As more stringent requirements to eliminate Sanitary Sewer Overflows (SSOs) are enforced, peak flow management becomes a more challenging task for wastewater treatment managers. The simplest approach to peak wet weather flow management, from a regulatory point of view, is onsite storage, where excess influent flows are diverted to side-stream storage basins to prevent the washout and long-term effects on biological processes. In the past, methods of sizing peak flow storage ranged from using a 5-year 24-hour storm hydrograph, to developing longer periods of wet weather events and calculating the resulting flows due to back-to-back storm events. This new methodology is intended to identify a storage volume that balances construction costs with the level of risk for potential storm events. It allows a utility to evaluate storm event coverage against the cost to build storage.

Membrane Aerated Biofilm Reactor (MABR) and continuous flow densification employ unique mechanisms to deliver process intensification. MABR uses a gas-transfer membrane for oxygen delivery that also serves as a biofilm support to grow a nitrogen removal biofilm that reduces the tank volume required for nitrogen removal. Continuous flow densification selects for densified biomass with improved settling characteristics to increase secondary clarifier throughput. These two intensification approaches have been successfully applied independently. This paper explores how combining the technologies delivers complementary benefits and has the potential to yield process synergies that deliver more value for utilities.

The Living with the Bay Resiliency Strategy helped New York State administer HUD's Community Development Block Grant-Disaster Recovery (CDBG-DR) funds. More specifically, it focused on mitigating local risk from flooding, while incorporating environmental co-benefits such as water quality improvements, ecological restoration, and aquifer recharge. In addition, social co-benefits were considered such as creating public access to the waterfront, reconnecting communities with nature, and public education.

This manuscript summarizes the development and implementation of the forecasting tool for Fort Wayne City Utilities’ collection system model. The tool uses an embedded Python script in PCSWMM to obtain the NOAA rainfall forecast and NWS river stage forecast to simulate 48 hours into the future for operational insight. In addition to the forecasting tool, a cloud-based Azure Function was developed to run the model in the cloud and reduce wear and tear on City computers. Located at the confluence of the St. Marys, St. Joseph, and Maumee Rivers, the collection system is influenced by high river stages. The NWS river forecast is obtained by a simple web scraping technique while the NOAA forecasts are obtained using the MetPy library (Unidata, 2021). The three forecasts are then formatted to PCSWMM’s structure, automatically added to the model as rainfall, climate, and river stage time series files, with the model simulation time set to the reflect the forecast period. Figure 2 presents City Utilities’ model and the embedded script, which can be used to automatically run the model after obtaining the forecast. Similar to many combined sewer communities with a Consent Decree, City Utilities frequently simulates a typical 5-year period as part of assessing combined sewer overflow (CSO) controls. To ease the burden on City Utilities’ staff laptops, an Azure Function was developed to run the model in the Microsoft Azure cloud environment as a lower-cost alternative to a virtual machine (VM).

This manuscript summarizes the past 18 months of deploying a predictive operational dashboard leveraging machine learning (ML) to forecast inflow into Citizens Energy Group’s DigIndy Tunnel system. We will present our lessons learned in the deployment of the ML tool in the cloud, managing the ML tool, and the integration of meter data to evaluate the accuracy of the forecast. Citizens Energy Group is the combined water, wastewater, natural gas, steam, and chilled water utility in Indianapolis. Citizens is presently implementing a 20-year CSO consent decree with required completion in 2025. Consent decree compliance is primarily achieved through the DigIndy Tunnel system, a 28-mile long, 18-foot diameter tunnel network. Citizens is also implementing a pair of storage facilities in the Upper White River and Upper Pogues Run watersheds. Our ML tool consists of an artificial neural network fit to long-term datasets to predict inflows to the DigIndy Tunnel using the 72-hour NOAA radar rainfall forecast (Sutton et. al, 2022). The neural network and automated tools to obtain actual gauged rainfall and metered tunnel inflow are fully contained within Citizens’ Azure Synapse enterprise cloud environment, with data storage in Azure Data Lake. The forecasted rainfall and tunnel inflow and actual data is presented in an online Power BI dashboard hosted in Citizens’ enterprise architecture that is accessible to all engineering and operations staff.

Selectively wasting poorer settling filaments and lighter mixed liquor particles and exposing mixed liquor to high F/M unaerated selectors can promote better settling sludge that can also yield additional benefits. Such benefits may include selective retainment of microorganisms that are beneficial for biological nutrient removal (BNR) and a reduction in pathogens that must be disinfected. This paper summarizes results from a pilot study of selective sludge wasting using hydrocyclones at a water resource recovery facility (WRRF) in Wichita, Kansas including improved settleability, more robust nitrification, and reduced E. coli counts in the clarified effluent. This paper also presents the design criteria and layout for a full-scale hydrocyclone facility that will be constructed from 2024-2027 as part of a BNR upgrade at the City’s Plant 2 WRRF.

