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Dynamic Process Modeling of Wastewater-Energy Systems

  • Author(s): Reifsnyder, Samuel
  • Advisor(s): Rosso, Diego
  • et al.
Abstract

Over the last quarter century, the intensification of human activities in urbanized areas, coupled with an increase in frequency and length of extreme droughts, has resulted in the growing demand for safe, reliable, and sustainable water. Municipal and industrial water reclamation has been the subject of much interest since it provides a more balanced solution on metrics such as land-use footprint, energy and operational costs, and reliability when compared to other alternative water solutions. As water stressed cities are increasingly relying on recycled water, treated wastewater effluent standards are becoming progressively stricter due to concerns associated with human exposure. Therefore, significant efforts have been devoted towards the optimization of conventional and advanced wastewater treatment processes in order to improve the sustainability of their operations in terms of effluent quality, energy use, greenhouse gas emissions and costs.

Dynamic modeling continues to provide a powerful tool for the evaluation of novel optimization strategies in the field of municipal and industrial wastewater treatment. Therefore, in this dissertation, three different systems have been carefully analyzed due to their growing interest in the field of wastewater treatment and reclamation. Through the use of dynamic process models, the optimization of these systems was evaluated. The topics presented are: i) optimization of air supply system in municipal water resource recovery facilities (WRRFs); ii) optimization of produced water clarifiers in petrochemical wastewater treatment facilities (PWTFs); and iii) optimization of hybrid WRRFs systems.

In the first topic, in-situ off-gas testing coupled with dynamic simulations of a full-scale air supply system have highlighted the significant impact that an imbalance in airflow distribution can have on the overall performance of a municipal WRRFs. Along with a family of dynamic process models, a multi-criteria analysis of an air supply model was performed by parametrizing the manual valves used to distribute the airflow to the various reactor zones. A trade-offs analysis showed potential energy savings of up to 13.6% and improvement of effluent quality for NH4+ (up to 68.5%) and NOx (up to 81.6%).

In the second topic, a novel clarifier model is proposed for the dynamic description of clarification tanks used to concurrently separate solids and oils dispersion in petrochemical wastewaters. Batch settling tests of samples collected from a petrochemical wastewater treatment plant in China revealed that the gravity separation of oils and solids behaves according to discrete particle dynamics. Therefore, a Stokesian particle separation model was incorporated into the produced water clarifier model, which is based on measured particle settling velocity distribution (PSVD) curves. Furthermore, through an ensemble of Monte Carlo simulations it was possible to analyze the separation performance of various flow configurations of the underflow and water effluent streams. It was in general possible to observe a marked trade-off between the competing goals of solids thickening and oil recovery.

In the third topic, a new conceptual framework for the dynamic management of hybrid WRRFs systems comprised of both centralized and satellite WRRFs is introduced. The underlying concept of such strategy relies on exploiting the hydraulic delay of sewer trunk lines for the deferral of the treatment intensity between hydraulically connected facilities during specific hours of the day. This study provides a novel insight into the dynamic management of hybrid wastewater treatment systems and highlights its potential to reduce the greenhouse gases, power demand, energy use and costs associated with treating the wastewater. Results show the potential to reduce by 25% the total power demand exerted during grid ramping hours. Furthermore, total costs, energy and greenhouse gas emissions could be reduced by 8.5%, 4.1%, and 4.5% respectively.

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