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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 Efficient electric heat pumps have the potential to significantly reduce greenhouse gas emissions from heating and cooling buildings. However, heat pumps’ initial costs can be prohibitively high and their lifetime costs are only situationally competitive with incumbent technologies. Here we show that a business model based on heat purchase agreements could lower these barriers to heat pump adoption. In this business model, a user hosts a heat pump owned by an aggregator. The aggregator installs the heat pump at low or no initial cost to the user. The user buys the heat pump’s heat or cooling output from the aggregator. The aggregator buys the heat pump’s input electricity in the wholesale energy market and sells the flexibility of their aggregate electrical load in ancillary service markets. This paper presents the first economic analysis of heat purchase agreements as a third-party ownership model for electric heat pumps. We derive conditions under which a heat purchase agreement is mutually beneficial to the user and the aggregator. We also provide a method to fairly price heat and cooling. A case study of a typical United States home shows that a heat purchase agreement could more than double the value of a heat pump investment relative to the incumbent business model. The potential impact of this work is to reduce emissions both directly, by accelerating replacement of fossil-fueled or inefficient heating or cooling equipment, and indirectly, by helping power system operators reliably integrate wind and solar generation.

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.

Abstract The optimized performance of two advanced CO2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

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.

Abstract This paper examines methods to simultaneously improve efficiency and reduce emissions from a 34 cc air-cooled two-stroke engine configured to operate on natural gas, using tuned intake plus exhaust resonators designed from Helmholtz resonance theory to promote effective scavenging. The engine was developed for novel application in a small, free piston engine for decentralized power generation. Operation occurred at wide-open-throttle at a speed of 5400 RPM for both port injection (PI) and low-pressure direct injection (LPDI). In both cases, the electronic ignition timing was adjusted for maximum brake torque, while the fuel was adjusted such that both rich and lean combustion were examined. The LPDI engine was then operated over a variety of engine speeds. An energy balance was completed for both PI and LPDI operation, which quantified all energy pathways, as engine operating regimes changed. Results showed that exhaust chemical energy (ECE) for LPDI was reduced significantly when compared to PI, mostly due to less fuel slip. The fuel slip rate ranged from 35 to 40% for PI operation while it was in the range of 6–20% for LPDI operation. However, mixture stratification with LPDI operation increased carbon monoxide emissions. The start-of-injection (SOI) was also examined, targeting a SOI to provide the highest trapping efficiency and most stable combustion. For LPDI operation, trapping efficiency, which is function of engine speed, showed significant effects on overall efficiency and fuel slip. Air volumetric efficiency increased due to the absence of gaseous fuel within the intake manifold. It was shown that, relative to PI, the overall engine indicated efficiency increased by 90% to a maximum indicated efficiency of 30% for LPDI operation with a SOI of 180 CAD BTDC.

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.