Optical Property Measurements of Mixed Coal Fly Ash and Particulate Carbon Aerosols Likely Emitted during Activated Carbon Injection for Mercury Emissions Control

The most mature technology for controlling mercury emissions from coal combustion is the injection into the flue gas of powdered activated carbon (PAC) adsorbents having chemically treated surfaces designed to rapidly oxidize and adsorb mercury. However, carbonaceous particles are known to have low electrical resistivity, which contributes to their poor capture in electrostatic precipitators (ESPs), the most widely used method of particulate control for coal-fired power plants worldwide. Thus, the advent of mercury emissions standards for power plants has the potential for increased emissions of PAC. Our previous analyses have provided estimates of PAC emission rates resulting from PAC injection in the U.S. and extrapolated these estimates globally to project their associated climate forcing effect. The present work continues our examination by conducting the first comparative measurements of optical scattering and absorption of aerosols comprising varying mixtures of coal combustion fly ash and PAC. A partially fluidized bed (FB) containing fly ash-PAC admixtures with varying PAC concentrations elutriates aerosol agglomerates. A photoacoustic extinctiometer (PAX) extractively samples from the FB flow, providing measurements of optical absorption and scattering coefficients of fly ash (FA) alone and FA-PAC admixtures. Extracted aerosol samples from the FB flow provide particulate loading measurements, thermogravimetric analysis (TGA) provides estimations of the carbon content of the particulates collected from the FB emission, and SEM images of the collected aerosols provide qualitative insight into the aerosols’ size distributions and agglomeration state. Soot from an oil lamp flame provides a comparative benchmark. The results indicate that the increase of carbonaceous particles in the FB emissions can cause a significant linear increase of their mass absorption cross sections (MACs). Thus, widespread adoption of activated carbon injection (ACI) in conjunction with ESPs has the potential to constitute a new source of light absorbing particle emissions which can absorb light efficiently and potentially act like black carbon in the atmosphere

Automated semantic segmentation of bridge point cloud based on local descriptor and machine learning

In recent years, monitoring the health condition of existing bridges has become a common requirement. By providing an information management system, Bridge Information Model (BrIM) can highly improve the efficiency of health inspection and the reliability of condition evaluation. However, the current modeling processes still largely rely on manual work, where the cost outweighs the benefits. The main barrier lies in the challenging step of semantic segmentation of point clouds. Efforts have been made to identify and segment the structural components of bridges in existing research. But these methods are either dependent on manual data preprocessing or need big training dataset, which, however, has rendered them unpractical in real-world applications. This paper presents a combined local descriptor and machine learning based method to automatically detect structural components of bridges from point clouds. Based on the geometrical features of bridges, we design a multi-scale local descriptor, which is then used to train a deep classification neural network. In the end, a result refinement algorithm is adopted to optimize the segmentation results. Experiments on real-world reinforced concrete (RC) slab and beam-slab bridges show an average precision of 97.26%, recall of 98.00%, and intersection over union (IoU) of 95.38%, which significantly outperforms PointNet. This method has provided a potential solution to semantic segmentation of infrastructures by small sample learning and will contribute to the fulfillment of the automatic BrIM generation of typical highway bridges from the point cloud in the future

Reaction rates for the fundamental subsystem.

<p>Note: is the reaction rate for ; is for ; is for , is for , and is the fusion-concentration constant.</p

A comparison to the good-of-fit level between the numerical results by the inverse problem analysis and the original experimental data from [9].

<p>The error is about .</p

A brief summary for the stabilizing analysis of the fundamental subsystems without regulation.

<p>A brief summary for the stabilizing analysis of the fundamental subsystems without regulation.</p

The whole process of fusion used in the mathematical model is shown.

<p>One direction arrows and symbol of represent the reaction between proteins, ions and complexes, while full direction arrows connect two parts of a single reaction. Modified from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038699#pone.0038699-Weber1" target="_blank">[1]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038699#pone.0038699-Burgoyne2" target="_blank">[6]</a>.</p

XRD data

XRD data of talc, TN 450, TN 550, and TN 65

BET and Pore size data

Nitrogen adsorption and desorption curves and pore size distribution of talc and TN 450, TN 550, and TN 65

A Bayesian phase I–II clinical trial design to find the biological optimal dose on drug combination

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I–II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose–response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.</p