The NEWgeneratorTM non-sewered sanitation system: Long-term field testing at an informal settlement community in eThekwini municipality, South Africa.

Globally, there is a dire need for a new class of advanced non-sewered sanitation systems (NSSS) to provide onsite wastewater treatment that is capable of meeting stringent discharge or reuse criteria. These systems need to be simple to operate and maintain, reliable, and resilient to unreliable electrical service. The NEWgenerator (NG) is a compact, automated, solar-powered wastewater treatment system comprised of three major treatment processes: anaerobic membrane bioreactor (AnMBR), nutrient capture system (NCS) with ion exchange and carbon sorption, and electrochlorination (EC). The NG system operated at an informal settlement community in South Africa over a 534 d period, treating high-strength blackwater (BW) and yellow water (YW) from a public toilet facility. Over three test stages (BW, BW + YW, BW) that included several periods of dormancy, the NG system was able to provide a high level of removal of total suspended solids (97.6 ± 3.1%), chemical oxygen demand (94.5 ± 5.0%), turbidity (96.3 ± 9.7%), color (92.0 ± 10.5%), total nitrogen (82.1 ± 24.0%), total phosphorus (43.0 ± 22.1%), E. coli (7.4 ± 1.5 LRV, not detected in effluent), and helminth ova (not detected in effluent). The treatment levels met most of the ISO 30500 NSSS standard for liquid effluent and local water reuse criteria. A series of maintenance events were successfully conducted onsite over the 534 d field trial: two membrane cleanings, two NCS regenerations, and granular activated carbon replacement. Desludging, a major pain point for onsite sanitation systems, was unnecessary during the field trial and thereby not performed. The AnMBR performed well, removing 94.5 ± 5.0% of the influent COD across all three stages. The high COD removal rate is attributed to the sub-micron separation provided by the ultrafiltration membrane. The NCS was highly efficient at removing total nitrogen, residual COD and color, but the regeneration process was lengthy and is a topic of ongoing research. The EC provided effective disinfection, but frequent prolonged run cycles due to power supply and water quality issues upstream limited the overall system hydraulic throughput. This extended field trial under actual ambient conditions successfully demonstrated the feasibility of using advanced NSSS to address the global water and sanitation crises.

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System.

Developing advanced onsite wastewater treatment systems (OWTS) requires accurate and consistent water quality monitoring to evaluate treatment efficiency and ensure regulatory compliance. However, off-line parameters such as chemical oxygen demand (COD), total suspended solids (TSS), and Escherichia coli (E. coli) require sample collection and time-consuming laboratory analyses that do not provide real-time information of system performance or component failure. While real-time COD analyzers have emerged in recent years, they are not economically viable for onsite systems due to cost and chemical consumables. This study aimed to design and implement a real-time remote monitoring system for OWTS by developing several multi-input and single-output soft sensors. The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, including COD, TSS, and E. coli concentrations. The temporal and spatial water quality data of an existing field-tested OWTS operated for almost two years (n = 56 data points) were used to evaluate the prediction performance of four machine learning algorithms. These algorithms, namely, partial least square regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN), were chosen as candidate algorithms for their prior application and effectiveness in wastewater treatment predictions. Water quality parameters that can be measured in-line, including turbidity, color, pH, NH4+, NO3-, and electrical conductivity, were selected as model inputs for predicting COD, TSS, and E. coli. The results revealed that the trained SVR model provided a statistically significant prediction for COD with a mean absolute percentage error (MAPE) of 14.5% and R2 of 0.96. The CUB model provided the optimal predictive performance for TSS, with a MAPE of 24.8% and R2 of 0.99. None of the models were able to achieve optimal prediction results for E. coli; however, the CUB model performed the best with a MAPE of 71.4% and R2 of 0.22. Given the large fluctuation in the concentrations of COD, TSS, and E. coli within the OWTS wastewater dataset, the proposed soft sensor models adequately predicted COD and TSS, while E. coli prediction was comparatively less accurate and requires further improvement. These results indicate that although water quality datasets for the OWTS are relatively small, machine learning-based soft sensors can provide useful predictive estimates of off-line parameters and provide real-time monitoring capabilities that can be used to make adjustments to OWTS operations.

Advancing the Economic and Environmental Sustainability of the NEWgenerator Nonsewered Sanitation System.

Achieving safely managed sanitation and resource recovery in areas that are rural, geographically challenged, or experiencing rapidly increasing population density may not be feasible with centralized facilities due to space requirements, site-specific concerns, and high costs of sewer installation. Nonsewered sanitation (NSS) systems have the potential to provide safely managed sanitation and achieve strict wastewater treatment standards. One such NSS treatment technology is the NEWgenerator, which includes an anaerobic membrane bioreactor (AnMBR), nutrient recovery via ion exchange, and electrochlorination. The system has been shown to achieve robust treatment of real waste for over 100 users, but the technology's relative life cycle sustainability remains unclear. This study characterizes the financial viability and life cycle environmental impacts of the NEWgenerator and prioritizes opportunities to advance system sustainability through targeted improvements and deployment. The costs and greenhouse gas (GHG) emissions of the NEWgenerator (general case) leveraging grid electricity were 0.139 [0.113-0.168] USD cap-1 day-1 and 79.7 [55.0-112.3] kg CO2-equiv cap-1 year-1, respectively. A transition to photovoltaic-generated electricity would increase costs to 0.145 [0.118-0.181] USD cap-1 day-1 but decrease GHG emissions to 56.1 [33.8-86.2] kg CO2-equiv cap-1 year-1. The deployment location analysis demonstrated reduced median costs for deployment in China (-38%), India (-53%), Senegal (-31%), South Africa (-31%), and Uganda (-35%), but at comparable or increased GHG emissions (-2 to +16%). Targeted improvements revealed the relative change in median cost and GHG emissions to be -21 and -3% if loading is doubled (i.e., doubled users per unit), -30 and -12% with additional sludge drying, and +9 and -25% with the addition of a membrane contactor, respectively, with limited benefits (0-5% reductions) from an alternative photovoltaic battery, low-cost housing, or improved frontend operation. This research demonstrates that the NEWgenerator is a low-cost, low-emission NSS treatment technology with the potential for resource recovery to increase access to safe sanitation.