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Hybrid models are powerful predictors of performance in Water Resource Recovery Facilities (WRRFs). When used in controllers, hybrid models can help inform manipulated variable setpoints in feedforward controllers. This idea was tested for chlorine disinfection and ammonia-based aeration control.
Digital twins (DTs) are on the rise in the wastewater treatment industry and, with ever-increasinguse of online data in more complex applications, it is bringing us towards a digital revolution withinwater resource recovery facilities (WRRFs). An integral data requirement is dynamic real-timeinfluent concentrations. However, few WRRFs have the ability to deploy sensors to generate therequired dynamic influent concentrations. This paper presents an innovative approach to generatedynamic influent profiles with a soft sensor primarily using bioreactor airflow rates and DT models.It has been implemented on three different full-scale facilities and demonstrate it is feasible toretrieve 15-minute interval diurnal influent concentrations. This study overcomes a key bottleneckin DT applications - the lack of realistic high-resolution influent profiles - fostering the developmentof DTs. The fact that the data used in this study are commonly available ensures wide applicabilityfor most WRRFs and prepares them for the advanced controls that digital twins can unlock.
Rotating algae biofilm reactors (RABRs) can reduce energy requirements for wastewater reclamation but require further optimization for implementation at water resource recovery facilities (WRRF). Optimizing RABR operation is challenging because conditions at WRRF change frequently, and disregarding interaction terms related to these changes can produce incorrect conclusions about RABR behavior. This study evaluated the two-way interaction and main effects of four factors on the biomass productivity and phosphorus removal efficiency of a microalgae-bacteria biofilm grown in municipal anaerobic digester centrate, with factor levels and operating conditions selected to mimic a pilot RABR at a WRRF in Utah. Two-way interactions harvesting period*light intensity (LI), harvesting period*temperature, and LI*hydraulic retention time (HRT) had significant effects on biomass productivity: at high temperature and low LI, highest biomass productivity was achieved with a 14-day harvesting period, but at medium temperature and high LI, highest biomass productivity was achieved with a 7-day harvesting period. At high HRT, highest biomass productivity occurred at low LI, but at low HRT, highest biomass productivity occurred at high LI. Phosphorus removal was strongly influenced by LI and occurred most rapidly during the first 2 days HRT, which suggests precipitation contributed significantly to phosphorus removal. These observations provide insight for further RABR optimization.
Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.
A case study was developed for the fiscal year 2024 to examine the potential of wastewater-grown microalgae as a feedstock for hydrothermal liquefaction (HTL) to produce fuels and other products. In this year’s case study, scenarios are investigated in which the algae cultivation process is placed at different points within the water resource recovery facility (WRRF). Additionally, the supply of biomass for HTL is boosted by blending with wastewater solids collected at different locations within the WRRF.
Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.
Hydrothermal liquefaction (HTL) has the potential to improve resource recovery at water resource recovery facilities (WRRF), but the production of a high-strength aqueous by-product (HTL-aq) is hampering HTL implementation. The formation of biofilms in anaerobic digestion have been shown to be useful when degrading recalcitrant compounds present in HTL-aq due to the promotion of direct interspecies electron transfer (DIET) and increase in the microbial activity of syntrophic and methanogenic populations. The Recirculating Anaerobic Dynamic Membrane Bioreactor (RAnDMBr) was able to degrade 65% of the chemical oxygen demand (COD) at 1.5 ± 0.2 g COD LR −1 day −1 and 5.6 ± 2.3 days producing 0.19 ± 0.02 LCH4 gCOD fed −1 . However, adding a solution rich in nutrients on a daily basis was necessary. The system presented microbial populations able to degrade aromatic compounds (i.e., Anaerolinaceae) to perform DIET and syntrophy (i.e., Syntrophus) and methanogens (i.e., Methanobacterium and Methanosarcina) with the biofilm having a higher relative abundance of methanogens than the suspended biomass. Increasing the organic loading rate to 2 g COD LR −1 day −1 caused inhibition in the system by accumulation of volatile fatty acids, probably due to an increase in phenol, N-heterocyclic and aromatic compounds. Overall, this research shows that the RAnDMBr can be used to treat HTL-aq in WRRF without inhibition at OLRs of 1.5 ± 0.2 g COD LR −1 day −1 or lower, making HTL-aq treatment more feasible. Future research should focus co-digestion of HTL-aq with a co-substrate rich in nutrients and on fouling mitigation strategies that will allow to increase the recirculation ratio to promote advective substrate transport through the biofilm.
Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine a full-scale, intensive WRRF utilizing mixed microalgae for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (D INVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55 %-80 %) that achieved strict nutrient removal (effluent TP < 0.04 mg·L -1 ) and steady biomass concentration (TSS monthly avg . = 400–700 mg·L −1 ). Operational variables including pH, alkalinity, and influent ammonium (NH 4 + ), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO 2 − accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Altogether, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA).
