Tomislav Bolanča

Simultaneous Determination of Six Inorganic Anions in Drinking Water by Non - Suppressed Ion Chromatography

A non-suppressed ion chromatographic method with conductometric detection is described for the simultaneous determination of six inorganic anions: fluoride, chloride, nitrite, bromide, nitrate and sulphate. The separation was achieved on a low-capacity anion-exchange column Metrohm IC Anion Column Super Sep, with a mobile phase consisting of phtalic acid dissolved in high-purity water, 2-amino-2-hydroxymethyl-1,3-propendiol and acetonitrile. In this work computer optimization procedures, using computer programs to select chromatographic conditions have been used, leading to the achievement of a desired separation. By using the different optimization methods in an integrated manner it is, however, possible to both speed method development, by reducing unnecessary experimentation, and to overcome the many shortcomings of each method, because of the different approaches. The purpose of this work is to improve and characterise the method for simultaneous determination of six inorganic anions in drinking water by non-suppressed ion chromatography, using optimization procedures, in order to be applied to the routine analysis. The proposed method has numerous advantages over the other widely used non-suppressed ion chromatography methods: higher selectivity, shorter analysis time, lower quantitation and detection limits. The performance characteristics of the method were established by determining the following validation parameters: precision and accuracy, linearity, detection limits and quantitation limits.

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixons outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.

Optimization strategies in ion chromatography

Abstract The ion chromatographer is often concerned with the separation of complex mixtures with a variable behavior of their components, which makes good resolution and reasonable analysis time sometimes extremely difficult. Several optimization strategies have been proposed to solve this problem. The most reliable and less time consuming strategies apply resolution criteria based on theoretical or empirical retention models to describe the retention of particular components. This review focuses on optimization strategies in ion chromatography with a detailed description of the ion chromatographic retention model, objective functions, multi criteria decision making, and peak modeling.

SELECTION OF CRITERIA FOR COMPARING AND EVALUATING THE OPTIMIZATION OF SEPARATION IN ION CHROMATOGRAPHY

Optimization procedures in Ion Chromatography require unambiguous goals. Optimization criteria express such goals in mathematical terms. If the retention factor tR, varies as a function of the parameters to be optimized, criteria should be selected that enable simultaneous optimization of retention and selectivity. The non — suppressed Ion Chromatographic method with conductometric detection is described for simultaneous determination of six inorganic anions: fluoride, chloride, nitrite, bromide, nitrate, and sulphate. It is demonstrated that the result of the optimization process depends on the optimization criterion selected. The computer-simulated chromatograms were used for the comparison of optima calculated using four different criteria. General recommendations for double criteria optimization of separation in ion chromatography are suggested.

Application of Different Artificial Neural Networks Retention Models for Multi-Criteria Decision-Making Optimization in Gradient Ion Chromatography

In this work, the principles of multi-criteria decision-making were used to develop an efficient optimization strategy in gradient elution ion chromatographic analysis. Two different artificial neural network retention models (multi-layer perceptron and radial basis function), three different separation criterion functions (chromatography response function, separation factor product and normalized retention difference product), and four different robustness criterion functions (CR1-CR4) were examined. The shape of the calculated separation vs the robustness response surface was used as principal criterion. Analysis time and minimum separation of adjacent peaks were additional criteria. The results showed that the radial basis artificial neural network retention model in combination with normalized retention difference product separation criterion function and CR3 robustness criterion function provided the optimal gradient ion chromatographic analysis.

Application of a Gradient Retention Model Developed by Using Isocratic Data for the Prediction of Retention, Resolution, and Peak Asymmetry in Ion Chromatography

Abstract In this work a model was developed for the prediction of retention time, resolution, and peak asymmetry in gradient elution mode by using isocratic experimental data. The predictive performance and generalization ability of the developed model was extensively tested by using an external experimental data set. The analysis of errors was performed in order to discuss and explain characteristics of the model. It was shown that the model performed satisfactorily and that it could be used for a modeling procedure in the optimization part of the ion chromatography method development.

Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions.

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.

Prediction of biodegradability of aromatics in water using QSAR modeling

The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD5) and chemical (COD) oxygen demand values determined for parent compounds ((BOD5/COD)0), as well as for their reaction mixtures in half-life achieved by UV-C/H2O2 process ((BOD5/COD)t1/2). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD5/COD)0 and (BOD5/COD)t1/2, respectively, according to the values of statistical parameters R2 and Q2. Hence, 3-variable model predicting (BOD5/COD)0 possessed R2=0.863 and Q2=0.799 for training set, and R2=0.710 for test set, while 5-variable model predicting (BOD5/COD)1/2 possessed R2=0.886 and Q2=0.788 for training set, and R2=0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H2O2.

Prediction of diesel fuel cold properties using artificial neural networks

In this paper, two neural networks, multilayer perceptron and networks with radial-basis function, were used to predict important cold properties of commercial diesel fuels, namely cloud point and cold filter plugging point. The developed models predict the named properties using cetane number, density, viscosity, contents of total aromatics, and distillation temperatures at 10, 50, and 90 vol. % recovery as input data. The training algorithms, number of hidden layer neurons, and number of training data points were optimized in order to obtain a model with optimal predictive ability. The results indicated better prediction of cloud and cold filter plugging points in the case of multilayer perceptron networks. The obtained absolute error mean for the optimal neural network models (0.58°C for the cloud point and 1.46°C for the cold filter plugging point) are within the range of repeatability of standard cold properties determination methods.

Prediction of the chromatographic signal in gradient elution ion chromatography

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).