Mahmoud Nasr

Artificial intelligence for greywater treatment using electrocoagulation process

Abstract Treatment of greywater by electrocoagulation using aluminum electrodes was studied. The effects of current-density, electrolysis-time, and inter-electrode-gap on turbidity-removal and electrical-energy consumption were investigated. Under the optimal conditions (J = 12.5 mA/cm2, t = 30 min, and l = 0.5 cm), pollutants removal were: CODtotal = 52.8%, CODsoluble = 31.4%, BODtotal = 32.8%, BODsoluble = 27.6%, SS = 64.6%, TN = 30.1%, and TP = 13.6%. The consumed electrical-energy recorded 4.1 kWh/m3 with an operating cost 0.25 US

Biosorption of Cr(VI) from aqueous solution using agricultural wastes, with artificial intelligence approach

/m3. Artificial intelligence was developed to simulate the influence of variables on the turbidity-removal. A 3–6–1 neural network achieved R-values: 0.99 (training), 0.84 (validation) and 0.89 (testing). An adaptive neuro-fuzzy inference system indicated that current-density is the most influential input.

In-situ Biological Water Treatment Technologies for Environmental Remediation: A Review

ABSTRACT Removal of Cr(VI) from aqueous solution by date-palm-leaves (DPL) and broad-bean-shoots (BBS) was investigated. FTIR, SEM, and EDAX showed that DPL has higher ability for ion-exchange to remove Cr(VI). Langmuir and Freundlich adsorption isotherms and kinetics revealed that DPL exhibited higher biosorption capacity. At Cr(VI) 100 mg/L, biosorbent-dose 5 g/L and 60 min contact-time, maximum Cr(VI) removal for DPL (98%) and BBS (95%) was achieved at pH 2 and 1, respectively. Adaptive-neuro fuzzy inference system determined the most important factor affecting Cr(VI) removal. The model indicated that DPL is more tolerant to pH levels, while BBS is a pH-sensitive adsorbent.

Experimental and theoretical approaches for Cd(II) biosorption from aqueous solution using Oryza sativa biomass

Water treatment technologies can be classified as in-situ or ex-situ technologies. In-situ biological techniques include the use of aquatic plants, aquatic animals, and microbial remediation. Approaches to alleviate surface water pollution should use bioremediation methods as a primary technique. These methods should be tested not only on rivers and lakes, but also on other polluted surface streams. Furthermore, bioremediation processes need to be optimized depending on flow condition and nutrient availability. This paper comprehensively reviews the latest surface water remediation techniques that are suitable for in-situ applications, focusing on bioremediation technologies as effective techniques to remedy polluted water.

Phycoremediation: An Eco-friendly Algal Technology for Bioremediation and Bioenergy Production

ABSTRACT Biomass of Oryza sativa (OS) was tested for the removal of Cd(II) ions from synthetic and real wastewater samples. Batch experiments were conducted to investigate the effects of operating parameters on Cd(II) biosorption. Fourier transform infrared spectroscopy, scanning electron microscopy, and energy-dispersive x-ray spectroscopy were used to examine the surface characteristics of the Cd(II)-loaded biomass. The maximum removal efficiency of Cd(II) was 89.4% at optimum pH 6.0, biosorbent dose 10.0 g L−1, initial Cd(II) 50 mg L−1, and biosorbent particle size 0.5 mm. The applicability of Langmuir and Freundlich isotherms to the sorbent system implied the existence of both monolayer and heterogeneous surface conditions. Kinetic studies revealed that the adsorption process of Cd(II) followed the pseudo-second-order model (r2: 0.99). On the theoretical side, an adaptive neuro-fuzzy inference system (ANFIS) was applied to select the operating parameter that mostly influences the Cd(II) biosorption process. Results from ANFIS indicated that pH was the most influential parameter affecting Cd(II) removal efficiency, indicating that the biomass of OS was strongly pH sensitive. Finally, the biomass was confirmed to adsorb Cd(II) from real wastewater samples with removal efficiency close to 100%. However, feasibility studies of such systems on a large-scale application remain to be investigated.

Artificial Intelligence for Electrocoagulation Treatment of Olive Mill Wastewater

