Isaac Chairez

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.

Reactivity of NiO for 2,4-D degradation with ozone: XPS studies.

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.

Wavelet Differential Neural Network Observer

State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with a state observation problem when the dynamic model of a plant contains uncertainties or it is completely unknown. Differential neural network (NN) approach is applied in this uninformative situation but with activation functions described by wavelets. A new learning law, containing an adaptive adjustment rate, is suggested to imply the stability condition for the free parameters of the observer. Nominal weights are adjusted during the preliminary training process using the least mean square (LMS) method. Lyapunov theory is used to obtain the upper bounds for the weights dynamics as well as for the mean squared estimation error. Two numeric examples illustrate this approach: first, a nonlinear electric system, governed by the Chuas equation and second the Lorentz oscillator. Both systems are assumed to be affected by external perturbations and their parameters are unknown.

Robust Trajectory Tracking of a Delta Robot Through Adaptive Active Disturbance Rejection Control

This paper describes the adaptive control design to solve the trajectory tracking problem of a Delta robot with uncertain dynamical model. This robot is a fully actuated, parallel closed-chain device. The output-based adaptive control was designed within the active disturbance rejection framework. An adaptive nonparametric representation for the uncertain section of the robot model was obtained using an adaptive least mean squares procedure. The adaptive algorithm was designed without considering the velocity measurements of the robot joints. Therefore, a simultaneous observer-identifier scheme was the core of the control design. A set of experimental tests were developed to prove the performance of the algorithm presented in this paper. Some reference trajectories were proposed which were successfully tracked by the robot. In all the experiments, the adaptive scheme showed a better performance than the regular proportional-integral-derivative (PID) controller with feed-forward actions as well as a nonadaptive active disturbance rejection controller. A set of numerical simulations was developed to show that even under five times faster reference trajectories, the adaptive controller showed better results than the PID controller.

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.

Observer design for a class of parabolic PDE via sliding modes and backstepping

Observation problem for systems governed by Partial Differential Equations (PDE) has been a research field of its own for a long time. In this paper it is presented an observer design for a class or parabolic PDEs using sliding modes theory and bacstepping-like procedure in order to achieve exponential convergence. A Volterra-like integral transformation is used to change the coordinates of the error dynamics into exponentially stable target systems using the backstepping-like procedure. This gives as a result the output injection functions of the observer which are obtained by solving a hyperbolic PDE system. Sliding modes are used to find an explicit solution to the hyperbolic PDE system and to make the observer gains to be discontinuous which have well known advantages. Theoretical results were proved using the Lyapunov theory. A numerical example demonstrates the proposed method effectiveness.

Differential Neuro-Fuzzy Controller for Uncertain Nonlinear Systems

In general, output-based controller design remains an important research area in control theory. Most of the existing solutions use a state estimation algorithm to reconstruct a plausible approximation of the real state. Then, one can apply a nonlinear controller, based on fuzzy logic, for example, to enforce the system trajectories to a desirable stable equilibrium point. Nevertheless, the aforementioned method may not be suitable for uncertain systems affected by external noises. State observers based on the systems structure cannot be applied in those cases. However, some sort of adaptive estimation may be developed. This paper deals with a fuzzy controller that was designed using the state observer solution when the dynamic model of a plant contains uncertainties or it is partially unknown. Differential neural network (DNN) approach is applied in this uninformative situation. A new learning law, containing an adaptive adjustment rate, is suggested to enforce the stability condition for the observers free parameters. On the other hand, nominal weights are adjusted during the preliminary training process using the least mean square method. Lyapunov theory is used to obtain the upper bounds for the weights dynamics. The proposed method seems to be a more advanced option to control uncertain systems when the state available information is reduced. Even when several options exist to control this class of nonlinear systems such as PID, the method introduced here uses the knowledge on the system behavior and enforces the reconstruction of the immeasurable states. This last issue is an extra advantage because it serves as a general software sensor. The well-known two-link manipulator is used to show the effectiveness of the proposed algorithm. A couple of cases are used here: the full actuated and the under-actuated systems. In both situations, the controller achieves a better performance than the well-known PID controllers and a fuzzy controller using the estimated states produced by a high-order sliding-mode observer. A practical example showing how the fuzzy controller based on the estimated states produced by the differential neural network observer is also presented. The system used to test the controller is the anaerobic digestion. In this case, the benefits of this output-based controller are also demonstrated.

Nonlinear discrete time neural network observer

State estimation for uncertain systems affected by external noises has been recognized as an important problem in control theory for either discrete and continuous plants. This paper deals with the state observation problem when the discrete-time dynamic model of a plant is partially unknown and it is affected by some sort of uncertainties and external perturbations. Recurrent Neural Networks (RNN) have shown several advantages to treat many different control and state estimation problems. In this paper, a new discrete-time Luenberger-like observer using the structure of a RNN is proposed. The class of discrete-time nonlinear system just has the input-output pairs as available information. The neural observer is training off-line using a class of least mean square method for matrix parameters. Lyapunov theory is employed to obtain the upper bounds for the weights dynamics as well for the estimation error and the learning laws to ensure the convergence of the observer. Simulation results using the van der Pol oscillator as data generator are presented to demonstrate the effectiveness of the proposed neural observer.

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.

Robust disturbance rejection control of a biped robotic system using high-order extended state observer☆

This study addressed the problem of robust control of a biped robot based on disturbance estimation. Active disturbance rejection control was the paradigm used for controlling the biped robot by direct active estimation. A robust controller was developed to implement disturbance cancelation based on a linear extended state observer of high gain class. A robust high-gain scheme was proposed for developing a state estimator of the biped robot despite poor knowledge of the plant and the presence of uncertainties. The estimated states provided by the state estimator were used to implement a feedback controller that was effective in actively rejecting the perturbations as well as forcing the trajectory tracking error to within a small vicinity of the origin. The theoretical convergence of the tracking error was proven using the Lyapunov theory. The controller was implemented by numerical simulations that showed the convergence of the tracking error. A comparison with a high-order sliding-mode-observer-based controller confirmed the superior performance of the controller using the robust observer introduced in this study. Finally, the proposed controller was implemented on an actual biped robot using an embedded hardware-in-the-loop strategy.