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Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4 mg, a pH of 5, a CV concentration of 40.1 mg/L, and a temperature of 20 °C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.

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A major environmental problem on a global scale is the contamination of water by dyes, particularly from industrial effluents. Consequently, wastewater treatment from various industrial wastes is crucial to restoring environmental quality. Dye is an important class of organic pollutants that are considered harmful to both people and aquatic habitats. The textile industry has become more interested in agricultural-based adsorbents, particularly in adsorption. The biosorption of Methylene blue (MB) dye from aqueous solutions by the wheat straw (T. aestivum) biomass was evaluated in this study. The biosorption process parameters were optimized using the response surface methodology (RSM) approach with a face-centred central composite design (FCCCD). Using a 10 mg/L concentration MB dye, 1.5 mg of biomass, an initial pH of 6, and a contact time of 60 min at 25 °C, the maximum MB dye removal percentages (96%) were obtained. Artificial neural network (ANN) modelling techniques are also employed to stimulate and validate the process, and their efficacy and ability to predict the reaction (removal efficiency) were assessed. The existence of functional groups, which are important binding sites involved in the process of MB biosorption, was demonstrated using Fourier Transform Infrared Spectroscopy (FTIR) spectra. Moreover, a scan electron microscope (SEM) revealed that fresh, shiny particles had been absorbed on the surface of the T. aestivum following the biosorption procedure. The bio-removal of MB from wastewater effluents has been demonstrated to be possible using T. aestivum biomass as a biosorbent. It is also a promising biosorbent that is economical, environmentally friendly, biodegradable, and cost-effective.

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The current study aimed to assess how high concentrations of ozone (O3) and suspended particulate matter (SPM) alter biochemical properties of high yielding wheat cultivars (i.e., HD3086 and HD2967) grown under 10 km radius in 8 villages, located around Thermal Power Plant (TPP), Auraiya, Uttar Pradesh, India. Significant foliar damage was brought on by O3 and SPM exposure in both wheat cultivars and noted for consecutive 2 years as per emission patterns, air movement and biochemical defense capabilities. The detected air pollutants at the chosen experimental site ranged from 34 to 46 ppb O3 and 139-189 µg/m3 SPM. Range of biochemical parameter for both cultivars are as pH 6.6-7.1, relative water content (RWC) 44-62%, chlorophyll 0.23-0.35 mg/g, ascorbic acid (AA) 54-68 mg/g and air pollution tolerance index (APTI) 47-72. It has been observed that SPM deposition had a meaningful impact (P-value = 0.05) on the chlorophyll, pH, RWC and APTI.

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Most of the dyes are toxic and non-biodegradable in textile industry wastewaters. Therefore, removal of textile dye using agriculture waste becomes crucial for the environment. This can be accomplished by the biosorption process which is the passive uptake of pollutants by agricultural waste. In this study, Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to obtain optimum conditions for Methylene Blue (MB) removal using sugarcane bagasse and peanut hulls as low-cost agricultural waste. The experimental design was carried out to study the effect of temperature, pH, biosorbent amount and dye concentration. The maximum MB dye removal considering the effect of total dissolved solids from aqueous solutions of 74.49% and 67.99% by sugarcane bagasse and peanut hulls, respectively. The models specify that they could predict biosorption with high accuracy having R2-value above 0.9. Statistical studies for RSM, ANFIS and ANN models were compared. Further, the models were optimized for maximum dye removal was at 1.21 g of biosorbent, pH 5.24, 31.24 mg/L MB concentration, 22.29°C of dye solution using sugarcane bagasse and at 1.37 g of biosorbent, pH 5.77, 36.7 mg/L MB concentration, 26.8°C of dye solution using peanut hulls. Additionally, Fourier Transform Infra-Red (FTIR) spectral analysis was also carried out to confirm the biosorption.

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Soil decontamination and restoration continue to be a key environmental concern around the globe. The degradation of soil resources due to the presence of potentially toxic elements (PTEs) has a substantial influence on agricultural production, food security, and human well-being, and as a result, urgent action is required. PTEs pollution is not a threat to the agroecosystems but also a serious concern to human health; thereby, it needs to be addressed timely and effectively. Hence, the development of improved and cost-effective procedures to remove PTEs from polluted soils is imperative. With this context in mind, current review is designed to distinctly envisage the PTEs removal potential by the single and binary applications of biochar (BC) and nanomaterials (NMs).2 Recently, BC, a product of high-temperature biomass pyrolysis with high specific surface area, porosity, and distinctive physical and chemical properties has become one of the most used and economic adsorbent materials. Also, biochar's application has generated interest in a variety of fields and environments as a modern approach against the era of urbanization, industrialization, and climate change. Likewise, several NMs including metals and their oxides, carbon materials, zeolites, and bimetallic-based NMs have been documented as having the potential to remediate PTEs-polluted environments. However, both techniques have their own set of advantages and disadvantages, therefore combining them can be a more effective strategy to address the growing concern over the rapid accumulation and release of PTEs into the environment.

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This research compares the performance efficiencies of Nanofiltration (NF), Reverse osmosis (RO), and Nanofiltration-Reverse Osmosis (NF-RO) hybrid membrane filtration systems, for treatment of brackish groundwater in Delhi-NCR region. Central composite design (CCD) of response surface methodology (RSM) were applied to formulate predictive models for the optimization and simulation of various responses, viz. Water flux, salt rejection, permeate recovery and specific energy consumption (SEC). Three different input factors (feed concentration, pH and pressure) were evaluated. Significance of RSM model was tested and validated with use of an analysis of variance (ANOVA). The models were also compared graphically for their prediction ability. The optimum conditions were determined by numerical optimization of the NF and RO membrane pilot plants. Finally, hybrid configurations of NF and RO were investigated utilising the optimum conditions to identify the technology suitable for the remediation of brackish groundwater. Despite, RO membrane eliminating over 99% of TDS from groundwater, the lower recovery rate renders it environmentally unfavourable. Result indicates that recovery of the hybrid system (40.35%) was greater than that of RO alone (18.796%) and SEC also decreases (5.090-3.8 kW H/m3). Hence, combining a hybrid membrane arrangement with NF-RO to treat brackish groundwater for improved recovery and lower SEC is a viable alternative.

