Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)
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
... s (MWCNTs) with the addition of alum. Also, chlorination experiments were conducted with water reservoirs from TLW, BLW and ULW, with different water quality regarding 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.