(15b) A Framework for the Optimization of Water Treatment Processes Under Uncertainty Assessed through Process Operability

The objectives of water treatment processes are diverse with interests including water reuse, resource recovery, waste valorization, and water-energy nexus integration, among others. Newly researched approaches to water treatment provide avenues for improved performance, economics, and sustainability [1]. Process systems engineering (PSE) analyses can be employed to inform full-scale optimal designs by utilizing models based on lab- and pilot-scale validation. As such, PSE tools depend on underlying process models to simulate results. The applications of PSE tools in water treatment have previously been challenged by the availability of electrolyte property and water treatment technology models within unified simulation frameworks. However, with the advancement of water treatment simulation and optimization tools, initial demonstrations of process-scale parametric optimization for selected water treatment processes have been valuable for assessing emerging designs [2–7].

The objective of this work is to demonstrate a framework for the optimization of water treatment processes under uncertainty assessed through process operability. This effort contributes to the interest in advancing the application of PSE tools such as optimization to other water treatment processes and, additionally, developing PSE frameworks that cater specifically to the features of water treatment processes. Traditional optimization problems are formulated to optimize process designs operating at a nominal point at which the facility has control over the quality of chemical feedstocks. Considering that feedwaters for water treatment are commonly variable due to seasonal, geographical, and/or other influences, an advanced optimization approach would provide a more informative analysis. Robust optimization is proposed to incorporate statistically characterized feedwater variations into an optimal design [8]. Subsequently, an operability analysis of robust optimal designs is implemented to quantify the tradeoffs between guaranteeing design robustness and the economics of process overdesign [9]. The framework utilizing Python-based tools including WaterTAP, PyROS, and Opyrability is exemplified through a case study of designing and optimizing an industrial water treatment process [7–9].

Disclaimer:

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

References:

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