GroupedDBID | -4Y -58 -5G -BR -EM -Y2 -~C .86 .VR 06C 06D 0R~ 0VY 1N0 1SB 2.D 28- 29B 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2XV 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 642 67Z 6NX 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAIKT AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ ASPBG AVWKF AXYYD AZFZN B-. B0M BA0 BBWZM BDATZ BGNMA BSONS CAG COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS EIOEI EJD EMK EPL ESBYG ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F IAO IEP IHE IJ- IKXTQ ITC IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C JBSCW JCJTX JZLTJ KDC KOW L8X LAK LLZTM M4Y MA- ML- N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P9N PF0 PT4 PT5 QOK QOR QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCG SCLPG SCM SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 W4F WJK WK8 XU3 YLTOR Z5O Z7R Z7S Z7V Z7W Z7Z Z86 Z8M Z8N Z8P Z8Q Z8T ~8M ~A9 ~EX ~KM AAPKM AAYXX ABDBE ABFSG ABJIA ABRTQ ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR CITATION |
| ID | FETCH-LOGICAL-c325t-4ec69984b9ec7f4cd982942cc99660a7ce5340e4f800667c2aeeaf8d54b0e3913 |
| IEDL.DBID | U2A |
| ISSN | 0009-3092 |
| IngestDate | Wed Oct 01 03:16:15 EDT 2025 Thu Apr 24 22:58:09 EDT 2025 Fri Feb 21 02:36:54 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | corrosion rate comparison analysis prediction model ACE algorithm carbon steel |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c325t-4ec69984b9ec7f4cd982942cc99660a7ce5340e4f800667c2aeeaf8d54b0e3913 |
| PageCount | 12 |
| ParticipantIDs | crossref_primary_10_1007_s10553_016_0664_7 crossref_citationtrail_10_1007_s10553_016_0664_7 springer_journals_10_1007_s10553_016_0664_7 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20160100 2016-1-00 |
| PublicationDateYYYYMMDD | 2016-01-01 |
| PublicationDate_xml | – month: 1 year: 2016 text: 20160100 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Chemistry and technology of fuels and oils |
| PublicationTitleAbbrev | Chem Technol Fuels Oils |
| PublicationYear | 2016 |
| Publisher | Springer US |
| Publisher_xml | – name: Springer US |
| References | Li, Jia (CR18) 2011; 6 Yong (CR17) 2014; 35 CR16 CR38 CR15 CR14 CR13 CR12 Wen, Cai, Liu (CR29) 2009; 51 Birbilis, Cavanaugh, Sudholz (CR24) 2011; 53 CR31 Shang, Zhu (CR21) 2007; 19 Zhu, Bai, Liu (CR35) 2008; 29 Liu, Control (CR11) 2005 Long, Zheng, Chen (CR37) 2005; 26 Yin (CR9) 2013; 40 Ma, Wang (CR10) 2007; 61 Li, Yu, Zeng (CR2) 2009; 65 Sakhanenko, Luger (CR27) 2006; 17 Chau (CR19) 2006; 329 Wang, Murphy (CR39) 2004; 2 CR5 CR8 CR7 Smola, Scholkopf (CR26) 2004; 14 CR28 Shahriar, Sadiq, Tesfamariam (CR3) 2012; 25 CR23 Zhao, Lu (CR36) 2008; 23 CR22 Chen, Yu (CR34) 2006 Lahiri, Ghanta (CR4) 2008; 63 El-Abbasy, Senouci, Zayed, Mirahadi (CR1) 2014; 45 CR40 Engelhardt, Macdonald (CR6) 2004; 46 Wang, Zhang, Zhou (CR32) 2014; 44 Li, Wang, Li (CR33) 2014; 28 Vapnik (CR25) 1995 Liao, Yao, Wu (CR20) 2012; 5 Fang, Wang, Qi (CR30) 2008; 44 S Yong (664_CR17) 2014; 35 KW Chau (664_CR19) 2006; 329 CJ Li (664_CR18) 2011; 6 664_CR28 V Vapnik (664_CR25) 1995 R-d Chen (664_CR34) 2006 A Shahriar (664_CR3) 2012; 25 J-k Liu (664_CR11) 2005 NA Sakhanenko (664_CR27) 2006; 17 MS El-Abbasy (664_CR1) 2014; 45 S-d Zhu (664_CR35) 2008; 29 G Engelhardt (664_CR6) 2004; 46 664_CR15 664_CR16 664_CR38 664_CR13 664_CR14 YF Wen (664_CR29) 2009; 51 G-x Zhao (664_CR36) 2008; 23 664_CR12 N Birbilis (664_CR24) 2011; 53 SK Lahiri (664_CR4) 2008; 63 664_CR31 F-Y Ma (664_CR10) 2007; 61 664_CR8 664_CR7 664_CR5 D Wang (664_CR39) 2004; 2 J Shang (664_CR21) 2007; 19 F-l Long (664_CR37) 2005; 26 K-X Liao (664_CR20) 2012; 5 664_CR40 SF Fang (664_CR30) 2008; 44 X-G Wang (664_CR32) 2014; 44 AJ Smola (664_CR26) 2004; 14 S-X Li (664_CR2) 2009; 65 664_CR22 664_CR23 M-S Yin (664_CR9) 2013; 40 Z-m Li (664_CR33) 2014; 28 |
