Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Trotochaud L

PROVIDER: S-EPMC7745533 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Trotochaud Lena L Andrus Rebecca M RM Tyson Kayana J KJ Miller Graham H GH Welling Claire M CM Donaghy Patrick E PE Incardona Joseph D JD Evans William A WA Smith Paul K PK Oriard Tim L TL Norris Ian D ID Stoner Brian R BR Guest Jeremy S JS Hawkins Brian T BT

Environmental science & technology 20201203 24

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 ...[more]

PMID: 33269914

Similar Datasets

Project description: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.

Project description:BackgroundAlcohol-to-jet (ATJ) is one of the technical feasible biofuel technologies. It produces jet fuel from sugary, starchy, and lignocellulosic biomass, such as sugarcane, corn grain, and switchgrass, via fermentation of sugars to ethanol or other alcohols. This study assesses the ATJ biofuel production pathway for these three biomass feedstocks, and advances existing techno-economic analyses of biofuels in three ways. First, we incorporate technical uncertainty for all by-products and co-products though statistical linkages between conversion efficiencies and input and output levels. Second, future price uncertainty is based on case-by-case time-series estimation, and a local sensitivity analysis is conducted with respect to each uncertain variable. Third, breakeven price distributions are developed to communicate the inherent uncertainty in breakeven price. This research also considers uncertainties in utility input requirements, fuel and by-product outputs, as well as price uncertainties for all major inputs, products, and co-products. All analyses are done from the perspective of a private firm.ResultsThe stochastic dominance results of net present values (NPV) and breakeven price distributions show that sugarcane is the lowest cost feedstock over the entire range of uncertainty with the least risks, followed by corn grain and switchgrass, with the mean breakeven jet fuel prices being $0.96/L ($3.65/gal), $1.01/L ($3.84/gal), and $1.38/L ($5.21/gal), respectively. The variation of revenues from by-products in corn grain pathway can significantly impact its profitability. Sensitivity analyses show that technical uncertainty significantly impacts breakeven price and NPV distributions.ConclusionsTechnical uncertainty is critical in determining the economic performance of the ATJ fuel pathway. Technical uncertainty needs to be considered in future economic analyses. The variation of revenues from by-products plays a significant role in profitability. With the distribution of breakeven prices, potential investors can apply whatever risk preferences they like to determine an appropriate bid or breakeven price that matches their risk profile.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets