Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 1: Hydrocracking Processes
This paper presents a workflow to develop, validate, and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes, a medium-pressure hydrocracking (MP HCR) uni...
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Veröffentlicht in: | Energy & fuels 2011-11, Vol.25 (11), p.5264-5297 |
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description | This paper presents a workflow to develop, validate, and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes, a medium-pressure hydrocracking (MP HCR) unit with a feed capacity of 1 million tons per year and a high-pressure hydrocracking (HP HCR) unit with a feed capacity of 2 million tons per year in the Asia Pacific. The units include reactors, fractionators, and hydrogen recycle systems. With catalyst and hydrogen, the process converts heavy feedstocks, such as vacuum gas oil, into valuable low-boiling products, such as gasoline and diesel. We present the detailed procedure for data acquisition to ensure accurate mass balances and for implementing the workflow using Excel spreadsheets and a commercial software tool, Aspen HYSYS/Refining. Our procedure is equally applicable to other commercial software tools. The workflow includes special tools to facilitate an accurate transition from lumped kinetic components used in reactor modeling to the pseudo-components based on boiling point ranges required in the rigorous stage-by-stage simulation of fractionators. We validate the two models with 4 months of plant data, and the resulting models accurately predict unit performance, product yields, and fuel properties from the corresponding operating conditions. The MP HCR model predicts the yields of heavy naphtha, diesel fuel, and bottom products with average absolute deviations (AADs) of at most 3.4 wt %, predicts the specific gravities of heavy naphtha, diesel fuel, and bottom oil with AADs below 0.0184, predicts the flash point and freezing point of diesel fuel with AADs of 3.6 and 4.1 °C, respectively, and predicts the outlet temperatures of catalyst beds with AADs of 1.9 °C. The HP HCR model predicts the yields of liquefied petroleum gas (LPG), light naphtha, heavy naphtha, jet fuel, and residue oil with AADs below 1.7 wt %, predicts the specific gravities of light naphtha, heavy naphtha, jet fuel, and residue oil with AADs less than 0.134, predicts the flash point and freezing point of jet fuel with AADs of 1.6 and 2.3 °C, respectively, and predicts the outlet temperatures of catalyst beds of the two HCR reactors with AADs of 1.8 and 3.2 °C. We apply the validated plantwide model to quantify the effect of the H2/oil ratio on product distribution and catalyst life and the effect of HCR reactor temperature and feed |
doi_str_mv | 10.1021/ef2007497 |
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Part 1: Hydrocracking Processes</title><source>American Chemical Society Journals</source><creator>Chang, Ai-Fu ; Liu, Y. A</creator><creatorcontrib>Chang, Ai-Fu ; Liu, Y. A</creatorcontrib><description>This paper presents a workflow to develop, validate, and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes, a medium-pressure hydrocracking (MP HCR) unit with a feed capacity of 1 million tons per year and a high-pressure hydrocracking (HP HCR) unit with a feed capacity of 2 million tons per year in the Asia Pacific. The units include reactors, fractionators, and hydrogen recycle systems. With catalyst and hydrogen, the process converts heavy feedstocks, such as vacuum gas oil, into valuable low-boiling products, such as gasoline and diesel. We present the detailed procedure for data acquisition to ensure accurate mass balances and for implementing the workflow using Excel spreadsheets and a commercial software tool, Aspen HYSYS/Refining. Our procedure is equally applicable to other commercial software tools. The workflow includes special tools to facilitate an accurate transition from lumped kinetic components used in reactor modeling to the pseudo-components based on boiling point ranges required in the rigorous stage-by-stage simulation of fractionators. We validate the two models with 4 months of plant data, and the resulting models accurately predict unit performance, product yields, and fuel properties from the corresponding operating conditions. The MP HCR model predicts the yields of heavy naphtha, diesel fuel, and bottom products with average absolute deviations (AADs) of at most 3.4 wt %, predicts the specific gravities of heavy naphtha, diesel fuel, and bottom oil with AADs below 0.0184, predicts the flash point and freezing point of diesel fuel with AADs of 3.6 and 4.1 °C, respectively, and predicts the outlet temperatures of catalyst beds with AADs of 1.9 °C. The HP HCR model predicts the yields of liquefied petroleum gas (LPG), light naphtha, heavy naphtha, jet fuel, and residue oil with AADs below 1.7 wt %, predicts the specific gravities of light naphtha, heavy naphtha, jet fuel, and residue oil with AADs less than 0.134, predicts the flash point and freezing point of jet fuel with AADs of 1.6 and 2.3 °C, respectively, and predicts the outlet temperatures of catalyst beds of the two HCR reactors with AADs of 1.8 and 3.2 °C. We apply the validated plantwide model to quantify the effect of the H2/oil ratio on product distribution and catalyst life and the effect of HCR reactor temperature and feed flow rate on product distribution. The results agree well with experimental observations reported in the literature. Our resulting models only require typical operating conditions and routine analysis of feedstock and products and appear to be the only reported integrated HCR models that can quantitatively simulate all key aspects of reactor operation, fractionator performance, hydrogen consumption, product yield, and fuel properties.