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Rigorous new regulations in coal-fired pollutants emissions from power plant strengthen the requirements for electrostatic precipitator (ESP) technology. The study was inspired by the requirement to use the new composite collectors as substitutes for typical steel collector to save energy and structural materials. A bench scale and a pilot scale experimental system have been set up to investigate the performance of the novel wet composite collector. Contrastive collection efficiencies of different assembled modes were investigated. The removals of coal-fired pollutants including acid aerosol, ammonia aerosol, slurry droplets and fine particles were performed in new wet ESP, as well as to quantify the key properties of the new wet ESP. The results show that the collection efficiency of the new composite collector was higher than that by typical collectors using a thimbleful of water penetrating the surface via capillary flow. The new wet ESP device acts in synergy with WFGD and SCR systems for controlling coal-fired pollutants emissions and solves the adverse impacts caused by wet flue gas desulfurization (WFGD) and selective catalytic reduction (SCR) systems at the same time.

Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. In EDAs there are neither crossover nor mutation operators. Instead, the new population of individuals is sampled from a probability distribution, which is estimated from a database that contains the selected individuals from the previous generation. Thus, the interrelations between the different variables that represent the individuals may be explicitly expressed through the joint probability distribution associated with the individuals selected at each generation.

In this paper, we study the ability limit of EDAs to effectively solve problems in relation to the number of interactions among the variables. More in particular, we numerically analyze the learning limits that different EDA implementations encounter to solve problems on a sequence of additively decomposable functions (ADFs) in which new sub-functions are progressively added. The study is carried out in a worst-case scenario where the sub-functions are defined as deceptive functions. We argue that the limits for this type of algorithm are mainly imposed by the probabilistic model they rely on. Beyond the limitations of the approximate learning methods, the results suggest that, in general, the use of bayesian networks can entail strong computational restrictions to overcome the limits of applicability.

Wet electrostatic precipitators (wet ESPs) are good options for effective control of PM2.5 emission. However, the common problems of uniform water film formed on rigid collector limited the applicability of typical wet ESPs, thus could be tend to cause "spark-over" and "back-corona". This research was designed to test the performance of the polypropylene fibrous as collection electrodes for PM2.5 control in wet ESP. Meanwhile, drop spreading feature and volume resistivity measurements of polypropylene fibrous collector were investigated. The results show that drop spreading existed in imbibitions experiments between hydrophobic fabrics contacted with water. The difference of spreading characteristics was accordance to the physical woven properties of fabrics. The relationship between the volume resistivity of polypropylene fibrous and the water addition rate has been investigated. The secondary current by polypropylene collection electrode was 5~20 percent higher than that by FRP. The average number collection efficiencies by polypropylene collector amounted to 89.21% for PM2.5 when the gas residence time was 1.33s at 60kV. The relationship between water addition rate and average number collection efficiency was not more regular than other factors such as gas treatment time or applied voltage.

Numerical simulation and field measurement were carried out to investigate into laws of deformation and movement and the evolving characteristics of the plastic region around the roadway based on engineering conditions of deep soft rock roadway in Wangfenggang colliery, Huainan Mining area. The mechanism of controlling the surrounding rock stability of soft rock roadway in deep coal mine was demonstrated. The supporting of soft rock roadway in deep coal mine must be compatible with deformation and failure characteristics of surrounding rock, and it can keep the stability of surrounding rock. The combined supporting with high strength and prestress bolting-anchoring and integral surrounding rock grouting reinforcement can effectively control the surrounding rock deformation of soft rock roadway in deep coal mine. But every working step must be pay attention to sequence on the time and space so that it can play an integral supporting effect. Research results are put into practice accordingly and good control effect has been achieved.

According to the imagery coordinate’s mapping relation between the three points’(A,C,E) geometric limited condition on target surface ( length, perpendicular) and the image’s coordinate on the imagery plane surface to construct non- lineable equation models, we make use of the method of abstracting the center of a circle to get the coordinates of the three points 、 、 .In order to improve the precision and decrease the quantity of operation, we take the accelerateNewton Newton iterative method to evaluate the original numerical value by adding its repeating times to increase the precision of model, then to solve out the numerical value of coordinates of the three points A,C,E on the target’s surface, on the basis of this numerical value, we can fix the solution of the target’s surface’s equation. Try to assure the precision of the constructed model through these erroneous values, meanwhile, to make out the concrete analysis about the model’s stability.