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Abstract Adsorbent-based carbon capture is only feasible if adsorption-desorption cycles are both fully regenerating and efficient. This work proposes a regenerative pH swing process and a pH swing regenerative adsorbent that are inspired by natural CO2 conversion by carbonic anhydrase biocatalysts found in mammalian red blood cells. The main objective is to develop, test and analyze a synthetic pH Swing Adsorption (pHSA) system as well as a pHSA compatible solid adsorbent to capture CO2 from a simulated ambient air gas stream. The lead developed adsorbent is a carbon black co-activated with potassium carbonate and nitrogenous copolymer that is impregnated with immobilized bovine carbonic anhydrase and thereby deemed “BCA/KN-CB”. BCA/KN-CB has preliminarily demonstrated both competitive CO2 adsorption capacity and limited regenerative ability under experimental pHSA conditions. In addition, BCA-based adsorbents achieved higher adsorption capacities than non-BCA adsorbent counterparts. The BCA/KN-CB adsorbent displayed both large point of zero charge (PZC) swings and regenerative stability. The proposed pHSA system requires essentially zero energy expenditure to achieve intended environments for capture and regeneration. With 1 kg of adsorbent, pHSA has the ability to capture 1 kg CO2 in less than 4 h of cycling. The tested pHSA adsorbent can also capture more than 96% of total CO2 in a given raw gas stream flowing through the capture chamber. This proof-of-concept study of a pH swing adsorption/biocatalytic adsorbent system suggests the potential to effectively operate under ambient conditions and exhibit advantageous operational efficiencies to other high-profile CO2 capture systems.

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract Bioproducts from biomass feedstocks and organic wastes have shown great potential to address challenges across food-energy-water systems. However, bioproducts production is at an early, nascent stage that requires new inventions and cost-reducing approaches to meet market needs. Biochar, a byproduct of the pyrolysis process, derived from nutrient-rich biomass feedstocks (e.g., cattle manure and poultry litter) is one of these bioproducts that has numerous applications, such as improving soil fertility and crop productivity. This study investigates the market opportunity and sustainability benefits of converting manure to biochar on-site, using a portable refinery unit. Techno-economic and environmental impact assessments are conducted on a real case study in Twin Falls, Idaho, USA. The techno-economic analysis includes a stochastic optimization model to calculate the total cost of biochar production and distribution. The environmental study employs a life cycle assessment method to evaluate the global warming potential of manure-to-biochar production and distribution network. The total cost of biochar production from cattle manure near the feedlots is approximately $237 per metric ton, and total emission is 951 kg CO2 eq. per metric ton. The on-site operation and manure moisture content are two key parameters that can reduce biochar unit price and carbon footprint of manure management. It is concluded that converting cattle manure, using the presented strategy and process near the collection sites can address upstream and midstream sustainability challenges and stimulate the biochar industry.

Abstract Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the battery health condition by estimating the battery capacity over the life-time. This paper presents a data-driven method for estimating the capacity of Li-ion battery based on the charge voltage and current curves. The contributions of this paper are three-fold: (i) the definition of five characteristic features of the charge curves that are indicative of the capacity, (ii) the development of a non-linear kernel regression model, based on the k-nearest neighbor (kNN) regression, that captures the complex dependency of the capacity on the five features, and (iii) the adaptation of particle swarm optimization (PSO) to finding the optimal combination of feature weights for creating a kNN regression model that minimizes the cross validation (CV) error in the capacity estimation. Verification with 10 years’ continuous cycling data suggests that the proposed method is able to accurately estimate the capacity of Li-ion battery throughout the whole life-time.

A performance analysis and optimization of a open-cycle regenerator gas-turbine power-plant is performed in this paper. The analytical formulae about the relation between power output and cycle overall pressure-ratio are derived taking into account the eight pressure-drop losses in the intake, compression, regeneration, combustion, expansion and discharge processes and flow process in the piping, the heat-transfer loss to the ambient environment, the irreversible compression and expansion losses in the compressor and the turbine, and the irreversible combustion loss in the combustion chamber. The power output is optimized by adjusting the mass-flow rate and the distribution of pressure losses along the flow path. Also, it is shown that the power output has a maximum with respect to the fuel-flow rate or any of the overall pressure-drops and the maximized power output has an additional maximum with respect to the overall pressure-ratio. The numerical example shows the effects of design parameters on the power output and heat-conversion efficiency.

A stand-alone analytical model using the NTU (number of transfer units) effectiveness approach was developed for a residential desuperheater integrated to a thermal-energy storage (TES) system. This paper describes the basic heat-transfer analysis and presents the characteristics of the model. The output prediction of the desuperheater model was the desuperheater rate. Error analyses of the desuperheater rate indicated that the model predictions were within 12% of the experimental values. A factorial-design sensitivity analysis was conducted to show that the variables Tri and Twi as well as interactions Twi∗hrefrig, hrefrig∗F, Tri∗hrefrig, and hwtr∗hrefrig∗F had significant effects on the desuperheater rate. Although the model involved various assumptions, the results were considered helpful for the design and understanding of the behaviour of the desuperheater.