This chapter provides a comprehensive overview of the main steps for algorithmic sensor signal quality assessment, which can enhance the decision-making process for water resource recovery facility (WRRF) operation and optimization. It introduces the concept of redundancy as the basis for data quality assessment. It also explains the typical data processing pipeline, which consists of preliminary analysis, data pre-processing, and specific algorithmic approaches. Each of these processes is presented and discussed in three separate sections. Importantly, this chapter introduces the main approaches for data quality assessment, provides guidelines for selecting the most suitable one and the key performance indicators to evaluate them and explains how to collect metadata through such an algorithmic approach.
PNNL has developed Hydrothermal Liquefaction (HTL) technology, which can directly convert sewage sludge to a biocrude oil that can be refined into renewable diesel fuel and provide a significant reduction in the volume of biosolids produced as ash with far less potential environmental impacts associated with disposal. HTL also produces an aqueous byproduct stream that requires treatment before it can be returned to the headworks of the Wastewater Resource Recovery Facilities (WRRF). The goal of this project is to evaluate synergies of using HTL and advanced AD to enable the treatment and recycle of HTL aqueous phase. This CRADA will; 1) formalize and strengthen the working relationship between PNNL and GLWA, 2) jointly evaluate HTL and AD scenarios for WRRFs and 3) help enable the implementation of HTL/AD technology at GLWA and other WRRFs.
The fiscal year (FY) 2021 State of Technology (SOT) Assessment for the hydrothermal liquefaction (HTL) of wastewater (WW)-grown microalgae and biocrude upgrading system was completed and reported here. An industrial partner, Gross-Wen Technologies (GWT), provided algae feedstock cultivated on a revolving algal biofilm (RAB) system by using the primary effluent from a water resource recovery facility (WRRF). This provided algae was tested at PNNL for HTL processing. The experimental results provided the major design basis of the HTL process of the SOT baseline case. The primary effluent of the Metropolitan Water Reclamation District (MWRD) of Greater Chicago was assumed to be the nutrients source for algae growth and the algae yield data per gallon wastewater provided by GWT were used to estimate the total algae production rate and thus the HTL conversion plant scale. Considering different cultivation technologies and wastewater streams with different flow rates and nutrients contents can be used to produce algae, this SOT assessment just provided an example case study for WW-grown algae based HTL conversion to fuels systems. A preliminary economic analysis was developed based on process simulation results. Sensitivity analysis was implemented to evaluate cost impacts of plant scales, potential cost improvements and other key factors.
Hydrothermal liquefaction (HTL) is a thermal process that converts wet biomass to renewable hydrocarbon fuel blendstocks (i.e., renewable naphtha, renewable diesel, and sustainable aviation fuel (SAF)). It can utilize a wide range of pure and blended wet feedstocks, including sewage sludge from water resource recovery facilities (WRRF), food and agriculture wastes, algae, fats, oils and greases (FOG) and blends of dry and wet wastes/feedstocks. Historically, techno-economic analysis (TEA) and annual state of technology (SOT) assessments with standard economic assumptions used by the Bioenergy Technologies Office (BETO) were conducted for the wet waste HTL pathway leveraging experimental data collected from Pacific Northwest National Laboratory’s (PNNL) continuous flow reactor systems. The objective of the SOT assessment has been to guide and track progress of BETO’s HTL research and development (R&D) toward reduced cost and greenhouse gas (GHG) emissions for the pathway. However, gaps exist between BETO’s traditional SOT updates and the needs of key external stakeholders that – if addressed – will accelerate technology adoption. This Business Case Study aims to bridge this gap by providing an updated design, TEA, and LCA based on PNNL’s FY23 R&D with added analyses and information that provide enhanced relevance for stakeholders of the HTL technology. This includes specific siting, regional wet waste resource inventory and transportation cost analyses, fuel market information, sustainable fuel policy impacts, economic metrics of net present value (NPV) and internal rate of return (IRR), greenhouse gas (GHG) emissions analysis, and statistical analysis of cost and technical uncertainties of the HTL plant design. The study focuses on the “Detroit combined statistical area (CSA)” region for siting of a wet waste HTL plant adjacent to the Great Lakes Water Authority (GLWA) facility with guidance from industry participants. Regional resource and siting analyses were conducted to identify feedstock availability, scale, and cost, as well as a beneficial site location. TEA with detailed rigorous capital cost estimation for the specific site application was conducted to evaluate the key economic metrics of most value to industrial partners. These include total capital investment, operating costs, minimum fuel selling price (MFSP) of the biocrude and fuel blendstock, and NPV and internal rate of return IRR with sustainable fuel credits. Life cycle analysis was conducted to evaluate the supply chain greenhouse gas (GHG) emissions for the wet waste HTL process as compared with petroleum derived diesel. This study is also informed by years of R&D and process de-risking learnings and was conducted with a basic engineering HTL plant design and costing that akin to a “first-of-a-kind” plant economics. This differs from our conventional “nth plant ” SOT assessments. Specifically, the HTL process model has been updated with more operationally reliable methods for feed heating and phase separations. Further, we have implemented additional spare equipment for redundancy, a more rigorous installed equipment cost estimation approach, and additional costs associated with feed formatting and delivery, building, piping and site development. An Excel-based cost sheet based on the basic engineering design is also released alongside the report that allows users to conduct customized TEA with their own feed composition and financial assumptions.
