Laboratory Demonstration and Preliminary Techno-Economic Analysis of an Onsite Wastewater Treatment System

Providing safe and reliable sanitation services to the billions of people currently lacking them will require a multiplicity of approaches. Improving onsite wastewater treatment to standards enabling water reuse would reduce the need to transport waste and fresh water over long distances. Here, we describe a compact, automated system designed to treat the liquid fraction of blackwater for onsite water reuse that combines cross-flow ultrafiltration, activated carbon, and electrochemical oxidation. In laboratory testing, the system consistently produces effluent with 6 ≤ pH ≤ 9, total suspended solids (TSS) < 30 mg L–1, and chemical oxygen demand (COD) < 150 mg L–1. These effluent parameters were achieved across a wide range of values for influent TSS (61–820 mg L–1) and COD (384–1505 mg L–1), demonstrating a robust system for treating wastewater of varying strengths. A preliminary techno-economic analysis (TEA) was conducted to elucidate primary cost drivers and prioritize research and development pathways toward commercial feasibility. The ultrafiltration system is the primary cost driver, contributing to >50% of both the energy and maintenance costs. Several scenario parameters showed an outsized impact on costs relative to technology parameters. Specific technological improvements for future prototype development are discussed.


S2
A: Supplementary Details of Prototype Design and Laboratory Testing  integrity of the electrolysis cell was achieved using polypropylene screws inserted into each corner of the electrode and secured using hex nuts.
A 12 V DC power supply located within the control panel was used to apply a potential to the electrodes. The DC electrical signal passes through a custom circuit board with an onboard microprocessor before it reaches the EC oxidation reactor. The microprocessor continually monitors the electrochemical disinfection process. If a minimum chloride concentration in the wastewater has been detected, the control algorithm will run the EC oxidation reactor in its energyefficient mode which terminates the electrolysis reaction when a predetermined mass of chlorine disinfectant has been produced; the system was programmed based on previous laboratory testing to determine the amount of chlorine needed to reliably achieve a 6-log reduction of E. coli when treating urine spiked with fecal sludge. Otherwise, the control algorithms will run the EC oxidation reactor for a fixed period of time (60 minutes).

Process logic
A schematic of the process logic can be seen in Figure S1. The system runs on an automated batch treatment cycle consisting of an UF/GAC phase (Phase 1) followed by an EC oxidation phase (Phase 2); system processes are controlled by a programmable logic controller (PLC) based on the status of capacitive level sensors (LS Gems, #230079, L-type, non-embeddable) attached to the outsides of the tanks ( Figure S1). When sufficient liquid has accumulated in the feed tank, This cycle proceeds until either the liquid level in the feed tank drops to the low-level limit triggered by LS2 (the tank is not emptied to keep the pump primed) or the EC oxidation tank is full (LS4). In the former case, the first phase is paused until more liquid enters the feed tank. In the latter case, the second phase begins. During the second phase, power to the UF subsystem is turned off and the EC subsystem is energized. the liquid is vigorously stirred and a potential (12 V) is applied via the MMO electrodes to oxidize chloride into chlorine and disinfect the liquid.
The EC unit runtime is calculated by the system based on the composition of the liquid (typical runtime is 30-40 minutes in wastewater). During the second phase, untreated liquid can continue to flow into the feed tank, but no additional liquid is added to the EC oxidation tank, as the UF subsystem is idle. At the end of this second phase, the EC oxidation tank is emptied by a diaphragm pump and the system reverts to the first phase (UF subsystem is turned back on) if more liquid has accumulated in the feed tank, or to idle if it has not. Figure S1. Process logic flow chart. The state of the level sensors (LS) determine what processes are run. LS1 sits at the highest point of the feed tank, and determines whether the inlet valve is open to receive influent. LS2 sits at the midpoint of the feed tank, and controls the activation of the feed pump. LS3 sits near the bottom of the feed tank and acts a backup to LS2 in the event of sensor failure, to prevent the feed pump from running dry. LS4 is located at the high level of the EC treatment tank, and its activation initiates the EC process, which runs for a predetermined time.
At the completion of the EC process LS5 controls the outlet pump; once the outlet pump has pumped out the EC tank, the system reverts back to control based on the state of LS2.

Note on datasets
The three datasets presented in the main text were collected between Dec. 2018 -Feb.
2020. Specific dates for each experiment can be found in the raw data file included in the Supporting Information. There is a temporal gap in the data presented in this manuscript between Jan. 2019 and Dec. 2019. However, the wastewater treatment system was not idle during this entire period. The system was tested with a multitude of configurations and wastewater compositions between Jan. -Dec. 2019; those data are not relevant for the results reported here and we plan to publish the results of these experiments in subsequent manuscripts.

Correlation plots
Correlation plots comparing influent and effluent values for pH, TSS, COD, total N, and total P are shown in Figures S2 -S6. A strong correlation between influent and effluent values indicates that the system performance depends in part on the influent concentration; this was observed for total N and total P. In contrast, no correlation between influent and effluent values shows that the system performance does not depend on the influent concentration and is an indicator of the robustness of the treatment process; this was observed for TSS and COD, where a wide range of influent concentrations consistently resulted in very low effluent concentrations.     Comparison of total N to NH3 and total P to reactive P The ISO 30500 standard sets percent removal requirements for total N and total P, which were measured for every experiment. We also measured concentrations of NH3 and reactive P (orthophosphate) for select experiments in dataset 2 and dataset 3. Figure S7 compares the concentrations of (a) total N, (b) total P, (c) NH3, and (d) reactive P for experiments where these data were collected. (Note that the data in Figure S7a and S7b are therefore a subset of the data shown in Figure 3 of the main text.)    Table S2 contains data from one experiment where a 1 L grab sample was extracted immediately after ultrafiltration (labelled UF) but before GAC treatment, and compared with a 1 L grab sample drawn immediately after GAC treatment but before electrochemical oxidation (UF-GAC). (The GAC column was operated in up-flow configuration during this experiment.) Table   S2 shows that 88.0% of COD is removed during ultrafiltration, and COD removed increases to 96.7% after GAC filtration. Table S2. Water chemistry parameters measured after each subsystem, with percent reduction values given in brackets. Equations are shown below without all unit conversions (e.g. hours to days, L to m 3 ) for simplicity. Note that all necessary unit conversions were performed in Python. Table S3. Fixed parameters (FP) and uncertain parameters (UP) used to model the UF unit process. Fixed parameters are assigned an exact value because they cannot vary due to design and/or product specification. The exact value for uncertain parameters is unknown, so a range is specified to encompass the most probable value.

Parameter
Unit Calculate the minimum daily user fee required to break even