| References_xml | – ident: CR22 – volume: 40 start-page: 2767 year: 2013 end-page: 2775 ident: CR9 article-title: Fifteen years of grey system theory research: A historical review and bibliometric analysis publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.11.002 – volume: 5 start-page: 3892 year: 2012 end-page: 3907 ident: CR20 article-title: A numerical corrosion rate prediction method for direct assessment of wet gas gathering pipelines internal corrosion publication-title: Energies doi: 10.3390/en5103892 – volume: 51 start-page: 349 year: 2009 end-page: 355 ident: CR29 article-title: Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression publication-title: Corrosion Science doi: 10.1016/j.corsci.2008.10.038 – ident: CR14 – volume: 44 start-page: 647 year: 2008 end-page: 655 ident: CR30 article-title: Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials publication-title: Computational Materials Science doi: 10.1016/j.commatsci.2008.05.010 – ident: CR16 – volume: 17 start-page: 1313 year: 2006 end-page: 1325 ident: CR27 article-title: Shock physics data reconstruction using support vector regression publication-title: Int. J. Mod. Phys. doi: 10.1142/S0129183106009813 – ident: CR12 – volume: 14 start-page: 199 year: 2004 end-page: 222 ident: CR26 article-title: A tutorial on support vector regression publication-title: Stat. Comput. doi: 10.1023/B:STCO.0000035301.49549.88 – volume: 46 start-page: 1159 year: 2004 end-page: 1187 ident: CR6 article-title: Estimation of corrosion cavity growth rate for predicting system service life publication-title: Corrosion Science doi: 10.1016/j.corsci.2003.09.011 – volume: 53 start-page: 168 year: 2011 end-page: 176 ident: CR24 article-title: A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys publication-title: Corrosion Science doi: 10.1016/j.corsci.2010.09.013 – ident: CR8 – ident: CR40 – volume: 65 start-page: 162 year: 2009 end-page: 166 ident: CR2 article-title: Predicting corrosion remaining life of underground pipelines with a mechanically-based probabilistic mode publication-title: Journal of Petroleum Science and Engineering doi: 10.1016/j.petrol.2008.12.023 – ident: CR23 – year: 1995 ident: CR25 publication-title: The Nature of Statistical Learning Theory doi: 10.1007/978-1-4757-2440-0 – volume: 28 start-page: 624 year: 2014 end-page: 635 ident: CR33 article-title: Accurate determination of the CO2-brine interfacial tension using graphical alternating conditional expectation publication-title: Energy & Fuels doi: 10.1021/ef401815q – volume: 19 start-page: 225 issue: 3 year: 2007 end-page: 228 ident: CR21 article-title: Application of genetic algorithms neural networks in prediction corrosion rate of carbon steel publication-title: Corrosion Science and Protection Technology – ident: CR15 – year: 2006 ident: CR34 publication-title: Mathematical Model and Mathematical Modeling – ident: CR38 – volume: 329 start-page: 3 year: 2006 end-page: 4 ident: CR19 article-title: Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2006.02.025 – volume: 35 start-page: 107 issue: 3 year: 2014 end-page: 