Mohsen Niazi; Ali Naghizadeh; Mansour Baziar
Abstract
AbstractBackground and purposeThe turbidity of treated water is measured as an important parameter in determining the quality of drinking or industrial water in all treatment plants. Due to the importance of the prevalence of pathogens such as Giardia and Cryptosporidium, which cause dangerous diseases ...
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AbstractBackground and purposeThe turbidity of treated water is measured as an important parameter in determining the quality of drinking or industrial water in all treatment plants. Due to the importance of the prevalence of pathogens such as Giardia and Cryptosporidium, which cause dangerous diseases such as dysentery, the relationship between reducing turbidity and increasing the elimination of these microorganisms has been proven in studies.Materials and methodsIn this study, an artificial neural network (ANN) model and multiple linear regression(MLR) were developed and their performance was compared to predict the turbidity of treated water of Tabas water treatment plant. Total dissolved solids, pH, temperature and input turbidity of raw water were used as input parameters of the models in the predictions. The best backpropagation algorithm and number of neurons were determined to optimize the model architecture.ResultsThe results showed that the Levenberg–Marquardt algorithm was selected as the best algorithm and the number of optimal neurons was determined to be 16.Also, the results of the sensitivity analysis of the neural network model showed that the input turbidity with a value of 29% is the most important parameter in the development of the ANN model.ConclusionThe results of correlation coefficient of MLR and ANN models were obtained for training data 0.63 and 0.8921 and for testing data 0.60 and 0.8571, respectively, which show the superiority of ANN model in Predicting the turbidity of the output of Tabas water treatment plant.
Ali Naghizadeh
Abstract
Background an Objectives: Natural organic matters because of production of disinfection by products such as trihalomethanes, which are often carcinogenic disinfection, are of particular importance. Carbon nanotubes due to large surface area, and many other applications, are effective adsorbents for the ...
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Background an Objectives: Natural organic matters because of production of disinfection by products such as trihalomethanes, which are often carcinogenic disinfection, are of particular importance. Carbon nanotubes due to large surface area, and many other applications, are effective adsorbents for the removal of natural organic matter. The present study aimed to investigate the removal of natural organic compounds from aqueous solution by single-walled carbon nanotubes and kinetics and equilibrium adsorption process. Methods: in present study, single wall carbon nanotubes used for removal of natural organic matters from aqueous solution. Different variables such as pH of zero point of charge, pH and different concentration of natural organic matters were investigated. Results: pH survey show that with decreasing pH adsorption capacity increased also pH of zero charge was 6.7. Adsorption capacity of single wall carbon nanotubes for initial concentration of natural organic matters of 10, 5 and 3 mg/L were 66.24, 40.63 and 29.77, respectively. Conclusion: Single-walled carbon nanotubes due to features such as high surface area have great potential for the removal of natural organic matter from aqueous solution