Zobrazení zdroje stránky Rosenarmstrong3412

In this work, particles of activated carbon supported by Fe-N-TiO2 (Fe-N-TiO2/AC) were synthesized and used as the three-dimensional (3D) particle electrode for folic acid wastewater treatment in the 3D electrolysis and photocatalysis coupling process. The structure, morphology, and physical and electrochemical properties of the Fe-N-TiO2/AC particles were characterized, and the results showed that Fe-N-TiO2 was bound on the surface of AC particles by chemical attachment, and the Fe-N-TiO2/AC particles had better capability of adsorption and charge transfer as compared with the TiO2/AC particles. [https://www.selleckchem.com/products/dss-crosslinker.html DSS Crosslinker] The effects of key operating parameters in the reaction process, including the current density (optimum 0.6 mA/cm2), aeration (optimum 5 L/min), pH value (optimum 5) and the ratio of Fe-N-TiO2/AC particles to cellulose acetate film coating AC particles (optimum 41), were optimized regarding the total oxygen carbon (TOC) removal. Under the optimum conditions, TOC removal from folic acid wastewater reached 82.4% during 120 min photoelectrocatalysis. The kinetic analysis and mechanism study showed that the degradation process fitted to the second-order kinetic model better than to the first-order, and the system exhibited synergistic effects in inhibiting photogenic electron-hole recombination and improving electrolytic efficiency. At the same time, this system has the ability to overcome the interference of the strong ionic strength in folic acid wastewater.Most currently employed textile effluent decolourization methods use physical and chemical processes where dyes do not get degraded instead concentrated or transferred into a solid phase. Therefore, further treatment processes are required to destroy dyes from the environment. In contrast, biological decolourization may result in degradation of the dye structure due to microbial activities and hence biological processes can be considered environmentally friendly. In the present study, bacterial strains with dye decolourization potential were isolated from the natural environment and their ability to decolourize four different reactive textile dyes was studied individually and in a bacterial consortium. The developed bacterial consortium composed with Proteus mirabilis, Morganella morganii and Enterobacter cloacae indicated more than 90% color removals for all four dyes and optimum decolourization of the dye mixture was observed at 40 °C and pH 7. The developed bacterial consortium decolourized 60% of dyes in textile industry effluent at 35 °C and pH 7 showing their ability to endure in highly complex and toxic environments and application in textile industry wastewaters.In the present study, real car wash wastewater was purified by different coagulation/flocculation methods. As coagulant, polyaluminum chloride ('BOPAC'), conventional iron(III) chloride, iron(III) sulfate, and aluminum(III) chloride were used, while as flocculant non-ionic and anionic polyelectrolytes were investigated. The effects of added clay mineral (Na-bentonite) and cationic surfactant (hexadecyltrimethyl ammonium bromide - 'HTABr') were also investigated. The use of BOPAC was significantly more effective than conventional coagulants. Extra addition of clay mineral was also beneficial in relation to both the sediment volume and sedimentation speed, while polyelectrolyte addition enhanced further the sedimentation. Moreover, the simultaneous addition of HTABr significantly enhanced the color removal efficiency due to the successful in-situ generation of organophilic bentonite. In summary, the application of 100 mg L-1 Na-bentonite with 20 mg L-1 Al3+ (from BOPAC) and 0.5 mg L-1 anionic polyelectrolyte resulted in the efficient reduction of the turbidity (4-6 NTU), the COD (158 mg L-1) and the extractable oil content (4 mg L-1) with efficiencies of 98%, 59%, and 85%, respectively. By applying organophilic bentonite in high concentration (500 mg L-1) with identical concentrations of BOPAC and anionic polyelectrolyte, significant color removal (5 times lower absorbance at λ = 400 nm) and 27% lower sediment volume were achieved.Diatomite was modified by chitosan to prepare modified diatomite, and the modified diatomite in an optimized ratio was utilized in coal bio-flocculation. The interaction behavior and flocculation mechanism of modified diatomite on coal slurry water were investigated by single factor experiments, infrared spectroscopy, Brunauer-Emmett-Teller (BET) measurements, and zeta potential measurements. The single factor experiments showed that when the amount of microbial flocculant added was 1.5 ml, the temperature of coal slurry water was 39 °C, the pH was 5, and the amount of modified diatomite was 0.2 g, after 30 min of sedimentation, the flocculation transmittance of the coal slurry water reached 84.3%. The infrared spectra showed that the -NH2 and -OH of the chitosan molecule had a polar interaction with the Si-OH bond in diatomite. The BET measurements showed that the specific surface area of diatomite was not a decisive factor affecting the flocculation effect. Zeta potential measurements indicated that the amino protonation of chitosan increased the isoelectric point (IEP) of modified diatomite. These results showed that modified diatomite has a good effect on coal bio-flocculation.Successful application of one-dimensional advection-dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefﬁcient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural rivers that is based on a hybrid method of granular computing (GRC) and an artificial neural network (ANN) model (GRC-ANN). Also, adaptive neuro-fuzzy inference system (ANFIS) and ANN models were developed to investigate the accuracy of three credible artificial intelligence (AI) models and the performance of these models in different LDC values. By comparing with empirical models developed in other studies, the results revealed the superior performance of GRC-ANN for LDC estimation. The sensitivity analysis of the three intelligent models developed in this study was done to determine the sensitivity of each model to its input parameters, especially the most important ones. The sensitivity analysis results showed that the W/H parameter (W channel width; H flow depth) has the most significant impact on the output of all three models in this research.