Mechanistic and data-driven modeling of carbon respiration with bio-electrochemical sensors.

Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs.

Integrating Bio-Electrochemical Sensors and Machine Learning to Predict the Efficacy of Biological Nutrient Removal Processes at Water Resource Recovery Facilities.

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 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.

Integrated Agricultural Practices and Engineering Technologies Enhance Synergies of Food-Energy-Water Systems in Corn Belt Watersheds.

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 co-benefits of FEWS in Corn Belt watersheds.

Mapping the National Phosphorus Recovery Potential from Centralized Wastewater and Corn Ethanol Infrastructure.

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.

Gaining deeper insights into the bioflocculation process occurring in a high loaded membrane bioreactor used for the treatment of synthetic greywater.

In the present study, a high loaded membrane bioreactor (HL-MBR) operated at a hydraulic retention time (HRT) of 1.5 h, and three different sludge retention times (SRTs) in the range of 0.5-2 days, was used for the treatment of synthetic greywater.