With the abundance of data availability across treatment facilities, artificial intelligence is paving the way for unique optimization opportunities. The examples provided encompass the Agua Nueva Water Reclamation Facility (WRF), which concentrates on minimizing aeration energy expenses and enhancing nutrient management, as well as the Wilmington WWTF, emphasizing the optimization of disinfection chemicals. Several artificial intelligence (AI) and Bayesian modeling frameworks were developed with both sites using AI algorithms with mean absolute percentage errors < 10% for both forecasting of future conditions at a facility, as well as causal inferencing for predicting water quality. Post deployment, both sites are regularly achieving anywhere from 10-30% savings in energy or chemical usage. These case-study demonstrates significant progress in the successful implementation of AI at a treatment facility and the ability to aid in the empowerment of treatment plant operators while improving efficiencies in wastewater treatment.

Blue Plains Advanced wastewater treatment plant (AWWTP) was the first utility in the USA to install Cambi thermal hydrolysis process (THP) together with anaerobic digester (AD) to reduce solids production, increase energy resource recovery and produce class A biosolids. The new facilities were commissioned at the Blue Plains Advanced Wastewater Treatment Plant (AWTP) in 2015. The technology choice provided a major benefit on dewatering process by improving the cake TS, which has a large economic impact on treatment operations by decreasing the hauling costs, and by improving the quality of the "Bloom” biosolids product that is regionally marketed (Li, 2021). Dynamic changes in operational condition of the THP and AD process, sludge characteristics, and seasonal impacts (Li, 2021) result in high variety of cake TS (%) from 24 to 38% TS and filtrate TSS from 50 to 1530 mg TSS/L at the final dewatering belt filter presses. Poor cake TS causes higher hauling costs, while unreliable filtrate TSS significantly impacts the filtrate treatment facility. In addition, the polymer cost also may increase year by year depending on the market price, so picking the right optimal polymer dose range can optimize operational cost. In practice, the polymer dose is set manually by the operator and requires constant attention to minimize polymer dose while maintaining good operational throughput. Polymer dose decision making is thus mainly driven by human factors. Therefore, this study focused on investigating if the polymer dosing decision made by operators is adequate or if there is room to improve dewatering performance, which helps to define research development needs for helping optimizing polymer costs at the plant.

The primary source of nitrogen and phosphorus in municipal wastewater treatment plants is human urine. One of the primary concerns associated with the use of urine-based fertilizers in agriculture is the presence of micropollutants, including pharmaceutical residues, which are co-excreted with nutrients in urine. The present study experimented sewage sludge biochar pyrolyzed at a temperature of 700°C and its modified form using dimethyl sulfoxide (DMSO) for selective adsorption of antibiotics in human urine. The adsorption isotherm and kinetics can be adequately described by the Langmuir and pseudo-second order, respectively. The maximum adsorption capacities of azithromycin, sulfamethoxazole, trimethoprim, and tetracycline were found to be 385, 270, 357, and 1,000 μg/g, respectively.

The City of Sunnyvale Water Pollution Control Plant (WPCP) is a 12 MGD Average Dry Weather Flow tertiary wastewater treatment facility (1). As part of the secondary system, the plant operates oxidation ponds (2) for removal of organic matter and some ammonia. The ponds are rich in nutrients and as such propagates algae. Prior to 2021, the dominant algae species in the ponds were nano-, micro- and mesoplankton species that consisted of some cyanobacteria, protozoa, small algae, and some large algae ranging in size from 2 micron to 2 mm. These species served the plant well with photosynthesis providing needed oxygen for the oxidation reactions and nutrient uptake by algae. During the early part of 2021, the ponds became dominated by picoplankton species, namely a unicellular cyanobacterium (also known as bluegreen algae(3) ) named Synechocystis with a size range of 0.2 to 2 micron. Granted Synechocystis continued to serve the plant well with photosynthesis and nutrient uptake, however, it upended our plant by not allowing itself to be removed in our Dissolved Air Flotation (DAF) tanks where the pond effluent is treated for solids/liquid separations. As a result, the plant violated its NPDES permit limit on multiple occasions for solids discharging to the lower San Francisco Bay(4), resulting in possible fines to the City. This paper discusses the research work conducted this year in terms of water chemistry at the DAF in determining the most optimal condition to remove Synechocystis from the pond effluent. This effort not only prevented the plant from violating the NPDES permit limit for solids again but also provided the City with a cost-effective solution.

With the abundance of data availability across treatment facilities, artificial intelligence is paving the way for unique optimization opportunities. The examples provided encompass the Agua Nueva Water Reclamation Facility (WRF), which concentrates on minimizing aeration energy expenses and enhancing nutrient management, as well as the Wilmington WWTF, emphasizing the optimization of disinfection chemicals. Several artificial intelligence (AI) and Bayesian modeling frameworks were developed with both sites using AI algorithms with mean absolute percentage errors < 10% for both forecasting of future conditions at a facility, as well as causal inferencing for predicting water quality. Post deployment, both sites are regularly achieving anywhere from 10-30% savings in energy or chemical usage. These case-study demonstrates significant progress in the successful implementation of AI at a treatment facility and the ability to aid in the empowerment of treatment plant operators while improving efficiencies in wastewater treatment.

