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Dudchenko, Alexander V

Publications and source records attributed to Dudchenko, Alexander V.

Tracking Spatiotemporal Electric Potential in Batteries Using High-Resolution Operando X‑ray Transmission Imaging

The formation of significant concentration gradients across electrolytes in batteries affects the rate at which electrochemical reactions occur. In this work, we use high-resolution operando X-ray transmission imaging to capture spatiotemporal salt concentration profiles c(x,t) in a symmetric cell comprising a polymer electrolyte sandwiched between two lithium–indium alloy electrodes during a constant-current experiment followed by open-circuit relaxation. The decay of open-circuit potential is related to the concentration dependence of the potential across concentration cells, U. We show how operando c(x,t) data can be used to calculate the spatiotemporal electric potential “inside” the polarized electrolyte. We track the spatial- and time-dependent cell potential during the constant-current step and distinguish its two contributions: a concentration overpotential governed by U. and an ohmic contribution governed by ionic conductivity. Over most of the time window, the concentration overpotential dominates. At steady state, it is a factor of 7 larger than the ohmic contribution. Such findings indicate that efforts to design new polymer electrolytes should focus on minimizing concentration gradients.

Electrical conductivity↗

Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Dudchenko, Alexander V↗

WaterTAP 0.12 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.11 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