Fluoride removal using a rotating anode electro-coagulation reactor: Parametric optimization using response surface methodology, isotherms and kinetic studies, economic analysis and sludge characterization.

The presence of fluoride in drinking water can cause various diseases, such as dental fluorosis and skeletal fluorosis. The present study aims to intensify the fluoride removal using a rotating anode electro-coagulation (EC) reactor with providing the proper hydrodynamics conditions. This fluoride removal is modeled and optimized using Response Surface Methodology (RSM) and central composite design (CCD) with varying operational parameters (rotation speed: 20-80 RPM, current: 0.2-1.0 A, initial fluoride concentration: 8-40 mg/L and time: 15-75 min). The maximum fluoride removal is obtained as 96.87% (predicted) and 95.40% (experimental) for the optimized process parameters, initial concentration of 32 mg/L, 0.8 A current, 60 min, and 60 RPM of rotating speed. Kinetic analysis reveals that the removal process adheres to a second-order kinetic model, suggesting that the rate of fluoride removal is dependent on the concentration of fluoride ions present. Isothermal studies indicate that the effective sorption of fluoride onto the generated flocs follows a sips isotherm. The optimal cost analysis is carried out to determine the operational cost as 0.256 USD/m3 for F removal of 93.49% at initial concentration 24 mg/L, time 50 min, current 0.7 A, and rotation 70 rpm and presenting a cost-effective solution for fluoride mitigation. Further, characterizations of the resultant sludge through X-Ray Diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FTIR), and the Toxicity Characteristic Leaching Procedure (TCLP) confirmed the safe disposal potential of the sludge. The findings show a promising approach for fluoride removal, combining high efficiency, economic viability, and environmental safety.

Pollutant intrusion modeling in water distribution networks using artificial neural networks.

The development and implementation of water quality models for water distribution systems have been growing interest for both environment and hydraulic researchers. It is imperative that the system is able to distribute disinfectants and/or chemicals efficiently for specified quality standards and recover the actual quality of water in case of intrusion of a pollutant into the distribution network. The present work presents hydraulic and quality analysis in a typical water distribution system to obtain the concentration at the sources (pumping station or tanks) affected by typical pollutants utilizing water quality at monitoring points as inputs to artificial neural network (ANN) model. The universal function approximation property of the ANN architecture is being employed for inverse mapping to predict the water quality at the source using the water quality at arbitrary monitoring locations in the distribution system. The optimal monitoring points are identified by water age analysis. The performance evaluation results are encouraging and demonstrate the potential applicability of the methodology.

Fuzzy rule based estimation of agricultural diffuse pollution concentration in streams.

Outflow from the agricultural fields carries diffuse pollutants like nutrients, pesticides, herbicides etc. and transports the pollutants into the nearby streams. It is a matter of serious concern for water managers and environmental researchers. The application of chemicals in the agricultural fields, and transport of these chemicals into streams are uncertain that cause complexity in reliable stream quality predictions. The chemical characteristics of applied chemical, percentage of area under the chemical application etc. are some of the main inputs that cause pollution concentration as output in streams. Each of these inputs and outputs may contain measurement errors. Fuzzy rule based model based on fuzzy sets suits to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. In this study, the property of fuzzy sets to address the uncertainty in input-output relationship is utilized to obtain the estimate of concentrations of a herbicide, atrazine, in a stream. The data of White river basin, a part of the Mississippi river system, is used for developing the fuzzy rule based models. The performance of the developed methodology is found encouraging.