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 ...
Read More
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.
Alireza Ehsanzadeh; Farhad Nejadkoorki; Sattar Khodadoostan
Abstract
Background & objectives: Air particulate matters which have natural and human made origins have significant effects on the climate, the environment and human health. Several epidemiological studies have shown a direct relationship among the concentrations of suspended particles with different ...
Read More
Background & objectives: Air particulate matters which have natural and human made origins have significant effects on the climate, the environment and human health. Several epidemiological studies have shown a direct relationship among the concentrations of suspended particles with different adverse health effects. The general purpose of this research was to determine the most important parameters affecting on the concentration of PM10 in Tajrish station (Tehran) and develop an estimator model for PM10. Materials & methods: In this study, a model is constructed using principal component regression (PCR) for the relationship between the hourly concentration of particulate matter smaller than 10 microns with meteorological parameters (WD, WS, T, P, H) and air pollution parameters (CO, NO2, SO2, NOx, NMHC, THC) in Tajrish station (Tehran). The results of the performance evaluation of PCR model were measured in training and testing stages using RMSE, MAE, R and IA as statistical indicators. Results: The results of principal components import into multiple regression model showed that the most important variable affecting on the concentrations of PM10, are air temperature and the wind speed. Also, CO and SO2 emissions were known as synergic factors for PM10 concentration. The results showed that PCR model is able to estimate 41% of PM10 concentrations in the testing. Conclusion: The principal components regression analysis showed that meteorological parameters are one of most important factors affecting on the reduction of PM10 concentration in Tajrish station (Tehran).