ANN Prediction of Equilibrium Constants in Aqueous Solutions
| Autoři | |
|---|---|
| Rok publikování | 2002 |
| Druh | Článek ve sborníku |
| Konference | Book of Abstracts of International Chemometric Conference CHEMOMETRICS VI |
| Fakulta / Pracoviště MU | |
| Citace | |
| Obor | Analytická chemie, separace |
| Klíčová slova | artificial neural networks; equilibrium constants |
| Popis | The knowledge of stability constants is important in all branches of chemistry, chemical technology, environment, etc. The equilibrium (formation, stability) constants in analytical chemistry are used in order to understand speciation in development of analytical procedures or in the environment. The measurement of large numbers of equilibrium constants of different reactions varying experimental conditions (ionic strength, temperature, etc.) is not attractive option. Therefore accurate and reliable methods for determination of equilibrium constants are desirable. In practice, different equations are used for prediction of equilibrium constants for given experimental conditions (ionic strength, temperature, etc.). The precision of prediction is dependent on the number of experimental points and relationship applied in the fitting procedure. Recently we proposed the application of artificial neural networks (ANN's) for evaluation of equilibrium constants from experimental data obtained by means of different experimental techniques. In this contribution, the method of equilibrium constants prediction for different ionic strengths and temperatures using "soft" modelling with ANN's was examined and compared with results obtained by "hard" modelling. This proposed methodology allows rapidly and with sufficient accuracy to predict formation constants for given experimental conditions. The results are independent on the model and also are not sensitive to error of formation constants. This alternative model-free approach for prediction of stability constants can be used in practice. |
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