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C6 rat glioma cells are one of the most aggressive carcinogenic tumors, due to its high mortality rate in human beings and animals. The current treatment for this illness includes surgery, radio and chemotherapy, showing relapse in patients treated with those therapies. Since the ozone was found to be an effective bioreactive to inhibit growth of several carcinoma cells in vitro and in vivo. In this research, therapeutic peritoneum insufflation of ozone/oxygen dissolved in the physiological solution of NaCl 0.9% was dosed for fifteen days on different female mice groups in an advanced stage of C6 tumor (n=6). The first of them was the control group which had no treatment, the second group was dosage with oxygen every second day, the third group was dosed with ozone every second day, and finally the fourth group was dosed with ozone dissolved every fifth day. The size of the tumor was higher in both groups dosage by ozone, nevertheless tumor activity measured by microPET was 98% less in the fourth group compared with the control group. That result proves that ozone provokes an increase in the tumor volume even though the decrease of the cell activity. Those results were confirmed by the quantification of hydroperoxides, total cholesterol and total triglycerides.

This paper describes a fixed-time convergent step-by-step high order sliding mode observer for a certain type of aerobic bioreactor system. The observer was developed using a hierarchical structure based on a modified super-twisting algorithm. The modification included nonlinear gains of the output error that were used to prove uniform convergence of the estimation error. An energetic function similar to a Lyapunov one was used for proving the convergence between the observer and the bioreactor variables. A nonsmooth analysis was proposed to prove the fixed-time convergence of the observer states to the bioreactor variables. The observer was tested to solve the state estimation problem of an aerobic bioreactor described by the time evolution of biomass, substrate and dissolved oxygen. This last variable was used as the output information because it is feasible to measure it online by regular sensors. Numerical simulations showed the superior behavior of this observer compared to the one having linear output error injection terms (high-gain type) and one having an output injection obtaining first-order sliding mode structure. A set of numerical simulations was developed to demonstrate how the proposed observer served to estimate real information obtained from a real aerobic process with substrate inhibition.

Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov-Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

This paper deals with a switching robust tracking feedback design for a corona-effect ozone generator. The generator is considered as a switched systems in the presence of bounded model uncertainties as well as external perturbations. Three nonlinear dynamic models under arbitrary switching mechanisms are considered assuming that a sample-switching times are known. The stabilization issue is achieved in the sense of a practical stability. We apply the newly elaborated (extended) version of the conventional attractive ellipsoid method (AEM) for this purpose. The same analysis was efficient to obtain the minimal size of region where the tracking error between the trajectories of the ozone generator and reference states converges. The numerically implementable sufficient conditions for the practical stability of systems are derived based on bilinear matrix inequalities (BMIs)., (Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.)

Terephthalic acid (TA) is considered as a refractory model compound. For this reason, the TA degradation usually requires a prolonged reaction time to achieve mineralization. In this study, vanadium oxide (VxOy) supported on titanium oxide (TiO2) served as a photocatalyst in the ozonation of the TA with light-emitting diodes (LEDs), having a bandwidth centered at 452 nm. The modified catalyst (VxOy/TiO2) in combination with ozone and LEDs improved the TA degradation and its by-products. The results obtained by this system were compared with photolysis, single ozonation, catalytic ozonation, and photocatalytic ozonation of VxOy/TiO2 with UV lamp. The LED-based photocatalytic ozonation showed almost the same decomposition efficiency of the TA, but it was better in comparison with the use of UV lamp. The oxalic acid accumulation, as the final product of the TA decomposition, was directly influenced by either the presence of VxOy or/and the LED irradiation. Several by-products formed during the TA degradation, such as muconic, fumaric, and oxalic acids, were identified. Besides, two unidentified by-products were completely removed during the observed time (60 min). It was proposed that the TA elimination in the presence of VxOy/TiO2 as catalyst was carried out by the combination of different mechanisms: molecular ozone reaction, indirect mechanism conducted by ·OH, and the surface complex formation.

