Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO

Abstract Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process

Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm

Reduced graphene oxide-supported Fe3O4 (Fe3O4/rGO) composites were applied in this study to remove low-concentration mercury from aqueous solutions with the aid of an artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. The Fe3O4/rGO composites were prepared by the solvothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), N2-sorption, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and superconduction quantum interference device (SQUID). Response surface methodology (RSM) and ANN were employed to model the effects of different operating conditions (temperature, initial pH, initial Hg ion concentration and contact time) on the removal of the low-concentration mercury from aqueous solutions by the Fe3O4/rGO composites. The ANN-GA model results (with a prediction error below 5%) show better agreement with the experimental data than the RSM model results (with a prediction error below 10%). The removal process of the low-concentration mercury obeyed the Freudlich isotherm and the pseudo-second-order kinetic model. In addition, a regeneration experiment of the Fe3O4/rGO composites demonstrated that these composites can be reused for the removal of low-concentration mercury from aqueous solutions

Cracking and segregation in high-alloy steel 0.4C1.5Mn2Cr0.35Mo1.5Ni produced by thick continuous casting

Based on our innovative application of using thick continuous casting slab 0.4C1.5Mn2Cr0.35Mo1.5Ni (high alloy) for the production of high-quality mould steel, the present study investigated the high cracking susceptibility of high-alloy steel and segregation in continuous casting slab. The thermal expansion and the continuous cooling transformation (CCT) curve measurement, together with a high temperature in situ observation, confirmed the martensite phase transition happening at approximately 583 K that would result in an increase in the hardenability and cracking susceptibility. The cracking susceptibility zone was determined by high-temperature mechanical properties measurement. The high-alloy mould steel has no II brittle zone, and III brittle zone is 973–1148 K. As a conclusion, the straightening temperature should be above 1148 K to avoid the cracking during the continuous casting. Moreover, the elemental segregation of carbon, sulfur, chromium, and molybdenum along the cracking was examined by electron probe microanalysis (EPMA) quantitative analysis that might be another reason for the steel crack formation. It shows that Martensite phase transition happened at approximately 583 K that would result in an increase in the hardenability and cracking susceptibility

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA)

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model

Addendum: Shi, X.D.; Ruan, W.Q.; Hu, J.W.; Fan, M.Y.; Cao, R.S.; Wei, X.H. Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA). Nanomaterials 2017, 7, 134

The authors wish to make the following addendum to their paper [1]:[...

Study on the Control of Rare Earth Metals and Their Behaviors in the Industrial Practical Production of Q420q Structural Bridge Steel Plate

Rare earth (RE) addition can refine and change the shape/distribution of inclusions in steel to improve its strength and toughness. In this paper, the control of RE, specifically Ce and La, and their behaviors in the practical industrial production of high-strength structural steel with 420 MPa yield strength were studied. In particular, the interactions between RE and Al, Nb, S, O were investigated, with the aim of improving the steel toughness and welding performance. The impact energy of the plate with RE is approximately 50 J higher than the regular plate without RE. The toughness of the plate from ladle furnace (LF) refining with RE addition is better than the one from Ruhrstahl and Hereaeus (RH) refining. The RE inclusions could induce the intragranular ferrite and refine the grain size to the preferred size. After welding at the heat input of 200 kJ/cm, the grain size at the heat affected zone was found to be the finest in the plate from the LF process with RE addition. Notably, the microstructure of ferrite was quasi-polygonal

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0) were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG

Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R2 value than the pseudo-first-order model

Heavy Metal Pollution and Ecological Assessment around the Jinsha Coal-Fired Power Plant (China)

Heavy metal pollution is a serious problem worldwide. In this study, 41 soil samples and 32 cabbage samples were collected from the area surrounding the Jinsha coal-fired power plant (JCFP Plant) in Guizhou Province, southwest China. Pb, Cd, Hg, As, Cu and Cr concentrations in soil samples and cabbage samples were analysed to study the pollution sources and risks of heavy metals around the power plant. The results indicate that the JCFP Plant contributes to the Pb, Cd, As, Hg, Cu, and Cr pollution in nearby soils, particularly Hg pollution. Cu and Cr in soils from both croplands and forestlands in the study area derive mainly from crustal materials or natural processes. Pb, Cd and As in soils from croplands arise partly through anthropogenic activities, but these elements in soils from forestlands originate mainly from crustal materials or natural processes. Hg pollution in soils from both croplands and forestlands is caused mainly by fly ash from the JCFP Plant. The cabbages grown in the study area were severely contaminated with heavy metals, and more than 90% of the cabbages had Pb concentrations exceeding the permissible level established by the Ministry of Health and the Standardization Administration of the People’s Republic of China. Additionally, 30% of the cabbages had As concentrations exceeding the permissible level. Because forests can protect soils from heavy metal pollution caused by atmospheric deposition, close attention should be given to the Hg pollution in soils and to the concentrations of Pb, As, Hg and Cr in vegetables from the study area