Modeling and Optimizing of NH4+ Removal from Stormwater by Coal-Based Granular Activated Carbon Using RSM and ANN Coupled with GA

Abstract

As a key parameter in the adsorption process, removal rate is not available under most operating conditions due to the time and cost of experimental testing. To address this issue, evaluation of the efficiency of NH4+ removal from stormwater by coal-based granular activated carbon (CB-GAC), a novel approach, the response surface methodology (RSM), back-propagation artificial neural network (BP-ANN) coupled with genetic algorithm (GA), has been applied in this research. The sorption process was modeled based on Box-Behnben design (BBD) RSM method for independent variables: Contact time, initial concentration, temperature, and pH; suggesting a quadratic polynomial model with p-value < 0.001, R2 = 0.9762. The BP-ANN with a structure of 4-8-1 gave the best performance. Compared with the BBD-RSM model, the BP-ANN model indicated better prediction of the response with R2 = 0.9959. The weights derived from BP-ANN was further analyzed by Garson equation, and the results showed that the order of the variables’ effectiveness is as follow: Contact time (31.23%) > pH (24.68%) > temperature (22.93%) > initial concentration (21.16%). The process parameters were optimized via RSM optimization tools and GA. The results of validation experiments showed that the optimization results of GA-ANN are more accurate than BBD-RSM, with contact time = 899.41 min, initial concentration = 17.35 mg/L, temperature = 15 °C, pH = 6.98, NH4+ removal rate = 63.74%, and relative error = 0.87%. Furthermore, the CB-GAC has been characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The isotherm and kinetic studies of the adsorption process illustrated that adsorption of NH4+ onto CB-GAC corresponded Langmuir isotherm and pseudo-second-order kinetic models. The calculated maximum adsorption capacity was 0.2821 mg/g.

RSM, ANN-GA and ANN-PSO modeling of SDBS removal from greywater in rural areas via Fe2O3-coated volcanic rocks

“…The research showed that in response surface analysis if the contour shape is elliptic, it indicates significant interaction between the factors, while the opposite is true for a circle. 51 As can be seen intuitively from the contour plot, the interaction between the two factors is relatively significant. There is an optimal SDBS removal area, that is, the area with a contact time of 120–180 min and adsorbent dosage of 70–90 g L −1 , with SDBS removal efficiency of over 90% .…”

Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix

“…The benefits of using RSM for modelling and optimization studies include: (i) it allows for a reduced number of experimental trials vis-à-vis the conventional method of one factor at a time; (ii) it integrates mathematical modeling and experimental design; (iii) it possesses the ability to analyze the interaction of process variables with visual elucidation in spite of the inherent complexity; (iv) it has the capacity of high reasonable optimization degree and (v) it exists in varieties of classes such as Box-Behnken design (BBD), central composite design (CCD), hybrid design, three-level factorial design among others, that suit specific demands [54][55]. To improve the performance of RSM in achieving global optimization, the evolutionary algorithm such as the genetic algorithm, one of the choicest and most popular algorithms, is employed [56]. The adaptive neuro-fuzzy inference logic system or adaptive network-based fuzzy inference logic system (ANFILS) is a part of artificial intelligence (AI) algorithms that map input to output data with high precision accuracy.…”

Modeling and Optimization of Phosphate Solubilizing Bacteria Isolated from Rhizospheric Soils of the Coffee Plant using Artificial Neural Network (ANN) and Response Surface Methodology (RSM).

“…ANN has been widely used due to its suitability for modeling and simulation of various processes in real engineering applications [9]. ANN does not require a mathematical description of the phenomena in the process, so the simulation of the complicated systems could be performed more efficiently [10].…”