Tatyana Poznyak

Degradation of Aqueous Phenol and Chlorinated Phenols by Ozone

The degradation of phenol and its chlorinated derivatives with ozone is studied. The studied compounds are phenol (Ph), 4-chlorophenol (4-CPh) and 2,4-dichlorophenol (2,4-DCPh). The kinetic performances of each phenolic compound and their model mixture are examined. The pH influence on the decomposition dynamics for different phenolic compounds in the range 2–12 is investigated. The increase of the decomposition rate under increasing pH was observed. In the pH range studied, phenol and chlorophenols ozonation proceeds rapidly. The UV absorbency is used for the preliminary control of the degree of decomposition. The HPLC analysis is used to identify intermediates and final products formed during ozonation. It is shown that the basic intermediates are muconic and fumaric acids, malonic and maleic acids, catechol and hydroquinone. The final products are oxalic acid and formic acid. In the case of alkaline media, the principal final product is oxalic acid. Furthermore, intermediates and final decomposition products obtained at different pH are compared. According to the results obtained, the possible mechanism of ozonation by the reaction of hydroxylation and dechloration in the early stage is proposed. The BOD5/COD ratio is used as a biodegradability measure for the comparison of biodegradability of initial compounds and final products composition.

Remediation of lignin and its derivatives from pulp and paper industry wastewater by the combination of chemical precipitation and ozonation

In the present work the degradation of the lignin and its derivatives in the residual water of a paper industry by simple ozonation was investigated. The remediation of lignin was realized using the combination of the pre-treatment with chemical precipitation, using concentrated sulfuric acid (97.1%) at the pH 1 and 3, and of the simple ozonation of the filtered residual water at the pH 1, 3, 8 and 12. Since the high residues content (the initial chemical oxygen demand (COD) is 70,000 mg/L) in the experiments the diluted samples (1:10) were used. The previous precipitation has showed a significant effect on the reduction of the COD (77%) and color (96.1%). The sludge precipitated contents sulfolignin, which in the reaction with sulfuric acid was formed. In ozonation of the filtered residual water during 25 min at the pH 1, 3, 8 and 12 the follows by-products were formed: fumaric, maleic, malonic and formic acids. The biodegradability of the treated water in ozonation increases up 0.067-0.29. The effect of the precipitation and the ozonation conditions on the decolorization kinetics was evaluated.

Ozonation of Non-Biodegradable Mixtures of Phenol and Naphthalene Derivatives in Tanning Wastewaters

The decomposition of non-biodegradable mixtures of phenol and naphthalene derivatives in aqueous solution by ozonation is investigated. The toxic water pollutants are the intermediate synthesis products obtained via the sulfonation reaction of naphthalene and phenol. These organic compounds are phenol (PH), p-phenol sulfonic acid (p-PSA), and 2-naphthalene sulfonic acid (2-NSA). The kinetic study of ozonation is realized by the pH variation in the range of 2–12. The pH-dependence of the ozone reactivity for different organics is observed. The intermediates and final products determination is measured by UV absorbency and HPLC technique. It is shown that the main sub-products are muconic, fumaric, maleic and malonic acids and catechol. The final products are basically oxalic acid and formic acids as well as formaldehyde. In the initial stage of ozonation, hydroxylation and desulfuration of p-PSA and 2-NSA is detected. The effect of the preliminary ozonation on the biodegradability of the contaminants is estimated by BOD5/COD-ratio variation, which significantly increases from 0.15 to 0.88.

BTEX decomposition by ozone in gaseous phase

Environment management is turning its efforts to control the air pollution. Nowadays, gas phase contaminants coming from different sources are becoming into the main cause of serious human illness. Particularly, benzene, toluene, ethylbenzene and xylene (BTEX) are getting more and more attention from the scientific community due the high level of volatilization showed by these compounds and their toxicity. Decomposition of these compounds using different treatments is requiring lots of new strategies based on novel options. In the present work the use of ozone was proposed as possible alternative treatment in the gaseous phase of VOCs liberated from water by stripping. This study deals with the decomposition by ozone in gaseous phase of model mixtures of BTEX stripped from water. The experiments were realized in a tubular reactor with fixed length (1.5 m length and diameter of 2.5 cm). The experiments were conducted in two stages: in the first one, organics was ventilated by oxygen flow to liberate BTEX to the gaseous phase; second stage deals with the liberated BTEX decomposition by ozone in the tubular reactor. Ozonation efficiency was determined measuring the VOCs concentration at the output of the tubular reactor. This concentration was compared to the concentration obtained at the input of the reactor. The obtained results confirm the possibility to use of ozone for the VOCs decomposition in gaseous phase. Also, the dynamic relationship between degradation and liberation was studied and characterized.