This study compares the RTK, AMM, GIM, NAM, and QvI models’ ability to accurately predict RDII for 10 selected sites across the U.S. and 975 identified rain events. Tests were conducted using automated optimization to measure the goodness of fit, tendency to overfit, extrapolation performance, and accuracy of calibrating to a short data period. The AMM models were most accurate overall, but not without important tradeoffs. Results emphasize thoughtful model selection and the careful use of modeler judgment to reduce overfitting. This also provides a common dataset and framework for benchmarking other models in future study.

Wastewater collection and treatment system plays a critical role in facilitating sustainable urban environment. Maintaining complex wastewater infrastructure, with varied range of assets and functionalities, is extremely challenging. For the City of Houston (COH), providing quality service in the wastewater sector is even more demanding due to rapidly increasing population and diverse industrial and commercial growth. To confront the challenge with modern technology, COH envisioned an intelligent planning tool to prioritize and optimize rehabilitation of its gravity pipe network by integrating asset inventory, field inspections and machine-learning models into a data-driven predictive analytics platform. This paper illustrates the underlying modules of the tool with their analytical framework. It also explains application of the tool to analyze future risk levels in different scenarios and optimize rehabilitation strategies.

The utility industry is faced with the challenge of providing reliable treatment, distribution, and collection of water/wastewater services to a large infrastructure of customers, while being good environmental stewards. Thousands of assets comprise the treatment, distribution, and collection system across multiple facilities. Standard equipment operation is critical to providing customers with high quality water/wastewater services that meet regulatory requirements. To ensure proper operation of critical equipment, a PM strategy is utilized at KUB. Multiple resources are utilized when executing preventive maintenance, such as labor, materials, and equipment, among others. Aligning resources in an optimized manner can be challenging. A common method for solving optimization problems such as scheduling is linear programming. This paper discusses the utilization of a linear program to define an optimized PM schedule for the water and wastewater treatment plants, and the sustainment of the gains through tracking KPIs.

AECOM partnered with the City of Penticton (Canada) to investigate various process modifications to its existing biological nutrient removal (BNR) process to promote the growth and retention of granular sludge. The continuous flow AGS demonstration pilot utilizes two completely independent trains, operated side-by-side. The pilot train configuration was modified to include an inDENSE hydrocyclone skid to selectively waste floccular biomass. The pilot train transitioned to a hybrid-floccular sludge, achieving an average SVI-30 of 70 -75 mL/g. Based on the particle size distribution analysis the average particle size in the pilot train was 210 µm vs 86 µm in the control train. Assuming the threshold size for granules is 200 µm, the granules composed approximately 12% in the control train vs 44% in the pilot train. The pilot also indicated that operating the last aerobic cells at low DO to maximize SND significantly reduced effluent TN by reducing nitrate concentration.

The Santa Clara Valley Water District (Valley Water) initiated a 2018 planning effort to expand existing recycled water distribution systems and develop up to 24,000 acre-feet per year (AFY) of purified water by 2040 to augment potable supplies. To reach this goal, Valley Water partnered with local recycled water producers to create the Countywide Water Reuse Master Plan (CoRe Plan) in 2021, a roadmap for advancing potable and non-potable reuse by integrating past studies and new opportunities. Successful CoRe Plan implementation is supported by two key strategies: flexibility and collaboration. Flexibility in implementation means right-sizing infrastructure to meet near-term demands and lessen immediate rate impacts while including modular features to enable a variety of future options for direct potable reuse expansion. Collaboration in implementation means working with the public, recycled water producers, regulators, water retailers, and reuse experts.

With the proliferation of artificial intelligence, machine learning, and other advanced computing solutions, utilities are looking to take advantage of the massive amounts of data at their disposal by using it to make real-time decisions that improve operational reliability and performance. But when is an organization ready to adopt a data-centered culture? This paper will discuss what’s needed for the successful adoption of advanced data analytics tools and share the results of a pilot project with the goal to transform a wastewater utility’s maintenance strategies from reactive to proactive using a predictive analytics software that leveraged the data collected by an existing process control system. The objectives of the pilot were: 1) to determine whether operational deviations could be detected ahead of conventional process control system alarms, and 2) to provide actionable intelligence prior to equipment/operational failure.