</description><identifier>ISSN: 0887-0624</identifier><identifier>EISSN: 1520-5029</identifier><identifier>DOI: 10.1021/ef2007497</identifier><language>eng</language><publisher>American Chemical Society</publisher><subject>Catalysts ; Diesel fuels ; Hydrocracking ; Jet fuels ; Mathematical models ; Naphtha ; Process Engineering ; Reactors ; Workflow</subject><ispartof>Energy & fuels, 2011-11, Vol.25 (11), p.5264-5297</ispartof><rights>Copyright © 2011 American Chemical Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a329t-a7539feedaf9d4271a5e1e916caf1c96f030234d60c136513b7fcc8e82c97e63</citedby><cites>FETCH-LOGICAL-a329t-a7539feedaf9d4271a5e1e916caf1c96f030234d60c136513b7fcc8e82c97e63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/ef2007497$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/ef2007497$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,780,784,2765,27076,27924,27925,56738,56788</link.rule.ids></links><search><creatorcontrib>Chang, Ai-Fu</creatorcontrib><creatorcontrib>Liu, Y. A</creatorcontrib><title>Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 1: Hydrocracking Processes</title><title>Energy & fuels</title><addtitle>Energy Fuels</addtitle><description>This paper presents a workflow to develop, validate, and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes, a medium-pressure hydrocracking (MP HCR) unit with a feed capacity of 1 million tons per year and a high-pressure hydrocracking (HP HCR) unit with a feed capacity of 2 million tons per year in the Asia Pacific. The units include reactors, fractionators, and hydrogen recycle systems. With catalyst and hydrogen, the process converts heavy feedstocks, such as vacuum gas oil, into valuable low-boiling products, such as gasoline and diesel. We present the detailed procedure for data acquisition to ensure accurate mass balances and for implementing the workflow using Excel spreadsheets and a commercial software tool, Aspen HYSYS/Refining. Our procedure is equally applicable to other commercial software tools. The workflow includes special tools to facilitate an accurate transition from lumped kinetic components used in reactor modeling to the pseudo-components based on boiling point ranges required in the rigorous stage-by-stage simulation of fractionators. We validate the two models with 4 months of plant data, and the resulting models accurately predict unit performance, product yields, and fuel properties from the corresponding operating conditions. The MP HCR model predicts the yields of heavy naphtha, diesel fuel, and bottom products with average absolute deviations (AADs) of at most 3.4 wt %, predicts the specific gravities of heavy naphtha, diesel fuel, and bottom oil with AADs below 0.0184, predicts the flash point and freezing point of diesel fuel with AADs of 3.6 and 4.1 °C, respectively, and predicts the outlet temperatures of catalyst beds with AADs of 1.9 °C. The HP HCR model predicts the yields of liquefied petroleum gas (LPG), light naphtha, heavy naphtha, jet fuel, and residue oil with AADs below 1.7 wt %, predicts the specific gravities of light naphtha, heavy naphtha, jet fuel, and residue oil with AADs less than 0.134, predicts the flash point and freezing point of jet fuel with AADs of 1.6 and 2.3 °C, respectively, and predicts the outlet temperatures of catalyst beds of the two HCR reactors with AADs of 1.8 and 3.2 °C. We apply the validated plantwide model to quantify the effect of the H2/oil ratio on product distribution and catalyst life and the effect of HCR reactor temperature and feed flow rate on product distribution. The results agree well with experimental observations reported in the literature. Our resulting models only require typical operating conditions and routine analysis of feedstock and products and appear to be the only reported integrated HCR models that can quantitatively simulate all key aspects of reactor operation, fractionator performance, hydrogen consumption, product yield, and fuel properties.