Developing a Statistical Model to Improve Drinking Water Quality for Water Distribution System by Minimizing Heavy Metal Releases
| Autor: | Rene V. Mayorga, Wei Peng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
lcsh:Hydraulic engineering Optimization problem 0208 environmental biotechnology Geography Planning and Development Water supply release of heavy metals (HMR) 02 engineering and technology Aquatic Science 01 natural sciences Biochemistry water quality (WQ) Standard deviation 010104 statistics & probability lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Robustness (computer science) 0101 mathematics dual response surface optimization (DRSO) Water Science and Technology lcsh:TD201-500 blending business.industry Statistical model 6. Clean water 020801 environmental engineering Nonlinear system 13. Climate action Environmental science multiple source waters blending optimization (MSWBO) Water quality Focus (optics) business |
| Zdroj: | Water Volume 10 Issue 7 Water, Vol 10, Iss 7, p 939 (2018) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w10070939 |
| Popis: | 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&rsquo s applicability, computational efficiency, and robustness. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|