Application of ANN to predict the apparent viscosity of waxy crude oil
BPNN structure to predict the Apparent Viscosity of Waxy Crude Oil The Back Propagation Neural Network (BPNN) is optimized with Genetic Algorithm (GA). All the chromosomes are decoded to calculate their respective fitness. The high fitness chromosomes will be selected and a better population will be...
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| creator | Zhang, Fan Fadul Mukhtar, Yasir M. Liu, Ben Li, Jiajun |
| description | BPNN structure to predict the Apparent Viscosity of Waxy Crude Oil
The Back Propagation Neural Network (BPNN) is optimized with Genetic Algorithm (GA). All the chromosomes are decoded to calculate their respective fitness. The high fitness chromosomes will be selected and a better population will be obtained through genetic algorithms. After meeting the requirement (maximum number of generations or goal error), the training will stop and the optimal chromosome can be obtained. The six parameters (K0, n0, Kj, nj, sg and cspw) are input variables of GA-BPNN model. The consistency coefficient (KT) and the flow behavior index (nT) are the output of the model, by which the apparent viscosity of PPD-treated crude oil with shear effect of viscous flow can be determined.
[Display omitted]
•The GA-BPNN model suits different kinds of crude oil, avoids the longtime of experiments and maintains high accuracy.•The entropy generation caused by viscous flow rates is the most important variable in apparent viscosity prediction.•The apparent viscosity should be used with the effect of shear history to design and operate the waxy crude oil pipeline.
This study investigated the apparent viscosity of waxy crude oil treated with pour point depressant (PPD). It considered the shear history and thermal history as the main factors to affect the apparent viscosity of the crude oil when transported by a long-distance pipeline. According to previous practice and present laboratory works, the apparent viscosity that can be determined according to the conventional test specifications without taking into account the effect of shear history cannot be used to successfully design and operate a waxy crude oil pipeline.
Thus, with the help of entropy generation (sg) combination, which is caused by the viscous flow of crude in the pipeline and the back propagation artificial neural networks (ANN) optimized by a genetic algorithm, a prediction model was developed to determine the viscosity of PPD treated waxy crude oil, which was affected by shear. The performance of the model was evaluated through four statistical indices, such as the Mean Absolute Percentage Error (MAPE). The MAPE of all data of the apparent viscosity was 12.20%. The influence of each variable on the apparent viscosity was investigated through a sensitivity analysis, which revealed that sg caused by viscous flow rates was the most important variable in viscosity prediction. |
| doi_str_mv | 10.1016/j.fuel.2019.115669 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2281144302</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S001623611931021X</els_id><sourcerecordid>2281144302</sourcerecordid><originalsourceid>FETCH-LOGICAL-c409t-f0c9a02d57880a0e83dd43d9c8470102f37fabfa617842706112777c69fd83fa3</originalsourceid><addsrcrecordid>eNp9kE1LxDAQQIMouH78AU8Bz62TpG1S8LIsrgrLetFziGmCKbWpSbq6_96WevY0l_dmhofQDYGcAKnu2tyOpsspkDonpKyq-gStiOAs46Rkp2gFE5VRVpFzdBFjCwBclMUKbdfD0DmtkvM99hav93ucPB6CaZxOOH0YrIZBBdMnfHBR--jScQa_1c8R6zA2BnvXXaEzq7porv_mJXrbPrxunrLdy-PzZr3LdAF1yizoWgFtSi4EKDCCNU3BmlqLggMBahm36t2qinBRUA4VIZRzrqvaNoJZxS7R7bJ3CP5rNDHJ1o-hn05KSgUhRcGAThRdKB18jMFYOQT3qcJREpBzL9nKuZece8ml1yTdL5KZ_j84E2TUzvR6ChGMTrLx7j_9F4Ywcjc</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2281144302</pqid></control><display><type>article</type><title>Application of ANN to predict the apparent viscosity of waxy crude oil</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Zhang, Fan ; Fadul Mukhtar, Yasir M. ; Liu, Ben ; Li, Jiajun</creator><creatorcontrib>Zhang, Fan ; Fadul Mukhtar, Yasir M. ; Liu, Ben ; Li, Jiajun</creatorcontrib><description>BPNN structure to predict the Apparent Viscosity of Waxy Crude Oil
The Back Propagation Neural Network (BPNN) is optimized with Genetic Algorithm (GA). All the chromosomes are decoded to calculate their respective fitness. The high fitness chromosomes will be selected and a better population will be obtained through genetic algorithms. After meeting the requirement (maximum number of generations or goal error), the training will stop and the optimal chromosome can be obtained. The six parameters (K0, n0, Kj, nj, sg and cspw) are input variables of GA-BPNN model. The consistency coefficient (KT) and the flow behavior index (nT) are the output of the model, by which the apparent viscosity of PPD-treated crude oil with shear effect of viscous flow can be determined.
[Display omitted]
•The GA-BPNN model suits different kinds of crude oil, avoids the longtime of experiments and maintains high accuracy.•The entropy generation caused by viscous flow rates is the most important variable in apparent viscosity prediction.•The apparent viscosity should be used with the effect of shear history to design and operate the waxy crude oil pipeline.
