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self-adjusted elastic action and its CBM pool-forming effect of the high rank coal reservoir

Chinese Science Bulletin 2005年第S1期50卷 99-103页

作者： QIN Yong FU Xuehai WU Caifang FU Guoyou BU Yingying Institute of Mineral Resources and Geoscience China University of Mining&TechnologyXuzhou 221008China

The coal reservoir is a kind of geologic body with considerable elasticity, and its response to stress is more sensitive than the routine ones. In the pool-forming process of the coalbed methane, (CBM) the action of many external geodynamic factors in the coal reservoir is represented by the ability of CBM’s diffusion and migration in the coal reservoir. Therefore, the occurrence of the natural fissures in the coal reservoir becomes a key that determines whether the CBM pool can be formed or not. Based on the principle, the authors have designed a comprehensive method of physical modeling to study the elasticity of the coal matrix block with different coal ranks in the light of the solid to fluid coupling, have established the model of the volume deformation (self-adjusted elastic effect) of the coal reservoir under the condition of the effective stress-adsorption/desorption, and have obtained a correct understanding of the law of the elastic deformation. Afterwards, a new viewpoint, named as the self-closing elastic effect for forming the CBM pool in the high rank reservoir, is put forward, providing an initial point and a scientific basis for a further investigation into its dynamic factors, possible mechanism and role in the CBM energy-balancing system.

关键词： coalbed methane reservoir elasticity self-adjusted action pool-forming effect.

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Traffic prediction using a self-adjusted evolutionary neural network

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Journal of Modern Transportation 2019年第4期27卷 306-316页

作者： Shiva Rahimipour Rayehe Moeinfar Mehdi Hashemi Department of Mathematics and Computer Science Amirkabir University of Technology (Tehran’s Polytechnic)

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control *** aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic ***'dependency on parameter setting is the major challenge in using them as a *** the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN ***,it is viable to set the best combination of the parameters according to a specific traffic *** the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural *** paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term ***-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation *** model addresses the challenge of optimizing a multi-output NN in a self-adjusted *** of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban *** are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually *** achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.

关键词： Traffic prediction Neural networks Genetic algorithm self-adjusted framework

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