Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil
In this paper, an artificial neural network (ANN) model was used to model and predict the desulfurization efficiency of AL-Ahdab crude oil (AHD). This study implements the artificial neural network (ANN) in modeling and predicting sulfur removal from AL-Ahdab crude oil for a better understanding and...
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| creator | Alardhi, Saja M. Jabbar, Noor M. AL-Jadir, Thaer Ibrahim, Neran K. Dakhil, Ali M. Al-Saedi, Noor Dh Al-Saedi, Haneen Dh Adnan, Mustafa |
| description | In this paper, an artificial neural network (ANN) model was used to model and predict the desulfurization efficiency of AL-Ahdab crude oil (AHD). This study implements the artificial neural network (ANN) in modeling and predicting sulfur removal from AL-Ahdab crude oil for a better understanding and optimizing of the process operation. This study was based on data sets collected from previous work on AHD sour crude oil (3.9 wt% sulfur content). The developed model's accuracy was assessed by the mean squared error and goodness of fit (R2). In this study, fifteen neural network models for the desulfurization process were designed and validated. Results show that the developed model (9) is in excellent agreement with experimental data. Model (9) is the best model with the lowest mean square error (0.001), two hidden layers and 20 neurons and the value of the correlation coefficient (R2) is 0.999. |
| doi_str_mv | 10.1063/5.0091975 |
| format | Conference Proceeding |
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This study implements the artificial neural network (ANN) in modeling and predicting sulfur removal from AL-Ahdab crude oil for a better understanding and optimizing of the process operation. This study was based on data sets collected from previous work on AHD sour crude oil (3.9 wt% sulfur content). The developed model's accuracy was assessed by the mean squared error and goodness of fit (R2). In this study, fifteen neural network models for the desulfurization process were designed and validated. Results show that the developed model (9) is in excellent agreement with experimental data. Model (9) is the best model with the lowest mean square error (0.001), two hidden layers and 20 neurons and the value of the correlation coefficient (R2) is 0.999.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0091975</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Artificial neural networks ; Correlation coefficients ; Crude oil ; Desulfurizing ; Goodness of fit ; Model accuracy ; Sulfur ; Sulfur removal</subject><ispartof>AIP Conference Proceedings, 2022, Vol.2443 (1)</ispartof><rights>Author(s)</rights><rights>2022 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c208t-7c3837d69966d0250508a1f0670eebdb0f238b5f983fb77ebac3bf89fc3ec0b33</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0091975$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,780,784,789,790,794,4512,23930,23931,25140,27924,27925,76384</link.rule.ids></links><search><contributor>Al Rbeawi, Salam Jabbar Hussain</contributor><contributor>Taghikhani, Vahid</contributor><contributor>Abbas, Ali Hassan</contributor><contributor>Al-Rubaiey, Najem Abdulkadhim</contributor><contributor>Rigby, Sean</contributor><contributor>Albayati, Talib M</contributor><contributor>Ramadhan, Ahmad Abdullah</contributor><creatorcontrib>Alardhi, Saja M.</creatorcontrib><creatorcontrib>Jabbar, Noor M.</creatorcontrib><creatorcontrib>AL-Jadir, Thaer</creatorcontrib><creatorcontrib>Ibrahim, Neran K.</creatorcontrib><creatorcontrib>Dakhil, Ali M.</creatorcontrib><creatorcontrib>Al-Saedi, Noor Dh</creatorcontrib><creatorcontrib>Al-Saedi, Haneen Dh</creatorcontrib><creatorcontrib>Adnan, Mustafa</creatorcontrib><title>Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil</title><title>AIP Conference Proceedings</title><description>In this paper, an artificial neural network (ANN) model was used to model and predict the desulfurization efficiency of AL-Ahdab crude oil (AHD). This study implements the artificial neural network (ANN) in modeling and predicting sulfur removal from AL-Ahdab crude oil for a better understanding and optimizing of the process operation. This study was based on data sets collected from previous work on AHD sour crude oil (3.9 wt% sulfur content). The developed model's accuracy was assessed by the mean squared error and goodness of fit (R2). In this study, fifteen neural network models for the desulfurization process were designed and validated. Results show that the developed model (9) is in excellent agreement with experimental data. Model (9) is the best model with the lowest mean square error (0.001), two hidden layers and 20 neurons and the value of the correlation coefficient (R2) is 0.999.