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Forward osmosis (FO) technology has been acknowledged as an energy-efficient cutting-edge water treatment innovation; however, the inefficient performance of polymer-based membranes remains a tailback in the practical utilization of FO. A significant issue in FO is membrane fouling, which negatively influences the flux efficiency, working expenses and membrane life expectancy. Membranes having high water flux and minimum reverse solute flux at low operating pressures are the ideal membranes for this process. This study reports a thin-film nanocomposite (TFNC) membrane for the treatment of textile industry wastewater utilizing fertilizer as draw solution fabricated via the phase inversion process. The chemical structure and morphology of the synthesized manganese oxide (MnO2) incorporated membrane were studied by various characterization techniques like X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy-energy-dispersive X-ray spectroscopy, contact angle and gravimetry. The outcomes demonstrated that the nanoparticles were bonded to cellulose acetate polymer via covalent bonds and showed very hydrophilic membrane surface, along with an increased osmotic water flux of 52.5 L.m2.h-1 and reverse salt flux of 10.9 g.m2.h-1, when deionized wastewater and potassium chloride were used as the feed solution and the draw solution, respectively. In this manner, incorporating manganese oxide into the FO membrane may introduce its extraordinary possible application for the production of diluted fertilizer solution with balanced nutrients.

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Water is crucial for enhancing the yield of agricultural land to meet the growing demand. Forward osmosis (FO) is a developing technology that utilizes the natural osmotic gradient of solutions. In this study, fertilizer drawn FO setup was considered by using potassium chloride (KCl) as the draw solution (DS) for treating textile wastewater as the feed solution (FS). This study investigated the effects of FS temperature, pH, and FS and DS concentrations. The performance investigation involved the study in terms of water flux, reverse salt flux, and specific reverse salt flux. DS and FS properties, osmotic potential, and temperature played a vital role in the performance. At 30°C FS temperature, the highest water flux (5.5 LMH) was observed. Reverse salt flux increased due to the increase in solute diffusivity. The highest value of water flux was obtained at a DS of 1.150 M and FS of 1000 mg/L. The permeation of water improved due to the difference in DS and FS concentrations at pH values above 7. The results of this study suggest that KCl as DS has a higher potential for the treatment of textile wastewater at a temperature of 30°C. Additionally, the functional groups attached to the FO membrane were identified through Fourier-transform infrared (FTIR) spectroscopic study. PRACTITIONER POINTS: Treatment of textile wastewater with the use of fertilizer draw solution (KCl) by forward osmosis process as carried out. The performance was assessed in terms of water flux, reverse salt flux, and specific reverse salt flux. The effects of feed and fertilizer draw solution concentrations; pH and temperature were evaluated on the performance of FO process.

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Artificial neural network (ANN) and response surface methodology (RSM) were employed to develop models for process optimisation of pilot scale nanofiltration (NF) and reverse osmosis (RO) membrane filtration system for the treatment of brackish groundwater. The process variables for this study were feed concentration, temperature, pH and pressure. The performance of NF/RO was assessed in terms of permeate flux, water recovery, salt rejection and specific energy consumption, which were considered as responses. The experimental design was employed to develop both RSM and ANN models. RSM model was validated for the whole range of experimental levels, while the ANN model was considered for the whole range of experimental design. RSM and ANN models were statistically analysed using analysis of variance (ANOVA). Further, the models were graphically compared for its predictive capacity. Numerical optimisation of NF and RO pilot plant to determine the optimum conditions were verified. Finally, using the optimum conditions, three hybrid configurations of NF and RO were studied to determine the best mode for the treatment of brackish groundwater. It was found that parallel NF-RO had a recovery of 57.18% and rejection of 44.89%, for RO-concentrate-NF (RO-C-NF) recovery was 49.55% and rejection of 38.64% & for NF-concentrate-RO (NF-C-RO), the recovery of 39.53% and rejection of 49.66% was obtained. Results obtained also suggested that the mode of configurations and the feed concentration affect the performance of the hybrid system.

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The present study aims at evaluating a small-scale brackish water reverse osmosis (RO) process using parameter optimization. Experiments were carried out using formulated artificial groundwater, and a predictive model was developed by using response surface methodology (RSM) for the optimization of input process parameters of brackish water RO process to simultaneously maximize water recovery and salt rejection while minimizing energy demand. The result of multiple response optimization along with analysis of variance for RSM predictions showed that the optimal water recovery (19.18%), total dissolved solids rejection (89.21%) and specific energy consumption (17.60 kWh/m³) occurred at 31.94 °C feed water temperature, 0.78 MPa feed pressure, 1500 mg/L feed salt concentration and 6.53 pH. Furthermore, confirmation of RSM predictions was carried out by an artificial neural network (ANN) model trained by RSM experimental data. Predicted values by both RSM and ANN modelling methodologies were compared and found within the acceptable range. Finally, a membrane validation experiment was carried out successfully at proposed optimal conditions, which proves the accuracy of employed RSM and ANN models. Present methodology can be used as a generalized way for the optimization of different RO membranes available in the market in terms of increased water recovery and salt rejection with least energy consumption to make it commercially competent.

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