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Fred Ghanem

Publications and source records attributed to Fred Ghanem.

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Optimal Control of Chromate Removal via Enhanced Modeling using the Method of Moments

Single-use anion-exchange resins can reduce hazardous chromates to safe levels in drinking water. However, since most process control strategies monitor effluent concentrations, detection of any chromate leakage leads to premature resin replacement. Furthermore, variations in the inlet chromate concentration and other process conditions make process control a challenging step. In this work, we capture the uncertainty of the process conditions by applying the Ito process of Brownian motion with drift into a stochastic optimal control strategy. The ion exchange process is modeled using the method of moments which helps capture the process dynamics, later formulated into mathematical objectives representing desired chromate removal. We then solved our developed models as an optimal control problem via Pontryagin's maximum principle. The objectives enabled a successful control via flow rate adjustments leading to higher chromate extraction. Such an approach maximized the capacity of the resin and column efficiency to remove toxic compounds from water while capturing deviations in the process conditions.

math.OC

Modeling Chromate Removal Using Ion Exchangers in Drinking Water Applications

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.

stat.AP