Pregled bibliografske jedinice broj: 124334
Artificial Neural Networks in Ion Chromatography
Artificial Neural Networks in Ion Chromatography // Proceedings of the 7th International Symposium Advances in Analytical Separation Science, Chromatography and Elecrtophoresis
Pörtschach am Wörthersee, 2002. (pozvano predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 124334 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Artificial Neural Networks in Ion Chromatography
Autori
Cerjan Stefanović, Štefica ; Srečnik, Goran ; Debeljak, Željko ; Bolanča, Tomislav
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 7th International Symposium Advances in Analytical Separation Science, Chromatography and Elecrtophoresis
/ - Pörtschach am Wörthersee, 2002
Skup
International Symposium Advances in Analytical Separation Science, Chromatography and Elecrtophoresis (7 ; 2002)
Mjesto i datum
Pörtschach am Wörthersee, Austrija, 03.07.2002. - 05.07.2002
Vrsta sudjelovanja
Pozvano predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
artificial neural networks; ion chromatography
Sažetak
The aim of the presented work was to develop the simple and rapid Ion Chromatographic (IC) method for monitoring and analysis of inorganic anions in leachate from electrical furance slag. Leachate samples consist small amount of inorganic anions, and high concentrations of inorganic cations, which results with overloading of the chromatographic column in case of the direct injection. Effect of inorganic cations must be dismissed. This can be done by treating the sample with strong acidic cation exchanger, followed by filtration m membrane filter prior analysis. The inorganic anions from slagmthrough a 0.45 were seperated using phtalic acid as eluent, and were measured by a conductivity detector. For analysis was used a 690 Ion Cromatograph (Metrohm) with IC Anion Column PRP-X100. for calculating the values of separation parametars / retention time, selectivity coefficient and number of theoretical plates / from known data, including mobile - phase composition, concentration and flow rate, diffusion coefficient of analites, ion - exchangeconstanta, the sorbend ion - exchange capacity and particle size parametars, are presented. The model was developed that simulated satisfactory the observed changes and could be used to optimize practical optimizations.
Izvorni jezik
Engleski
Znanstvena područja
Kemijsko inženjerstvo
