Akshay received his B.S. and M.S. from Purdue University in Mechanical Engineering and Computational Science, and his Ph.D. in Civil and Environmental Engineering from Stanford University. His research focuses on developing algorithmic approaches to design and operate of electrified industrial separations. Akshay was named a Siebel Scholar in Energy Sciences for his contributions in this space.
Team Members
Overview
Municipal and industrial water treatment facilities face increasingly complex technology decisions for treating nontraditional waters, water reuse, and resource recovery. Current tools provide limited capability to rigorously compare alternatives, predict performance under variable conditions, or justify R&D and pilot investments. This results in conservative design decisions, costly and time-intensive piloting programs, and missed opportunities for innovation. WaterTAP is an open-source, physics-based modeling framework for designing and evaluating advanced water treatment systems. WaterTAP process models are built within an optimization framework to solve for cost-optimal design and operation. WaterTAP enables researchers and engineers to simulate complex treatment trains; screen and prioritize technologies for piloting; integrate facility data into predictive models; and quantify the value of innovation.
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Team Members
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.
