Publications by authors named "Nasser Talebbeydokhti"

3 Publications

  • Page 1 of 1

Handling uncertainty in optimal design of reservoir water quality monitoring systems.

Environ Pollut 2020 Nov 10;266(Pt 2):115211. Epub 2020 Jul 10.

Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran. Electronic address:

In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed methodology is based on the concept of nonlinear interval number programming and information theory, while handling uncertainties of temperature, reservoir inflow, and inflow constituent concentration. A reference-point-based non-dominated sorting genetic algorithm (NSGA-III) is used to deal with the many-objective optimization problem. The proposed model is developed for the Karkheh reservoir system in Iran as a real-world problem. The results show excellent performance of the optimized water quality sampling locations instead of all potential ones in providing adequate information about the reservoir water quality status. The presented uncertainty-based model leads to a 55.73% reduction in the radius of the uncertain interval caused by different scenarios. Handling uncertainties in a spatio-temporal many-objective optimization problem is the main contribution of this study, yielding a reliable and robust design of a reservoir monitoring system that is less sensitive to various scenarios.
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November 2020

Enhanced removal of phosphate from aqueous solutions using a modified sludge derived biochar: Comparative study of various modifying cations and RSM based optimization of pyrolysis parameters.

J Environ Manage 2018 Nov 30;225:75-83. Epub 2018 Jul 30.

Department of Civil and Environmental Engineering, Shiraz University, Iran.

Different biochars produced by the impregnation of Mg, Ca, Al, Cu, and Fe were compared for the phosphate (P) uptake capacity and the effect on solution pH. Among them, Ca- and Mg-rich biochars demonstrate better sorption ability to P and have less effect on pH change. The optimum conditions of the pyrolysis processes were determined using response surface methodology. Comparison of the P removal efficiency of these two biochars under optimum conditions imply the superior adsorption capability of Ca-rich biochar. According to XRD analysis, calcite is the dominant mineral on the biochar surface, indicating the potential of Ca-rich biochar for P removal by adsorption and precipitation. Predictive second-order kinetic and linear Langmuir isotherm models could adequately interpret the P sorption process for optimized Ca-rich biochar. The maximum P sorption capacity of Ca-rich biochar of 153.85 mg/g is superior to other adsorbents reported in literature.
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November 2018

Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran.

J Environ Health Sci Eng 2016 9;14:13. Epub 2016 Aug 9.

Research Engineer at EnTech Engineering, PC11 broadway 21st floor, New York, NY 10004 USA.

Background: Extensive human activities and unplanned land uses have put groundwater resources of Shiraz plain at a high risk of nitrate pollution, causing several environmental and human health issues. To address these issues, water resources managers utilize groundwater vulnerability assessment and determination of protection. This study aimed to prepare the vulnerability maps of Shiraz aquifer by using Composite DRASTIC index, Nitrate Vulnerability index, and artificial neural network and also to compare their efficiency.

Methods: The parameters of the indexes that were employed in this study are: depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity, and land use. These parameters were rated, weighted, and integrated using GIS, and then, used to develop the risk maps of Shiraz aquifer.

Results: The results indicated that the southeastern part of the aquifer was at the highest potential risk. Given the distribution of groundwater nitrate concentrations from the wells in the underlying aquifer, the artificial neural network model offered greater accuracy compared to the other two indexes. The study concluded that the artificial neural network model is an effective model to improve the DRASTIC index and provides a confident estimate of the pollution risk.

Conclusions: As intensive agricultural activities are the dominant land use and water table is shallow in the vulnerable zones, optimized irrigation techniques and a lower rate of fertilizers are suggested. The findings of our study could be used as a scientific basis in future for sustainable groundwater management in Shiraz plain.
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August 2016