No abstract

Real-time carbon monitoring of wastewater using bio-electrochemical sensors coupled with advanced data analysis methods provides WRRFs with an opportunity for efficient wastewater quality monitoring and an early warning tool for plant upsets.

Integrated watershed modeling is needed to couple water resource recovery facilities (WRRFs) with agricultural management for holistic watershed nutrient management. Surrogate modeling can facilitate model coupling. This study applies artificial neural networks (ANNs) as surrogate models for WRRF models to efficiently evaluate the long-term treatment performance and cost under influent fluctuations. Specifically, we first developed five WRRFs, including activated sludge, activated sludge with chemical precipitation (ASCP), enhanced biological phosphorus removal (EBPR), EBPR with acetate addition (EBPR-A), and EBPR with struvite recovery (EBPR-S), in a high-fidelity simulation program (GPS-X). The five WRRFs were based on an existing plant that treats combined domestic and industrial wastewater. The ANNs have satisfactory performance in capturing nonlinear biological behaviors for all five WRRFs, even though the prediction performance (R-square) slightly decreases as the model complexity increases. We advanced ANNs application in WRRF models by simulating long-term (10-yr) performance with monthly influent fluctuations using ANNs trained by simulation data from steady-state models and evaluated their performance on Phosphorus (P) and Nitrogen (N) removal. EBPR-S shows the most resilience, while EBPR is more sensitive to influent characteristics impacted by stormwater inflow. When comparing life cycle costs of N and P removal for each layout over the 10-yr simulation period, EPBR-S is the most cost-effective alternative, highlighting both the operational and cost benefits of side-stream P recovery. By capturing both nonlinear behaviors of biological treatment and operating costs with computationally lean ANNs, this study provides a paradigm for integrating complex WRRF models within integrated watershed modeling frameworks.

Anthropogenic discharge of excess phosphorus (P) to water bodies and increasingly stringent discharge limits have fostered interest in quantifying opportunities for P recovery and reuse. To date, geospatial estimates of P recovery potential in the United States (US) have used human and livestock population data, which do not capture the engineering constraints of P removal from centralized water resource recovery facilities (WRRFs) and corn ethanol biorefineries where P is concentrated in coproduct animal feeds. Here, renewable P (rP) estimates from plant-wide process models were used to create a geospatial inventory of recovery potential for centralized WRRFs and biorefineries, revealing that individual corn ethanol biorefineries can generate on average 3 orders of magnitude more rP than WRRFs per site, and all corn ethanol biorefineries can generate nearly double the total rP of WRRFs across the US. The Midwestern states that make up the Corn Belt have the largest potential for P recovery and reuse from both corn biorefineries and WRRFs with a high degree of co-location with agricultural P consumption, indicating the untapped potential for a circular P economy in this globally significant grain-producing region.

No abstract

Monitoring biological nutrient removal (BNR) processes at water resource recovery facilities (WRRFs) with data-driven models is currently limited by the data limitations associated with the variability of bioavailable carbon (C) in wastewater. This study focuses on leveraging the amperometric response of a bio-electrochemical sensor (BES) to wastewater C variability, to predict influent shock loading events and NO 3 − removal in the first-stage anoxic zone (ANX1) of a five-stage Bardenpho BNR process using machine learning (ML) methods. Shock loading prediction with BES signal processing successfully detected 86.9% of the influent industrial slug and rain events of the plant during the study period. Extreme gradient boosting (XGBoost) and artificial neural network (ANN) models developed using the BES signal and other recorded variables provided a good prediction performance for NO 3 − removal in the ANX1, particularly within the normal operating range of WRRFs. A sensitivity analysis of the XGBoost model using SHapley Additive exPlanations indicated that the BES signal had the strongest impact on the model output and current approaches to methanol dosing that neglect C availability can negatively impact nitrogen (N) removal due to cascading impacts of overdosing on nitrification efficacy.

Interconnected food, energy, water systems (FEWS) require systems level understanding to design efficient and effective management strategies and policies that address potentially competing challenges of production and environmental quality. Adoption of agricultural best management practices (BMPs) can reduce nonpoint source phosphorus (P) loads, but there are also opportunities to recover P from point sources, which could also reduce demand for mineral P fertilizer derived from declining geologic reserves. Here, we apply the Integrated Technology-Environment-Economics Model to investigate the consequences of watershed-scale portfolios of agricultural BMPs and environmental and biological technologies (EBTs) for cobenefits of FEWS in Corn Belt watersheds. Via a pilot study with a representative agro-industrial watershed with high P and nitrogen discharge, we show achieving the nutrient reduction goals in the watershed; BMP-only portfolios require extensive and costly land-use change (19% of agricultural land) to perennial energy grasses, while portfolios combining BMPs and EBTs can improve water quality while recovering P from corn biorefineries and wastewater streams with only 4% agricultural land-use change. The potential amount of P recovered from EBTs is estimated as 2 times as much as the agronomic P requirement in the watershed, showing the promise of the P circular economy. These findings inform solution development based on the combination of agricultural BMPs and EBTs for the cobenefits of FEWS in Corn Belt watersheds.