Solids thickening is an indispensable unit process for process intensification of biosolid s treatment. The fluctuations of feed sludge characteristics often impact the stability of the thickening process, reduce process capability, and increase operation and maintenance costs. Often, the optimization of sludge thickening is focused on meeting performance requirements as detailed in specifications. This study expands the assessment of sludge thickening by including both thickening equipment and its up- and downstream infrastructure from a process capability perspective. The presentation will benefit engineers and utility operators in better design and troubleshooting of sludge thickening processes with designed thickened sludge solids total equal to or higher than 6%. The presentation will provide practical suggestions for improving the process capabilities of sludge thickening, including pumping improvement, pipe flushing using digested sludge, and improvements on sensors.

Water utilities are under increasing pressure to maintain plant performance while reducing operational cost and carbon, due to population density, ageing infrastructure and climate change. Digital technology can address these challenges through the application of data science and advanced analytics. The Rosedale Wastewater Treatment Plant in Auckland, New Zealand, consists of primary, secondary, and tertiary treatment and serves a population equivalent of 240,000. After creating a digital twin to support commissioning of a fourth reactor in 2020, it was identified that the digital twin could provide new insights into operational cost and greenhouse gas and support decisionsmaking, to help Watercare achieve their objective to reduce operational greenhouse gas by 50%. Live dashboards were developed to provide new insights into operational greenhouse gas emissions and cost which leveraged real-time biological modelling and emissions monitoring data. Soft sensors were developed to improve the instrumentation data quality through early identification of sensor drift.

A major source of pharmaceuticals in the environment is the discharge of wastewater contaminated with active pharmaceutical ingredients (API) at manufacturing facilities. API wastewaters are often segregated and trucked offsite for disposal. Electrochemical oxidation (EOx) allows onsite treatment of pharmaceutical wastewaters including stable fluorinated APIs without generating secondary waste streams, eliminating the need for offsite disposal. The EOx process can break extremely stable bonds and is ideally suited for API removal. Fundamentals of electrochemical oxidation, catalyst selection, and typical operating conditions are reviewed. EOx systems are typically operated as batch reactors with first order kinetics dominating API removal rate, and complementary processes can improve performance. Process trains integrating EOx with concentration/separation technologies are illustrated. A case study and operating data are presented of an API removal system currently operating at a Fortune 500 pharmaceutical manufacturer utilizing an integrated ultrafiltration, reverse osmosis, and EOx treatment train.

This paper presents the integrated approach by the City of Austin to address odor concerns in the Greater Walnut Creek Area and its sewershed. The project focused on the identification of the odor sources and their potential impact on the community using dispersion modeling. The project also used sewer process models to comprehend the entire collection system to determine H2S hot spots and to screen sulfide control strategies considering plant impacts. Results showed that the baseline scenario for the Walnut Creek WWTP exceeded the odor threshold set forth. The City is going through an expansion of the Walnut Creek WWTP that incorporates these findings into the design of odor control facilities to mitigate outside impacts. The resulting optimized strategy for the collection system relies on the use of pH adjusters; the WWTP currently uses it in its process thus representing ‘free’ chemical by dosing it upstream for odor control.

Renewable Water Resources (ReWa) in Greenville, SC, initiated a dewatering evaluation in 2021 to explore alternatives for dewatering anaerobically digested biosolids from three of their Water Resource Recovery Facilities, Mauldin Road, Pelham and Lower Reedy. The objective of this project was to provide engineering services for evaluation of the remaining useful life or replacement of existing belt filter press (BFP) sludge dewatering equipment with new BFP, centrifuge, or screw press dewatering equipment. Bench Scale testing was conducted considering several technologies and onsite pilot testing of a centrifuge was conducted and compared to the existing BFP. A unique part of the study included comparing the fecal coliform regrowth and odor potential from both the centrifuge cake and BFP biosolids. All facilities showed fecal coliform regrowth and increased total volatile organic sulfur compounds with centrifuge dewatering, as expected, but one facility also showed regrowth with belt filter press dewatering.

DC Water’s Potomac Interceptor system has suffered from corrosion for many years. According to recent CCTV inspection, approximately 95% of the Potomac Interceptor has shown signs of corrosion. Inputting field data to sewer process models that integrate ventilation resulted in recommendations focusing on the operation of existing forced ventilation odor control systems to support reduction in corrosion. These recommendations have been adopted in the PI operation protocol. In addition, DC Water developed a long-term corrosion prevention program implementing various corrosion mitigation measures identified in the case study.

Ammonia is known to be acutely toxic to fish and other aquatic organisms at low concentrations. In effort to predict regulatory non-compliance due to ammonia discharged from two of Metro Vancouver’s wastewater treatment plants, regression models were developed to project final effluent ammonia concentrations at each facility over a 25-year period. These ammonia projections were compared with regulatory requirements, and the risk of non-compliance was quantified over the period under various operational scenarios, such as increased centrifuge operation and per capita flow reduction. The risk assessment concluded that some degree of noncompliance was observed in most scenarios evaluated; to target full compliance with the regulations it is estimated that a 13% reduction in effluent ammonia at LIWWTP and a 22% reduction at AIWWTP under baseline conditions is required. At both facilities, implementing side stream ammonia removal on the dewatering centrate stream would increase compliance to 100% at baseline conditions.