{"title":"lewait MP64和Dowex吸附废水中Cr (VI)的人工神经网络建模1×8","authors":"A. E. Tümer, Serpil Edebali","doi":"10.1109/PICECE.2019.8747199","DOIUrl":null,"url":null,"abstract":"In this study, an artificial neural network model was developed to estimate the removal efficiency of Cr (VI) ion from waste water by Lewatit MP64 and Dowex 1×8 resins. For this purpose, 36 experimental data obtained in a laboratory batch study. In the developed model, contact time, adsorbent dosage, pH and concentration were used as the input parameters, and removal efficiency for Lewatit MP64 and Dowex 1×8 were also used as output parameters. The model performances were determined by the mean square error and the coefficient of determination. The model using the Levenberg-Marquardt backpropagation algorithm (TrainLM) was found the best prediction. This model also has a hidden layer and 15 neurons (4-15-1). The coefficient of determination between experimental and estimates was found to be 0.99 removal efficiency for Lewatit MP64 and 0.92 for Dowex 1×8. The results show that removal efficiency can be predicted successfully with artificial neural networks.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"160 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Neural Network Approach for Modeling of Cr (VI) Adsorption from Waste Water by Lewatit MP64 and Dowex 1×8\",\"authors\":\"A. E. Tümer, Serpil Edebali\",\"doi\":\"10.1109/PICECE.2019.8747199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, an artificial neural network model was developed to estimate the removal efficiency of Cr (VI) ion from waste water by Lewatit MP64 and Dowex 1×8 resins. For this purpose, 36 experimental data obtained in a laboratory batch study. In the developed model, contact time, adsorbent dosage, pH and concentration were used as the input parameters, and removal efficiency for Lewatit MP64 and Dowex 1×8 were also used as output parameters. The model performances were determined by the mean square error and the coefficient of determination. The model using the Levenberg-Marquardt backpropagation algorithm (TrainLM) was found the best prediction. This model also has a hidden layer and 15 neurons (4-15-1). The coefficient of determination between experimental and estimates was found to be 0.99 removal efficiency for Lewatit MP64 and 0.92 for Dowex 1×8. The results show that removal efficiency can be predicted successfully with artificial neural networks.\",\"PeriodicalId\":375980,\"journal\":{\"name\":\"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)\",\"volume\":\"160 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PICECE.2019.8747199\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICECE.2019.8747199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial Neural Network Approach for Modeling of Cr (VI) Adsorption from Waste Water by Lewatit MP64 and Dowex 1×8
In this study, an artificial neural network model was developed to estimate the removal efficiency of Cr (VI) ion from waste water by Lewatit MP64 and Dowex 1×8 resins. For this purpose, 36 experimental data obtained in a laboratory batch study. In the developed model, contact time, adsorbent dosage, pH and concentration were used as the input parameters, and removal efficiency for Lewatit MP64 and Dowex 1×8 were also used as output parameters. The model performances were determined by the mean square error and the coefficient of determination. The model using the Levenberg-Marquardt backpropagation algorithm (TrainLM) was found the best prediction. This model also has a hidden layer and 15 neurons (4-15-1). The coefficient of determination between experimental and estimates was found to be 0.99 removal efficiency for Lewatit MP64 and 0.92 for Dowex 1×8. The results show that removal efficiency can be predicted successfully with artificial neural networks.
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