An attempt has been made to develop water quality index (WQI), using six water quality parameters pH, dissolved oxygen, biochemical oxygen demand, electrical conductivity, nitrate nitrogen and total coliform measured at three different stations along the Sabarmati river basin from the year 2005 to 2008. Rating scale is developed based on the tolerance limits of inland waters and health point of view. Weighted arithmetic water quality index method was used to find WQI along the stretch of the river basin. It was observed from this study that the impact of human activity and sewage disposal in the river was severe on most of the parameters. The station located in highly urban area showed the worst water quality followed by the station located in moderately urban area and lastly station located in a moderately rural area. It was observed that the main cause of deterioration in water quality was due to the high anthropogenic activities, illegal discharge of sewage and industrial effluent, lack of proper sanitation, unprotected river sites and urban runoff.
In the study the electrochemical oxidation process for decolorization of Rhodamine-B dye was studied using an anode coated with mixed metal oxides: TiO2, RuO2, and IrO2. Batch experimental studies were conducted to assess the effect of four important performance variables, current density, electrolyte concentration, initial pH and electrolysis time, on the decolorization and energy consumption. The process was modeled using an artificial neural network. Response surface methodology using central composite design (CCD) was utilized for optimization of the decolorization process. Based on the experimental design given by CCD, the results obtained by the statistical analysis show that the electrolysis time was the most influential parameter for decolorization whereas the current density had the greatest influence on the energy consumption. According to the optimized results given by the CCD model, maximum color removal of 97% and minimum energy consumption of 1.01 kWh/m3 were predicted in 4.9 minute of electrolysis time, using 0.031 M NaCl concentration at current density 10 mA/cm2 and an initial pH of 3.7. A close conformity was observed between the optimized predicted results and experimental results. The process was found to be efficient and consisted of indirect chemical oxidation producing strong oxidizing agents such as Cl2, HClO and OCl−.
An artificial neural network (ANN) approach with response surface methodology (RSM) technique has been applied to model and optimize the removal process of Brilliant Green dye by batch electrocoagulation process. A multilayer perceptron (MLP) - ANN model has been trained by four input neurons which represent the reaction time, current density, pH, NaCl concentration, and two output neurons representing the dye removal efficiency (%) and electrical energy consumption (kWh/kg). The optimized hidden layer neurons were obtained based on a minimum mean squared error. The batch electrocoagulation process was optimized using central composite design with RSM once the ANN network was trained and primed to anticipate the output. At optimized condition (electrolysis time 10 min, current density 80 A/m 2 , initial pH 5 and electrolyte NaCl concentration 0.5 g/L), RSM projected decolorization of 98.83% and electrical energy consumption of 14.99 kWh/kg. This study shows that the removal of brilliant green dye can be successfully carried out by a batch electrocoagulation process. Therefore, the process is successfully trained by ANN and optimized by RSM for similar applications.
This study evaluated the effect of the addition of green iron microparticles (Fe-MPs) as a three-dimensional electrode on efficiency of the electrochemical oxidation process. Polyphenols present in green tea extract act as a reducing and capping agent during green synthesis of the Fe-MPs. Scanning electron microscopy and energy-dispersive X-ray spectroscopy analysis indicates that the average size of particles is 100 µm, with about ~47 wt % of Fe in oxide form. The addition of Fe-MPs as a third electrode in the conventional electro-oxidation (EO) process converts it into a three-dimensional (3D) catalytic EO process to enhance the decolorization efficiency. Green synthesized Fe-MPs function as several microelectrodes in the process. Adsorption study indicated that only 12% of decolorization is due to adsorption on the Fe-MPs surface. Moreover, improvement in generation of hydroxyl radicals was validated by applying dimethyl sulfoxide as scavenger, and it was observed that generation of hydroxyl radicals decreased with the addition of DMSO. Results showed that decolorization efficiency increased in the 3D EO process with Fe-MPs by about 24% compared to the conventional 2D process without the Fe-MPs dosing, and initial pH as well as the Fe-MPs dose has a significant effect on decolorization efficiency during the 3D process. It is observed that reaction works better at highly acidic pH (2-4), and decolorization efficiency improved with higher doses of Fe-MPs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.