Optimization of specific energy consumption for Bomaplex Red CR-L dye removal from aqueous solution by electrocoagulation using Taguchi-neural method

Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

Waste streams with high ammonia nitrogen (NH3-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH3-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH3-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH3-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH3-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH3-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH3-N from wastewater under different operating conditions.

Electrochemical technique for paper mill effluent degradation using concentric aluminum tube electrodes (CATE

In this study, Taguchi experimental design was used to the optimize operating parameters for the degradation of paper mill effluent using electrochemical (EC) process with two-dimensional concentric aluminum tube electrodes (CATE). For this purpose, four significant experimental factors were used in four levels pH (6-9), electrolysis time (10-40 min), voltage (6-12 V) and surface area (357-624 cm2). The process parameters were optimized, through performing L16 orthogonal array of Taguchi technique, for the removal of chemical oxygen demand (COD) and turbidity. The percent COD and turbidity reductions were transferred into an accurate S/N ratio for a larger value is the better (LBT) response. The study presents a unique method of finding optimum combination of process parameters to illustrate their effect on the turbidity and COD reduction. The treatment conditions for the maximum elimination of the pollutants were second level of pH (7), third level of ET (30 min), fourth level of voltage (12 V) and second level of surface area (446 cm2). The confirmation experiment results were within the confidence intervals (CI) indicating an acceptable agreement between predicted and observed values. Based on the p-values, the electrolysis time and voltages were found to be the most significant factors for both COD and turbidity reduction. The findings of research indicated, that the Taguchi method can be used successfully for the treatment of paper industry effluent by electrochemical technique.

Modelling dye removal by adsorption onto water treatment residuals using combined response surface methodology-artificial neural network approach

In this study, response surface methodology (RSM)-artificial neural network (ANN) approach was used to optimise/model disperse dye removal by adsorption using water treatment residuals (WTR). RSM was first applied to evaluate the process using three controllable operating parameters, namely WTR dose, initial pH (pH_initial) and dye concentration, and optimal conditions for colour removal were determined. In the second step, the experimental results of the design data of RSM were used to train the neural network along with a non-controllable parameter, the final pH (pH_final). The trained neural networks were used for predicting the colour removal. A colour removal of 52.6 ± 2.0% obtained experimentally at optimised conditions (pH_initial 3.0, adsorbent dose 30 g/L and dye concentration 75 mg/L) was comparable to 52.0% and 52.2% predicted by RSM and RSM-ANN, respectively. This study thus shows that optimising/predicting the colour removal process using the RSM-ANN approach is possible, and it also indicates that adsorption onto WTR could be used as a primary treatment for removal of colour from dye wastewater.