Developing a Statistical Model to Improve Drinking Water Quality for Water Distribution System by Minimizing Heavy Metal Releases

Abstract

This paper proposes a novel statistical approach for blending source waters in a public water distribution system to improve water quality (WQ) by minimizing the release of heavy metals (HMR). Normally, introducing a new source changes the original balanced environment and causes adverse effects on the WQ in a water distribution system. One harmful consequence of blending source water is the release of heavy metals, including lead, copper and iron. Most HMR studies focus on the forecasting of unfavorable effects using precise and complicated nonlinear equations. This paper uses a statistical multiple objectives optimization, namely Multiple Source Waters Blending Optimization (MSWBO), to find optimal blending ratios of source waters for minimizing three HMRs in a water supply system. In this paper, three response surface equations are applied to describe the reaction kinetics of HMR, and three dual response surface equations are used to track the standard deviations of the three response surface equations. A weighted sum method is performed for the multi-objective optimization problem to minimize three HMRs simultaneously. Finally, the experimental data of a pilot distribution system is used in the proposed statistical approach to demonstrate the model's applicability, computational efficiency, and robustness.Keywords: water quality (WQ); blending; release of heavy metals (HMR); dual response surface optimization (DRSO); multiple source waters blending optimization (MSWBO)

Advances in Water Distribution Networks

“…The methodologies adopted are multi-faceted, ranging from hydraulic [40][41][42][43] and water quality [46,47] modelling, to the graph theory [44,45], statistical [42,48], and optimization [41,47] techniques.…”

“…Methodologies are currently being developed to improve water quality and to estimate the vulnerability of WDNs, to avoid the risk of contamination of supplied water by undesired substances. As for water quality, two papers were presented in the special issue, concerning assessment of vulnerability to trihalomethane [46] and development of a statistical optimization model to improve drinking water quality through the minimization of heavy metal releases [47], respectively.…”

“…The results obtained on five case studies suggest that the introduced indexes identify different critical areas in terms of elevated concentrations of TTHMs. This allows identification of the higher risk nodes in terms of different kinds of exposure (short period of exposure to high TTHMs values, or chronic exposure to low concentrations); • Peng and Mayorga [47] propose a statistical multiple objectives optimization, namely Multiple Source Waters Blending Optimization (MSWBO), to find optimal blending ratios of source waters for minimizing three heavy metals (HMR) in a WDN. Three response surface equations are applied to describe the reaction kinetics of HMR, and three dual response surface equations are used to track the standard deviations of the three response surface equations.…”

A Mathematical Model for Control and Optimization of Industrial Rotary Alumina Kiln Process

“…Vining and Myers [28] fitted two second-order polynomial models for the mean response and standard deviation of responses separately. In their research, the optimization of one of the polynomial models subjected to an appropriate constraint given by the other [31]. A general DRSM model is developed for the industrial rotary kiln as follows:…”