Coal bursts occurring in steeply inclined coal seams (SICSs) are increasingly severe. To solve this problem, a mechanical model for the distribution of static stress on coal‐rock masses along panels and the distribution of dynamic load induced by the breakage of thick and hard roofs with propagation distance was established. The stress characteristics after a superposition of dynamic and static loads on the roof and floor roadways ( R r and R f ) were determined. In addition, precursory information characteristics and index sensitivities of four indices for dynamic loads and the CT index for static loads based on seismic tomography were separately analyzed. The monitoring and warning indices for SICSs and flat seams were compared. The results showed that the static stress of R r was significantly higher than that of R f , which provided a basis for the stress‐triggering coal burst behaviors. Three indices for dynamic loads and seismic tomography results exhibited remarkable precursory information and high sensitivity. However, the performance of lack of shock index is poor. The continuous anomaly and the contradiction of indices at R r and R f can be considered as precursory information for predicting coal bursts.
In view of the coal burst induced by roof breakage in the steeply inclined coal seam (SICS) roadway and its mechanism, a mechanical model was established to investigate the distribution of dynamic and static stresses in the coal seam before and after the breakage of a thick hard roof. The aim of this research is to study failure laws of SICS roadways under the superposition of dynamic load induced by roof breakage and asymmetric static load. For this purpose, response characteristics including acoustic emission (AE), static stress, and acceleration were analyzed by applying different dynamic loads to different horizontal slices with a self‐made similarity simulation test apparatus under combined dynamic and static loads. The theoretical model and simulation results were verified by analyzing characteristics of coal burst occurrence in the field, microseismic (MS) events, and tomographic imaging of microseismic waves. The study demonstrates the following: (1) The abutment pressure of the roof plays a dominant role in stress distribution of the coal seam slice before the breakage of the thick hard roof with the stress of the roof roadway ( R r ) being obviously higher than that of the floor roadway ( R f ). (2) High‐energy MS events and AE events are concentrated on the roof side after the breakage of the thick hard roof, and coal bursts are more easily induced by the superposition of high dynamic and static stresses on the roof side. Coal burst in the roadway is jointly determined by dynamic and static stresses. Under the same static stress, response characteristics increase with the rise of intensity of dynamic loads. When dynamic stress is the same, coal burst easily occurs in the roadway with high static stress.
Multi-objective competitive location problem with cooperative coverage for distance-based attractiveness is introduced in this paper. The potential facilities compete to be selected to serve all demand points which are determined by maximizing total collective attractiveness of all demand points from assigned facilities and minimizing the fixed and distance costs between all demand points and selected facilities. Facility attractiveness is represented as a coverage of the facility with full, partial and none coverage corresponding to maximum full and partial coverage radii. Cooperative coverage, which the demand point is covered by at least one facility, is also considered. The problem is formulated as a multi-objective optimization model and solution procedure based on elitist non-dominated sorting genetic algorithms (NSGA-II) is developed. Experimental example demonstrates the best non-dominated solution sets obtained by developed solution procedure. Contributions of this paper include introducing competitive location problem with facility attractiveness as a distance-based coverage of the facility, re-categorizing facility coverage classification and developing solution procedure base upon NSGA-II.
The electricity system can be divided into power generation, transmission and distribution subsystems from the structure point of view. Electricity business is positioned as a public business and regulated by pricing and quantities due to the high publicly utilization. The power transmission system is responsible for power transmission from power generation plants to the areas of power utilization. Since there is the characteristic that electricity needs to produce and sell with immediate and instant balance, it is required to construct the complete power transmission network in charge of the power dispatching in order to provide stable electricity. The price of purchases and sales is closely related with the necessity of power supply and demand. The purpose of this paper is to introduce the optimization model of power supply and demand in the independent power transmission network for the maximization of consumer and producer's surplus in the liberalization of electricity business.We apply this model to discussing the relationships of electricity quantity and price of purchases and sales. In addition, the sensitive analysis is introduced as well. We conclude that the maximal profit of power transmission network will be decreased when quantity of power utilization, cost of power generation and maintenance cost of transmission network are increasing. Instead, the maximal profit of power transmission network will be increased when power transmission rate is increasing.
This study is to investigate the nonlinearly constrained various signal detector location allocation problems in which the types of detectors and the corresponding numbers and locations can be determined at the same time so as to minimize the maximum detecting failure rate in a specified area. In other words, the objective of the detector location allocation problem is to minimize the maximum failure rate by determining the best possible conjunction of three types of decision variables, i.e., the type of detector, the numbers of each detector type and where to build up each of them with a limited resources. So, the quality of reliability of event detecting can be assured and consistent. By the way, the signal intensity usually disintegrates proportionally to some power of the distance from the detector. That makes the longer distance far away the detector, the bigger failure rate in detection of the event. The signal detector allocation problem is described as a mixed-integer nonlinear programming model, usually using math programming or heuristic optimization methods for finding the optimal solution or near optimal solution. While using the both methods, the difficulties encountered are the amount of decision variables and the difficulty of not violating the constraints. In this study, a two-phase evolutionary computation approach based on the immune algorithm and particle swarm optimization has been developed for overcoming the difficulties and finding the optimal solutions for the detector allocation problems effectively. Finally, the performance of the proposed methodology has been evaluated with the commercial optimization software. Numerical results illustrate that our approach is with well performance for the constrained detector allocation problems considered in this paper. As reported, solutions acquired by using our approach are as well as or better than those found by using LINGO®.
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