Residual oil hydrogenation device yield prediction method based on machine learning
The invention discloses a method for predicting the yield of a residual oil hydrogenation device based on machine learning, which comprises the following steps of: starting from data driving, constructing a two-layer model by utilizing a machine learning algorithm, and constructing a model I for rep...
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| creator | ZHOU HAN REN XIAOTIAN TANG XIAOJIN BAO DI |
| description | The invention discloses a method for predicting the yield of a residual oil hydrogenation device based on machine learning, which comprises the following steps of: starting from data driving, constructing a two-layer model by utilizing a machine learning algorithm, and constructing a model I for representing intrinsic dynamics by fully utilizing a large amount of small-scale experimental data obtained in the development process of a residual oil hydrogenation process; a model II representing reactor factors is constructed by considering the reactor amplification effect of a pilot plant or an industrial device; according to the method, complex reaction mechanism and reaction process simulation calculation are avoided, rapid calculation of reaction processes of residual oil hydrogenation devices of different scales can be achieved through the two-layer model, and the model is wide in application range, high in accuracy and high in practicability.
本发明公开一种基于机器学习的渣油加氢装置的收率预测方法，以数据驱动出发，利用机器学习算法构建两层模型，充分利用渣油加氢工艺开发过程 |
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本发明公开一种基于机器学习的渣油加氢装置的收率预测方法，以数据驱动出发，利用机器学习算法构建两层模型，充分利用渣油加氢工艺开发过程</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240301&DB=EPODOC&CC=CN&NR=117634279A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76294</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240301&DB=EPODOC&CC=CN&NR=117634279A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHOU HAN</creatorcontrib><creatorcontrib>REN XIAOTIAN</creatorcontrib><creatorcontrib>TANG XIAOJIN</creatorcontrib><creatorcontrib>BAO DI</creatorcontrib><title>Residual oil hydrogenation device yield prediction method based on machine learning</title><description>The invention discloses a method for predicting the yield of a residual oil hydrogenation device based on machine learning, which comprises the following steps of: starting from data driving, constructing a two-layer model by utilizing a machine learning algorithm, and constructing a model I for representing intrinsic dynamics by fully utilizing a large amount of small-scale experimental data obtained in the development process of a residual oil hydrogenation process; a model II representing reactor factors is constructed by considering the reactor amplification effect of a pilot plant or an industrial device; according to the method, complex reaction mechanism and reaction process simulation calculation are avoided, rapid calculation of reaction processes of residual oil hydrogenation devices of different scales can be achieved through the two-layer model, and the model is wide in application range, high in accuracy and high in practicability.
本发明公开一种基于机器学习的渣油加氢装置的收率预测方法，以数据驱动出发，利用机器学习算法构建两层模型，充分利用渣油加氢工艺开发过程</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNirEKwkAQBdNYiPoP6wdYxIjBUoJiZaH2Yb19yS2cd0fuFPL3ovgBVsMMMy2uFySVJzsK6siOMoQenrMGT4KXGtCocEJxgKj59geyDUJ3ThD6OBurHuTAg1ffz4tJxy5h8eOsWB4Pt-a0QgwtUmQDj9w257Kst9VmXe_21T_PG4IBOLw</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>ZHOU HAN</creator><creator>REN XIAOTIAN</creator><creator>TANG XIAOJIN</creator><creator>BAO DI</creator><scope>EVB</scope></search><sort><creationdate>20240301</creationdate><title>Residual oil hydrogenation device yield prediction method based on machine learning</title><author>ZHOU HAN ; REN XIAOTIAN ; TANG XIAOJIN ; BAO DI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117634279A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHOU HAN</creatorcontrib><creatorcontrib>REN XIAOTIAN</creatorcontrib><creatorcontrib>TANG XIAOJIN</creatorcontrib><creatorcontrib>BAO DI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHOU HAN</au><au>REN XIAOTIAN</au><au>TANG XIAOJIN</au><au>BAO DI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Residual oil hydrogenation device yield prediction method based on machine learning</title><date>2024-03-01</date><risdate>2024</risdate><abstract>The invention discloses a method for predicting the yield of a residual oil hydrogenation device based on machine learning, which comprises the following steps of: starting from data driving, constructing a two-layer model by utilizing a machine learning algorithm, and constructing a model I for representing intrinsic dynamics by fully utilizing a large amount of small-scale experimental data obtained in the development process of a residual oil hydrogenation process; a model II representing reactor factors is constructed by considering the reactor amplification effect of a pilot plant or an industrial device; according to the method, complex reaction mechanism and reaction process simulation calculation are avoided, rapid calculation of reaction processes of residual oil hydrogenation devices of different scales can be achieved through the two-layer model, and the model is wide in application range, high in accuracy and high in practicability.
本发明公开一种基于机器学习的渣油加氢装置的收率预测方法，以数据驱动出发，利用机器学习算法构建两层模型，充分利用渣油加氢工艺开发过程</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Residual oil hydrogenation device yield prediction method based on machine learning |
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