FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312096374> ?p ?o ?g. }

• W4312096374 endingPage "115073" @default.
• W4312096374 startingPage "115073" @default.
• W4312096374 abstract "Selenite (Se4+) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (REselenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (ICselenite) (10 mg/L - 60 mg/L) and hydraulic retention time (HRT) (24 h - 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The REselenite was maximized while minimizing the HRT for a preferably higher range of ICselenite. Thus, the most favourable optimum conditions were suggested as: ICselenite - 50.45 mg/L and HRT - 24 h, resulting in REselenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the REselenite in an ALR, and the process parameters can be effectively optimized using PSO." @default.
• W4312096374 created "2023-01-04" @default.
• W4312096374 creator A5000864309 @default.
• W4312096374 creator A5021935971 @default.
• W4312096374 creator A5024376941 @default.
• W4312096374 creator A5030100655 @default.
• W4312096374 creator A5055947342 @default.
• W4312096374 creator A5087082911 @default.
• W4312096374 creator A5088023379 @default.
• W4312096374 date "2023-02-01" @default.
• W4312096374 modified "2023-10-01" @default.
• W4312096374 title "Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization" @default.
• W4312096374 cites W1155290947 @default.
• W4312096374 cites W1525673011 @default.
• W4312096374 cites W1629989778 @default.
• W4312096374 cites W1967445830 @default.
• W4312096374 cites W1982127144 @default.
• W4312096374 cites W2003770836 @default.
• W4312096374 cites W2023149913 @default.
• W4312096374 cites W2038287612 @default.
• W4312096374 cites W2048224752 @default.
• W4312096374 cites W2068109735 @default.
• W4312096374 cites W2078475310 @default.
• W4312096374 cites W2083218192 @default.
• W4312096374 cites W2106214150 @default.
• W4312096374 cites W2124701854 @default.
• W4312096374 cites W2142019992 @default.
• W4312096374 cites W2150163713 @default.
• W4312096374 cites W2287662311 @default.
• W4312096374 cites W2288250765 @default.
• W4312096374 cites W2317320233 @default.
• W4312096374 cites W233535153 @default.
• W4312096374 cites W2407705879 @default.
• W4312096374 cites W2573137292 @default.
• W4312096374 cites W2573958722 @default.
• W4312096374 cites W2758223900 @default.
• W4312096374 cites W2775249024 @default.
• W4312096374 cites W2788085458 @default.
• W4312096374 cites W2788180525 @default.
• W4312096374 cites W2791001464 @default.
• W4312096374 cites W2801763041 @default.
• W4312096374 cites W280944552 @default.
• W4312096374 cites W2891778304 @default.
• W4312096374 cites W2902561652 @default.
• W4312096374 cites W2902567210 @default.
• W4312096374 cites W2952401064 @default.
• W4312096374 cites W2975995395 @default.
• W4312096374 cites W2984961307 @default.
• W4312096374 cites W2986617680 @default.
• W4312096374 cites W2994571956 @default.
• W4312096374 cites W2999332760 @default.
• W4312096374 cites W3000154186 @default.
• W4312096374 cites W3001585636 @default.
• W4312096374 cites W3010616240 @default.
• W4312096374 cites W3016624250 @default.
• W4312096374 cites W3022983348 @default.
• W4312096374 cites W3036829513 @default.
• W4312096374 cites W3101469305 @default.
• W4312096374 cites W3119158834 @default.
• W4312096374 cites W3152939552 @default.
• W4312096374 cites W3157149188 @default.
• W4312096374 cites W3158012516 @default.
• W4312096374 cites W3193021373 @default.
• W4312096374 cites W4290791011 @default.
• W4312096374 cites W848817176 @default.
• W4312096374 doi "https://doi.org/10.1016/j.envres.2022.115073" @default.
• W4312096374 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36535392" @default.
• W4312096374 hasPublicationYear "2023" @default.
• W4312096374 type Work @default.
• W4312096374 citedByCount "1" @default.
• W4312096374 countsByYear W43120963742023 @default.
• W4312096374 crossrefType "journal-article" @default.
• W4312096374 hasAuthorship W4312096374A5000864309 @default.
• W4312096374 hasAuthorship W4312096374A5021935971 @default.
• W4312096374 hasAuthorship W4312096374A5024376941 @default.
• W4312096374 hasAuthorship W4312096374A5030100655 @default.
• W4312096374 hasAuthorship W4312096374A5055947342 @default.
• W4312096374 hasAuthorship W4312096374A5087082911 @default.
• W4312096374 hasAuthorship W4312096374A5088023379 @default.
• W4312096374 hasConcept C101219045 @default.
• W4312096374 hasConcept C105795698 @default.
• W4312096374 hasConcept C11413529 @default.
• W4312096374 hasConcept C122357587 @default.
• W4312096374 hasConcept C127413603 @default.
• W4312096374 hasConcept C128990827 @default.
• W4312096374 hasConcept C139945424 @default.
• W4312096374 hasConcept C154945302 @default.
• W4312096374 hasConcept C155032097 @default.
• W4312096374 hasConcept C168170006 @default.
• W4312096374 hasConcept C178790620 @default.
• W4312096374 hasConcept C179717631 @default.
• W4312096374 hasConcept C185592680 @default.
• W4312096374 hasConcept C186060115 @default.
• W4312096374 hasConcept C2779593757 @default.
• W4312096374 hasConcept C2780092901 @default.
• W4312096374 hasConcept C33923547 @default.
• W4312096374 hasConcept C41008148 @default.
• W4312096374 hasConcept C50644808 @default.

• next