Predictive models using "cheap and easy" field measurements: Can they fill a gap in planning, monitoring, and implementing fecal sludge management solutions?

Rethinking the relationships between gel like structure and sludge dewaterability based on a binary gel like structure model: Implications for the online sensing of dewaterability

The digital transformation of sludge treatment processes requires online sensing of dewaterability. This topic has been attempted for many years based on macroscopic shear rheology. However, the relationship between rheological behavior and dewaterability remains noncommittal, and the reason is unclear. Herein, a binary gel-like structure model was proposed including the interactions network at the supra-flocs level and the gel-like structure at the flocs level. Multiple advanced techniques including optical tweezers were employed to precisely understand the binary gel-like structure and to classify the correlation mechanism between this gel-like structure, rheological behavior, and dewaterability. The analysis of sludge from eight wastewater treatment plants showed the binary gel-like structures at both supra-flocs and flocs levels have significant relationships with sludge dewaterability (p < 0.05). Further deconstruction of the sludge viscoelastic behavior illustrated that the gel-like structure at the supra-flocs level dominates the rheological behavior of sludge. Moreover, the direct description of the binary gel-like structure in four typical sludge treatment processes highlighted the importance of the flocs level's structure in determining the dewaterability. Overall, this study revealed that shear rheology may prefer to stress the interactions network at the supra-flocs level but mask the flocs level's structure, although the latter is important. This observation may provide a general guideline for the design of robust sensors for dewaterability.

Particle size as a driver of dewatering performance and its relationship to stabilization in fecal sludge

Poor and unpredictable dewatering performance of fecal sludge is a major barrier to sanitation provision in urban areas not served by sewers. Fecal sludge comprises everything that accumulates in onsite containments, and its characteristics are distinct from wastewater sludges and from feces. There is little fundamental understanding of what causes poor dewatering in fecal sludge. For the first time, we demonstrate that particle size distribution is a driver of dewatering performance in fecal sludge, and is associated with level of stabilization. Higher concentrations of small particles (<10 μm) and smaller median aggregate size (D50) corresponded to poor dewatering performance (measured by capillary suction time (CST) and supernatant turbidity) in field samples from Kenya and Uganda and in controlled laboratory anaerobic storage experiments. More stabilized fecal sludge (higher C/N, lower VSS/TSS) had better dewatering performance, corresponding to lower concentrations of small particles. Samples with the largest aggregates (D50 > 90 μm) had higher abundance of Gammaproteobacteria Pseudomonas, and samples with the smallest aggregates (D50 ≤ 50 μm) were characterized by higher abundance of Bacteroidetes Vadin HA17 and Rikenellaceae. Contrary to common perceptions, stabilization, particle size distribution, and dewatering performance were not dependent on time intervals between emptying of onsite containments or on time in controlled anaerobic storage experiments. Our results suggest that the stabilization process in onsite containments, and hence the dewaterability of sludge arriving at treatment facilities, is not dependent on time in containment but is more likely associated with specific microbial populations and the in-situ environmental conditions which promote or discourage their growth.

Towards globally relevant, small-footprint dewatering solutions: Optimal conditioner dose for highly variable blackwater from non-sewered sanitation

Globally, the sanitation needs of three billion people are met by non-sewered sanitation. Small-footprint treatment technologies are needed that are appropriate for dense urban areas. Blackwater (BW) (or fecal sludge), contains more than 95% liquid, and dewatering it without conditioning requires large footprints. Chemically-enhanced dewatering with conditioners is a promising option to increase dewatering performance and reduce required footprints. However, before implementation of this solution there is a need for increased knowledge on selection and dosing of conditioners. This study evaluated bio-based and synthetic conditioners (chitosan, tannin-, and starch-based, synthetic with and without poly-acrylamide) with 14 types of BW from five countries. The supernatant after settling with jar-tests was analyzed to quantify optimal dose and dewatering performance. The reduction of total chemical oxygen demand (COD) was >55%, achieved by removal of particulate constituents with mainly soluble COD remaining in the supernatant. A reduction in particulate COD could lead to increased efficiency of soluble COD in supernatant treatment. Bio-based conditioners are as effective as synthetic conditioners, and when performance was variable, it was due to differing properties of TSS, TS, EC and pH. Optimal conditioner dose for synthetic conditioners and chitosan could be predicted using concentrations of total solids (TS) (R² > 0.7), whereas optimal dose for starch- and tannin-based conditioners could be predicted with electrical conductivity (EC) (R² > 0.8), and colloid titration (R² > 0.8). In addition, real-time optical TSS and EC sensors could accurately predict chitosan dose for fresh BW treated at source (R² = 0.97, R² = 0.95). This study validates that use of conditioners for dewatering with highly variable BW can be implemented with real-time measurements for optimal dose, in globally relevant implementations.

A CFD-ML augmented alternative to residence time for clarification basin scaling and design

Particulate matter (PM), while not an emerging contaminant, remains the primary labile substrate for partitioning and transport of emerging and known chemicals and pathogens. As a common unit operation and also green infrastructure, clarification basins are widely implemented to sequester PM as well as PM-partitioned chemicals and pathogens. Despite ubiquitous application for urban drainage, stormwater clarification basin design and optimization lacks robust and efficient design guidance and tools. Current basin design and regulation primarily adopt residence time (RT) as presumptive guidance. This study examines the accuracy and generalizability of RT and nondimensional groups of basin geometric and dynamic similarity (Hazen, Reynolds, Schmidt numbers) to scale clarification basin performance (measured as PM separation and total PM separation). Published data and 160,000 computational fluid dynamics (CFD) simulations of basin PM separation over a wide range of basin configurations, loading conditions, and PM granulometry (particle size distribution [PSD], density) are examined. Based on the CFD database, a novel implementation of machine learning (ML) models: decision tree (DT), random forest (RF), artificial neural networks (ANN), and symbolic regression (SR) are developed and trained as surrogate models for basin PM separation predictions. Study results indicate that: (1) Models based solely on RT are not accurate or generalizable for basin PM separation, with significant differences between CFD and RT models primarily for RT < 200 hr, (2) RT models are agnostic to basin configurations and PM granulometrics and therefore do not reproduce total PM separation, (3) Trained ML models provide high predictive capability, with (R2) above 0.99 and prediction for total PM separation within ±15%. In particular, the SR model distilled from CFD simulations is entirely defined by only two compact algebraic equations (allowing use in a spreadsheet tool). The SR model has a physical basis and indicates PM separation is primarily a function of the Hazen number and basin horizontal and vertical aspect ratios, (4) With common presumptive guidance of 80% for PM separation, a Pareto frontier analysis indicates that the CFD-ML augmented SR model generates significant economic benefit for basin planning/design, and (5) CFD-ML models show that enlarging basin dimensions (increasing RT) to address impaired behavior can result in exponential cost increases, irrespective of land/infrastructure adjacency conflicts. CFD-ML applications can extend to intra-basin retrofits (permeable baffles) to upgrade impaired basins.