The NEWgeneratorTM Decentralized Wastewater Treatment System for Non-sewered Sanitation: Technology Advancement Through Field Evaluation and Modeling

Abstract

Non-sewered sanitation systems (NSSS) offer innovative solutions to sanitation challenges in areas lacking basic facilities, utilizing advanced treatment technologies for on-site water reuse. The NEWgenerator (NG), an advanced NSSS developed at the University of South Florida under the Bill & Melinda Gates Foundation’s Reinventing the Toilet Challenge, integrates an anaerobic membrane bioreactor (AnMBR), an ion exchange-based nutrient capture system (NCS), and an electrochlorinator for final disinfection. This study builds on previous field trials of the NG system, focusing on developing, evaluating, and optimizing its water reuse capabilities through field trials, system dynamics modeling, and advanced monitoring techniques.

In the first part of the study, a three-month field trial of the NG system in a South African informal settlement showed the NG system effectively treated high-strength blackwater for non-potable on-site reuse, thereby reducing the use of potable water for sanitation purposes. The NG system maintained effluent water quality, meeting local reuse standards and ISO 30500 requirements, and reduced overall water demand for public toilets by an average of 16%. However, significant leakage in the flushing led to an average wastage rate of 82%, indicating the need for specific improvements.

To further understand the wastewater treated by the NG system in South Africa, the study analyzed water use in public toilets within the informal settlement. It was found that laundry water accounted for 47% of total water consumption, with toilet flushing at 40%, generating about 4,523 L/d of blackwater primarily from toilets and urinals, and about 9,120 L/d of greywater from showers, handwashing, and laundry fixtures. Significant leakage in the flushing system was discovered, contributing to 65% of flushing needs. Addressing these leaks could significantly reduce blackwater production, easing the load on the treatment system. The findings also highlighted a high portion of greywater from laundry, suggesting the need for additional processing steps for detergent.

To evaluate the NG system’s performance and maintenance requirements under various environmental conditions, a system dynamics model was developed using STELLA Architect software. This model simulates the fate and transport of water, COD, nitrogen, phosphorus, pathogen exposure, energy, and techno-economic factors, combining biological and physical processes in the AnMBR with chemical, physical, and biological processes in the zeolite beds. Sensitivity analysis and scenario simulations indicated that the NG system could treat water for public toilet flushing for 100 users with minimal additional water and no grid power required. Financially, the photovoltaic energy deployment cost is approximately 0.042 USD/cap, with NCS O&M being major cost drivers. Adding urine to the system increases the nitrogen load, raising the per capita cost to 0.070 USD/cap. The ion exchange process during zeolite regeneration decreases zeolite capacity over time, underscoring the importance of zeolite bioregeneration in reducing operating costs. Overall, the NG system shows significant potential for providing safe and accessible sanitation in various settings.

The final part of the research addresses the need for real-time monitoring of NSSS. Traditional monitoring methods are costly and time-consuming, especially for small systems. This study developed multi-input and single-output soft sensors using machine learning algorithms to predict key water quality parameters, including COD, TSS, and E. coli concentrations. Field-tested data over nearly two years were used to evaluate the performance of partial least squares regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN) algorithms. The SVR model provided the best prediction for COD, while the CUB model was most effective for TSS. Although predictions for E. coli were less accurate, these models hold promise for real-time monitoring of NSSS. 

By integrating field trials, system dynamics modeling, and advanced monitoring techniques, this dissertation demonstrates the effectiveness and feasibility of the NG system for on-site water reuse. The findings highlight the potential of the NG system to address sanitation challenges in various settings, providing a sustainable and cost-effective solution for safe water reuse.