Water & Wastewater Systems Planning

Team Members

Caroline Adkins

MR

Meerashree Sundara Raju

MH

Madeline Hodge

Emily Winn

Overview

Increasing water stress is forcing state and local water resource managers to evaluate non-traditional water supplies. Complicating this process is the fact that local political, economic, social, technical, legal, and environmental (PESTLE) contexts for tapping alternative source waters vary widely by geographic location. The screening of alternative water sources to secure American water supplies (SAWS) tool aims to provide a centralized data repository, analysis, and mapping tool for factors influencing adoption of non-traditional water supplies at the county scale. SAWS integrates these previously disparate PESTLE datasets into a centralized, uniform viability assessment framework to enable comparisons across different water supplies and geographic regions. SAWS supports a regional supply portfolio optimization, comparative supply viability assessments, and goal prioritization for technological development, among other analyses. Finally, the ability to import new datasets, combined with native flexibility in how viability metrics are calculated and weighted, allows users to perform sensitivity analysis and assess the value of additional data collection. SAWS was designed to provide policy makers, water resource managers, consulting engineers, and other stakeholders with a common platform for gathering, interpreting, and visualizing pathways to enhanced water security, resilience, and affordability.

Links

Supported By

• National Science Foundation
• ExxonMobil

Team Members

Gina Kittleson

Dr. Marta Zaniolo

Overview

Unconventional new drought-resilient water technologies such as reuse and desalination offer the potential to diversify California's water mix and complement surface and groundwater sources. Our research is developing new modeling approaches to understand where, when, and how to target these technologies to develop a drought resilient water portfolio at low cost. Our approach integrates household-level water distribution network modeling with hydrological modeling of drought scenarios. An initial case study in Santa Barbara, CA focuses on identifying a least-cost pathway to water security by optimizing a portfolio of water augmentation actions to secure the city water supply in the face of hydrological and climate uncertainty. This project is a collaboration with Prof. Sarah Fletcher, PI of the Fletcher Lab

Links

Supported By

• National Alliance for Water Innovation
• Stanford Sustainability Accelerator

Team Members

Akshay Rao

Fletcher Chapin

Carson Tucker

Dr. Erin Musabandesu

Dr. Haochi Wu

Daly Wettermark

Dr. Alexander Dudchenko

Overview

Most pathways for decarbonizing the electricity sector by 2050 include substantial increases in intermittent renewable generation paired with several TWh of grid-scale energy storage. Grid scale storage includes the ability to shift or schedule large amounts of power over time and can be deployed via a diverse range of technologies, like batteries or pumped hydropower. This project aims to understand the degree to which we can use existing infrastructure as virtual storage, by scheduling the operations and consequently the power consumption of electrified industrial processes (e.g., water treatment plants, chemical manufacturing, data centers, etc.). Our group develops methods rooted in numerical optimization to design, operate, and evaluate energy flexibility strategies across a range of industrial sectors and geographic regions. By modeling these processes from the ground up, we aim to understand the cost and feasibility requirements to unlock more flexibility from existing infrastructure and strategies to design and deploy flexibility more effectively for next generation infrastructure.

Team Members

Sinan Abi Farraj

Overview

Wastewater treatment plants are under pressure to meet nutrient discharge regulations while maintaining rate payer affordability. This work introduces CLEANRWastewater, a decision support tool for valuing innovative staged deployment and coordinated infrastructure strategies for WWTPs sharing a regulated watershed. Formulated as a multi-period, mixed-integer optimization framework and applied to a case study of three treatment facilities in the San Francisco Bay, this tool demonstrates that coordinating capital and operational decisions across facilities reduces the costs of sub-embayment nutrient removal by up to 48% ($268 million). These jointly realized savings stem from facilities delaying capital-intensive upgrades and deploying lowest-cost treatment options at the sub-embayment level. The framework's multi-period formulation uniquely incorporates time-varying constraints and accommodates uncertainty analysis around future nutrient loads. Quantifying the financial benefits of innovative infrastructure planning strategies may motivate utility managers to establish nutrient trading markets and coordinate regional infrastructure investments.

Links

Supported By

• Stanford Woods Institute for the Environment

Team Members

Caroline Adkins

Carson Tucker

Dr. Alexander Dudchenko

Dr. Alison Fritz

Overview

Desalination at inland brackish groundwater treatment plants is currently limited by high costs of concentrate management and disposal. With current methods, 5-25% of the feed is disposed of as concentrate, with 98% of disposal using conventional methods (i.e. no byproduct recovery). The levelized cost of water (LCOW) for current plants in operation ranges from $0.42-1.5/m3. This project aims to assess the technical and economic feasibility of industrial ecosystems to desalinate brackish groundwater and supply existing and potential markets for clean concentrate or other bulk constituents from the brine. Specific project goals include: 1) Developing location specific byproduct revenue models using market assessments, and 2) Creating treatment train schemas in WaterTAP for treatment and valorization of brackish groundwater and establish pipe parity cost and performance targets for brackish groundwater treatment.

Links

Supported By

• National Alliance for Water Innovation (NAWI)

Team Members

Daly Wettermark

Carson Tucker

Overview

This project seeks to provide a framework for selecting water resource recovery facility (WRRF) modeling platforms based on their ability to incorporate effluent limits, resource recovery, and energy-flexible operation goals. By benchmarking mechanistic (e.g., GPS-X, SUMO) and data-driven platforms, and considering the role of numerical optimization (e.g., WaterTAP), we demonstrate how platform selection affects design decisions including aeration system sizing, time-of-use electricity cost optimization, and identification of process synergies that reduce overall lifetime cost. Although established platforms excel at simulation of a given design, integrated design and operation decisions across complex treatment trains benefit from systematic design space exploration.

Supported By

• Woods Institute for the Environment