Dark fermentation for bio-hydrogen (bio-H2) production is an easily operated and environmentally friendly technology. However, low bio-H2 production yield has been reported as its main drawback. Two strategies have been followed in the past to improve this fact: genetic modifications and adjusting the reaction conditions. In this paper, the second one is followed to regulate the bio-H2 release from the reactor. This operating condition alters the metabolic pathways and increased the bio-H2 production twice. Gas release was forced in the continuous culture to study the equilibrium in the mass transfer between the gaseous and liquid phases. This equilibrium depends on the H2, CO2, and volatile fatty acids production. The effect of reducing the bio-H2 partial pressure (bio-H2 pp) to enhance bio-H2 production was evaluated in a 30 L continuous stirred tank reactor. Three bio-H2 release strategies were followed: uncontrolled, intermittent, and constant. In the so called uncontrolled fermentation, without bio-H2 pp control, a bio-H2 molar yield of 1.2 mol/mol glucose was obtained. A sustained low bio-H2 pp of 0.06 atm increased the bio-H2 production rate from 16.1 to 108 mL/L/h with a stable bio-H2 percentage of 55% (v/v) and a molar yield of 1.9 mol/mol glucose. Biogas release enhanced bio-H2 production because lower bio-H2 pp, CO2 concentration, and reduced volatile fatty acids accumulation prevented the associated inhibitions and bio-H2 consumption.

Designing a proportional derivative (PD) controller has as main problem, to obtain the derivative of the output error signal when it is contaminated with high frequency noises. To overcome this disadvantage, the supertwisting algorithm (STA) is applied in closed-loop with a PD structure for multi-input multi-output (MIMO) second order nonlinear systems. The stability conditions were analyzed in terms of a strict non-smooth Lyapunov function and the solution of Riccati equations. A set of numerical test was designed to show the advantages of implementing PD controllers that used STA as a robust exact differentiator. The first numerical example showed the stabilization of an inverted pendulum. The second example was designed to solve the tracking problem of a two-link robot manipulator., (Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is characterized using the practical stability framework. Indeed, the region where the identification error converges is derived by the same Lyapunov method. This zone is defined by the power of uncertainties and perturbations affecting the complex-valued uncertain dynamics. Moreover, this convergence zone is reduced to its lowest possible value using ideas related to the so-called ellipsoid methodology. Two simple but informative numerical examples are developed to show how the identifier proposed in this paper can be used to approximate uncertain nonlinear systems valued in the complex domain.

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

2,4-Dichlorophenoxyacetic acid (2,4-D) is usually used as a refractory model compound that requires a prolonged reaction time for mineralization. In this study, we found that nickel oxide (NiO) significantly improved 2,4-D degradation and mineralization in reaction with ozone. Other metal oxides, such as titania, silica and alumina, were also tested in this reaction, so that, the mineralization degree was almost the same for all of them (ca. 25%), whereas NiO showed more than 60% in 1h. These outstanding results led us to study in more depth the role of NiO as catalyst in the degradation of 2,4-D. For instance, the optimum NiO loading amount was 0.3 g L(-1). The catalytic ozonation showed a high stability after three reaction cycles. With the aim of identifying the surface species responsible for the high activity of NiO, besides knowing the byproducts during the degradation of 2,4-D, XPS and HPLC were mainly used as analytical tools. According to the results, the mineralization of 2,4-D was directly influenced by the adsorbed chlorate organic compounds and oxalate group onto NiO. Therefore, NiO plays a true role as a catalyst forming surface compounds which are subsequently decomposed causing an increase in the mineralization efficiency. In addition, it was possible to identify several degradation byproducts (2,4-diclorophenol, glycolic, fumaric, maleic and oxalic acids) that were included in a rational reaction pathway. It was proposed that 2,4-D elimination in presence of NiO as catalyst is a combination of processes such as: conventional ozonation, indirect mechanism (OH) and surface complex formation., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Hydrogen (H2) production from the organic fraction of solid waste such as fruit and vegetable waste (FVW) is a novel and feasible energy technology. Continuous application of this process would allow for the simultaneous treatment of organic residues and energy production. In this study, batch experiments were conducted using glucose as substrate, and data of H2 production obtained were successfully adjusted by a logistic model. The kinetic parameters (μ max = 0.101 h(-1), K s = 2.56 g/L) of an H2-producing microbial culture determined by the Monod and Haldane-Andrews growth models were used to establish the continuous culture conditions. This strategy led to a productive steady state in continuous culture. Once the steady state was reached in the continuous reactor, a maximum H2 production of 700 mL was attained. The feasibility of producing H2 from the FVW obtained from a local market in Mexico City was also evaluated using batch conditions. The effect of the initial FVW concentration on the H2 production and waste organic material degradation was determined. The highest H2 production rate (1.7 mmol/day), the highest cumulative H2 volume (310 mL), and 25 % chemical oxygen demand (COD) removal were obtained with an initial substrate (FVW) concentration of 37 g COD/L. The lowest H2 production rates were obtained with relatively low initial substrate concentrations of 5 and 11 g COD/L. The H2 production rates with FVW were also characterized by the logistic model. Similar cumulative H2 production was obtained when glucose and FVW were used as substrates.

