Application of Artificial Neural Network and Multiple Linear Regression Retention Models for Optimization of Separation in Ion Chromatography by Using Several Criteria Functions

This work focuses on problems regarding empirical retention modelling and optimization of separation in ion chromatography. Influences of eluent flow rate and concentration of eluent competing ion (OH^–) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulphate, bromide, nitrate, and phosphate) were investigated. Artificial neural networks and multiple linear regression retention models in combination with several criteria functions were used and compared in global optimization process. It can be seen that general recommendations for optimization of separation in ion chromatography is application of chromatography exponential function criterion in combination with artificial neural networks retention model.     

Determination of Inorganic Ions in Fertilizer Industry Wastewater by Ion Chromatography

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.     

多壁碳纳米管结构与其电化学容量之间关系的研究

采用化学气相沉积法,通过改变催化剂的成分、碳源、反应和后处理条件来制 备不同管径、管长、石墨化程度的多壁碳纳米管。经电化学容量性能测试、透射电 子显微镜观察和N_2吸附等结构表征,发现管径分布为30.0～40.0 nm、管长越短、 石墨化程度越低、比表面积越大、孔容越大的多壁碳纳米管具有更好的电化学容量 。     

Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model

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.     

Development of Gradient Retention Model in Ion Chromatography. Part I: Conventional QSRR Approach

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.     

Novel criteria for fast searching for optimal method in gradient ion chromatography: an integrated approach

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.     

SYNTHESIS AND CHARACTERIZATION OF BLOCK POLYMER OF ALTERNATING COPOLYMERS

Block polymer of alternating copolymers, poly[(ST-a-MA)-b(IP-a-MMA)-b-(ST-a-MA)](I), has been synthesized in the presence of AlEt_(1.5)Cl_(1.5),by using Mn_2(CO)_(10)/CBr_4. initiation system.The GPC results indicate that there is no unreacted prepolymer in I purified by precipitation andfractionation. The study of NMR shows that every block of polymer I has alternating structure. The dynamicmechanical properties of I measured by torsional braid method indicate that the polymer has two T_g s,being 38 and 75℃, respectively. The transmission electron micrographs of I show they have two phasestructure and the domain size increases with increasing molecular weight of hard block.     

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).