</description><subject>Catalysts</subject><subject>Diesel fuels</subject><subject>Hydrocracking</subject><subject>Jet fuels</subject><subject>Mathematical models</subject><subject>Naphtha</subject><subject>Process Engineering</subject><subject>Reactors</subject><subject>Workflow</subject><issn>0887-0624</issn><issn>1520-5029</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNptkMlOwzAQhi0EEmU58Aa-IMEhZewsjrmhshSpiAi4R4MzrgJpDLaLlFfgqQkUceI0_4y-fzbGjgRMBUhxRlYCqEyrLTYRuYQkB6m32QTKUiVQyGyX7YXwAgBFWuYT9ll5aloT2w_id66hru2X3Fm-QL-k5NFgR_y2j7T0GKnhD2TbnvwwChxNrufYN_zabxL8qTwOIdIqcOvdilcd9pFfYsQpr9BHLs75fGi8M6Pn9XtYNWoKgcIB27HYBTr8jfvs6frqaTZPFvc3t7OLRYKp1DFBlafaEjVodZNJJTAnQVoUBq0wurCQgkyzpgAj0iIX6bOyxpRUSqMVFek-O9m0ffPufU0h1qs2GOrGRcmtQy0UCCiEFmpETzeo8S4ET7Z-8-0K_VALqL_fXf-9e2SPNyyaUL-4te_HG_7hvgD_kH9f</recordid><startdate>20111117</startdate><enddate>20111117</enddate><creator>Chang, Ai-Fu</creator><creator>Liu, Y. A</creator><general>American Chemical Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7SR</scope><scope>7TB</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20111117</creationdate><title>Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 1: Hydrocracking Processes</title><author>Chang, Ai-Fu ; Liu, Y. A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a329t-a7539feedaf9d4271a5e1e916caf1c96f030234d60c136513b7fcc8e82c97e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Catalysts</topic><topic>Diesel fuels</topic><topic>Hydrocracking</topic><topic>Jet fuels</topic><topic>Mathematical models</topic><topic>Naphtha</topic><topic>Process Engineering</topic><topic>Reactors</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Ai-Fu</creatorcontrib><creatorcontrib>Liu, Y. A</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Energy & fuels</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Ai-Fu</au><au>Liu, Y. A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 1: Hydrocracking Processes</atitle><jtitle>Energy & fuels</jtitle><addtitle>Energy Fuels</addtitle><date>2011-11-17</date><risdate>2011</risdate><volume>25</volume><issue>11</issue><spage>5264</spage><epage>5297</epage><pages>5264-5297</pages><issn>0887-0624</issn><eissn>1520-5029</eissn><abstract>This paper presents a workflow to develop, validate, and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes, a medium-pressure hydrocracking (MP HCR) unit with a feed capacity of 1 million tons per year and a high-pressure hydrocracking (HP HCR) unit with a feed capacity of 2 million tons per year in the Asia Pacific. The units include reactors, fractionators, and hydrogen recycle systems. With catalyst and hydrogen, the process converts heavy feedstocks, such as vacuum gas oil, into valuable low-boiling products, such as gasoline and diesel. We present the detailed procedure for data acquisition to ensure accurate mass balances and for implementing the workflow using Excel spreadsheets and a commercial software tool, Aspen HYSYS/Refining. Our procedure is equally applicable to other commercial software tools. The workflow includes special tools to facilitate an accurate transition from lumped kinetic components used in reactor modeling to the pseudo-components based on boiling point ranges required in the rigorous stage-by-stage simulation of fractionators. We validate the two models with 4 months of plant data, and the resulting models accurately predict unit performance, product yields, and fuel properties from the corresponding operating conditions. The MP HCR model predicts the yields of heavy naphtha, diesel fuel, and bottom products with average absolute deviations (AADs) of at most 3.4 wt %, predicts the specific gravities of heavy naphtha, diesel fuel, and bottom oil with AADs below 0.0184, predicts the flash point and freezing point of diesel fuel with AADs of 3.6 and 4.1 °C, respectively, and predicts the outlet temperatures of catalyst beds with AADs of 1.9 °C. The HP HCR model predicts the yields of liquefied petroleum gas (LPG), light naphtha, heavy naphtha, jet fuel, and residue oil with AADs below 1.7 wt %, predicts the specific gravities of light naphtha, heavy naphtha, jet fuel, and residue oil with AADs less than 0.134, predicts the flash point and freezing point of jet fuel with AADs of 1.6 and 2.3 °C, respectively, and predicts the outlet temperatures of catalyst beds of the two HCR reactors with AADs of 1.8 and 3.2 °C. We apply the validated plantwide model to quantify the effect of the H2/oil ratio on product distribution and catalyst life and the effect of HCR reactor temperature and feed flow rate on product distribution. The results agree well with experimental observations reported in the literature. Our resulting models only require typical operating conditions and routine analysis of feedstock and products and appear to be the only reported integrated HCR models that can quantitatively simulate all key aspects of reactor operation, fractionator performance, hydrogen consumption, product yield, and fuel properties.</abstract><pub>American Chemical Society</pub><doi>10.1021/ef2007497</doi><tpages>34</tpages></addata></record> |
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subjects | Catalysts Diesel fuels Hydrocracking Jet fuels Mathematical models Naphtha Process Engineering Reactors Workflow |
title | Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 1: Hydrocracking Processes |
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