110 ident: CR17 article-title: The research and application of genetic algorithm publication-title: Software – volume: 29 start-page: 724 issue: 12 year: 2008 end-page: 726 ident: CR35 article-title: Influence of Ca and Mg on corrosion rate of N80 publication-title: Corrosion & Protection – ident: CR31 – ident: CR13 – volume: 63 start-page: 1497 year: 2008 end-page: 1509 ident: CR4 article-title: Development of an artificial neural network correlation for prediction of slurry transport in pipeline publication-title: Chemical Engineering Science doi: 10.1016/j.ces.2007.11.030 – volume: 6 start-page: 452 year: 2011 end-page: 459 ident: CR18 article-title: Adaptive genetic algorithm for steady-state operation optimization in natural gas networks publication-title: J. Softw. – volume: 44 start-page: 82 issue: 7 year: 2014 end-page: 87 ident: CR32 article-title: Grey combination forecast model for corrosion rate of pipelines based on the least squares support vector machine publication-title: Practice and Theory of Mathematics – volume: 25 start-page: 505 year: 2012 end-page: 523 ident: CR3 article-title: Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis publication-title: Journal of Loss Prevention in the Process Industries doi: 10.1016/j.jlp.2011.12.007 – ident: CR5 – volume: 2 start-page: 329 year: 2004 end-page: 346 ident: CR39 article-title: Estimation optimal transformations for multiple regression using the ACE algorithm publication-title: Journal of Data Science – ident: CR7 – volume: 61 start-page: 998 year: 2007 end-page: 1001 ident: CR10 article-title: Prediction of pitting corrosion behavior for stainless SUS 630 based on grey system theory publication-title: Materials Letters doi: 10.1016/j.matlet.2006.06.053 – volume: 45 start-page: 50 year: 2014 end-page: 65 ident: CR1 article-title: Artificial neural network models for predicting condition of offshore oil and gas pipelines publication-title: Automation in Construction doi: 10.1016/j.autcon.2014.05.003 – ident: CR28 – volume: 26 start-page: 290 issue: 7 year: 2005 end-page: 293 ident: CR37 article-title: Influence of temperature, CO2 partial pressure, flow rate and pH value on uniform corrosion rate of X65 pipeline steel publication-title: Corrosion & Protection – volume: 23 start-page: 74 issue: 4 year: 2008 end-page: 78 ident: CR36 article-title: Effect of temperature on the corrosion rate of oil tubing and casing publication-title: Journal of Xi’an Shiyou University (Natural Science Edition) – year: 2005 ident: CR11 publication-title: Corrosion rate prediction of pipeline based on BP artificial neural network – volume: 6 start-page: 452 year: 2011 ident: 664_CR18 publication-title: J. Softw. – volume: 46 start-page: 1159 year: 2004 ident: 664_CR6 publication-title: Corrosion Science doi: 10.1016/j.corsci.2003.09.011 – ident: 664_CR13 – ident: 664_CR28 doi: 10.1163/156939308783122788 – volume: 63 start-page: 1497 year: 2008 ident: 664_CR4 publication-title: Chemical Engineering Science doi: 10.1016/j.ces.2007.11.030 – ident: 664_CR5 – volume: 65 start-page: 162 year: 2009 ident: 664_CR2 publication-title: Journal of Petroleum Science and Engineering doi: 10.1016/j.petrol.2008.12.023 – volume: 44 start-page: 82 issue: 7 year: 2014 ident: 664_CR32 publication-title: Practice and Theory of Mathematics – ident: 664_CR15 doi: 10.4028/www.scientific.net/AMR.652-654.1088 – volume: 19 start-page: 225 issue: 3 year: 2007 ident: 664_CR21 publication-title: