Response Surface Methodology Box Research Articles

Adsorptive removal of malachite green dye from aqueous solution using Rumex abyssinicus derived activated carbon

The potential for malachite green dye saturated effluent to severely affect the environment and human health has prompted the search for effective treatment technologies. Thus, this study was conducted with the goal of developing activated carbon from Rumex abyssinicus for the adsorptive removal of malachite green dye from an aqueous solution. Unit operations such as drying, size reduction, impregnation with H3PO4, and thermal activation were used during the preparation of the activated carbon. An experiment was designed considering four main variables at their respective three levels: initial dye concentration (50, 100, and 150 mg/L), pH (3, 6, and 9), contact period (20, 40, and 60 min), and adsorbent dosage (0.05, 0.01, and 0.15 g/100 mL). Optimization of the batch adsorption process was carried out using the Response Surface methodology's Box Behnken approach. The characterization of the activated carbon was described by SEM for surface morphology with cracks and highly porous morphology, FTIR for multi-functional groups O–H at 3506.74 cm−1 and 3290.70 cm−1, carbonyl group stretching from aldehyde and ketone (1900–1700 cm−1), stretching motion of aromatic ring C=C (1543.12 cm−1), stretching motion of –C–H (1500–1200 cm−1), vibrational and stretching motion of –OH (1250.79 cm−1), and vibrational motion of C–O–C (1049.32 cm−1), pHpzc of 5.1, BET for the specific surface area of 962.3 m2/g, and XRD for the presence of amorphous structure. The maximum and minimum dye removal efficiencies of 99.9% and 62.4% were observed at their respective experimental conditions of (100 mg/L, 0.10 mg/100 mL, pH 6, and 40 min) and (100 mg/L, 0.15 mg/100 mL, pH 3, and 20 min), respectively. Langmuir, Freundlich, Toth, and Koble-Corrigan models were used to evaluate the experimental data, in which Koble-Corrigan model was found to be the best fit with the highest value of R2 0.998. In addition to this, the kinetic studies were undertaken using pseudo-first-order, pseudo-second-order, intraparticle diffusion, and Boyd models, and as a result, the pseudo-second-order model proved to have a better fit among the kinetic models. The kinetics and isotherm analysis revealed that the nature of the adsorption to be homogenous and monolayer surfaces driven by chemosorption. Furthermore, the thermodynamics study revealed the nature of adsorption to be feasible, spontaneous, and endothermic. On the other hand, the reusability study depicted the fact that the adsorbent can be utilized for five cycles with a negligible drop in the removal efficiencies from 99.9 to 95.2%. Finally, the low-cost, environmentally benign, and high adsorption capacity of the adsorbent material derived from Rumex abyssinicus stem could be used to treat industrial effluents.

Prediction of the Mechanical Properties of Fibre-Reinforced Quarry Dust Concrete Using Response Surface and Artificial Neural Network Techniques

The focus of this study is to forecast the 28-day compressive strength and split tensile strength of concrete with various percentages of jute and coconut fibres mixed with quarry dust. The response surface methodology (RSM) and the artificial neural networks (ANN) methods were adopted for 3 variable process modelling (coconut fibres of 0% to 2.5%, jute fibres of 0% to 2.5%, and quarry dust of 0% to 25% by weight of cement). The RSM Box−Behnken design (BBD) method was adopted to design the experiments. Test results showed that compressive strength of 34.6 N/mm2 was obtained for concrete with 0% jute, 0% coir, and 12.5% quarry dust. Similarly, the maximum split tensile strength of 3.8 N/mm2 was obtained for concrete with 1.25% jute fibres, 1.25% coconut fibres, and 12.5% quarry dust. ANOVA and Pareto charts were used to assess regression models for response data. Each progression variable’s statistical significance was assessed, and the resulting models were expressed as second-order polynomial equations. Levenberg−Marquardt (LM) algorithm with feed-forward back propagation neural network was used for assessing the compressive strength and split tensile strength of concrete. The statistical data, root mean square error (RMSE), mean absolute error (MAE), mean absolute and percentage error (MAPE), and determination coefficient (R2) show that both techniques, ANN and RSM, are effective tools for predicting compressive strength and split tensile strength. Furthermore, RSM and ANN models have a high correlation with experimental data. However, the response surface methodology model is more accurate.

Response surface modelling of the biosorption of Zn(II) and Pb(II) onto Micropogonias undulatus scales: Box\u2013Behnken experimental approach

In this study, the scales of Micropogonias undulatus fish were investigated as precursors for the development of low-cost biosorbent for the removal of Pb(II) and Zn(II) from aqueous media.The biosorbent was characterised using Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and X-ray diffractometry (XRD). Adsorption parameters (temperature, biosorbent dosage and contact time) were optimised using response surface methodology Box–Behnken experimental design. The optimal factors for Zn(II) removal by croaker fish scale were 145.5 min, 7.01 g/L biosorbent dosage, 30 °C and pH 5.4. The optimal factors for the removal of Pb(II) were 179.3 min, 6.61 g/L biosorbent dosage, 20 °C and pH 3.4. The numerical optimisation revealed that the optimal removal efficiency for Zn(II) and Pb(II) sorption is 96.45% and 98.76%, respectively. The biosorption of both heavy metals was best fit to Freundlich isotherm and pseudo-second-order kinetic models. Thermodynamics studies revealed that the biosorption process was exothermic and spontaneous.