Dual-purpose optimization of dye-polluted wastewater decontamination using bio-coagulants from multiple processing techniques via neural intelligence algorithm and response surface methodology

Two-stage injection of polymer and microsand during ballasted flocculation for treating kaolin waters with or without humic acid: Floc evolutional characteristics, performance and mechanisms

Ballasted flocculation is regarded as a most promising water treatment technology in aspects of retrofit and high-rate applications. To deep understand the incorporation behaviors of ballasting agent into ballasted floc growth, two distinct injection modes (namely a two-stage injection of polyacrylamide (PAM) alone, and a two-stage injection of both PAM and microsand) were developed in this study. Then, ballasted flocculation tests of kaolin and kaolin-HA (humic acid) waters were conducted at varying split ratios for fixed total dosages of both PAM and microsand. The experimental results showed that for either two-stage injection mode, the higher the second percentage of each split ratio, the greater the average size of maturated flocs at the second sub-stage of maturation. Meanwhile, the turbidity and UV254 values of settled water became lower at 30 and 180 s of sedimentation, suggesting that varying split ratios significantly affected the kinetics of ballasted floc growth. Moreover, it was suggested that the selection of either two-stage injection mode or corresponding split ratios played a more pronounced role in the HA removal than the total dosage of PAM. This suggestion was supported by SEM, FTIR and XPS analyses for surface morphological details, functional groups and chemical states of maturated flocs eventually formed in the kaolin-HA water through both two-stage injection modes. Accordingly, newly-established conceptual models of ballasted floc growth were proposed to explore the potential influencing mechanisms of varying split ratios on the ballasted flocculation performance. At each sub-stage of maturation, an appropriate dosage ratio between PAM and microsand was of great importance to effectively incorporate microsand particles into ballasted floc formation, besides the hydrolyzed produces of AS coagulant formed at the coagulation stage of ballasted flocculation. This study is expected to provide valuable insights for making ballasted flocculation more effective, economical and sustainable in water treatment engineering.

Effective removal of malachite green from local dyeing wastewater using zinc-tungstate based materials

The frequent use of an industrial dye such as malachite green (MG) has caused major water body deterioration and is one of the most pressing global challenges, demanding effective treatment techniques. To solve these issues, a simplistic method was developed to synthesize zinc-tungstate (ZnWO4) nanoparticles and also dope the surface matrix of the ZnWO4 nanoparticles using nonmetals of boron (B), carbon (C), and nitrogen (N) at different ratios for enhanced MG removal from wastewater. The prepared nanomaterials were characterized by different methods for crystal structure composition, surface properties, surface morphology, microstructures, functional groups, and elemental oxidation states. The BET analysis revealed a mesoporous structure with surface areas of 30.740 m2/g for ZnWO4, 38.513 m2/g for ZnWO4@BCN, 37.368 m2/g for ZnWO4@BCN/B, 39.325 m2/g for ZnWO4@BCN/C, and 45.436 m2/g for ZnWO4@BCN/N nanocomposites. The best removal of MG was accomplished at pH (8), contact period (50 min), nanoadsorbent dose (0.8 g/L), initial MG concentration (20 mg/L), and temperature (303 K). The maximum adsorption capacities of ZnWO4 and ZnWO4@BCN/N towards MG were 218.645 and 251.758 mg/g, respectively. At equilibrium, the Freundlich isotherm and pseudo-second-order kinetic models were the best fits for the experimental data of MG adsorption on both nanoadsorbents. After eight cycles of adsorption and desorption, both ZnWO4 and ZnWO4@BCN/N were found to be good at removing MG, with efficiencies of 71.00 and 74.20%, respectively. Thermodynamic investigations further validated the spontaneity and endothermic nature of the adsorption process. All study findings confirm the nanoadsorbents exceptional capability and economic feasibility for removing MG dye.

A comparative study of black-box and white-box data-driven methods to predict landfill leachate permeability

Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), planning and operation of solid waste management systems are challenging for decision-makers. In this regard, data-driven methods can be considered robust approaches to modeling this problem. In this paper, three black-box data-driven models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and support vector regression (SVR), and also three white-box data-driven models, including the M5 model tree (M5MT), classification and regression trees (CART), and group method of data handling (GMDH), were developed for modeling and predicting landfill leachate permeability ([Formula: see text]). Based on a previous study conducted by Ghasemi et al. (2021), [Formula: see text] can be formulated as a function of impermeable sheets ([Formula: see text]) and copper pipes ([Formula: see text]). Hence, in the present study, [Formula: see text] and [Formula: see text] were adopted as input variables for the prediction of [Formula: see text] and evaluated for the performance of the suggested black-box and white-box data-driven models. Scatter plots and statistical indices such as coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were used for qualitative and quantitative evaluations of the effectiveness of the suggested methods. The outcomes indicated all of the provided models successfully predicted [Formula: see text]. However, ANN and GMDH had higher accuracy between the proposed black-box and white-box data-driven models. ANN with R² = 0.939, RMSE = 0.056, and MAE = 0.017 was marginally better than GMDH with R² = 0.857, RMSE = 0.064, and MAE = 0.026 in the testing stage. Nevertheless, an explicit mathematical expression provided by GMDH to predict k was easier and more understandable than ANN.