Corrosion Science and Protection Technology – volume: 29 start-page: 724 issue: 12 year: 2008 ident: 664_CR35 publication-title: Corrosion & Protection – ident: 664_CR38 doi: 10.1080/01621459.1985.10478157 – volume: 2 start-page: 329 year: 2004 ident: 664_CR39 publication-title: Journal of Data Science doi: 10.6339/JDS.2004.02(4).156 – volume: 5 start-page: 3892 year: 2012 ident: 664_CR20 publication-title: Energies doi: 10.3390/en5103892 – ident: 664_CR22 doi: 10.1179/cmq.2010.49.1.99 – volume-title: The Nature of Statistical Learning Theory year: 1995 ident: 664_CR25 doi: 10.1007/978-1-4757-2440-0 – volume: 53 start-page: 168 year: 2011 ident: 664_CR24 publication-title: Corrosion Science doi: 10.1016/j.corsci.2010.09.013 – volume-title: Mathematical Model and Mathematical Modeling year: 2006 ident: 664_CR34 – ident: 664_CR8 – ident: 664_CR14 – ident: 664_CR31 – ident: 664_CR12 – volume: 26 start-page: 290 issue: 7 year: 2005 ident: 664_CR37 publication-title: Corrosion & Protection – ident: 664_CR16 doi: 10.1179/1743278211Y.0000000001 – volume: 17 start-page: 1313 year: 2006 ident: 664_CR27 publication-title: Int. J. Mod. Phys. doi: 10.1142/S0129183106009813 – volume: 23 start-page: 74 issue: 4 year: 2008 ident: 664_CR36 publication-title: Journal of Xi’an Shiyou University (Natural Science Edition) – volume: 14 start-page: 199 year: 2004 ident: 664_CR26 publication-title: Stat. Comput. doi: 10.1023/B:STCO.0000035301.49549.88 – volume: 44 start-page: 647 year: 2008 ident: 664_CR30 publication-title: Computational Materials Science doi: 10.1016/j.commatsci.2008.05.010 – volume: 28 start-page: 624 year: 2014 ident: 664_CR33 publication-title: Energy & Fuels doi: 10.1021/ef401815q – volume-title: Corrosion rate prediction of pipeline based on BP artificial neural network year: 2005 ident: 664_CR11 – volume: 25 start-page: 505 year: 2012 ident: 664_CR3 publication-title: Journal of Loss Prevention in the Process Industries doi: 10.1016/j.jlp.2011.12.007 – volume: 35 start-page: 107 issue: 3 year: 2014 ident: 664_CR17 publication-title: Software – volume: 45 start-page: 50 year: 2014 ident: 664_CR1 publication-title: Automation in Construction doi: 10.1016/j.autcon.2014.05.003 – ident: 664_CR23 doi: 10.1016/j.corsci.2009.06.004 – volume: 51 start-page: 349 year: 2009 ident: 664_CR29 publication-title: Corrosion Science doi: 10.1016/j.corsci.2008.10.038 – ident: 664_CR40 doi: 10.1016/j.petrol.2006.12.001 – volume: 40 start-page: 2767 year: 2013 ident: 664_CR9 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.11.002 – ident: 664_CR7 doi: 10.1016/S0167-6911(82)80025-X – volume: 61 start-page: 998 year: 2007 ident: 664_CR10 publication-title: Materials Letters doi: 10.1016/j.matlet.2006.06.053 – volume: 329 start-page: 3 year: 2006 ident: 664_CR19 publication-title: J. Hydrol. |
| SSID | ssj0009723 |
| Score | 2.0004795 |
| Snippet | Based on dynamic corrosion experiments, we propose a new model for predicting corrosion rate that is based on an alternating conditional expectation (ACE)... |
| SourceID | crossref springer |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 728 |
| SubjectTerms | Chemistry Chemistry and Materials Science Ecology Geotechnical Engineering & Applied Earth Sciences Industrial Chemistry/Chemical Engineering Mineral Resources |
| Title | Prediction of Carbon Steel Corrosion Rate Based on an Alternating Conditional Expectation (Ace) Algorithm |
| URI | https://link.springer.com/article/10.1007/s10553-016-0664-7 |
