Mohamed Ateia

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

Elucidating Adsorptive Fractions of Natural Organic Matter on Carbon Nanotubes

/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.

The relationship between molecular composition and fluorescence properties of humic substances

Natural organic matter (NOM) is a heterogeneous mixture of organic compounds that is omnipresent in natural waters. To date, the understanding of the adsorption of NOM components by carbon nanotubes (CNTs) is limited because of the limited number of comprehensive studies in the literature examining the adsorption of NOM by CNTs. In this study, 11 standard NOM samples from various sources were characterized, and their adsorption behaviors on four different CNTs were examined side-by-side using total organic carbon, fluorescence, UV-visible spectroscopy, and high-performance size-exclusion chromatography (HPSEC) analysis. Adsorption was influenced by the chemical properties of the NOM, including aromaticity, degree of oxidation, and carboxylic acidity. Fluorescence excitation-emission matrix (EEM) analysis showed preferential adsorption of decomposed and terrestrial-derived NOM compared to freshly produced and microbial-derived NOM. HPSEC analysis revealed preferential adsorption of fractions in the molecular weight range of 0.5-2 kDa for humic acids but in the molecular weight range of 1-3 kDa for all fulvic acids and reverse-osmosis isolates. However, the smallest characterized fraction (MW < 0.4 kDa) in all samples did not adsorb on the CNTs.

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

The quantity and quality of dissolved organic matters have been widely characterized by fluorescence spectroscopy, yet the relationship between the fluorescence properties of dissolved organic matters and its molecular composition remains poorly described in the literature. Here, we measured the fluorescence excitation–emission matrix of 17 well-characterized humic substance standards to determine a range of fluorescence parameters, including classical fluorescence indices (e.g., fluorescence index, biological index and humification index) and parameters derived from parallel factor analysis (e.g., component contribution). Relationships between humic substance’s fluorescence and compositional parameters were then statistically examined using canonical correspondence and simple correlation analyses. The canonical correspondence analysis generally suggested that most fluorescence parameters determined here are highly associated with the amount of aliphatic and aromatic compounds in humic substances. However, the correlation analysis between single molecular and fluorescence parameters indicated that the fluorescence properties of humic substances including the parallel factor analysis component contribution also significantly correlate well with several aspects of the molecular composition of humic substances, such as elemental composition, carbon species, acidic functional group and iron complexation. Overall, our results suggest that measurement of humic substance’s fluorescence is beneficial in understanding the molecular composition and environmental functions of dissolved organic matters in natural and engineered waters.

Green and facile approach for enhancing the inherent magnetic properties of carbon nanotubes for water treatment applications

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.

Natural organic matter undergoes different molecular sieving by adsorption on activated carbon and carbon nanotubes

Current methods for preparing magnetic composites with carbon nanotubes (MCNT) commonly include extensive use of treatment with strong acids and result in massive losses of carbon nanotubes (CNTs). In this study we explore the potential of taking advantage of the inherent magnetic properties associated with the metal (alloy or oxide) incorporated in CNTs during their production. The as-received CNTs are refined by applying a permanent magnet to a suspension of CNTs to separate the high-magnetic fraction; the low-magnetic fraction is discarded with the solvent. The collected MCNTs were characterized by a suite of 10 diffraction and spectroscopic techniques. A key discovery is that metallic nano-clusters of Fe and/or Ni located in the interior cavities of the nanotubes give MCNTs their ferromagnetic character. After refinement using our method, the MCNTs show saturation magnetizations up to 10 times that of the as-received materials. In addition, we demonstrate the ability of these MCNTs to repeatedly remove atrazine from water in a cycle of dispersion into a water sample, adsorption of the atrazine onto the MCNTs, collection by magnetic attraction and regeneration by ethanol. The resulting MCNTs show high adsorption capacities (> 40 mg-atrazine/g), high magnetic response, and straightforward regeneration. The method presented here is simpler, faster, and substantially reduces chemical waste relative to current techniques and the resulting MCNTs are promising adsorbents for organic/chemical contaminants in environmental waters.

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

We have comprehensively compared the molecular sieving of natural organic matter (NOM) by adsorption on activated carbon (AC) and multi-walled carbon nanotubes (CNT) using different types of NOM. All water samples were characterized using UV-visible and fluorescence spectroscopies as well as high-performance size-exclusion chromatography (HPSEC) before and after adsorption. Adsorption isotherm results fitted well with Freundlich model (R2 = 0.95-0.99) and the model parameters indicated higher adsorption of NOM on CNT than AC. Fluorescence index (FI) and freshness index (BIX) showed preferential adsorption of microbial derived and fresh NOM on AC, whereas, terrestrial derived and decomposed NOM were preferentially adsorbed on CNT. Further, HPSEC revealed that AC adsorbed NOM fractions with small molecular weight (MW) (<0.4 kDa) faster than the fractions with higher MW. In contrast, CNT adsorbed NOM fractions characterized by high MW (>1 kDa) while the smallest fraction (<0.4 kDa) was not adsorbed, possibly due to its hydrophilic character. Our results also demonstrated a good correlation between FI and average MW of NOM (R2 > 0.93). These findings illustrate the influence of the adsorbents type and characteristics (i.e., porosity and pore size distribution) on the preferential adsorption of different NOM fractions.

Modeling the Effects of Operational Parameters on Algae Growth

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.

Nonlinear Relationship of Near-Bed Velocity and Growth of Riverbed Periphyton

An adequate description of algal growth is one of the most important intentions in the field of ecological and water quality modeling. Algal biomass productivity is naturally complex due to their nonlinear responses to several environmental parameters such as temperature, light irradiance, and nutrient availability (in terms of nitrogen and phosphorus). Mathematical modeling is a useful tool applied for bio-process design and optimization. Mathematical models were previously developed to predict reliable parameters such as specific growth rate of algal species. Effects of light irradiance and temperature on the growth rate of algae can be described by the mathematical models of Steele (Primary production in aquatic environments, notes on some theoretical problem in production ecology. Goldmen CR: Memorial Institute of Idrobiology, University of California Press. Berkeley, 1965), Platt and Jassby (J Phycol 102:5083–5092, 2011), and Peeters and Eilers (Hydrobiol Bull 12:134–136, 1978). Nutrient limitation of algae growth rates can either be described by the Monod model or by the Droop model. The Monod model correlates the growth rate to the concentration of limiting nutrient, whereas the Droop model relates the specific growth rate to the cell quota. Thus, this chapter attempts to represent the mathematical models used to define the optimal parameters for algal growth as well as their interactions.