Abstract: Industries using conventional fossil fuels have an
interest in better understanding the mechanism of particulate
formation during combustion since such is responsible for emission
of undesired inorganic elements that directly impact the atmospheric
pollution level. Fine and ultrafine particulates have tendency to
escape the flue gas cleaning devices to the atmosphere. They also
preferentially collect on surfaces in power systems resulting in
ascending in corrosion inclination, descending in the heat transfer
thermal unit, and severe impact on human health. This adverseness
manifests particularly in the regions of world where coal is the
dominated source of energy for consumption.
This study highlights the behavior of calcium transformation as
mineral grains verses organically associated inorganic components
during pulverized coal combustion. The influence of existing type of
calcium on the coarse, fine and ultrafine mode formation mechanisms
is also presented. The impact of two sub-bituminous coals on particle
size and calcium composition evolution during combustion is to be
assessed. Three mixed blends named Blends 1, 2, and 3 are selected
according to the ration of coal A to coal B by weight. Calcium
percentage in original coal increases as going from Blend 1 to 3.
A mathematical model and a new approach of describing
constituent distribution are proposed. Analysis of experiments of
calcium distribution in ash is also modeled using Poisson distribution.
A novel parameter, called elemental index λ, is introduced as a
measuring factor of element distribution.
Results show that calcium in ash that originally in coal as mineral
grains has index of 17, whereas organically associated calcium
transformed to fly ash shown to be best described when elemental
index λ is 7.
As an alkaline-earth element, calcium is considered the
fundamental element responsible for boiler deficiency since it is the
major player in the mechanism of ash slagging process. The
mechanism of particle size distribution and mineral species of ash
particles are presented using CCSEM and size-segregated ash
characteristics. Conclusions are drawn from the analysis of
pulverized coal ash generated from a utility-scale boiler.
Abstract: This purpose of this paper is to present the acceptance single sampling plan when the fraction of nonconforming items is a fuzzy number and being modeled based on the fuzzy Poisson distribution. We have shown that the operating characteristic (oc) curves of the plan is like a band having a high and low bounds whose width depends on the ambiguity proportion parameter in the lot when that sample size and acceptance numbers is fixed. Finally we completed discuss opinion by a numerical example. And then we compared the oc bands of using of binomial with the oc bands of using of Poisson distribution.
Abstract: The objective of this paper is to present explicit analytical formulas for evaluating important characteristics of Double Moving Average control chart (DMA) for Poisson distribution. The most popular characteristics of a control chart are Average Run Length ( 0 ARL ) - the mean of observations that are taken before a system is signaled to be out-of control when it is actually still incontrol, and Average Delay time ( 1 ARL ) - mean delay of true alarm times. An important property required of 0 ARL is that it should be sufficiently large when the process is in-control to reduce a number of false alarms. On the other side, if the process is actually out-ofcontrol then 1 ARL should be as small as possible. In particular, the explicit analytical formulas for evaluating 0 ARL and 1 ARL be able to get a set of optimal parameters which depend on a width of the moving average ( w ) and width of control limit ( H ) for designing DMA chart with minimum of 1 ARL
Abstract: C-control chart assumes that process nonconformities follow a Poisson distribution. In actuality, however, this Poisson distribution does not always occur. A process control for semiconductor based on a Poisson distribution always underestimates the true average amount of nonconformities and the process variance. Quality is described more accurately if a compound Poisson process is used for process control at this time. A cumulative sum (CUSUM) control chart is much better than a C control chart when a small shift will be detected. This study calculates one-sided CUSUM ARLs using a Markov chain approach to construct a CUSUM control chart with an underlying Poisson-Gamma compound distribution for the failure mechanism. Moreover, an actual data set from a wafer plant is used to demonstrate the operation of the proposed model. The results show that a CUSUM control chart realizes significantly better performance than EWMA.