Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

Abstract

In this study, the co-precipitation method was used to synthesize the cerium oxide tetraethylenepentamine (CTEPA) hybrid material with the variation of the molar concentration of the metal oxide. Physicochemical techniques, like FESEM, XRD, FTIR, HR-TEM and TGA-DSC, were used to characterize the hybrid material. The adsorption experiment was carried out to estimate the optimum condition for adsorption with the variation of adsorbent dose, pH of the solution, time and initial concentration of the adsorbate. These variables are further used to develop an approach for the evaluation of As(III) removal from water by using evolutionary genetic programming techniques (GP) and least square support vector models (LS-SVM). GP model was found to be the best performing model for understanding the nonlinear behavior and prediction of As(III) removal (minimum standard error GP training 0.411, GP testing 0.658). The adsorption process in this study followed second order kinetics. The experimental data were best fitted to linearly transformed Langmuir isotherm with an R 2 (correlation coefficient) value of >0.99 and having a maximum adsorption capacity of 124.8 mg/g at 25±2°C. The type of adsorption process was derived from the DubininRadushkevich isotherm model. A comparison between the model and actual experimental values gave a high correlation coefficient value (R 2 training (GP) 0.988, R 2 testing (GP) 0.977).

Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices

“…Thus, the developed EA-based MEP models are not merely the correlations but can be helpful for practitioners and decision-makers that will eventually save the time and money required for monitoring water quality parameters. [4]; prg [11] = prg [10] − prg[0]; prg [12] = prg [9]/prg [2]; prg [13] = prg [4] * prg [12]; prg [14] = prg [9] * prg [9]; prg [15] = x [4]; prg [16] = prg[0] − prg [13]; prg [17] = prg [13] − prg[0]; prg [18] = x[0]; prg [19] = prg [14] + prg [5]; prg [20] = x[0]; prg [21] = log(prg [20]); prg [22] = x [7]; prg [23] = prg [16] * prg [21]; prg [24] = prg [13] − prg [18]; prg [25] = prg [12] + prg [23]; prg [26] = prg [19] + prg [25]; prg [27] = prg [7]/prg [24]; prg [28] = prg [16] − prg [17]; prg…”

“…During the last couple of decades, the sub-field of artificial intelligence (AI) i.e., machine learning (ML) has been widely used to tackle varied ecological technical challenges, especially water quality index simulation [2,[16][17][18][19]. ML techniques are indeed a technological breakthrough in the development on management and surveillance of many engineering activities [20][21][22].…”

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

“…Typical applications include the following, among many others: predicting the dispersion coefficient (D) in a river ecosystem (Antonopoulos et al 2015); modelling the permeability losses in permeable reactive barriers (Santisukkasaem et al 2015); estimating the reference evapotranspiration (ET 0 ) in India (Adamala et al 2015); calculating the dynamic coefficient in porous media ; predicting Indian monsoon rainfall (Azad et al 2015); modeling of arsenic (III) removal (Mandal et al 2015); predicting effluent biochemical oxygen demand (BOD) in a wastewater treatment plant (Heddam et al 2016); modeling Secchi disk depth (SD) in river (Heddam 2016a); and predicting phycocyanin (PC) pigment concentration in river (Heddam 2016b). Unsurprisingly, regarding the high capabilities of ANNs in developing environmental models, they have rapidly gained much popularity.…”

Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA

“…Artificial intelligence (AI) techniques have been frequently applied in environmental modelling. Some of these applications include, among others, the following: prediction of reservoir permeability from porosity measurements (Handhal 2016); predictive modeling of discharge in compound open channel ; automatic inversion tool for geoelectrical resistivity (Raj et al 2015); forecasting monthly groundwater level ; predicting the dispersion coefficient (D) in a river ecosystem (Antonopoulos et al 2015); modelling the permeability losses in permeable reactive barriers (Santisukkasaem et al 2015); estimating the reference evapotranspiration (ET 0 ) (Adamala et al 2015); calculating the dynamic coefficient in porous media (Das et al 2015); predicting Indian monsoon rainfall (Azad et al 2015), and modeling of arsenic (III) removal (Mandal et al 2015). Although RBFNN has been applied for modelling DO concentration, to the best of our knowledge, there have been no studies done on the application of RBFNN for forecasting DO in rivers; hence the present study aims to investigate the capabilities of the RBFNN in comparison to the standard MLPNN for simultaneous modelling and forecasting of hourly DO concentration.…”