Cocoyam powder extracted from Colocasia antiquorum as a novel plant-based bioflocculant for industrial wastewater treatment: Flocculation performance and mechanism

In the current investigation, the comparative study of cocoyam bioflocculant (CYBF) and chemical flocculant for the removal of heavy metals, COD, BOD, TDS, TSS, sulphate and nitrate from tannery effluent, and dyes from synthetic dye wastewater were examined. Different analytical techniques, including Fourier transforms infrared (FTIR), X-ray powder diffraction (XRD), Scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDX), were used to characterise the extracted bioflocculant. FTIR spectral measurement of the bioflocculant demonstrated the presence of hydroxyl, carboxyl, and amino groups. By using bioflocculant, the highest removal of TSS (85.5%), TDS (76.2%), BOD (74%), COD (50.5%), sulphate (54.4%), nitrate (52%), Lead (65%), Chromium (60%), Nickel (57.9%), from tannery effluent was achieved at pH 6 and bioflocculant dosage of 8 mg/L. While, 80% congo red, 79% methyl orange, 73% safranin, and 72% methylene blue were removed from synthetic dye wastewater by cocoyam bioflocculant. Two flocculation mechanisms were found for dye removal, electrostatic force of attraction, and hydrogen bonding. In the case of metal adsorption, only electrostatic interactions were observed between metal ions and functional groups of bioflocculant. The cocoyam bioflocculant exhibited excellent flocculation efficacy and thus can be used in wastewater treatment to remove heavy metals and other pollutants.

Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models

Three artificial intelligence (AI) data-driven techniques, including artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were applied for modeling and predicting turbidity removal from water using graphene oxide (GO). Based on partial mutual information (PIM) algorithm, pH, GO dosage, and initial turbidity were selected as the input variables for developing the models. The prediction performance of the AI-based models was compared with each other and with the response surface methodology (RSM) model, previously reported by the authors, as well. The models' estimation accuracy was assessed through statistical measures, including mean-squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Among the evaluated models, ANN had the highest estimation accuracy as it showed the highest R² for the validation data (0.949) and the lowest MSE, RMSE, and MAE values. Furthermore, ANN predicted 76.1% of data points with relative errors (RE) less than 10%. In contrast, the weakest prediction performance belonged to the SVR model with the lowest R² for both calibration (0.712) and validation (0.864) data. Besides, only 57.1% of the SVR's predictions were characterized by RE < 10%. The ANFIS and RSM models exhibited a more or less similar performance in terms of R² for the validation data (0.877 and 0.871, respectively) and other statistical parameters. According to the results, the ANN technique is proposed as the best option for modeling the process. Nevertheless, as the RSM technique provides valuable information about the contribution of the independent operational parameters and their complex interaction effects using the least number of experiments, simulating the process by this technique before modeling by ANN is inevitable.

Evaluation of Zn Adenine-Based Bio-MOF for Efficient Remediation of Different Types of Dyes

As an eco-friendly material, Zn-adeninate bio-metal-organic framework (bio-MOF) was investigated as an efficient adsorbent for both anionic and cationic dyes. The adsorption capability of the synthesized Zn-adeninate bio-MOF was confirmed by its notable surface area of 52.62 m2 g−1 and total pore volume of 0.183 cm3 g−1. The bio-MOF adsorption profiles of anionic direct red 81 (DR-81) and cationic methylene blue (MB) dyes were investigated under different operating parameters. The optimum dosages of Zn-adeninate bio-MOF were 0.5 g L−1 and 1 g L−1 for MB and DR-81 decolorization, respectively. The pHPZC of Zn-adeninate bio-MOF was 7.2, and maximum monolayer adsorption capacity was 132.15 mg g–1 for MB, which decreased to 82.54 mg g–1 for DR-81 dye. Thermodynamic data indicated the spontaneous and endothermic nature of the decolorization processes. Additionally, the adsorption processes were in agreement with the Langmuir and pseudo-second-order kinetic models. The synthesized Zn-adeninate bio-MOF could be reused several times with high decolorization ability. These findings demonstrated that the synthesized Zn bio-MOF is an effective and promising adsorbent material for the removal of both cationic and anionic dyes from polluted water.

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

Decontamination and reuse of greywater in rural areas has attracted increasing attention. Typical contaminants in grey water are SDBS, which has a stubborn molecular structure. In this study, Fe₂O₃-coated volcanic rocks (Fe₂O₃-VR) prepared from FeCl₃ solution by a heating evaporation method can reach 95% removal of SDBS, which is 80% higher than before. The effect of contact time, pH, initial concentration, FeCl₃ solution concentration, adsorbent dosage and calcination temperature on the removal rate was researched and modeled by response methodology (RSM) and artificial neural network (ANN). Based on the univariate test, the Box-Behnken design method was used to establish the data sample, which represented a quadratic polynomial model with p-value <0.001, R² = 0.9872, while the ANN model has the better performance with R² = 0.9961. The weights of the BP-ANN model were further analyzed using the Garson equation, and the results showed that the validity ranking of the variables was as follows: contact time (37.31%) > calcination temperature (29.43%) > dosage (24.44%) > initial concentration (17.18%) > FeCl₃ solution concentration (17.18%) > pH (11.56%). Genetic algorithm (GA) and particle swarm optimization (PSO) were selected to optimize the process parameters. The results showed that ANN-PSO methodology presented a satisfactory alternative and the predicted removal efficiency was 99.9982% with relative error = 0.2230. The optimum level of contact time, pH, initial SDBS concentration, FeCl₃ solution concentration, adsorbent dosage and calcination temperature is 136.45 min, 5.64, 22.4 mg L⁻¹, 0.3 mol L⁻¹, 83.21 g L⁻¹, 274.02 °C, respectively. Moreover, Fe₂O₃-VR was characterized via instrumental analyses (SEM-EDS, FTIR, XRD, BET).

This paper provides a new method for SDBS removal and parameter optimization of the adsorption process using RSM and ANN models.