Štefica Cerjan-Stefanović

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.

Removal of metal-complex dyestuffs by croatian clinoptilolite

One of the most significant commercial applications of natural zeolites is the removal of metal ions from wastewater1–5. Some metal-complex dyes used for wool dyeing are copper and chromium complexes6,7. Chromium has the advantage over copper because of its greater stability to acids and alkalis. This paper deals with the possibility of eliminating of metal-ions from metal complex dyestuffs after the dyeing process by natural zeolites. The sorption properties of pre-treated and raw clinoptilolite for copper(II) and chromium(III) ions from metal complex dyes were investigated. The copper(II) complexes were prepared with two ligands: a 2-(2-pyridylmethylene-amino) phenol (PMAP) and 2-(2-quinolylmethylene-amino) phenol (QMAP)8. The chromium(III) complexes were prepared with 3-methyl-1-phenyl-5-pyrazolone (C. I. 18744) and 1-amino-2-naphtol-4-sulfonic acid (C. I. 14880). It was shown that various factors influence the Cu2+ and Cr3+ bonding capacities to natural zeolites, including chemical treatment of natural zeolite and particle size of zeolites. This study demonstrated that the pre-treated and raw clinoptilolite can be used as an effective adsorbent for the treatment of wastewater containing copper(II) and chromium(III) ions from metal compex-dyestuff.

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.

Separation of silver from waste waters by ion-exchange resins and concentration by microbial cells

SummaryWaste waters of film processing plants are rich with silver. Part of the silver is regenerated electrochemically, but the rest (0.5 g) remains in waste waters and is sent to sewers. This is a bad politic from both the environmental (toxic waste waters) and the economical point of view (a waste of silver). In this work, the silver was isolated by ion-exchange resins and then concentrated by microorganisms. For exchange of silver, Ionenaustauscher I, II and IV were used. The batch method was used to obtain a static equilibrium. Silver elution from exchangers is based on silver transformation to a stable cation or anion complex. By varying the ligands, pH and eluent concentrations, optimum elution is found at 1 mol/l Na2S2O3, 1 mol/l NH3, 2 mol/l HNO3 and 1 mol/l (NH2)2CO. The concentration of silver in the eluent is about 50 mg/l. The silver ion uptake from solutions after ion exchange by mixed bacterial culture isolated from photographic waste water drain and pure bacterial cultures Escherichia coli 3009 and Bacillus subtilis 3053. was studied. Experiments were carried out in submerse culture at pH 7 with different Ag+ concentrations (4, 8 and 40 mg/l) on a rotary shaker (100 rpm) at 37°C. At the lower Ag+ concentrations a good growth and simultaneous removal of Ag+ from the solutions was achieved. At Ag+ concentration of 40 mg/l growth and removal of Ag+ by mixed and pure culture differed significantly. Thus mixed bacterial culture grew well and at the same time removed efficiently Ag+ (approximately 90%) from medium. Pure bacterial cultures on the contrary were unable to grow at 40 mg/l Ag+, though their biomass showed to be an effective biosorbent for Ag+ (approximately 80% of Ag+ removal).

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.

Separation and detection of traces of copper, iron and manganese in cotton materials by TLC

SummaryA TLC method for the fast detection of copper, iron and managanese ions in cotton materials is described. The optimal solvent system is 8∶1∶2 (v/v), acetone: HCl:H2O and the locating reagent is rubeanic acid followed by exposure to ammonia vapour. It was found that in cotton materials, metal ions can be detected at a lower limit of 20 μg per gram of material.

Phenol adsorption on active carbon by means of thin-layer chromatography

SummaryThe effect of the acidity of the media on the adsorption of phenol and creasols was investigated using TLC and using the RF-values as a measure of the adsorption ability of active carbon. Three types of chromatographic layers were employed: silica gel, silica gel containing 3% of active carbon and silica gel containing 6% of active carbon. Standard solutions of phenol, m-cresol and o-cresol were used as the samples. The acetic acid content of the solvent mixture significantly influences the adsorption of phenol and cresols on the active carbon layer. An increase in the acetic acid content results in a decrease of the adsorption of phenols. However, under specific conditions [81∶5∶7 hexane-diethyl ether-acetic acid, and 48∶2∶8 benzene-acetone-acetic acid developers] the competitive adsorption of phenols and acetic acid may take place, which has been observed by a steep increase in the adsorption of phenol and cresols.