Improved Models for Chromate Removal Using Ion Exchangers in Drinking Water Applications

Ghanem, Fred; Jerpoth, Swapana S.; Yenkie, Kirti M.

doi:10.1061/(asce)ee.1943-7870.0001997

Cited by 4 publications

(7 citation statements)

References 38 publications

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“…With changes in the experimental chromate concentrations as well as the flow rates to the ion exchange resin column, the empirical model was optimized in previous work and summarized below (Ghanem, Jerpoth, and Yenkie 2022). In that work, it was demonstrated that fixed parameters cannot capture accurately fluctuations in inlet chromate concentration and flow rates.…”

Section: Setting Up the Empirical Modelmentioning

confidence: 99%

“…Therefore a 1ppb variation, while small, is considered a dramatic variation that can lead to a premature ion exchange failure. Therefore, the inlet chromate concentration is the most important system characteristic that can affect the performance of the system (Ghanem, Jerpoth, and Yenkie 2022).…”

Section: Setting Up the Empirical Modelmentioning

confidence: 99%

“…ii-variations with the predictive model parameters, 𝑲 𝑻 and 𝒒 𝒎 : In previous work (Ghanem, Jerpoth, and Yenkie 2022), the accuracy of the parameters to reflect the system conditions were seen to fall within a 30% window, which needs to be captured in our stochastic process representation.…”

Section: Setting Up the Empirical Modelmentioning

confidence: 99%

“…Modeling challenging processes via used of minimization of the error difference between the measured and simulated data are necessary in many applications (Han et al 2023). Previous work was published by the authors to increase the accuracy of such models in chromate extraction (Ghanem, Jerpoth, and Yenkie 2022). Since all purification systems depend on successful process control, predicting the dynamics and subsequently implementing a better control strategy will help to design efficient and smaller-scale ion exchange systems.…”

Section: Introductionmentioning

confidence: 99%

“…These models also depend on empirical parameters that are not fixed when system characteristics, such as flow rates and inlet concentrations, fluctuate (Recepoğlu et al 2018). In this work, we use the Thomas model to represent the chromate removal process due to its accuracy when compared to other models, as described in detail in prior work (Ghanem, Jerpoth, and Yenkie 2022).…”

Section: Introductionmentioning

confidence: 99%

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Modeling chromate removal using type 1 quaternary amine anion exchangers in drinking water applications

Ghanem¹,

Yenkie²

2020

Preprint

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Chromates are widely used for their anticorrosive properties. Unfortunately, they are highly hazardous with environmental agencies regulating their levels to below 10 ppb in drinking water. As anion exchange resins are typically used for removal, predictive dynamic models are necessary to make quick decisions rather than relying on experimental data that could take several days to implement. With various dynamic models currently applied to simulate the ion exchange process, the Thomas model was picked for its simplicity and better accuracy when compared to other models. The Thomas model contains two parameters, the constant (KT) and the maximum resin capacity (qm), which are empirically calculated. Unfortunately, the model demonstrated large parameter fluctuations with no correlation to varying contact times or inlet chromate concentrations. Therefore, fixing both parameters will lead to failed model predictive behavior. By fixing the value of qm and proposing a linear relationship of KT with resin contact time and inlet chromate concentration, the accuracy of the model was improved five-fold, demonstrating its potential for better process controls.

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Section: Setting Up the Empirical Modelmentioning

confidence: 99%

Section: Setting Up the Empirical Modelmentioning

confidence: 99%

Section: Setting Up the Empirical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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