In this research, ozonation of Reactive Black 5 (RB5) and Direct Red 28 (DR28) under the presence of Na2SO4 and Na2CO3 used as textile additives was investigated. The effect of these salts on discoloration, degradation dynamics, and the composition of the final compounds were studied. Different systems were evaluated; such as RB5-Na2SO4 (100 g/L), RB5-Na2CO3 (30 g/L), RB5-Na2SO4/Na2CO3 (100 g/ L/30 g/L), and DR28-Na2SO4 (10 g/L, 40 g/L, and 80 g/L) with dye concentrations of 50, 150, and 250 mg/L without pH adjustment. Discoloration of RB5 and DR28 with and without additives was determined by visible and UV (UV-Vis) spectroscopy. Decomposition of the dyes and the dynamics of intermediates and final byproducts were followed by high performance liquid chromatography. The presence of additives accelerated discoloration and decomposition for both dyes (more than 50%). The accumulation of oxalic and formic acids was observed. Possible mechanism schemes of ozonation for both dyes are proposed.

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 VOC's 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 VOC's 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 VOC's decomposition in gaseous phase. Also, the dynamic relationship between degradation and liberation was studied and characterized., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

One of the most important elements for the rehabilitation process regards to the correct evaluation of the biomechanical and the electrophysiological responses. This evaluation must be done during the therapy. In general, the improvements achieved by the treatment are slightly and difficult to be distinguished. This is a difficult task when the changes in the signals obtained by the bio-amplifiers (EMG, electro-goniometry, etc) are evaluated by a wired system because the patient cannot interact with its environment freely. The present work tackles the design, construction and implementation of a platform to carry out biomechanical analysis for upper and lower limbs. The included variables in the biomechanical system are the angular position, linear acceleration, electromyography signals and force executed by the limbs. The designed scheme considers the wireless monitoring of relevant signals; such variables allow us to analyze the effectiveness achieved by the therapy. Processing and data exhibition are carry out in a personal computer. Two application examples regarding the biomechanical wrist evaluation and the EMG correlation are presented. Nonlinear algorithms to analyze the information obtained in the system are used to evaluate the biomechanical responses produced in different patients.

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.

In this work a new technique dealing with differential neural network observer (DNNO), which is related with differential neural networks (DNN) approach, is applied to estimate the anthracene dynamics decomposition and to identify the kinetic parameters in a contaminated model soil treatment by simple ozonation. To obtain the experimental data set, the model soil (sand) is combined with an initial anthracene concentration of 3.24mg/g and treated by ozone (with the ozone initial concentration 16mg/L) during 90min in a reactor by the "fluid bed" principle. The anthracene degradation degree was controlled by UV-vis spectrophotometry and HPLC techniques. Based on the HPLC data, the obtained results confirm that anthracene may be decomposed completely in the solid phase by simple ozonation during 20min and by-products of ozonation are started to be destroyed after 30min of treatment. In the ozonation process the ozone concentration in the gas phase at the reactor outlet is registered by an ozone detector. The variation of this parameter is used to obtain the summary characteristic curve of the anthracene ozonation (ozonogram). Then, using the experimental decomposition dynamics of anthracene and the ozonogram, the proposed DNNO is trained to reconstruct the anthracene decomposition and to estimate the anthracene ozonation constant using the DNN technique and a modified Least Square method.

Presented in this study, a dynamic neural network (DNN) is employed to estimate the states dynamics of the phenols-ozone-water system. A new technique based on the dynamic neural network observer (DNNO) with relay (signum) term is applied to estimate the decomposition dynamics of phenols and to identify their kinetic parameters without any mathematical model usage. The decomposition of phenols (phenol (PH), 4-chlorophenol (4-CPH) and 2,4-dichlorophenol (2,4-DCPH)) and their mixture by ozone, realized in a semi-batch reactor, is considered as a process with uncertain model ("black-box"). Only one parameter monitoring, namely, the ozone concentration in gas phase in the reactor outlet, is measured during ozonation. The variation of this variable is used to obtain the summary characteristic curve for the phenols ozonation. Then, using the experimental decomposition dynamics of phenols and of their mixture, obtained by HPLC method, the proposed DNNO is applied to estimate the ozonation constants of phenols at the different pH 2-12. A good correspondence between the decomposition dynamics and the estimated ones by DNNO is obtained.