FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313332806> ?p ?o ?g. }

• W4313332806 endingPage "101" @default.
• W4313332806 startingPage "91" @default.
• W4313332806 abstract "Chemical recycling via thermal processes such as pyrolysis is a potentially viable way to convert mixed streams of waste plastics into usable fuels and chemicals. Unfortunately, experimentally measuring product yields for real waste streams can be time- and cost-prohibitive, and the yields are very sensitive to feed composition, especially for certain types of plastics like poly(ethylene terephthalate) (PET) and polyvinyl chloride (PVC). Models capable of predicting yields and conversion from feed composition and reaction conditions have potential as tools to prioritize resources to the most promising plastic streams and to evaluate potential preseparation strategies to improve yields. In this study, a data set consisting of 325 data points for pyrolysis of plastic feeds was collected from the open literature. The data set was divided into training and test sub data sets; the training data were used to optimize the seven different machine learning regression methods, and the testing data were used to evaluate the accuracy of the resulting models. Of the seven types of models, eXtreme Gradient Boosting (XGBoost) predicted the oil yield of the test set with the highest accuracy, corresponding to a mean absolute error (MAE) value of 9.1%. The optimized XGBoost model was then used to predict the oil yields from real waste compositions found in Municipal Recycling Facilities (MRFs) and the Rhine River. The dependence of oil yields on composition was evaluated, and strategies for removing PET and PVC were assessed as examples of how to use the model. Thermodynamic analysis of a pyrolysis system capable of achieving oil yields predicted using the machine-learned model showed that pyrolysis of Rhine River plastics should be net exergy producing under most reasonable conditions." @default.
• W4313332806 created "2023-01-06" @default.
• W4313332806 creator A5015088086 @default.
• W4313332806 creator A5021376706 @default.
• W4313332806 creator A5027655988 @default.
• W4313332806 creator A5033865362 @default.
• W4313332806 creator A5044580263 @default.
• W4313332806 creator A5044688435 @default.
• W4313332806 creator A5056138758 @default.
• W4313332806 creator A5072608793 @default.
• W4313332806 date "2022-12-29" @default.
• W4313332806 modified "2023-10-14" @default.
• W4313332806 title "Machine Learning Predictions of Oil Yields Obtained by Plastic Pyrolysis and Application to Thermodynamic Analysis" @default.
• W4313332806 cites W172742023 @default.
• W4313332806 cites W1966805331 @default.
• W4313332806 cites W1976364950 @default.
• W4313332806 cites W1980087458 @default.
• W4313332806 cites W1985492666 @default.
• W4313332806 cites W2012773593 @default.
• W4313332806 cites W2023692280 @default.
• W4313332806 cites W2030541808 @default.
• W4313332806 cites W2042930890 @default.
• W4313332806 cites W2054511090 @default.
• W4313332806 cites W2055764609 @default.
• W4313332806 cites W2080103777 @default.
• W4313332806 cites W2093822768 @default.
• W4313332806 cites W2117023110 @default.
• W4313332806 cites W2119654329 @default.
• W4313332806 cites W2146427903 @default.
• W4313332806 cites W2152092413 @default.
• W4313332806 cites W2428475944 @default.
• W4313332806 cites W2479598871 @default.
• W4313332806 cites W2487770199 @default.
• W4313332806 cites W2604316091 @default.
• W4313332806 cites W2737217596 @default.
• W4313332806 cites W2762862468 @default.
• W4313332806 cites W2791727655 @default.
• W4313332806 cites W2809418529 @default.
• W4313332806 cites W2916636227 @default.
• W4313332806 cites W2946512733 @default.
• W4313332806 cites W2961542673 @default.
• W4313332806 cites W2963754836 @default.
• W4313332806 cites W2969280700 @default.
• W4313332806 cites W2971422153 @default.
• W4313332806 cites W2972785004 @default.
• W4313332806 cites W2979714976 @default.
• W4313332806 cites W2988788791 @default.
• W4313332806 cites W2995894766 @default.
• W4313332806 cites W3003888078 @default.
• W4313332806 cites W3004187029 @default.
• W4313332806 cites W3020178450 @default.
• W4313332806 cites W3023099481 @default.
• W4313332806 cites W3033087305 @default.
• W4313332806 cites W3094895370 @default.
• W4313332806 cites W3112472111 @default.
• W4313332806 cites W3112547496 @default.
• W4313332806 cites W3113090645 @default.
• W4313332806 cites W3119324337 @default.
• W4313332806 cites W3122970510 @default.
• W4313332806 cites W3135027315 @default.
• W4313332806 cites W3140398190 @default.
• W4313332806 cites W3162629371 @default.
• W4313332806 cites W3177211565 @default.
• W4313332806 cites W3185856877 @default.
• W4313332806 cites W3210490984 @default.
• W4313332806 cites W3211431567 @default.
• W4313332806 cites W4210257751 @default.
• W4313332806 cites W4220823505 @default.
• W4313332806 cites W4224988655 @default.
• W4313332806 doi "https://doi.org/10.1021/acsengineeringau.2c00038" @default.
• W4313332806 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37096175" @default.
• W4313332806 hasPublicationYear "2022" @default.
• W4313332806 type Work @default.
• W4313332806 citedByCount "1" @default.
• W4313332806 countsByYear W43133328062023 @default.
• W4313332806 crossrefType "journal-article" @default.
• W4313332806 hasAuthorship W4313332806A5015088086 @default.
• W4313332806 hasAuthorship W4313332806A5021376706 @default.
• W4313332806 hasAuthorship W4313332806A5027655988 @default.
• W4313332806 hasAuthorship W4313332806A5033865362 @default.
• W4313332806 hasAuthorship W4313332806A5044580263 @default.
• W4313332806 hasAuthorship W4313332806A5044688435 @default.
• W4313332806 hasAuthorship W4313332806A5056138758 @default.
• W4313332806 hasAuthorship W4313332806A5072608793 @default.
• W4313332806 hasBestOaLocation W43133328061 @default.
• W4313332806 hasConcept C127413603 @default.
• W4313332806 hasConcept C134121241 @default.
• W4313332806 hasConcept C159985019 @default.
• W4313332806 hasConcept C192562407 @default.
• W4313332806 hasConcept C21880701 @default.
• W4313332806 hasConcept C2776866176 @default.
• W4313332806 hasConcept C36759035 @default.
• W4313332806 hasConcept C39432304 @default.
• W4313332806 hasConcept C41008148 @default.
• W4313332806 hasConcept C548081761 @default.
• W4313332806 hasConceptScore W4313332806C127413603 @default.
• W4313332806 hasConceptScore W4313332806C134121241 @default.
• W4313332806 hasConceptScore W4313332806C159985019 @default.

• next