Thermographic Phosphors in Improved Thermal Management of Fossil-Fuel Power Plants
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We show that the average aromatic cluster size in complex carbonaceous materials can be accurately determined using fast magic-angle spinning (MAS) NMR at a high magnetic field. To accurately quantify the nonprotonated aromatic carbon, we edited the 13C spectra using the recently reported MAS-synchronized spin–echo, which alleviated the problem of rotational recoupling of 1H-13C dipolar interactions associated with traditional dipolar dephasing experiments. The dependability of this approach was demonstrated on selected Argonne Premium coal standards, for which full sets of basic structural parameters were determined with high accuracy.
This report summarizes technical progress achieved during the project supported by the Department of Energy under Award Number DE-FG26-07NT4306. The aim of the project was to conduct basic research into battery-free wireless sensing mechanism in order to develop novel wireless sensors and sensor network for physical and chemical parameter monitoring in a harsh environment. Passive wireless sensing platform and five wireless sensors including temperature sensor, pressure sensor, humidity sensor, crack sensor and networked sensors developed and demonstrated in our laboratory setup have achieved the objective for the monitoring of various physical and chemical parameters in a harsh environment through remote power and wireless sensor communication, which is critical to intelligent control of advanced power generation system. This report is organized by the sensors developed as detailed in each progress report.
The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.
Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.
2014 Pittsburgh Coal Conference Presentation on indirect supercritical CO2 Cycles with the following sections: - Introduction - sCO2 Recompression Brayton Cycle - Screening of Fossil-based Heat Sources - Potential Benefits for Coal-Based Indirect sCO2 Cycles Conclusions include that (1) potential for significant efficiency gains over conventional steam cycles depending on the configuration and temperature; (2) applicable to multiple coal-based platforms; (3) costs currently uncertain; and, (4) coal-based systems have a potential for a 5-15% reduction in COE under certain assumptions.
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Here, we discuss a new paper in Nature by Welsby et al., which makes clear that pursuing efforts to limit global average temperature increase to 1.5°C above pre-industrial levels—the most frequently stated goal of the 2015 Paris Agreement—implies the vast majority of coal, crude oil, and natural gas must remain underground and undeveloped.
Resin production and post-use pathways have cross-database discrepancies due to different temporal/geographic representation, technologies, and assumptions. These differences can distort comparisons across alternatives and confound efforts to establish standards based on the plastics’ life-cycle impacts. Thus, this study quantifies the degree and identifies the sources of cross-database discrepancies across four LCA databases (GREET, USLCI, Ecoinvent, and GaBi) for five resin production pathways and three post-use phases. For resin production pathways, all resins showed significant cross-database discrepancy in their global warming impacts: the degree of discrepancy was significant to recommend a consistent choice of database to users any LCA comparisons across different products. For post-use phases, landfill datasets had relatively lower degree of cross-database discrepancy than incineration and mechanical recycling. Different metadata characteristics were the sources of some cross-database discrepancies while other parts could be explained by the original differences in the life-cycle inventory sourced from different producers and plants.
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We examine the opportunities offered by advancements in solid-state NMR (SSNMR) methods, which increasingly rely on the use of high magnetic fields and fast magic angle spinning (MAS), in the studies of coals and other carbonaceous materials. The sensitivity of one- and two-dimensional experiments tested on several Argonne Premium coal samples is only slightly lower than that of traditional experiments performed at low magnetic fields in large MAS rotors, since higher receptivity per spin and the use of 1H detection of low-gamma nuclei can make up for most of the signal loss due to the small rotor size. The advantages of modern SSNMR methodology in these studies include improved resolution, simplicity of pulse sequences, and the possibility of using J-coupling during mixing.
Terrestrial vegetation removes CO 2 from the atmosphere; an important climate regulation service that slows global warming. This 119 Pg C per annum transfer of CO 2 into plants—gross primary productivity (GPP)—is the largest land carbon flux globally. While understanding past and anticipated future GPP changes is necessary to support carbon management, the factors driving long-term changes in GPP are largely unknown. In this study, we show that 1901 to 2010 changes in GPP have been dominated by anthropogenic activity. Our dual constraint attribution approach provides three insights into the spatiotemporal patterns of GPP change. First, anthropogenic controls on GPP change have increased from 57% (1901 decade) to 94% (2001 decade) of the vegetated land surface. Second, CO 2 fertilization and nitrogen deposition are the most important drivers of change, 19.8 and 11.1 Pg C per annum (2001 decade) respectively, especially in the tropics and industrialized areas since the 1970’s. Third, changes in climate have functioned as fertilization to enhance GPP (1.4 Pg C per annum in the 2001 decade). These findings suggest that, from a land carbon balance perspective, the Anthropocene began over 100 years ago and that global change drivers have allowed GPP uptake to keep pace with anthropogenic emissions.
Despite the known biochemical production of a range of aromatic compounds by plants and the presence of benzenoids in floral scents, the emissions of only a few benzenoid compounds have been reported from the biosphere to the atmosphere. Here, using evidence from measurements at aircraft, ecosystem, tree, branch and leaf scales, with complementary isotopic labeling experiments, we show that vegetation (leaves, flowers, and phytoplankton) emits a wide variety of benzenoid compounds to the atmosphere at substantial rates. Controlled environment experiments show that plants are able to alter their metabolism to produce and release many benzenoids under stress conditions. The functions of these compounds remain unclear but may be related to chemical communication and protection against stress. We estimate the total global secondary organic aerosol potential from biogenic benzenoids to be similar to that from anthropogenic benzenoids (~10 Tg y -1 ), pointing to the importance of these natural emissions in atmospheric physics and chemistry.