Statistical Modeling and Optimization of Process Parameters for 2,4-Dichlorophenoxyacetic Acid Removal by Using AC/PDMAEMA Hydrogel Adsorbent: Comparison of Different RSM Designs and ANN Training Methods

Abstract

In this study, the response surface methodology (RSM) and artificial neural network (ANN) were employed to study the adsorption process of 2,4-dichlorophenoxyacetic acid (2,4-D) by using modified hydrogel, i.e., activated carbon poly(dimethylaminoethyl methacrylate) (AC/PDMAEMA hydrogel). The effect of pH, the initial concentration of 2,4-D and the activated carbon content on the removal of 2,4-D and adsorption capacity were investigated through the face-centered composite design (FCCD), optimal design and two-level factorial design. The response surface plot suggested that higher removal of 2,4-D and adsorption capacity could be achieved at the higher initial concentration of 2,4-D and lower pH and activated carbon content. The modeling and optimization for the adsorption process of 2,4-D were also carried out by different design methods of RSM and different training methods of ANN. It was found that among the three design methods of RSM, the optimal design has the highest accuracy for the prediction of 2,4-D removal and adsorption capacity (R2 = 0.9958 and R2 = 0.9998, respectively). The numerical optimization of the optimal design found that the maximum removal of 2,4-D and adsorption capacity of 65.01% and 65.29 mg/g, respectively, were obtained at a pH of 3, initial concentration of 2,4-D of 94.52 mg/L and 2.5 wt% of activated carbon. Apart from the optimization of process parameters, the neural network architecture was also optimized by trial and error with different numbers of hidden neurons in the layers to obtain the best performance of the response. The optimization of the neural network was performed with different training methods. It was found that among the three training methods of the ANN model, the Bayesian Regularization method had the highest R2 and lowest mean square error (MSE) with the optimum network architecture of 3:9:2. The optimum condition obtained from RSM was also simulated with the optimized neural network architecture to validate the responses and adequacy of the RSM model.

Ultrasonic (US)-Assisted Electrocoagulation (EC) Process for Oil and Grease (O&G) Removal from Restaurant Wastewater

“…The fit of the model for the responses is determined by the coefficient of determination (R 2 ) and the coefficient of variation (C.V) values; the value of R 2 must be greater than 0.8 to indicate a good model for the data [62]. The C.V is the standard deviation-tomean ratio, expressed as a percentage (%); C.V less than 10% indicates that the model is reproducible [63][64][65]. In this study, all the responses had an R 2 greater than 0.90; as shown in Table 5, the R 2 for O&G and COD was 98.38% and 95.58%, respectively.…”

Activated Carbon Fabricated from Biomass for Adsorption/Bio-Adsorption of 2,4-D and MCPA: Kinetics, Isotherms, and Artificial Neural Network Modeling

“…Sridevi et al [45] employed an FFNN model to predict the adsorption of 2,4-D onto AC generated from Ulva prolifera biomass, and they recorded an R 2 = 0.96 and an RMSE = 0.1007. Dahlan et al [46] used an FFNN to model the adsorption process of 2,4-D onto a modified hydrogel using three input layers; the model complexity was substantially reduced, and they found high accuracy (R 2 = 0.99 and RMSE = 0.0004). Isiyaka et al [47] reported the use of an FFNN model to predict the remediation of MCPA in an aqueous medium using an aluminum-based metal-organic framework and recorded high accuracy (R 2 = 0.99 and RMSE = 0.0040).…”

“…Sridevi et al [45] employed an FFNN model t predict the adsorption of 2,4-D onto AC generated from Ulva prolifera biomass, and the recorded an R 2 = 0.96 and an RMSE = 0.1007. Dahlan et al [46] used an FFNN to model th…”