Response of carbon isotopic compositions of Early-Middle Permian coals in North China to palaeo-climate change

To investigate the magnitude to which the carbon isotopic ratio (delta C-13) varies in coals in response to their contemporary terrestrial environment, the Early-Middle Permian Huainan coals (including coals from the Shanxi Formation, Lower Shihezi Formation and Upper Shihezi Formation) in North China were systematically sampled. A 2.5 parts per thousand variation range of delta C-13 values (-25.15%o to -22.65%o) was observed in Huainan coals, with an average value of -24.06 parts per thousand. As coal diagenesis exerts little influence on carbon isotope fractionation, delta C-13 values in coals were mainly imparted by those of coal -forming flora assemblages which were linked to the contemporary climate. The delta C-13 values in coals from the Shanxi and Lower Shihezi Formations are variable, reflecting unstable climatic oscillations. Heavy carbon isotope is enriched in coals of the Capitanian Upper Shihezi Formation, implying a shift to high positive delta C-13 values of coeval atmospheric CO2. Notably, our study provides evidence of the Kamura event in the terrestrial environment for the first time

Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets

The number of clusters (i.e., the number of classes) for unsupervised classification has been recognized as an important part of remote sensing image clustering analysis. The number of classes is usually determined by cluster validity indices (CVIs). Although many CVIs have been proposed, few studies have compared and evaluated their effectiveness on remote sensing datasets. In this paper, the performance of 16 representative and commonly-used CVIs was comprehensively tested by applying the fuzzy c-means (FCM) algorithm to cluster nine types of remote sensing datasets, including multispectral (QuickBird, Landsat TM, Landsat ETM+, FLC1, and GaoFen-1) and hyperspectral datasets (Hyperion, HYDICE, ROSIS, and AVIRIS). The preliminary experimental results showed that most CVIs, including the commonly used DBI (Davies-Bouldin index) and XBI (Xie-Beni index), were not suitable for remote sensing images (especially for hyperspectral images) due to significant between-cluster overlaps; the only effective index for both multispectral and hyperspectral data sets was the WSJ index (WSJI). Such important conclusions can serve as a guideline for future remote sensing image clustering applications

Integration of liquid air energy storage with ammonia synthesis process for resource efficiency and cost-effectiveness

Liquid air energy storage (LAES) uses air or nitrogen as both energy storage medium and working fluid. Such a working fluid is directly exhausted during power recovery stage, leading to resource waste. The synthesis of ammonia, a promising hydrogen carrier, on the other hand, requires nitrogen as feed, which is produced by an air separation unit (ASU). The ASU is an energy intensive process, which often works continuously, consuming both peak and off-peak electricity and hence has a high operating cost. This work aims to address the issues by integrating the LAES with the ammonia synthesis process, denoted as LAES-NH3 thereafter. Such an integrated system works in the following manner: at off-peak hours, the ASU operates to provide nitrogen for both the ammonia synthesis and the charging cycle of the LAES; at peak hours, the exhausted nitrogen from the LAES power recovery process is fed to the ammonia synthesis process. In this way, the ASU only operates at off-peak times with low-cost electricity, thus reducing the operating costs of the ASU; the LAES will benefit from reduced capital costs by sharing some key components with the ASU, and the LAES is operated independently from the ammonia synthesis. Thermodynamic and economic analyses are conducted on the proposed LAES-NH3 system. Our analyses show ∼38 % reduction in the ASU operating cost during ammonia synthesis, and a reduced initial investment cost of the LAES by 11.3 %