Search

Gas drainage technology is closely related to the flow of methane in coal. Desorption and diffusion of gas in coal is an important basic research area, and particle size is one of the influencing factors. In this study, the desorption and diffusion characteristics of coal samples mixed with different particle sizes over time were measured under constant temperature and pressure. The results show that in the initial stage, the desorption volume and desorption rate of coal samples are relatively large. The improved desorption model can well fit the data and the order of desorption volume is negatively correlated to the mean particle size of coal samples. The single small particle coal sample has the largest desorption volume which reached 3.77ml/g. Both the unipore model and the constructed unsteady dynamic diffusion model can well describe the diffusion of gas in coal, the diffusion coefficient of coal sample decreases with time, and the decay rate decreases gradually with time until it remains unchanged. The calculation results of the two models show that the diffusion coefficient ranges of gas in coal between 10−10m2/s and 10−12m2/s. For single-particle coal samples, the diffusion coefficient is positively correlated with particle size. The diffusion coefficient D is affected by the particle size and the degree of compaction between coal particles. These observations indicate that coal samples with different particle sizes will affect the desorption and diffusion of gas, and it has a positive impact on methane recovery. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2009.09.031 Byline: Jun Wang (a), Jia Li (a), Yingpeng Xie (a), Chengwu Li (a), Guangxi Han (a), Liquan Zhang (b), Rui Xu (a), Xiangdong Zhang (a) Abstract: In this work, Er.sup.3+:YAlO.sub.3/ZnO-TiO.sub.2 and ZnO-TiO.sub.2 composites were prepared by the ultrasonic dispersion and liquid boiling method. In succession, they were then characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Acid red B as a model dye compound was degraded under solar light irradiation to evaluate the photocatalytic activities of the Er.sup.3+:YAlO.sub.3/ZnO-TiO.sub.2 and ZnO-TiO.sub.2 composites. We found that the photocatalytic activity of ZnO-TiO.sub.2 composite can be enhanced by adding an appropriate amount of Er.sup.3+:YAlO.sub.3. We reviewed influencing factors, such as Er.sup.3+:YAlO.sub.3 content, heat-treated temperature and heat-treated time on the photocatalytic activity of the Er.sup.3+:YAlO.sub.3/ZnO-TiO.sub.2 composites. In addition, the effects of solar light irradiation time, dye initial concentration, Er.sup.3+:YAlO.sub.3/ZnO-TiO.sub.2 amount and solution acidity on the photocatalytic degradation of acid red B dye in aqueous solution were investigated in detail. Simultaneously, the degradation and comparison of other dyes such as methyl orange (MO), rhodamine B (RM-B), azo fuchsine (AF), congo red (CG-R) and methyl blue (MB) were also reviewed. In addition, we attempted to explore both the principle of possible excitation of Er.sup.3+:YAlO.sub.3/ZnO-TiO.sub.2 under solar light irradiation and the mechanism of photocatalytic degradation. Author Affiliation: (a) Department of Chemistry, Liaoning University, Shenyang 110036, PR China (b) Department of Pharmacy, Liaoning University, Shenyang 110036, PR China Article History: Received 1 March 2009; Revised 3 September 2009; Accepted 21 September 2009

Summary: As a form of energy dissipation, electromagnetic radiation (EMR) is gradually becoming a mainstream method prediction method for damage monitoring of coal and rock. To better improve the prediction accuracy of EMR, it is crucial to investigate time‐frequency characteristics of EMR and the influence of coal and rock properties on EMR that occurs in the fracture process. In this paper, the deformation and fracture process of raw coal, shaped coal, and cement specimens under compression are observed, and time‐frequency evolution characteristics of EMR below ultralow frequency (ULF) under different loading stages are evaluated. Then, the inherent reasons of time‐frequency evolution of EMR reflecting the damage performance are analyzed. Meanwhile, the effect of loading condition and composition and structure of materials on EMR is discussed. Results show that there is obvious EMR below ULF in the deformation and fracture process of coal and rock materials, and the combined denoising method of ensemble empirical mode decomposition (EEMD) and wavelet is suitable for extraction of EMR. Besides, the amplitude is approximately inversely proportional to frequency of electromagnetic signals, and the relationship between pulse count of EMR and damage is related to the homogeneity of coal and rock materials. Moreover, the time‐frequency characteristic parameter of EMR, such as amplitude, pulse count, and frequency spectrum, can indirectly reflect the damage evolution process of coal and rock. These results can provide a theoretical basis for remote monitoring of coal and rock dynamic disaster using EMR below ULF. [ABSTRACT FROM AUTHOR]

In the process of intelligent manufacturing, appropriate learning algorithm and intelligent model are necessary. In this work, a novel learning algorithm named random vibration and cross particle swarm optimization algorithm was proposed. The proposed algorithm is used for the prediction and optimization of machining process. Tool wear is an important factor that affects the machined surface quality during machining process, so it is necessary to find qualified tool wear prediction model and obtain the best combination of machining parameters to prolong tool life. In this study, the adaptive network–based fuzzy inference system was established to predict the tool wear width size. The random vibration and cross particle swarm optimization algorithm was tested using benchmark functions, and the results showed that random vibration and cross particle swarm optimization algorithm is able to find global optimum. Compared with the adaptive network–based fuzzy inference system trained by particle swarm optimization algorithm and adaptive network–based fuzzy inference system trained by differential evolution models, the results showed that the adaptive network–based fuzzy inference system trained by random vibration and cross particle swarm optimization algorithm can give a more accurate predicted value for offline prediction of the tool wear width size. In order to obtain the best combinations of cutting parameters under different removal area, the multi-objective optimization based on random vibration and cross particle swarm optimization algorithm was established. The optimized cutting parameters were verified and could be accepted to prolong tool life and improve machining efficiency. [ABSTRACT FROM AUTHOR]