Processing of signals from an ion-elective electrode array by a neural network

Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous determination of potassium, calcium, nitrate and chloride in mixtures of potassium and calcium chlorides and ammonium nitrate. The measurements for the Ca2+/Cu2+ determinations were done with a pH-glass electrode and calcium and copper ion-selective electrodes; results were accurate to ±8%. For the K+/Ca2+NO−3/Cl− determinations, the measurements were made with the relevant ion-selective electrodes and a glass electrode; the mean relative error was ±6%, and for the worst cases the error did not exceed 20%

Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K¿ and Kß lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges

Modelling the permeability of polymers: a neural network approach

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a polymer to its permeability. The underlying assumption is that the chemical information hidden in the IR spectrum is sufficient for the prediction. The best neural network investigated so far does indeed show predictive capabilities

Space-time discontinuous Galerkin method for the compressible Navier-Stokes equations on deforming meshes

An overview is given of a space-time discontinuous Galerkin finite element method for the compressible Navier-Stokes equations. This method is well suited for problems with moving (free) boundaries which require the use of deforming elements. In addition, due to the local discretization, the space-time discontinuous Galerkin method is well suited for mesh adaptation and parallel computing. The algorithm is demonstrated with computations of the unsteady \ud ow field about a delta wing and a NACA0012 airfoil in rapid pitch up motion

Hollow-fibre membrane for sample introduction in a flow-injection system : Determination of carbon disulphide in air

A hollow-membrane fibre is used for the introduction of gaseous compounds into a flow-injection system. The sampling system consists of a certain length of asymmetric hollow-fibre membrane in which an acceptor stream is stopped for a fixed period of time. The analyte permeates from the surrounding environment through the membrane and is accumulated in the acceptor solution, then pumping is resumed. The method is tested for the determination of carbon disulphide in ambient air. The detection range of the method is from 3 to at least 30 mg l−1

A generalized approach for the calculation and automation of potentiometric titrations Part 1. Acid-Base Titrations

Fast and accurate calculation procedures for pH and redox potentials are required for optimum control of automatic titrations. The procedure suggested is based on a three-dimensional titration curve V = f(pH, redox potential). All possible interactions between species in the solution, e.g., changes in activity coefficients and influences of redox potential on pH variations, are taken into account. The number of titrant additions can be reduced considerably without loss of precision, by using the fact that the pH of a protolyte or mixture of protolytes at some fraction titrated does not depend strongly on the actual concentration

A knowledge-based system for the automatic chronopotentiometric elucidation of electrode reaction mechanisms

A knowledge-based system for the elucidation of electrode reaction mechanisms based on chronopotentiometric experiments is described. The system runs the diagnostic experiments and uses the results in the reasoning process. New mechanistic knowledge can be added directly to its knowledge base in the form of production rules. The system is fully modular and its domain- specific modules can easily be changed for application to other electrochemical techniques. Correct operation of the system is demonstrated with the familiar reduction mechanisms of cadmium (II), zinc (II), cystamine and cinnamaldehyde

Use of the hunt filter to optimize the determination of impulse-response functions of individual component parts of flow-injection manifolds

The dispersion behaviour of the various individual parts making up a flow-injection manifold can be expressed by means of impulse-response functions. These functions can be determined by deconvolution of the response curves obtained with and without the part concerned. Special attention is paid to a procedure to decrease the influence of noise. It is shown that good results can be obtained with a Hunt filter which operates in the Fourier domain