In drinking water treatment plants, chlorination is done for the disinfection. On one hand chlorine addition provides residual protection against recontamination of water in the distribution with pathogenic micro-organism but at the same time it reacts with natural organic matter (NOM) present in water to form certain by-products, which are harmful in long term consumption. Trihalomethanes (THMs) is one such group of by products which contains CHCl3, CHCl2Br, CHClBr2 and CHBr3. In drinking water formation of THMs is a function of pH, temperature, reaction time, total organic carbon (TOC), chlorine dose etc. Choosing important parameters to model the formation of THMs is useful alternative to chemical analysis. This paper presents the application of two empirical models for simulating and forecasting THMs concentrations within drinking water. The first is a linear autoregressive model with external inputs, known as ARX; the second is a non-linear artificial neural network (ANN) model. The results demonstrate the potential of an ANN model, which has a unique ability to detect non-linear complex relationships between data. In evaluating all the given data, simulation results show a similar performance for the linear and non-linear models. However, for specific water treatment conditions (very high and very low chlorine doses, pH and TOC), the ANN model gives better predictions than the ARX model.
Cite this article:
Sanjay Verma, Ashok Sharma, Sarita Sharma, Rajan K. Priyadarshi. Comparison of Different Modelling Approaches of Trihalomethanes (THMs) Formation in Drinking Water. Asian J. Research Chem. 4(4): April 2011; Page 537-541.