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The aim of this work is development of methodology for analysis of inorganic cations (sodium, ammonium, potassium, magnesium
and calcium) in fertilizer industry wastewater. Method development includes optimization of eluent flow rate and concentration
of eluent competing ion in order to obtain optimal separation within reasonable analysis time. For that purpose artificial
neural network retention model was developed and used in combination with normalized resolution product criteria function.
Developed artificial neural network retention model shows good predictive ability R2 ≥ 0.9983. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing
validation procedure and number of statistical tests it is shown that developed ion chromatographic method has superior performance
characteristic: linearity R2 ≥ 0.9984, recovery = 99.81% − 99.44%, repeatability RSD ≤ 0.52%. That result proves that proposed method can be used for
routine monitoring analysis in fertilizer industry. 相似文献
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Štefica Cerjan Stefanović Tomislav Bolanča Melita Luša Šime Ukić Marko Rogošić 《Analytica chimica acta》2012
This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study. 相似文献
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Šime Ukić Mirjana Novak Petar Žuvela Nebojša Avdalović Yan Liu Bogusław Buszewski Tomislav Bolanča 《Chromatographia》2014,77(15-16):985-996
New retention methodology that integrates the conventional quantitative structure-retention relationship (QSRR) approach and gradient retention modeling based on isocratic retention data is developed and presented in this paper. Such an integrated approach removes the general QSRR limitation of highly predefined application conditions (i.e., QSRR are generally applicable only under the conditions used during model development) and allows the prediction of retentions over a wide range of different elution conditions (practically for any isocratic or gradient elution profile). At the same time, it retains the ability to predict retention of components unknown to the model, i.e., the components that have not been used in modeling. Ion-exchange chromatography (IC) analysis of carbohydrates was selected as modeling environment. Three regression techniques were applied and compared during QSRR modeling, namely: stepwise multiple linear regression, partial least squares (PLS), and uninformative variable elimination–PLS regression. The obtained prediction results of the best QSRR model (root-mean-square error of prediction = 22.69 %) were similar to those found in the literature. The upgrade from QSRR to the integrated model did not diminish the predictive ability of the model, indicating an excellent potential of the developed methodology not only in IC but also in chromatography in general. 相似文献
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In this article, an integrated approach for prediction and optimization in ion chromatography (IC) was presented. The approach provides a fast and reliable insight in the elution behavior of an IC system. The predictions are based on a mathematical model that predicts ion retentions (for both isocratic and gradient modes) by using an empirical isocratic model. Other chromatographic values significant for the optimal elution conditions (resolution, peak asymmetry) are calculated quickly and easily from the predicted retention values of characteristic points of a chromatographic peak. Every day, IC users might find this approach a suitable tool for finding optimal IC elution conditions in a given system. 相似文献
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Tomislav Bolanča Štefica Cerjan Stefanović Šime Ukić Marko Rogošić Melita Luša 《Journal of separation science》2009,32(17):2877-2884
This study describes the development of a signal prediction model in gradient elution ion chromatography. The proposed model is based on a retention model and generalized logistic peak shape function which guarantees simplicity of the model and its easy implementation in method development process. Extensive analysis of the model predictive ability has been performed for ion chromatographic determination of bromate, nitrite, bromide, iodide, and perchlorate, using KOH solutions as eluent. The developed model shows good predictive ability (average relative error of gradient predictions 1.94%). The developed model offers short calculation times as well as low experimental effort (only nine isocratic runs are used for modeling). 相似文献
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Bolanca T Cerjan-Stefanović S Lusa M Rogosić M Ukić S 《Journal of chromatography. A》2006,1121(2):228-235
When facing separation problems in ion chromatography, chromatographers often lack guidelines to decide a priori if isocratic elution will give enough separation in a reasonable analysis time or a gradient elution will be required. This situation may be solved by the prediction of retention in gradient elution mode by using isocratic experimental data. This work describes the development of an ion chromatographic gradient elution retention model for fluoride, chloride, nitrite, bromide, nitrate, sulfate and phosphate by using isocratic experimental data. The isocratic elution retention model was developed by applying a polynomial relation between the logarithm of the retention factor and logarithm of the concentration of competing ions; the gradient elution retention model was based on the stepwise numerical integration of the corresponding differential equation. It was shown that the developed gradient elution retention model was not significantly affected by transferring data form isocratic experiment. The root mean squared prediction error for gradient elution retention model was between 0.0863 for fluoride and 0.7027 for bromide proving a very good predictive ability of developed gradient elution retention model. 相似文献
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Bolanca T Cerjan-Stefanović S Lusa M Ukić S Rogosić M 《Journal of separation science》2008,31(4):705-713
In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes. 相似文献
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Tomislav Bolanča Štefica Cerjan Stefanović Šime Ukić Melita Luša Marko Rogošić 《Chromatographia》2009,70(1-2):15-20
Gradient elution is used in ion chromatography to achieve rapid analysis with reasonable separation. Optimization and prediction of the gradient is clearly a multidimensional problem, however. One approach to prediction of gradient retention behavior is based on isocratic experimentation. In this work, a gradient model for simultaneous prediction of the retention behavior of fluoride, chlorite, chloride, chlorate, nitrate, and sulfate ions, on the basis of isocratic experimental data, is proposed. An artificial neural network was used to predict isocratic results; the network was optimized with regard to the number of data in the training set (25) and number of neurons in the hidden layer (6). A slight systematic error was observed in the isocratic prediction, but this did not effect gradient prediction. Good predictions were achieved for all the anions investigated (average error 1.79%). Deviations were somewhat higher for prediction of sulfate retention than for the other anions, probably because of the higher charge and larger size of sulfate in comparison with the other ions examined. 相似文献
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