Sinan is a Ph.D. student in Environmental Engineering at Stanford University and holds a B.Eng. in Materials Engineering and an M.Sc. in Chemical Engineering from McGill University. His doctoral research focuses on developing cost-optimization models for coordinating water and wastewater infrastructure investments to meet regional environmental targets.
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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.
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