Effect of Inorganic Additives in the Textile Dyes Removal by Ozonation

Treatment of industrial wastewaters based on oxidative efficiency of ozone is still of great interest due to the high removal percentages of the initial pollutants and their by-products. In particular, the industrial dyes and their wastewaters have received special attention considering the large volumes of water produced daily with high concentration of chemical oxygen demand. In addition, the dyeing processes use some chemical additives to enhance the final quality of dyeing. The effect of all these additives on the wastewater treatment has been insufficiently explored. This chapter is focused on the study of different additives commonly used in dyeing process (Na2SO4 and Na2CO3 for Reactive Black 5 – RB5, Na2SO4 at different concentrations for Direct Red 28 – DR28, and acetic acid for Basic Green 4 – BG4) and their effect on ozonation efficiency in discoloration and dye decomposition. Moreover, the distribution of by-products obtained throughout the ozonation was compared when the additives are or not participating in the reaction. The influence of additives and dyes’ chemical nature, their concentration, and the induced pH variation on dye solutions are explained using the results of ozone based on the treatment of the three dyes mentioned earlier. The characteristics of each dye combined with the corresponding additives over degradation and decomposition efficiency by ozone, and the by-product distribution was also studied

3D Nonparametric Neural Identification

This paper presents the state identification study of 3D partial differential equations (PDEs) using the differential neural networks (DNNs) approximation. There are so many physical situations in applied mathematics and engineering that can be described by PDEs; these models possess the disadvantage of having many sources of uncertainties around their mathematical representation. Moreover, to find the exact solutions of those uncertain PDEs is not a trivial task especially if the PDE is described in two or more dimensions. Given the continuous nature and the temporal evolution of these systems, differential neural networks are an attractive option as nonparametric identifiers capable of estimating a 3D distributed model. The adaptive laws for weights ensure the “practical stability” of the DNN trajectories to the parabolic three-dimensional (3D) PDE states. To verify the qualitative behavior of the suggested methodology, here a nonparametric modeling problem for a distributed parameter plant is analyzed

Ozone Dosage is the Key Factor of Its Effect in Biological Systems

The applications of ozone are not only restricted to environmental remediation or industrial areas. This gas has been applied in medicine to treat several diseases, where positive effects have been confirmed by many clinical studies. According to the European Medical Society of Ozone and the National Center of Scientific Investigation in Cuba, it has not been possible to validate ozone’s effectiveness by traditional analytical methods. Thus, this investigation proposed evaluating the effect that ozone has on biological substrates (murine models with induced carcinogenic tumors, inflammation, and wounds), studying the variations that ozone (dissolved in physiological solution or ozonated vegetable oils) provokes over the total unsaturation of lipids (TUL), and by using the so-called method double bond index (DB-index), make a correlation with the dynamic reactions obtained by several analytical methods according to each experimental stage considered in this study

Non-parametric Identification of Homogeneous Dynamical Systems

International audienceThe aim of this study is to design a non-parametric identifier for homogeneous systems based on a class of artificial neural networks with continuous dynamics. The identification algorithm is developed for input-affine systems with uncertain gains and diverse degrees of homogeneity. One of the main contributions of this study is the extension of the universal approximation property of neural networks for continuous homogeneous systems. Another contribution is the development of a differential non-parametric identifier based on the novel concept of homogeneous neural networks. The adjustment laws for the weights are obtained from a Lyapunov stability analysis taking homogeneity properties of the system into account. The ultimate boundedness of the origin for the identification error is demonstrated using the persistent excitation condition. The effectiveness of the proposed identifier is verified by the simulation of the three-tank homogeneous model. In this example, the proposed identification scheme is compared with a classical ANN identifier, and we present a statistical analysis of such comparison. It is shown in simulations that the identification error of the proposed homogeneous algorithm has faster convergence and less oscillations

Differential Neural Network Identification for Homogeneous Dynamical Systems ⋆

International audienceIn this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier

A New Homogeneous Quasi-Continuous Second Order Sliding Mode Control

International audienceA new homogeneous quasi-continuous second order sliding mode control algorithm is developed. The Lyapunov function for finite-time stability analysis is constructed. The homogeneity property of the algorithm is analyzed. The chattering attenuation procedure using Lyapunov function of the closed-loop system is discussed. The scheme for control parameters tuning based on linear matrix inequalities is elaborated. The theoretical results are supported by numerical simulations