Natural zeolites have a great potential as low-cost, non-toxic and highly selective sorbents in pollution control, especially in waste-water management. The potential of Croatian and Serbian ...clinoptilolite-rich (HEU-type zeolite) tuffs from four different deposits have been evaluated for their use in metal removal from aqueous solutions by the application of adsorption equilibrium studies, structural studies of metal-loaded zeolite tuffs and their regeneration or further treatment, which were performed in our laboratories or were reported by other authors. The results showed that metals like copper could be completely removed from aqueous solutions at low metal concentrations using Serbian and/or Croatian zeolites (up to 120mg Cu/L). At higher concentrations the efficiency of zeolites slowly decreased, also in accordance with the theoretical adsorption capacities. The studies confirmed that a higher content of zeolite in tuffs from Serbia (70–80% vs. 50% for Croatian tuff) resulted in their better adsorption performance for selected metals. Additionally, the pretreatment of zeolites with Na+ significantly enhanced the uptake of all metal cations, while the pretreatment with Fe3+, which resulted in stable iron-oxo-species on the zeolite surface and positive charge of the framework, enabled the adsorption of anions, like arsenites, arsenates and chromates. The reversibility of the metal uptake depended on the type of metal; for example, post-treatment of the samples with HCl, NaCl or NH4Cl solutions revealed irreversible adsorption of chromium and arsenic and mostly reversible adsorption of zinc and copper. The possible strategies for the regeneration or immobilization of the used metal-loaded zeolites, like immobilization in cements or the use as catalysts, were also considered.
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•Reviewing adsorption studies of metal species on Serbian and Croatian zeolites•Emphasis on zinc, chromium, copper and arsenic immobilization on clinoptilolite•Describing pretreatment of zeolite tuffs for optimized cation/anion adsorption•Comment on the utilization of studied clinoptilolites for water metal-pollution control•Suggest strategies for the regeneration/immobilization of used metal-loaded zeolites
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► Development of ad hoc methodology for anions determination in oilfield water. ► Artificial neural networks were used for modeling of anion retention in IC. ► Optimal networks were ...used for retention predictions in 47775 virtual experiments. ► Multi-criteria decision making was used for determination of optimal separation. ► Optimal method was finally validated and applied for real oilfield water sample.
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.
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).
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.
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.
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.
Optimization Strategies in Ion Chromatography Bolanča, Tomislav; Cerjan-Stefanović, Štefica
Journal of liquid chromatography & related technologies,
20/2/1/, Letnik:
30, Številka:
5-7
Journal Article
Recenzirano
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.
Gradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved ...(less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized two‐phase training algorithm that enables the researcher to make use of the advantages of first‐ and second‐order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed ANNs are the method of first choice for retention modelling of inorganic anions in IC.
The most important part of the complex ion chromatography method development process is retention modeling. It tries to integrate the demands for high quality ion chromatography with the demands for ...low consumption of chemicals, fast analysis, and the short time of method development. This work compares the properties of the cascade forward and back propagation artificial neural network in the development of temperature dependent retention models. The retention times of bromate, bromide, nitrite, iodide, and perchlorate were modeled in relation with temperature of separation process, concentration of hydroxide eluent competing ion, and eluent flow rate. Artificial neural networks were optimized in term of selecting the optimal training algorithm, optimal number of hidden layer neurons, activation function, and number of experiments needed for modeling procedure. The retention model based on cascade forward methodology exhibited superior predictive ability and, therefore, should be the method of first choice for the temperature dependent optimization in ion chromatography.
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