This study investigated the apparent viscosity of waxy crude oil treated with pour point depressant (PPD). It considered the shear history and thermal history as the main factors to affect the apparent viscosity of the crude oil when transported by a long-distance pipeline. According to previous practice and present laboratory works, the apparent viscosity that can be determined according to the conventional test specifications without taking into account the effect of shear history cannot be used to successfully design and operate a waxy crude oil pipeline.
Thus, with the help of entropy generation (sg) combination, which is caused by the viscous flow of crude in the pipeline and the back propagation artificial neural networks (ANN) optimized by a genetic algorithm, a prediction model was developed to determine the viscosity of PPD treated waxy crude oil, which was affected by shear. The performance of the model was evaluated through four statistical indices, such as the Mean Absolute Percentage Error (MAPE). The MAPE of all data of the apparent viscosity was 12.20%. The influence of each variable on the apparent viscosity was investigated through a sensitivity analysis, which revealed that sg caused by viscous flow rates was the most important variable in viscosity prediction.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2019.115669</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Apparent viscosity ; Artificial neural networks ; Back propagation ; Back propagation networks ; Back propagation neural network ; Crude oil ; Entropy ; Entropy generation ; Flow rates ; Flow velocity ; Food processing industry ; Genetic algorithm ; Genetic algorithms ; Neural networks ; Petroleum pipelines ; Pipelines ; Pour point depressant ; Prediction models ; Sensitivity analysis ; Shear ; Shear rate ; Viscosity ; Viscous flow ; Waxy crude oil</subject><ispartof>Fuel (Guildford), 2019-10, Vol.254, p.115669, Article 115669</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier BV Oct 15, 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-f0c9a02d57880a0e83dd43d9c8470102f37fabfa617842706112777c69fd83fa3</citedby><cites>FETCH-LOGICAL-c409t-f0c9a02d57880a0e83dd43d9c8470102f37fabfa617842706112777c69fd83fa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S001623611931021X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Zhang, Fan</creatorcontrib><creatorcontrib>Fadul Mukhtar, Yasir M.</creatorcontrib><creatorcontrib>Liu, Ben</creatorcontrib><creatorcontrib>Li, Jiajun</creatorcontrib><title>Application of ANN to predict the apparent viscosity of waxy crude oil</title><title>Fuel (Guildford)</title><description>BPNN structure to predict the Apparent Viscosity of Waxy Crude Oil
The Back Propagation Neural Network (BPNN) is optimized with Genetic Algorithm (GA). All the chromosomes are decoded to calculate their respective fitness. The high fitness chromosomes will be selected and a better population will be obtained through genetic algorithms. After meeting the requirement (maximum number of generations or goal error), the training will stop and the optimal chromosome can be obtained. The six parameters (K0, n0, Kj, nj, sg and cspw) are input variables of GA-BPNN model. The consistency coefficient (KT) and the flow behavior index (nT) are the output of the model, by which the apparent viscosity of PPD-treated crude oil with shear effect of viscous flow can be determined.
[Display omitted]
•The GA-BPNN model suits different kinds of crude oil, avoids the longtime of experiments and maintains high accuracy.•The entropy generation caused by viscous flow rates is the most important variable in apparent viscosity prediction.•The apparent viscosity should be used with the effect of shear history to design and operate the waxy crude oil pipeline.
This study investigated the apparent viscosity of waxy crude oil treated with pour point depressant (PPD). It considered the shear history and thermal history as the main factors to affect the apparent viscosity of the crude oil when transported by a long-distance pipeline. According to previous practice and present laboratory works, the apparent viscosity that can be determined according to the conventional test specifications without taking into account the effect of shear history cannot be used to successfully design and operate a waxy crude oil pipeline.
