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In 2021, the White House proposed a 50-52% reduction in greenhouse gas emissions by the year 2030; therefore, there is significant interest in energy sources and processes that reduce carbon dioxide emissions. This paper presents a sensitivity analysis of a nuclear microreactor-powered design for concurrent hydrogen (H2) and ammonia (NH3) production, with a focus on wastewater treatment plant applications. Wastewater with organic materials (e.g., municipal wastewater, swine lagoon waste, and food waste) are the analyzed feedstocks. The system integrates the anaerobic digestion of wastewater sludge with a Brayton cycle-based power generation unit heated by the microreactor. Using empirical data and an analytical model, the paper investigates the system's response to variations in key operational parameters. The sensitivity analysis explores the influence of parameters such as the chemical oxygen demand of the feedstock, compressor isentropic efficiency, and reactor temperature and pressure on H2 and NH3 production rates, Brayton cycle efficiency, and carbon dioxide emissions. Highlights from this analysis show a nonlinear dependence for Brayton efficiency on reactor temperature, the proportionality of ammonia and hydrogen production on chemical oxygen demand values, the major impact of compressor isentropic efficiency, and the minimal response from changing the pressure of steam methane reforming. These results signify opportunities to improve the system and ultimately lead to lowered greenhouse gas emissions.
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The energy implication of climate change on urban wastewater systems
Urban wastewater service provision is an important energy consumer as well as a potentially important energy producer. This study aims to advance understandings on the influence of climate change on the intra- and inter-annual patterns of wastewater treatment plants’ net life cycle energy consumption. Historic monthly operational data of a wastewater treatment plant in the northeast United States were obtained and its current net life cycle energy demand was investigated. Comprehensive multivariate and multiple linear regression analyses were then performed. The main climate variables (temperature, rainfall, and snowfall) and the wastewater characteristics (flow rate, water temperature, total suspended solids, 5-day biochemical oxygen demand, and chemical oxygen demand) were used to develop regression models for energy that is directly and indirectly consumed and generated at the treatment plant. Two different approaches, a lumped and a month-based method, for conducting the regression analysis were investigated. Whenever possible, these two approaches were combined to improve the predictive power of the regression models. The obtained result shows the treatment plant’s direct energy use consists of more than 86% of the total energy consumption currently. Various energy recovery strategies allow the treatment plant to offset more than 15% of its total energy consumption. The future annual wastewater influent of the plant was projected to decrease towards the end of the century under climate change, with a significantly larger seasonal variation. The influent wastewater quality is projected to decrease, leading to higher direct and indirect energy consumption for treatment. Projections of future intra-annual responses show that the seasonal variations of wastewater flowrate as well as the monthly cumulative energy demand can potentially experience a two-fold increase, resulting in more frequent system shocks and create operational difficulties.
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- Award ID(s):
- 1706143
- PAR ID:
- 10189216
- Date Published:
- Journal Name:
- Journal of cleaner production
- Volume:
- 267
- ISSN:
- 0959-6526
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/j.jclepro.2020.121905
