Preparation of hydrochar bio-based catalyst for fenton process in dye-containing wastewater treatment

A Novel Recyclable Magnetic Nano-Catalyst for Fenton-Photodegradation of Methyl Orange and Imidazole Derivatives Catalytic Synthesis

A magnetite chlorodeoxycellulose/ferroferric oxide (CDC@Fe3O4) heterogeneous photocatalyst was synthesised via treated and modified cotton in two steps. The designed nanocomposites were characterised by FTIR, TGA, XRD, SEM, and VSM analyses. The Fenton-photocatalytic decomposition efficiency of the synthesised magnetic catalyst was evaluated under visible sunlight using Methyl Orange (MO) as a model organic pollutant. The impacts of several degradation parameters, including the light source, catalyst load, irradiation temperature, oxidant dose, and pH of the dye aqueous solution and its corresponding concentration on the Fenton photodegradation performance, were methodically investigated. The (CDC@Fe3O4) heterogeneous catalyst showed a remarkable MO removal rate of 97.9% at 10 min under visible-light irradiation. (CDC@Fe3O4) nanomaterials were also used in a heterogeneous catalytic optimised protocol for a multicomponent reaction procedure to obtain nine tetra-substituted imidazole derivatives. The green protocol afforded imidazole derivatives in 30 min with good yields (91-97%) at room temperature and under ultrasound irradiation. Generally, a synthesised recyclable heterogeneous nano-catalyst is a good example and is suitable for wastewater treatment and organic synthesis.

Preconcentrations of Pb(II), Ni(II) and Zn(II) by solid phase bio-extractor using thermophilic Bacillus subtilis loaded multiwalled carbon nanotube biosorbent

An alternative biotechnological solid phase bio-extraction (SPE) method was developed. Bacillus subtilis loaded multiwalled carbon nanotube was designed and used as biosorbent for the preconcentrations of Pb(II), Ni(II), and Zn(II). The experimental parameters such as sample flow rate, pH of sample solution, amounts of Bacillus subtilis and multiwalled carbon nanotube, volume of sample solution and reusability of column which affects the analytical characteristics of the SPE method were investigated in details. Surface structures were examined by using FTIR, SEM. The best pH was determined as 5.0 and the percentages recoveries of Zn(II), Ni(II), and Pb(II) were determined as 99.1%, 98.7%, and 96.2%, respectively, at a flow rate of 3 mL/min. In this study, in which the profitable sample volume was determined as 400 mL and the amount of multiwalled carbon nanotube (MWCNT) as 50 mg. It was also observed that the column had a significant potential to preconcentrate Zn(II), Ni(II), and Pb(II) even after 25 reuses. The biosorption capacities for Zn(II), Ni(II) and Pb(II) were calculated as 39.67 mg/g, 45.98 mg/g and 51.34 mg/g respectively. The LOD values were calculated as 0.024 ng/mL for Pb(II), 0.029 ng/mL for Ni(II), and 0.019 ng/mL for Zn(II). The linear range was detected as 0.25-25 ng/mL. The concentrations of Pb(II), Ni(II), and Zn(II) in a variety of real food samples were determined by using developed method after application of certified reference sample.

Lignin microparticles-reinforced cellulose filter paper for simultaneous removal of emulsified oils and dyes

The presence of multiple pollutants in wastewater, often with complex interactions, poses a significant challenge for conventional membranes to effectively remove multiple pollutants simultaneously. Herein, a lignin microparticles-reinforced cellulose filter paper (FP@AL-LS-DA) was fabricated via an aldol condensation between lignin and cellulose filter paper and cross-linking with dopamine hydrochloride (DA), which showed desired rejection of oil-in-water emulsions and dyes. Characterizations revealed that the addition of lignin and DA effectively narrowed the pore size (from 4.45 μm to 2.01 μm) and enhanced the rigidity and stability of the cellulose filter paper, thus making it not easily damaged in the water environment and showing excellent tolerance to strong acid and high-salt environments. The oil-in-water emulsions removal efficiency was higher than 99 % even after ten times usage, and the oil flux was kept stable at 52.54 L·m^-2·h^-1, indicating that FP@AL-LS-DA had outstanding reusability and stability. Remarkably, FP@AL-LS-DA showed excellent removal efficiency (>99 %) for complex pollutants containing dyes and oil-in-water emulsions. In this work, we demonstrate a lignin microparticles-reinforced cellulose filter paper that is simple to prepare and can efficiently separate oil-in-water emulsions and remove dyes.

Deep convolutional neural network with sine cosine algorithm based wastewater treatment systems

Wastewater treatment systems are essential in today's business to meet the ever-increasing requirements of environmental regulations while also limiting the environmental impact of the sector's discharges. A new control and management information system is needed to handle the residual fluids. This study advises that Wastewater Treatment System (WWTS) operators use intelligent technologies that analyze data and forecast the future behaviour of processes. This method incorporates industrial data into the wastewater treatment model. Deep Convolutional Neural Network (DCNN) and Since Cosine Algorithm (SCA), two powerful artificial neural networks, were used to predict these properties over time. Remediation actions can be taken to ensure procedures are carried out in accordance with the specifications. Water treatment facilities can benefit from this technology because of its sophisticated process that changes feature dynamically and inconsistently. The ultimate goal is to improve the precision with which wastewater treatment models create their predictions. Using DCNN and SCA techniques, the Chemical Oxygen Demand (COD) in wastewater treatment system input and effluent is estimated in this study. Finally, the DCNN-SCA model is applied for the optimization, and it assists in improving the predictive performance. The experimental validation of the DCNN-SCA model is tested and the outcomes are investigated under various prospects. The DCNN-SCA model has achieved a maximum accuracy performance and proving that it outperforms compare with the prevailing techniques over recent approaches. The DCNN-SCA-WWTS model has shown maximum performance Under 600 data, DCNN-SCA-WWTS has a precision of 97.63%, a recall of 96.37%, a F score of 95.31%, an accuracy of 96.27%, an RMSE of 27.55%, and a MAPE of 20.97%.