Estimation of Contaminants Decomposition in Solid Phase with Ozone by Differential Neural Networks with Discontinuous Learning Law

A discontinuous learning law is implemented here to adjust an adaptive non-parametric identifier, based on the differential neural networks (DNNs) approximations. The learning law for DNN uses the vector form of an extended super-twisting algorithm as the output injection term in the DNN structure. The learning laws with discontinuous dynamics have been obtained from the application of a special class of strong lower semi-continuous Lyapunov function. The developed observer was tested on both modelled and experimental input-output information on the specific the ozonation process of a contaminated solid phase. A numerical example illustrates the observer performance when the input-output information is free of the observation noise. The observer has been evaluated using real experimental data, obtained by the direct laboratory analysis. In both cases, modelling and real experiments, the coincidence between the ozonation variables and the estimated states shows a remarkable correspondence.

• Publication year

  2018

• Venue

  International Workshop on Variable Structure Systems

• Publication date

  2018-07-01

• Fields of study

  Chemistry, Engineering, Environmental Science, Computer Science

• Identifiers
  DOI 10.1109/VSS.2018.8460461
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Phenanthrene degradation in soil by ozonation: Effect of morphological and physicochemical properties.

  2017cited by this paper
• Kinetic study of toxic pollutants decomposition by ozone in landfill leachate using a numerical adaptive method

  2011cited by this paper
• Degradation of pentachlorophenol in soil by pulsed corona discharge plasma.

  2010cited by this paper
• Evaluation of soil pH and moisture content on in-situ ozonation of pyrene in soils.

  2009cited by this paper
• Principles of 2-sliding mode design

  2007cited by this paper
• New Sliding-Mode Learning Law for Dynamic Neural Network Observer

  2006cited by this paper
• Degradation of polycyclic aromatic hydrocarbons by combined chemical pre-oxidation and bioremediation in creosote contaminated soil.

  2006cited by this paper
• Dynamic neural observers and their application for identification and purification of water by ozone

  2006cited by this paper
• Modeling aqueous ozone/UV process using oxalic acid as probe chemical.

  2005cited by this paper
• Differential neural networks for robust nonlinear control—identification, state estimation and trajectory tracking, A. S. Poznyak, E. N. Sanchez and W. Yu, World Scientific, 2001, 410 pages, ISBN 981-02-4624-2

  2004cited by this paper
• Degradation of polycyclic aromatic hydrocarbons in soil: The fenton reagent versus ozonation

  2004cited by this paper
• Variable structure systems : from Principles to Implementation

  2004cited by this paper
• Kinetics of soil ozonation: an experimental and numerical investigation.

  2004cited by this paper
• Ozone treatment of soil contaminated with aniline and trifluralin.

  2003cited by this paper
• Combined ozonation and biodegradation for remediationof mixtures of polycyclic aromatic hydrocarbons in soil

  2000cited by this paper
• Robust Control Via Variable Structure and Lyapunov Techniques

  1996cited by this paper
• Higher order sliding modes as a natural phenomenon in control theory

  1996cited by this paper
• A Luenberger-like observer for nonlinear systems

  1993cited by this paper

• No citing papers are available for this paper.