Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

Abstract

Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (Co) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R2: 0.925) and pseudo-second-order (PSO) kinetic model (R2: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10−5, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.

Poly(O‐Toluidine)‐polyvinylpyrrolidone copolymer submerged MgFe2O4/CS‐OXSWCNTs nanocomposite for removal of acid red dye from aqueous solutions

“…To examine the effect of temperatures on the adsorption of A.R dye by the POT-PVP/MgF NCs and POT-PVP/ MgF/CS-OXSWCNTs NCs, four different temperatures were considered as follows (10,20,35, and 50 C) while maintaining a constant contact time. As seen in Figure 11d, increasing the solution temperature from 10 to 50 C subsequently enhanced the removal of A.R dye by both POT-PVP/MgF NCs and POT-PVP/MgF/CS-OXSWCNTs NCs as an indication to endothermic adsorption process.…”

“…They are often preferred as they offer a cost-effective approach to water treatment. [19][20][21] Due to their many benefits such as high stability, low cost, excellent dispersion, conductivity, interfacial reactivity, and mechanical strength, polymers are being used more and more to address water pollution. [22][23][24][25] When polymers are treated with dye effluent, the resulting polymeric nanocomposite exhibits improved conductivity and uniform dispersion due to the integration of nanoparticles.…”

“…Among these methods, physical adsorption processes are considered reliable due to their efficiency and simplicity of operation and preparation. They are often preferred as they offer a cost‐effective approach to water treatment 19–21 …”

Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

“…In addition to using artificial neural network (ANN) methods, other techniques such as linear regression (LR), support vector regression (SVR), and neuro-fuzzy network methods have also been used in pollutant removal technology to predict changes in pollutant concentrations or other process parameters 14 – 19 . These methods (as shown in Table 1 ) have been proven effective in modeling the complex relationships between various factors and predicting pollutant concentrations, which helps to optimize the performance of the treatment process.…”

“…However, despite these models 14 – 17 performed quite well, their processing or environment is idealized. Most of them use simulated experimental conditions.…”