Carbon Capture Overview
Government research funding initially emphasizing pre-combustion (CO2 capture from syngas) has led to the realization that less-glamorous post-combustion (flue gas scrubbing) most likely represents the best option for timely, cost-effective Carbon Capture and Storage (CCS) in the immediate future. A better understanding of capture costs resulting from the renewed interest in post-combustion has prompted the U.S. Government to increase the acceptable target for increased cost of energy required for flue gas scrubbing from 20% in 2006 to 35% today, highlighting the importance of advancing the current technology. While acid gas removal from process streams using alkanolamines is a mature technology, flue gas scrubbing presents many new challenges still not adequately met on the scale necessary for GHG abatement. In addition to discussing those challenges, this paper reviews (1) flue-gas scrubbing facilities installed over the past 30+ years for commercial purposes such as EOR, urea manufacture, soda ash manufacture and food processing, and (2) current status of innovative technologies employing, for example, sterically hindered amines and aqueous ammonia. While the GHG contribution of sulphur recovery activities is arguably insignificant, potential monetary CO2 reduction incentives will likely improve the economics of reducing incineration fuel by simple, established means as discussed.
Carbon Dioxide Post-Combustion Capture: Solvent Technologies Overview, Status and Future Directions
One of the most promising approaches to tackle the high emission rate of carbon dioxide is the use of Carbon Capture and Storage (CCS) technology. This technology aims at capturing carbon dioxide from power stations and other industrial facilities, compressing, and then transporting it to underground storage locations. Three technological routes for carbon capture from power plants exist: pre-combustion, post-combustion and oxycombustion. Pre-combustion is the removal of the carbon element from fuel gas prior to combustion [1]. This process takes place in Integrated Gasification Combined Cycle (IGCC) plants and operates at high pressures for high concentrations of CO2. IGCC plants still face several obstacles to commercialization. For instance, only two IGCC demonstration plants are in operation in the power sector in the United States. The second option is oxy-fuel combustion, which involves the use of high purity oxygen (instead of air) for fuel combustion and produces a CO2/H2O stream from which water is easily condensed [2]. However, the air (or nitrogen-oxygen) separation step is considered a bottle-neck for this process due to its energy intensiveness and high capital and operational costs. Finally, postcombustion capture involves a highly energy intensive nitrogen-carbon dioxide separation step [3]. As an end-of-pipe technology, this process is easier to implement compared to the other capture routes. In this chapter, the focus will be placed on post-combustion capture technology due to its high maturity, ability to be retrofitted to existing power plants and operational flexibility in switching between capture and no-capture modes [4].
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.