FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3087528961> ?p ?o ?g. }

• W3087528961 endingPage "2476" @default.
• W3087528961 startingPage "2471" @default.
• W3087528961 abstract "Despite the age of the process, the blast furnace (BF) ironmaking is still crucial to iron and steel industry. To improve the competitiveness of enterprises, fuel ratio (FR) in BF ironmaking process needs to be kept its lowest level possibly. In the work, a prediction model was established to predict FR of the BF by using feature selection and support vector regression (SVR). GA-SVR (genetic algorithm - SVR) method was employed to select the most informative five features from the candidate features.The experimental results indicated that the SVR model brought high learning precision and excellent prediction generalization ability. To explore and discover the laws of BF production, the influences of the five features on FR were discussed by simulation analysis of the model. All the calculations were performed on the computational platform of data mining developed by us. The work can provide guides for the operators on modulating input parameters in advance. The methods outlined here can provide valuable hints into revealing mechanisms of BF ironmaking process and realizing controlled production of BF with guidance of quantitative analysis methods." @default.
• W3087528961 created "2020-09-25" @default.
• W3087528961 creator A5028134449 @default.
• W3087528961 creator A5053477060 @default.
• W3087528961 creator A5083054357 @default.
• W3087528961 date "2020-11-15" @default.
• W3087528961 modified "2023-10-15" @default.
• W3087528961 title "Fuel Ratio Optimization of Blast Furnace Based on Data Mining" @default.
• W3087528961 cites W1902366081 @default.
• W3087528961 cites W1972234764 @default.
• W3087528961 cites W1976112023 @default.
• W3087528961 cites W2000292092 @default.
• W3087528961 cites W2028216301 @default.
• W3087528961 cites W2037386801 @default.
• W3087528961 cites W2041099987 @default.
• W3087528961 cites W2061054558 @default.
• W3087528961 cites W2062634819 @default.
• W3087528961 cites W2062990026 @default.
• W3087528961 cites W2080294453 @default.
• W3087528961 cites W2091481568 @default.
• W3087528961 cites W2093806571 @default.
• W3087528961 cites W2109506152 @default.
• W3087528961 cites W2110495709 @default.
• W3087528961 cites W2139212933 @default.
• W3087528961 cites W2158994553 @default.
• W3087528961 cites W2186941161 @default.
• W3087528961 cites W2191513493 @default.
• W3087528961 cites W2313121853 @default.
• W3087528961 cites W2316030966 @default.
• W3087528961 cites W2339071283 @default.
• W3087528961 cites W2389595022 @default.
• W3087528961 cites W2514704736 @default.
• W3087528961 cites W2551428743 @default.
• W3087528961 cites W2560260256 @default.
• W3087528961 cites W2609523961 @default.
• W3087528961 cites W2617383318 @default.
• W3087528961 cites W2676567727 @default.
• W3087528961 cites W2728715864 @default.
• W3087528961 cites W2729369771 @default.
• W3087528961 cites W2757342969 @default.
• W3087528961 cites W2799747280 @default.
• W3087528961 cites W2804333359 @default.
• W3087528961 cites W2899505897 @default.
• W3087528961 cites W2916302197 @default.
• W3087528961 cites W2920289002 @default.
• W3087528961 cites W2965208434 @default.
• W3087528961 cites W2974000087 @default.
• W3087528961 cites W3014223259 @default.
• W3087528961 cites W4233107853 @default.
• W3087528961 doi "https://doi.org/10.2355/isijinternational.isijint-2020-238" @default.
• W3087528961 hasPublicationYear "2020" @default.
• W3087528961 type Work @default.
• W3087528961 sameAs 3087528961 @default.
• W3087528961 citedByCount "11" @default.
• W3087528961 countsByYear W30875289612021 @default.
• W3087528961 countsByYear W30875289612022 @default.
• W3087528961 countsByYear W30875289612023 @default.
• W3087528961 crossrefType "journal-article" @default.
• W3087528961 hasAuthorship W3087528961A5028134449 @default.
• W3087528961 hasAuthorship W3087528961A5053477060 @default.
• W3087528961 hasAuthorship W3087528961A5083054357 @default.
• W3087528961 hasBestOaLocation W30875289611 @default.
• W3087528961 hasConcept C111919701 @default.
• W3087528961 hasConcept C119857082 @default.
• W3087528961 hasConcept C12267149 @default.
• W3087528961 hasConcept C124101348 @default.
• W3087528961 hasConcept C127413603 @default.
• W3087528961 hasConcept C134306372 @default.
• W3087528961 hasConcept C139719470 @default.
• W3087528961 hasConcept C148483581 @default.
• W3087528961 hasConcept C153180895 @default.
• W3087528961 hasConcept C154945302 @default.
• W3087528961 hasConcept C162324750 @default.
• W3087528961 hasConcept C177148314 @default.
• W3087528961 hasConcept C18762648 @default.
• W3087528961 hasConcept C191897082 @default.
• W3087528961 hasConcept C192562407 @default.
• W3087528961 hasConcept C21880701 @default.
• W3087528961 hasConcept C2778348673 @default.
• W3087528961 hasConcept C2780269488 @default.
• W3087528961 hasConcept C33923547 @default.
• W3087528961 hasConcept C41008148 @default.
• W3087528961 hasConcept C78519656 @default.
• W3087528961 hasConcept C8880873 @default.
• W3087528961 hasConcept C98045186 @default.
• W3087528961 hasConceptScore W3087528961C111919701 @default.
• W3087528961 hasConceptScore W3087528961C119857082 @default.
• W3087528961 hasConceptScore W3087528961C12267149 @default.
• W3087528961 hasConceptScore W3087528961C124101348 @default.
• W3087528961 hasConceptScore W3087528961C127413603 @default.
• W3087528961 hasConceptScore W3087528961C134306372 @default.
• W3087528961 hasConceptScore W3087528961C139719470 @default.
• W3087528961 hasConceptScore W3087528961C148483581 @default.
• W3087528961 hasConceptScore W3087528961C153180895 @default.
• W3087528961 hasConceptScore W3087528961C154945302 @default.
• W3087528961 hasConceptScore W3087528961C162324750 @default.
• W3087528961 hasConceptScore W3087528961C177148314 @default.
• W3087528961 hasConceptScore W3087528961C18762648 @default.

• next