Experimental investigation of trihalomethane formation and its modeling in drinking waters

Özdemir, Kadir | Yıldırım, Yılmaz | Toröz, İsmail | Uyak, Vedat

Article | 2015 | Asian Journal of Chemistry27 ( 3 ) , pp.984 - 990

This research developed models using multiple linear regression analysis for the prediction of trihalomethane formation in coagulated Istanbul drinking water sources. The power-law model (model 1), using only ?UV272 as the designed parameter, proved the best model to describe the formation of trihalomethane. The other model (model 2), included pH, total organic carbon, chlorine dosages, ultraviolet absorbance at 254 nm (UV254), specific ultraviolet absorbance (SUVA) and differential absorbance at 272 nm (?UV272). The root-meansquare error (RMSE), normalization mean square error (NMSE), regression coefficient (R2) and index of agreem . . .ent (IA) were used as statistical variables to evaluate the model performance. The better prediction results were obtained by model 1 for root-mean-square error, normalization mean square error, R2 and index of agreement as 9.14, 0.015, 0.95 and 0.99, respectively. © 2015, Chemical Publishing Co. All rights reserved Daha fazlası Daha az

Relationship among chlorine dose, reaction time and bromide ions on trihalomethane formation in drinking water sources in Istanbul, Turkey

Özdemir, Kadir | Toröz, İsmail | Uyak, Vedat

Article | 2014 | Asian Journal of Chemistry26 ( 20 ) , pp.6935 - 6939

We investigate the effects of factors such as chlorine dose, reaction time and bromide ions on the formation and speciation of trihalomethanes during the chlorination of Istanbul reservoirs such as Terkos lake water, Büyükçekmece lake water and Ömerli lake water. The experimental results showed that approximately 50% of trihalomethane formation was observed in the first 4 h of reaction time in chlorinated Terkos lake water, Büyükçekmece lake water and Ömerli lake water, respectively. Trihalomethane concentrations increased with increasing chlorine dosage and reaction time. Chloroform was the major trihalomethane species forming as a . . . result of the chlorinated raw water samples. On the other hand, bromide ions play a great significant role in the distribution of trihalomethane species. The bromine and chlorine incorporation ratios were strongly related to natural organic matter precursors and bromide levels in Terkos lake water, Büyükçekmece lake water and Ömerli lake water. The percentage of bromine incorporation was much higher than that of chlorine in all chlorinated water samples Daha fazlası Daha az

Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)

Özdemir, Kadir | Güngör, Ömer

Article | 2018 | Water SA44 ( 4 ) , pp.680 - 690

This research developed models using the multiple linear regression technique for prediction of trihalomethane (THM) removal from chlorinated drinking water sources through a combination of a coagulation process with carbon nanotubes (CNTs). Terkos Lake water (TLW), Buyukçekmece Lake water (BLW) and Ulutan Lake water (ULW) samples were coagulated by a conventional coagulant (alum) and increasing doses of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) with the addition of alum. Also, chlorination experiments were conducted with water reservoirs from TLW, BLW and ULW, with different water quality re . . .garding bromide concentration and organic matter content. The factors studied affecting THM removal were contact time, chlorine dose, coagulation process, total organic carbon (TOC), and specific ultraviolet absorbance (SUVA). Statistical analysis of the results focused on the development of multiple regression models, as Models 1 and 2, for predicting total trihalomethane (TTHM) based on the use of contact time, SWCNTs and MWCNTs doses, chlorine dose and TOC. When the two models were compared, Model 1 proved best suited to describe THM removal for the three water sources. The developed models provided satisfactory estimations of THM removal; the model regression coefficients for Models 1 and 2 were 0.88 and 0.77, respectively. Furthermore, the root-mean-square error (RMSE) values of 0.083 and 0.126 confirm the reliability of the two models. The results show that THM removal can be simply predicted by using the multiple linear regression technique in chlorinated drinking water sources. © 2018, South African Water Research Commission. All rights reserved Daha fazlası Daha az

Optimization of arsenic removal from drinking water by electrocoagulation batch process using response surface methodology

Kobya, Mehmet | Demirbaş, Erhan | Geboloğlu, Uğur | Öncel, Mehmet Salim | Yıldırım, Yılmaz

Article | 2013 | Desalination and Water Treatment51 ( 34-36 ) , pp.6676 - 6687

In this investigation, arsenic removal from drinking water using electrocoagulation (EC) in a batch mode was studied by response surface methodology (RSM). The RSM was applied to optimize the operating variables viz. current density (CD, A/m2), operating time (tEC, min) and arsenic concentration (Co, µg/L) on arsenic removal in the EC process using iron electrodes. The combined effects of these variables were analyzed by the RSM using quadratic model for predicting the highest removal efficiency of arsenic from drinking water. The proposed model fitted very well with the experimental data. R2 adjusted correlation coefficients (AdjR2 . . .: 0.93) for arsenic removal efficiency showed a high significance of the model. The model predicted for a maximum removal of arsenic at the optimum operating conditions (112.3 µg/L, 5.64 A/m2 and 5 min) after the EC process was 93.86% which corresponded to effluent arsenic concentration of 6.9 µg/L. The minimum operating cost (OC) of the EC process was 0.0664 €/m3. This study clearly showed that the RSM was one of the suitable methods for the EC process to optimize the best operating conditions for target value of effluent arsenic concentration (<10 µg/L) while keeping the OC (energy and electrode consumptions) to minimal. © 2013 © 2013 Balaban Desalination Publications Daha fazlası Daha az

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