Hydrothermal liquefaction (HTL) is a thermal process that converts wet biomass to renewable hydrocarbon fuel blendstocks (i.e., renewable naphtha, renewable diesel, and sustainable aviation fuel (SAF)). It can utilize a wide range of pure and blended wet feedstocks, including sewage sludge from water resource recovery facilities (WRRF), food and agriculture wastes, algae, fats, oils and greases (FOG) and blends of dry and wet wastes/feedstocks. Historically, techno-economic analysis (TEA) and annual state of technology (SOT) assessments with standard economic assumptions used by the Bioenergy Technologies Office (BETO) were conducted for the wet waste HTL pathway leveraging experimental data collected from Pacific Northwest National Laboratory’s (PNNL) continuous flow reactor systems. The objective of the SOT assessment has been to guide and track progress of BETO’s HTL research and development (R&D) toward reduced cost and greenhouse gas (GHG) emissions for the pathway. However, gaps exist between BETO’s traditional SOT updates and the needs of key external stakeholders that – if addressed – will accelerate technology adoption. This Business Case Study aims to bridge this gap by providing an updated design, TEA, and LCA based on PNNL’s FY23 R&D with added analyses and information that provide enhanced relevance for stakeholders of the HTL technology. This includes specific siting, regional wet waste resource inventory and transportation cost analyses, fuel market information, sustainable fuel policy impacts, economic metrics of net present value (NPV) and internal rate of return (IRR), greenhouse gas (GHG) emissions analysis, and statistical analysis of cost and technical uncertainties of the HTL plant design. The study focuses on the “Detroit combined statistical area (CSA)” region for siting of a wet waste HTL plant adjacent to the Great Lakes Water Authority (GLWA) facility with guidance from industry participants. Regional resource and siting analyses were conducted to identify feedstock availability, scale, and cost, as well as a beneficial site location. TEA with detailed rigorous capital cost estimation for the specific site application was conducted to evaluate the key economic metrics of most value to industrial partners. These include total capital investment, operating costs, minimum fuel selling price (MFSP) of the biocrude and fuel blendstock, and NPV and internal rate of return IRR with sustainable fuel credits. Life cycle analysis was conducted to evaluate the supply chain greenhouse gas (GHG) emissions for the wet waste HTL process as compared with petroleum derived diesel. This study is also informed by years of R&D and process de-risking learnings and was conducted with a basic engineering HTL plant design and costing that akin to a “first-of-a-kind” plant economics. This differs from our conventional “nth plant ” SOT assessments. Specifically, the HTL process model has been updated with more operationally reliable methods for feed heating and phase separations. Further, we have implemented additional spare equipment for redundancy, a more rigorous installed equipment cost estimation approach, and additional costs associated with feed formatting and delivery, building, piping and site development. An Excel-based cost sheet based on the basic engineering design is also released alongside the report that allows users to conduct customized TEA with their own feed composition and financial assumptions.
A structured approach for recording data quality and contextual information about how and why a signal exists – i.e. metadata – is central to interpret and use sensor data correctly. This is becoming increasingly important with the global trend with data-driven applications such as digital twins and AI-models. But a structured metadata collection and organization of sensor data is not routine in most plants, which can result in lost information and missed opportunities to make use of the investments made in the data collection. Therefore, the IWA task group on Metadata Collection and Organization in wastewater resource recovery systems (MetaCO) was initiated in 2020 and recently delivered the IWA scientific and technical report number 31. The report gives and in-depth description about metadata in water resources recovery facilities (WRRFs) and is available as open access at IWA publishing. The report is the outcome of the collaboration between more than 80 water professionals with the intention to serve WRRF data users with a guide on how to structure and make use of metadata throughout the data pipeline in order to maximize the value of sensor data.
On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. Here, we develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.
Proportional-integral-derivative (PID) controllers in water resource recovery facilities (WRRFs) feedback control loops are commonplace. While simple to implement, such control loops are rarely tuned optimally or systematically. Heuristic tuning approaches are commonly applied with varying degrees of success using trial-and-error, ad hoc tuning rules, or duplication of tuning values from a similar system. However, there are effective methods, such as lambda tuning, produce acceptable tuning with limited effort. These are based on the step-response method, where a manual process perturbation is used to define the relationship between the manipulated and controlled variables. Based on such an experiment, a simple process model is constructed and used to determine the controller tuning values. In this work, we used the step-response method and lambda tuning for two control systems in full-scale WRRFs. This led to responsive and stable behavior of the controlled system as defined by the absolute average error of the controlled variable to setpoint and standard deviation of the manipulated variable. Tuning of feedback control loops can be completed successfully through a systematic approach, and this work suggests that tuning tools, like lambda, should be part of all wastewater treatment control engineers' toolbox.