Matches in SemOpenAlex for { <https://semopenalex.org/work/W4221136347> ?p ?o ?g. }
- W4221136347 endingPage "1053" @default.
- W4221136347 startingPage "1053" @default.
- W4221136347 abstract "To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations." @default.
- W4221136347 created "2022-04-03" @default.
- W4221136347 creator A5008040193 @default.
- W4221136347 creator A5022457238 @default.
- W4221136347 creator A5022677475 @default.
- W4221136347 creator A5027003305 @default.
- W4221136347 creator A5040856115 @default.
- W4221136347 creator A5075338259 @default.
- W4221136347 creator A5087190109 @default.
- W4221136347 date "2022-03-27" @default.
- W4221136347 modified "2023-10-03" @default.
- W4221136347 title "Enhancing Real-Time Prediction of Effluent Water Quality of Wastewater Treatment Plant Based on Improved Feedforward Neural Network Coupled with Optimization Algorithm" @default.
- W4221136347 cites W1573302072 @default.
- W4221136347 cites W1968462190 @default.
- W4221136347 cites W1983768933 @default.
- W4221136347 cites W1994152198 @default.
- W4221136347 cites W1999595821 @default.
- W4221136347 cites W2010681343 @default.
- W4221136347 cites W2013789039 @default.
- W4221136347 cites W2020876574 @default.
- W4221136347 cites W2025647636 @default.
- W4221136347 cites W2033577917 @default.
- W4221136347 cites W2050906118 @default.
- W4221136347 cites W2053185607 @default.
- W4221136347 cites W2055921525 @default.
- W4221136347 cites W2067336855 @default.
- W4221136347 cites W2081816223 @default.
- W4221136347 cites W2092105105 @default.
- W4221136347 cites W2095239580 @default.
- W4221136347 cites W2212491726 @default.
- W4221136347 cites W2317320233 @default.
- W4221136347 cites W2479755133 @default.
- W4221136347 cites W2611084095 @default.
- W4221136347 cites W2800358624 @default.
- W4221136347 cites W2884378147 @default.
- W4221136347 cites W2910535822 @default.
- W4221136347 cites W2914192042 @default.
- W4221136347 cites W2917234733 @default.
- W4221136347 cites W2931156301 @default.
- W4221136347 cites W2931813856 @default.
- W4221136347 cites W2953794436 @default.
- W4221136347 cites W2955584946 @default.
- W4221136347 cites W2963693536 @default.
- W4221136347 cites W2964288588 @default.
- W4221136347 cites W2966651448 @default.
- W4221136347 cites W2970591347 @default.
- W4221136347 cites W2981704843 @default.
- W4221136347 cites W2986617680 @default.
- W4221136347 cites W3004008875 @default.
- W4221136347 cites W3009865495 @default.
- W4221136347 cites W3021518412 @default.
- W4221136347 cites W3046635960 @default.
- W4221136347 cites W3082855882 @default.
- W4221136347 cites W3084312333 @default.
- W4221136347 cites W3094683717 @default.
- W4221136347 cites W3097106463 @default.
- W4221136347 cites W3119611932 @default.
- W4221136347 cites W3127383141 @default.
- W4221136347 cites W3200542697 @default.
- W4221136347 cites W3205785275 @default.
- W4221136347 cites W3210610608 @default.
- W4221136347 cites W4211108442 @default.
- W4221136347 cites W4213426537 @default.
- W4221136347 cites W4240679481 @default.
- W4221136347 cites W4250879370 @default.
- W4221136347 doi "https://doi.org/10.3390/w14071053" @default.
- W4221136347 hasPublicationYear "2022" @default.
- W4221136347 type Work @default.
- W4221136347 citedByCount "14" @default.
- W4221136347 countsByYear W42211363472022 @default.
- W4221136347 countsByYear W42211363472023 @default.
- W4221136347 crossrefType "journal-article" @default.
- W4221136347 hasAuthorship W4221136347A5008040193 @default.
- W4221136347 hasAuthorship W4221136347A5022457238 @default.
- W4221136347 hasAuthorship W4221136347A5022677475 @default.
- W4221136347 hasAuthorship W4221136347A5027003305 @default.
- W4221136347 hasAuthorship W4221136347A5040856115 @default.
- W4221136347 hasAuthorship W4221136347A5075338259 @default.
- W4221136347 hasAuthorship W4221136347A5087190109 @default.
- W4221136347 hasBestOaLocation W42211363471 @default.
- W4221136347 hasConcept C105795698 @default.
- W4221136347 hasConcept C119857082 @default.
- W4221136347 hasConcept C127413603 @default.
- W4221136347 hasConcept C132459708 @default.
- W4221136347 hasConcept C133731056 @default.
- W4221136347 hasConcept C147455438 @default.
- W4221136347 hasConcept C188287460 @default.
- W4221136347 hasConcept C18903297 @default.
- W4221136347 hasConcept C21880701 @default.
- W4221136347 hasConcept C2780797713 @default.
- W4221136347 hasConcept C33923547 @default.
- W4221136347 hasConcept C38858127 @default.
- W4221136347 hasConcept C39432304 @default.
- W4221136347 hasConcept C41008148 @default.
- W4221136347 hasConcept C47702885 @default.
- W4221136347 hasConcept C50644808 @default.
- W4221136347 hasConcept C57442070 @default.
- W4221136347 hasConcept C86803240 @default.