Multi-variable approach to determine treatment efficiency of wetland: size effect and electro-kinetic effects

Abstract

Empirical stochastic multi-variable models for prediction of treatment efficiency of wetlands are presented in this article. Wetlands of seven different shapes are visualized using tracer studies. Two different variants of experiments are carried out. Numerous flow rate variations are performed keeping surface area of the wetland constant. The experiment is also carried out with a variation in volume of the wetland which helps to study the effect of flow height on the hydrodynamics within the wetland. A multi-variable model for treatment efficiency in terms of change in tracer concentration as a function of shape, volumetric height of water within the wetland, time, and mass flow rate is considered. Further, another set of experiments is performed studying the treatment efficiency in terms of electro-kinetic parameters. This involves measuring the pH, turbidity, temperature, electrical conductivity, total dissolved salts at inlet and outlet and residence time with varying flow rate, and height of water for the seven different wetland models under study. The electro-kinetic parameters changes due to difference in concentration of the tracer dye which simulates impurities. In this case, treatment efficiency is expressed as a function of the above-discussed electro-kinetic variables, time variation, water height, as well as variation in the mass flow rate. The stochastic multi-parameter models, thus, empirically derived in the above two cases have high coefficient of determination. The models thus derived may be used as a tool for quick analysis of treatment efficiency of any shape and size of a three-dimensional wetland.

Comparative analysis of hydrodynamics of treatment wetlands using finite volume models with empirical data

“…It is observed that the dead zone volume is minimal in case of a volumetric flow rate of 500 ml/min and increases with decrease in wetland inflow velocity. Similar result is also reiterated by the experimental results in Gupta et al 2014. This is clearly illustrated in Fig.…”

“…This is also supported and illustrated by the increase in value of k for the square with islands Table 5 Coefficient of determination R 2 and error of the model S for predicting E-curve using ln Y ¼ a þ b 1 X 1 þ b 2 X 2 for flow rate = 300 ml/min and height = 15.5 cm Table 6 Coefficient of determination R 2 and error of the model S for predicting E-curve using ln X ln Y ¼ a þ b 1 X 1 þ b 2 X 2 for flow rate = 500 ml/min and a height = 15.5 cm Table 7 Coefficient of determination R 2 and error of the model S for predicting E-curve using ln X ln Y ¼ a þ b 1 X 1 for flow rate = 150 ml/min and height = 31 cm Table 8 Coefficient of determination R 2 and error of the model S for predicting E-curve using ln X ln Y ¼ a þ b 1 X 1 for flow rate = 300 ml/min and height = 31 cm Table 9 Coefficient of determination R 2 and error of the model S for predicting E-curve using ln X ln Y ¼ a þ b 1 X 1 for flow rate = 500 ml/min and height = 31 cm Table 11 Coefficient of determination R 2 and error of the model S for predicting E-curve using ln X ln Y ¼ a þ b 1 X 1 þ b 2 X 2 for flow rate = 500 ml/min and height = 45.5 cm model for the cases of heads of water at 15.5, 31 and 45.5 cm, respectively. The highest value of k was found to be 0.72 for the case of 500 ml/min flow rate at a head of 15.5 cm, indicating maximum occurrence of mixing [21]. A comparison of the results from Figs.…”

Clean drinking water solution for rural India: Portable sip-up

“…Where X is any electro-kinetic parameter The influent and filtrate are both dilute solutions in this experiment [21]. The porous media flow treatment can be effectively predicted on the basis of the gradient in electro-kinetic parameters of these dilute solutions as they traverse across the porous clay ceramic cylinders [22]. From Plappally et al 2010, the characteristic gradient of an electro-kinetic parameter will help predict the treatment provided by the clay ceramic to the water [23][24].…”

“…From Plappally et al 2010, the characteristic gradient of an electro-kinetic parameter will help predict the treatment provided by the clay ceramic to the water [23][24]. Therefore the gradient in turbidity between the influent and filtrate water from the sip-up can be predicted with an expression in Eq.2 [21,22].…”