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

机译:基于人工神经网络保留模型的离子色谱法测定油田水中无机阴离子的多标准决策开发

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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.
机译:本文描述了用于确定油田水中无机阴离子的临时方法的发展,因为它们的成分通常与平均值(组分和/或基质的浓度)存在显着差异。因此,必须进行快速而可靠的方法开发,以确保在新条件下监视所需特性。该方法的开发基于计算机辅助的多准则决策策略。使用的标准是:使用的目标函数的最大值,分离方法的最大鲁棒性,最小的分析时间以及两个最接近的组件之间的最大保留距离。人工神经网络用于阴离子保留的建模。通过验证性能特征,对开发方法的可靠性进行了广泛测试。基于验证结果,所开发的方法显示出令人满意的性能特征,证明了计算机辅助方法在所述案例研究中的成功应用。

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