| Volume | 51 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1573-8310 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009723 issn: 0009-3092 databaseCode: ABDBF dateStart: 20030501 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1573-8310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009723 issn: 0009-3092 databaseCode: ADMLS dateStart: 19970101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1573-8310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009723 issn: 0009-3092 databaseCode: AFBBN dateStart: 19650101 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1573-8310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009723 issn: 0009-3092 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1573-8310 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009723 issn: 0009-3092 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA9-IOqD6FScHyMPPvhBIU3Tpn2sc3OoE3EO5lNJ0nQOZie1_v_mutZtoIJPTck1hbu0d7mP3yF0KpXtB7ZgVuITbTEWc0sm1LWM8Stsj0sec6h37j54nT67HbiDso77o8p2r0KSxZ96rtjNdSH3x4OqeWbxZbTqApqX2cR9Gs6Qdjn9bp_mkIBWocyfllhURouR0ELBtLfRVmkZ4nAqyh20pNMaWm9WDdlqaK1VIEyb0eYciuAuGj1mEG0BDuNJgpsik2bUy7Ue4-YkM2-GmSdjVOIro7NibO5EisNx6QtMh4YMIteFVxAD-LGaBujxWaj0uaEcTrJR_vq2h_rt1nOzY5UdFCzlUDe3mFaeOU8xGWjFE6biwKcBo0rBKYcIrrTrMKKZERQkuyoqtBaJH7tMEu0EtrOPVtJJqg8QDsyc7QlHJgTWi6V0hGf7RHHCpNFpdUQqVkaqhBeHLhfjaAaMDNyPIKUMuB_xOrr4fuR9iq3xF_FlJZ-o_Mw-fqc-_Bf1EdqgxaYAz8oxWsmzT31ibI1cNtBqeN2978H15uWu1Sj22hdoNcyv |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZgCAEH3ojxzIEDD3VK07Rpj2MajKcQDAlOVZKmY2J0qCsXfj1J146HAGm3VHGjJE5rO7Y_A-wJafuBzakV-1hZlEbMEjFxLa38cttjgkXM5DtfXXute3r-4D4UedyDMtq9dEnmf-ovyW6ua2J_PJM1Ty02CVNU2yekAlP108eL5ifWLiOjAmoODkjpzPxtkO_i6LsvNBcxJwvQLic3jCx5rr1loibff-A2jjn7RZgvVE5UH56RJZhQyTLMNMpKb8sw3cyhq3Vr7gs84Qp0b1LjxjGsQ_0YNXgqdOsuU6qHGv1UL8j03GptFR1rYRgh_cQTVO8Vl4xJR5MZl3h-3YgMqrIcev7Rfl2qA03Z6afd7OllFe5Pmu1GyypKM1jSIW5mUSU9bahRESjJYiqjwCcBJVIa8wlzJpXrUKyoPgEmilYSrhSP_cilAisnsJ01qCT9RK0DCnSf7XFHxNiMFwnhcM_2sWSYCi0sq4BLDoWywC035TN64Sfistnb0MSqmb0NWRUOR6-8DkE7_iM-KjkWFt_v4G_qjbGod2Gm1b66DC_Pri82YZbk7DfXN1tQydI3ta0VmkzsFAf4A_FQ6TY |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ZT8MwDI44xPWAOMU488ADh6qladq0j6Vs4pwmYNLeqiRNx6TRoa78f-Ku5ZAAibdUcVPJdmXHx2eEjqWy_cAWzEp9oi3GEm7JlLqWcX6F7XHJEw79zvcd76rHbvpuv5pzOqmr3euU5LSnAVCasqL5mqTNL41vrgt1QB500DOLz6J5BjgJRqF7NPxE3eX0Y5SaQwJapzV_OuK7YfqeFS2NTXsNrVZeIg6nYl1HMzrbQEtRPZxtAy20SrRps1r5gii4iYbdHDIvwG08TnEkcmlWj4XWIxyNc_Nl2HkwDia-MPYrweZJZDgcVXHBbGDIIItdRggxACGrabIen4RKnxrKwTgfFs8vW6jXbj1FV1Y1TcFSDnULi2nlmbsVk4FWPGUqCXwaMKoU3HiI4Eq7DiOaGaFB4auiQmuR-onLJNFOYDvbaC4bZ3oH4cDs2Z5wZErgvERKR3i2TxQnTBr71kCkZmWsKqhxmHgxij9BkoH7MZSXAfdj3kBnH6-8TnE2_iI-r-UTV7_c5Hfq3X9RH6HF7mU7vrvu3O6hZVrqBwRc9tFckb_pA-OCFPKwVLN3ZqnQrw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+Carbon+Steel+Corrosion+Rate+Based+on+an+Alternating+Conditional+Expectation+%28Ace%29+Algorithm&rft.jtitle=Chemistry+and+technology+of+fuels+and+oils&rft.au=Chen%2C+Xing-yi&rft.au=Yuan%2C+Zong-ming&rft.au=Zheng%2C+Yun-ping&rft.au=Liu%2C+Wei&rft.date=2016-01-01&rft.pub=Springer+US&rft.issn=0009-3092&rft.eissn=1573-8310&rft.volume=51&rft.issue=6&rft.spage=728&rft.epage=739&rft_id=info:doi/10.1007%2Fs10553-016-0664-7&rft.externalDocID=10_1007_s10553_016_0664_7 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0009-3092&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0009-3092&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0009-3092&client=summon |