Substantial amount of the refractory organics; inorganic nutrients, mainly nitrogen and phosphorus; heavy metals; etc. is discharged in conventional wastewater treatments. The concentration of such contaminants in the discharged wastewater depends on the performance and maintenance of the wastewater treatment plants (WWTPs). Though further reduction in such contaminants is possible with an aid of some of the advance technologies and skilled manpower, it makes wastewater treatment more expensive. More importantly, the running and maintenance of WWTPs are uncommon in economically weaker countries especially in the rural areas. This leads to the hunt of economically viable and environmentally sustainable alternative wastewater treatments. The truism nowadays is to recognize the emergence of phycoremediation as an alternative. Algae-based bioremediation has been found excellent for the nutrient, organic, pathogen, heavy metal, etc. removal from various types of wastewater. Green microalgae possess the unique potential of high photosynthetic activity compared to food crops and terrestrial plants. Therefore, such systems are capable of high biomass production through CO2 sequestration from the air and nutrient and organic sequestration from water. The microalgal cells contain comparatively high lipid contents; thus, algal biomass serves as an excellent feedstock for biofuels. Therefore, the choice of algal species possessing excellent phytoremediation potential as well as capable of producing high biomass is important to consider while designing the phycoremediation-based treatment systems. In this chapter, a concise partial overview of the potential and uniqueness of phycoremediation in treating various types of water and production of algal biomass for biofuels has been discussed. The environmental sustainability and economic viability aspects of phycoremediation, factors influencing the wastewater treatment, and the limitations of such technologies are covered briefly.

Organic matter removal from saline agricultural drainage wastewater using a moving bed biofilm reactor.

An electrocoagulation system using bipolar aluminium electrodes was studied for the treatment of olive mill wastewater (OMW). Response surface methodology and adaptive neuro-fuzzy inference system (ANFIS) were employed to study the effects of operating parameters on the removal of chemical oxygen demand (COD). At the optimum condition of initial pH 4, current density 83 mA cm-2 and 20 min-electrolysis time, the estimated COD removal efficiency of 40.4% was close to the experimental result (42.7%) with a coefficient of determination r2=0.92. Results from ANFIS indicated that the order of operating parameters affecting the COD removal efficiency was pH>current density>electrolysis time. Additionally, the optimal combination of two inputs influencing the COD removal efficiency was current density × pH, since it recorded the least training root mean square error of 5.04. This study demonstrated that ANFIS could be used as a tool to describe the factors influencing electrocoagulation process.

Zero-valent iron nanoparticles for methylene blue removal from aqueous solutions and textile wastewater treatment, with cost estimation

We investigated the effect of salinity on the removal of organics and ammonium from agricultural drainage wastewater (ADW) using moving bed biofilm reactors (MBBRs). Under the typical salinity level of ADW (total dissolved solids (TDS) concentration up to 2.5 g·L(-1)), microorganisms were acclimated for 40 days on plastic carriers and a stable slime layer of attached biofilm was formed. Next, six batch mode MBBRs were set up and run under different salinity conditions (0.2-20 g-TDS·L(-1)). The removal efficiency of chemical oxygen demand (COD) and ammonium-nitrogen (NH4-N) in 6 hours decreased from 98 and 68% to 64 and 21% with increasing salt concentrations from 2.5 to 20 g-TDS·L(-1), respectively. In addition, at decreasing salt levels of 0.2 g-TDS·L(-1), both COD removal and nitrification were slightly lowered. Kinetic analysis indicated that the first-order reaction rate constant (k1) and specific substrate utilization rate (U) with respect to the COD removal remained relatively constant (10.9-11.0 d(-1) and 13.1-16.1 g-COD-removed.g-biomass(-1)·d(-1), respectively) at the salinity range of 2.5-5.0 g-TDS·L(-1). In this study, the treated wastewater met the standard criteria of organic concentration for reuse in agricultural purposes, and the system performance remained relatively constant at the salinity range of typical ADW.

Regression model, artificial intelligence, and cost estimation for phosphate adsorption using encapsulated nanoscale zero-valent iron

Nanoscale zero-valent iron (nZVI) particles were investigated for the removal of methylene blue (MB) from aqueous solutions and the treatment of textile industry effluents. The nZVI material was characterized by XRD, TEM, EDS, FTIR, and SEM. It was demonstrated that several functional groups such as C-H, C = C, C-C, and C-O contributed to MB reduction. At initial MB concentration of 70 mg/L, the optimum pH was 6, achieving a removal efficiency of 72.1% using an nZVI dosage of 10 g/L, stirring rate of 150 rpm, and temperature of 30 °C within 30 min. The adsorption isotherm was described by the Langmuir model with monolayer coverage of 5.53 mg/g, and the Freundlich equation with multilayer adsorption capacity of 1.59 (mg/g)·(L/mg)1/n. The removal mechanisms of MB included reduction into colorless leuco-MB, precipitation as Fe(II)-MB, adsorption as ZVI-MB or FeOOH-MB, and/or degradation using •OH radicals. The synthesized nZVI particles were applied to reduce various organic and inorganic compounds, as well as heavy metal ions from real textile wastewater samples. The removal efficiencies of COD, BOD, TN, TP, Cu2+, Zn2+, and Pb2+ reached up to 91.9%, 87.5%, 65.2%, 78.1%, 100.0%, 29.6%, and 99.0%, respectively. The treatment cost of 1 m3 of textile wastewater was estimated as 1.66

Regression model, artificial neural network, and cost estimation for biosorption of Ni(II)-ions from aqueous solutions by Potamogeton pectinatus

USD.