</description><subject>Artificial neural networks</subject><subject>Correlation coefficients</subject><subject>Crude oil</subject><subject>Desulfurizing</subject><subject>Goodness of fit</subject><subject>Model accuracy</subject><subject>Sulfur</subject><subject>Sulfur removal</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kE1LAzEYhIMoWKsH_0HAm7D1zaZJNsel-AUFLwrewubLpm43NZtV6q-3tgVvngaGhxlmELokMCHA6Q2bAEgiBTtCI8IYKQQn_BiNtu60KKf09RSd9f0SoJRCVCNk6pSDDyY0Le7ckHaSv2J6x6toXYt9THidnA0mh-4N54XD1vVD64cUvpscYoed_w1wndng6HHdFvXCNhqbNFiHY2jP0Ylv2t5dHHSMXu5un2cPxfzp_nFWzwtTQpULYWhFheVScm6hZMCgaogHLsA5bTX4klaaeVlRr4VwujFU-0p6Q50BTekYXe1z1yl-DK7PahmH1G0rVckrIQFYybfU9Z7qTci7AWqdwqpJG0VA_Z6omDqc-B_8GdMfqNbW0x84I3PC</recordid><startdate>20220711</startdate><enddate>20220711</enddate><creator>Alardhi, Saja M.</creator><creator>Jabbar, Noor M.</creator><creator>AL-Jadir, Thaer</creator><creator>Ibrahim, Neran K.</creator><creator>Dakhil, Ali M.</creator><creator>Al-Saedi, Noor Dh</creator><creator>Al-Saedi, Haneen Dh</creator><creator>Adnan, Mustafa</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20220711</creationdate><title>Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil</title><author>Alardhi, Saja M. ; Jabbar, Noor M. ; AL-Jadir, Thaer ; Ibrahim, Neran K. ; Dakhil, Ali M. ; Al-Saedi, Noor Dh ; Al-Saedi, Haneen Dh ; Adnan, Mustafa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c208t-7c3837d69966d0250508a1f0670eebdb0f238b5f983fb77ebac3bf89fc3ec0b33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural networks</topic><topic>Correlation coefficients</topic><topic>Crude oil</topic><topic>Desulfurizing</topic><topic>Goodness of fit</topic><topic>Model accuracy</topic><topic>Sulfur</topic><topic>Sulfur removal</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alardhi, Saja M.</creatorcontrib><creatorcontrib>Jabbar, Noor M.</creatorcontrib><creatorcontrib>AL-Jadir, Thaer</creatorcontrib><creatorcontrib>Ibrahim, Neran K.</creatorcontrib><creatorcontrib>Dakhil, Ali M.</creatorcontrib><creatorcontrib>Al-Saedi, Noor Dh</creatorcontrib><creatorcontrib>Al-Saedi, Haneen Dh</creatorcontrib><creatorcontrib>Adnan, Mustafa</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alardhi, Saja M.</au><au>Jabbar, Noor M.</au><au>AL-Jadir, Thaer</au><au>Ibrahim, Neran K.</au><au>Dakhil, Ali M.</au><au>Al-Saedi, Noor Dh</au><au>Al-Saedi, Haneen Dh</au><au>Adnan, Mustafa</au><au>Al Rbeawi, Salam Jabbar Hussain</au><au>Taghikhani, Vahid</au><au>Abbas, Ali Hassan</au><au>Al-Rubaiey, Najem Abdulkadhim</au><au>Rigby, Sean</au><au>Albayati, Talib M</au><au>Ramadhan, Ahmad Abdullah</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil</atitle><btitle>AIP Conference Proceedings</btitle><date>2022-07-11</date><risdate>2022</risdate><volume>2443</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>In this paper, an artificial neural network (ANN) model was used to model and predict the desulfurization efficiency of AL-Ahdab crude oil (AHD). This study implements the artificial neural network (ANN) in modeling and predicting sulfur removal from AL-Ahdab crude oil for a better understanding and optimizing of the process operation. This study was based on data sets collected from previous work on AHD sour crude oil (3.9 wt% sulfur content). The developed model's accuracy was assessed by the mean squared error and goodness of fit (R2). In this study, fifteen neural network models for the desulfurization process were designed and validated. Results show that the developed model (9) is in excellent agreement with experimental data. Model (9) is the best model with the lowest mean square error (0.001), two hidden layers and 20 neurons and the value of the correlation coefficient (R2) is 0.999.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0091975</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP Conference Proceedings, 2022, Vol.2443 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0091975 |
| source | AIP Journals Complete |
| subjects | Artificial neural networks Correlation coefficients Crude oil Desulfurizing Goodness of fit Model accuracy Sulfur Sulfur removal |
| title | Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil |
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