Neural numerical modeling for uncertain distributed parameter systems

In this paper a strategy based on differential neural networks for the identification of the parameters in a mathematical model described by partial differential equations is proposed. The identification problem is reduced to finding an exact expression for the weights dynamics using the differential neural networks properties. The adaptive laws for wieghts ensure the convergence of the neural network trajectories to the partial differential equation states. To investigate the qualitative behavior of the suggested methodology, here the non parametric modeling problem for a distributed parameter plant is analyzed: the tubular reactor system.

A CONTINUOUS TIME NEURO-OBSERVER FOR HUMAN IMMUNODEFICIENCY VIRUS (HIV) DYNAMICS

Abstract The continuous time observer design based on differential neural networks (DNN) is presented. It is tested by the application to an immunological interaction model for HIV. To give the corresponding data interpretation and to justify the observability property the Kirschners model is used. The suggested observer estimates the models variables behavior providing the evolution of the number of infected and not infected lymphocyte T-CD4 cells during 10 days approximately starting from the treatment using Zidovuina (AZT). This approach does not require any model of this process to be in use. The obtained results are shown to allow the programming a high-quality medication protocol for zero-positive patients or AIDS patients.

High order dynamic neuro observer: application for ozone generator

In this study the adaptive observing problem for nonlinear uncertain systems is analyzed. The considered nonlinear systems verify the so-called regular form. A new high-order sliding mode neural-observer is suggested to solve the afore-mentioned problem by means of the super-twisting algorithm. This observer is supplied with a new learning procedure which adaptive structure ensuring the practical stability for estimation scheme. The suggested approach was successfully applied for the state estimation of the corona discharge ozone generator.

DNN-state identification of 2D distributed parameter systems

There are many examples in science and engineering which are reduced to a set of partial differential equations (PDEs) through a process of mathematical modelling. Nevertheless there exist many sources of uncertainties around the aforementioned mathematical representation. Moreover, to find exact solutions of those PDEs is not a trivial task especially if the PDE is described in two or more dimensions. It is well known that neural networks can approximate a large set of continuous functions defined on a compact set to an arbitrary accuracy. In this article, a strategy based on the differential neural network (DNN) for the non-parametric identification of a mathematical model described by a class of two-dimensional (2D) PDEs is proposed. The adaptive laws for weights ensure the ‘practical stability’ of the DNN-trajectories to the parabolic 2D-PDE states. To verify the qualitative behaviour of the suggested methodology, here a non-parametric modelling problem for a distributed parameter plant is analysed.

Kinetic study of toxic pollutants decomposition by ozone in landfill leachate using a numerical adaptive method

This study describes a new method to perform the kinetic study of toxic pollutants decomposition by ozone in landfill leachate using a novel numerical adaptive method based on the classical least square method. The leachate was obtained from the Mexico City waste sanitary landfill (Bordo Poniente) and was treated by ozonation after a coagulation treatment with Fe2(SO4)3. For the partial identification of the initial organic matter composition, intermediates and final products of ozonation, the extraction of the initial and ozonated leachate with benzene, chloroform : methanol (2 : 1) and hexane was carried out, and then the extracts were analysed with a gas chromatography.

Use of Ozonation for Polymer Microstructure Characterization: Analytical Aspect of the Ozone Application

Ozonation is suggested as an effective tool for polymer microstructure characterization. It consists of the selective estimation of the carbon - carbon double bonds content (less than 0.01% mass) and their distribution within polymers. The developed technique consists of measuring the ozone absorbed by the olefin double bonds according to the ozonation reaction. The measurements were carried out by the “Double Bonds Analyzer” (DBA) device. The comparison of the proposed simple and quick method with traditional ones, like GPC, shows good correlation.