Thus, with the help of entropy generation (sg) combination, which is caused by the viscous flow of crude in the pipeline and the back propagation artificial neural networks (ANN) optimized by a genetic algorithm, a prediction model was developed to determine the viscosity of PPD treated waxy crude oil, which was affected by shear. The performance of the model was evaluated through four statistical indices, such as the Mean Absolute Percentage Error (MAPE). The MAPE of all data of the apparent viscosity was 12.20%. The influence of each variable on the apparent viscosity was investigated through a sensitivity analysis, which revealed that sg caused by viscous flow rates was the most important variable in viscosity prediction.</description><subject>Apparent viscosity</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Back propagation neural network</subject><subject>Crude oil</subject><subject>Entropy</subject><subject>Entropy generation</subject><subject>Flow rates</subject><subject>Flow velocity</subject><subject>Food processing industry</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Neural networks</subject><subject>Petroleum pipelines</subject><subject>Pipelines</subject><subject>Pour point depressant</subject><subject>Prediction models</subject><subject>Sensitivity analysis</subject><subject>Shear</subject><subject>Shear rate</subject><subject>Viscosity</subject><subject>Viscous flow</subject><subject>Waxy crude oil</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQQIMouH78AU8Bz62TpG1S8LIsrgrLetFziGmCKbWpSbq6_96WevY0l_dmhofQDYGcAKnu2tyOpsspkDonpKyq-gStiOAs46Rkp2gFE5VRVpFzdBFjCwBclMUKbdfD0DmtkvM99hav93ucPB6CaZxOOH0YrIZBBdMnfHBR--jScQa_1c8R6zA2BnvXXaEzq7porv_mJXrbPrxunrLdy-PzZr3LdAF1yizoWgFtSi4EKDCCNU3BmlqLggMBahm36t2qinBRUA4VIZRzrqvaNoJZxS7R7bJ3CP5rNDHJ1o-hn05KSgUhRcGAThRdKB18jMFYOQT3qcJREpBzL9nKuZece8ml1yTdL5KZ_j84E2TUzvR6ChGMTrLx7j_9F4Ywcjc</recordid><startdate>20191015</startdate><enddate>20191015</enddate><creator>Zhang, Fan</creator><creator>Fadul Mukhtar, Yasir M.</creator><creator>Liu, Ben</creator><creator>Li, Jiajun</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>20191015</creationdate><title>Application of ANN to predict the apparent viscosity of waxy crude oil</title><author>Zhang, Fan ; Fadul Mukhtar, Yasir M. ; Liu, Ben ; Li, Jiajun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-f0c9a02d57880a0e83dd43d9c8470102f37fabfa617842706112777c69fd83fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Apparent viscosity</topic><topic>Artificial neural networks</topic><topic>Back propagation</topic><topic>Back propagation networks</topic><topic>Back propagation neural network</topic><topic>Crude oil</topic><topic>Entropy</topic><topic>Entropy generation</topic><topic>Flow rates</topic><topic>Flow velocity</topic><topic>Food processing industry</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Neural networks</topic><topic>Petroleum pipelines</topic><topic>Pipelines</topic><topic>Pour point depressant</topic><topic>Prediction models</topic><topic>Sensitivity analysis</topic><topic>Shear</topic><topic>Shear rate</topic><topic>Viscosity</topic><topic>Viscous flow</topic><topic>Waxy crude oil</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Fan</creatorcontrib><creatorcontrib>Fadul Mukhtar, Yasir M.</creatorcontrib><creatorcontrib>Liu, Ben</creatorcontrib><creatorcontrib>Li, Jiajun</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Fan</au><au>Fadul Mukhtar, Yasir M.</au><au>Liu, Ben</au><au>Li, Jiajun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of ANN to predict the apparent viscosity of waxy crude oil</atitle><jtitle>Fuel (Guildford)</jtitle><date>2019-10-15</date><risdate>2019</risdate><volume>254</volume><spage>115669</spage><pages>115669-</pages><artnum>115669</artnum><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>BPNN structure to predict the Apparent Viscosity of Waxy Crude Oil
The Back Propagation Neural Network (BPNN) is optimized with Genetic Algorithm (GA). All the chromosomes are decoded to calculate their respective fitness. The high fitness chromosomes will be selected and a better population will be obtained through genetic algorithms. After meeting the requirement (maximum number of generations or goal error), the training will stop and the optimal chromosome can be obtained. The six parameters (K0, n0, Kj, nj, sg and cspw) are input variables of GA-BPNN model. The consistency coefficient (KT) and the flow behavior index (nT) are the output of the model, by which the apparent viscosity of PPD-treated crude oil with shear effect of viscous flow can be determined.
[Display omitted]
•The GA-BPNN model suits different kinds of crude oil, avoids the longtime of experiments and maintains high accuracy.•The entropy generation caused by viscous flow rates is the most important variable in apparent viscosity prediction.•The apparent viscosity should be used with the effect of shear history to design and operate the waxy crude oil pipeline.
This study investigated the apparent viscosity of waxy crude oil treated with pour point depressant (PPD). It considered the shear history and thermal history as the main factors to affect the apparent viscosity of the crude oil when transported by a long-distance pipeline. According to previous practice and present laboratory works, the apparent viscosity that can be determined according to the conventional test specifications without taking into account the effect of shear history cannot be used to successfully design and operate a waxy crude oil pipeline.
Thus, with the help of entropy generation (sg) combination, which is caused by the viscous flow of crude in the pipeline and the back propagation artificial neural networks (ANN) optimized by a genetic algorithm, a prediction model was developed to determine the viscosity of PPD treated waxy crude oil, which was affected by shear. The performance of the model was evaluated through four statistical indices, such as the Mean Absolute Percentage Error (MAPE). The MAPE of all data of the apparent viscosity was 12.20%. The influence of each variable on the apparent viscosity was investigated through a sensitivity analysis, which revealed that sg caused by viscous flow rates was the most important variable in viscosity prediction.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2019.115669</doi><oa>free_for_read</oa></addata></record> |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Apparent viscosity Artificial neural networks Back propagation Back propagation networks Back propagation neural network Crude oil Entropy Entropy generation Flow rates Flow velocity Food processing industry Genetic algorithm Genetic algorithms Neural networks Petroleum pipelines Pipelines Pour point depressant Prediction models Sensitivity analysis Shear Shear rate Viscosity Viscous flow Waxy crude oil |
| title | Application of ANN to predict the apparent viscosity of waxy crude oil |
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