Mobility and Dispersion Optimization of Nano Zerovalent Iron (nZVI) in Disinfection of Urban Wastewater with Pneumatic Nitrogen Gas Injection

Zero iron nanoparticle is considered as a universal enhancement agent. Its stabilization in aqueous environments with different coatings, reduces the efficiency of nanoparticles to a great extent. This study aimed to optimize the mobility and dispersion of nanoparticles to increase the inactivation efficiency of heterotrophic bacteria in urban sewage effluents. The experiment was carried out on Response Surface Methodology (RSM) and Central Composite Design (CCD) using Design Expert 10 software. Iron nanoparticles were synthesized in two types of carboxymethyl cellulose-coated and simple type. B-nZVI  was introduced into the effluent with by pneumatic injection of nitrogen gas. CMC-nZVI was also mixed with a mixer in the effluent. Comparison of the results was done with two HPC and cellular molecular techniques (Genetic sequencing of 16s rRNA bacteria). The highest inactivation efficiency (90%) was observed in minute 23 for pneumonic injection of B-nZVI at a flow rate of 10 L / min.  Finally, with the improvement of gas pressure and flow rate, the inactivation efficiency was recorded at 95.6% at 32 minutes. Final model obtained from this process agreed with the quadratic equation. General forecasting of the model was expressed by the correlation coefficient (R2=0.9447) that made good fitness for the response data. The statistical significance was determined using Fisher's statistics (F-value=13.29). For optimal use of nZVI in the inactivation of urban wastewater heterotrophic bacteria, nZVI can be injected into the wastewater by pneumatic injection in two steps with an inert gas such as nitrogen. In the nZVI pneumatic injection, the efficiency of deactivating bacteria in urban wastewater treatment plants was about 17% to 39% better than that of the coated-nZVI such as CMCs

A Study on Membrane Bioreactor for Water Reuse from the Effluent of Industrial Town Wastewater Treatment Plant

Background: Considering the toxic effects of heavy metals and microbial pathogens in industrial wastewaters, it is necessary to treat metal and microbial contaminated wastewater prior to disposal in the environment. The purpose of this study is to assess the removal of heavy metals pollution and microbial contamination from a mixture of municipal and industrial wastewater using membrane bioreactor. Methods: A pilot study with a continuous stream was conducted using a 32-L-activated sludge with a flat sheet membrane. Actual wastewater from industrial wastewater treatment plant was used in this study. Membrane bioreactor was operated with a constant flow rate of 4 L/hr and chemical oxygen demand, suspended solids concentration, six heavy metals concentration, and total coliform amounts were recorded during the operation. Results: High COD, suspended solids, heavy metals, and microbial contamination removal was measured during the experiment. The average removal percentages obtained by the MBR system were 81% for Al, 53% for Fe, 94% for Pb, 91% for Cu, 59% for Ni, and 49% for Cr which indicated the presence of Cu, Ni, and Cr in both soluble and particle forms in mixed liquor while Al, Fe, and Pb were mainly in particulate form. Also, coliforms in the majority of the samples were <140 MPN/100mL that showed that more than 99.9% of total coliform was removed in MBR effluent. Conclusion: The Membrane Biological Reactor (MBR) showed a good performance to remove heavy metals and microbial matters as well as COD and suspended solids. The effluent quality was suitable for reusing purposes

Removal capability of 4-Nonylphenol using new nano-adsorbents produced in sand filters of water treatment plants

Sand filters are a physical treatment unit in water treatment plants that have considerable potential for removing large suspended matter. However, these filters are somewhat inefficient in removing micro-pollutants. In this study, using waste leachate, carbon nanoparticles were coated on the silica particles to increase the surface adsorption capacity on silica substrates of rapid sand filters. The surface properties of nano-adsorbents produced by scanning electron microscopy, Raman spectroscopy and EDS test were investigated. Furthermore, the adsorption capacity of 4-Nonylphenol was examined using a new nanocomposite under different operational conditions (contact time, temperature and initial concentration) and after obtaining pHzpc, the effect of pH, total dissolved solids (TDS) and total organic carbon (TOC) on the efficacy of 4-Nonylphenol removal was tested. The adsorption isotherms in three temperature amounts of 15, 25, and 50 °C were also studied and Langmuir isotherm well fit the experimental data. To evaluate the thermal effect on the adsorption process, the thermodynamic study was also conducted. The results demonstrated that this reaction is spontaneous, endothermic and thermodynamically desirable. The experimental data also showed that the new engineered material is a good reusable adsorbent in water treatment

Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study

Klinkenberg permeability is an important parameter in tight gas reservoirs. There are conventional methods for determining it, but these methods depend on core permeability. Cores are few in number, but well logs are usually accessible for all wells and provide continuous information. In this regard, regression methods have been used to achieve reliable relations between log readings and Klinkenberg permeability. In this work, multiple linear regression, tree boost, general regression neural network, and support vector machines have been used to predict the Klinkenberg permeability of Mesaverde tight gas sandstones located in Washakie basin. The results show that all the four methods have the acceptable capability to predict Klinkenberg permeability, but support vector machine models exhibit better results. The errors of models were measured by calculating three error indexes, namely the correlation coefficient, the average absolute error, and the standard error of the mean. The analyses of errors show that support vector machine models perform better than the other models, but there are some exceptions. Support vector machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Herein, support vector machine was used to predict the Klinkenberg permeability of a tight gas reservoir and the performances of four regression techniques were compared