Taking No.11 flue gas desulfurization (FGD) site in Taiyuan Second Thermal Power Plant (TSTPP) as an example, the authors respectively deduced the models of technical performance indexes including SO2 removal efficiency and outlet SO2 concentration and the models of economical performance indexes including limestone consumption, power consumption and process water consumption. Then, by using the least square linear and nonlinear regression method, the authors obtained the practical mathematic models in the period of 21:00, Mar. 28th and 3:00, Mar. 29th, 2007. Finally, the authors calculated the optimal solutions of slurry pH value, calcium-sulfur (Ca/S) mole ratio and liquid-gas (L/G) ratio by utilizing the multiobjective programming method. Using this method, the desulfurization system works safely and economically.
The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.
Solid-state multilevel data storage devices based on ferroelectric materials possess significant potential for use as artificial synapses in building biomimetic neural networks with low energy consumption and efficient data processing...
In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions rather than the available deployed ones. Unfortunately, the existing approaches face grave challenges due to the vulnerability of identification to human biases and errors. Here, we have proposed a novel approach to determine the criticality of different deployed regions by ranking them based on risk. The probability of occurrence of the failure event in each deployed region is estimated by spatial statistics to measure the uncertainty of risk. The severity of risk consequence is measured for each deployed region based on the total cost caused by failure events. At the same time, hypothesis testing is used before the application of the proposed approach. By validating the availability of the proposed approach, it provides a strong credible basis and the falsifiability for the analytical conclusion. Finally, a case study is used to validate the feasibility of our approach to identify the critical regions. The results of the case study have implications for understanding the spatial heterogeneity of the occurrence of failure in a pipeline network. Meanwhile, the spatial distribution of risk uncertainty is a useful priori knowledge on how to guide the random deployment of wireless sensors, rather than adopting the simple assumption that each sensor has an equal likelihood of being deployed at any location.
In the face of the budget cuts and increased size of industry infrastructure, one of the top priorities for industry infrastructure protection is to identify critical regions by vulnerability analysis. Then, limited resources can be allocated to those critical regions. Unfortunately, difficulties can be observed in existing approaches of vulnerability analysis. Some of them are unavailable due to the insufficient data. Others are susceptible to human biases. Here, we propose an approach to overcome these difficulties based on the location data of failure events. The critical geographic regions are determined by the risk ranking of different candidate regions. Risk is calculated by integrating the probability of the failure event occurring (risk uncertainty) and total failure cost (the severity of failure consequences) in each candidate region. By changing the modeled object from the components to the region where the whole industry infrastructure is located, it collects the rarely failure events which are dispersed in different positions of the industry infrastructure to provide sufficient data, then the probability can be obtained by using a Poisson point process and kernel density estimation. Meanwhile, the application of hypothesis testing avoids the susceptibility of the approach to human biases by verifying the correctness of the assumptions used in the approach. Finally, a case study of this approach is performed on a pipeline network in Kansas, USA. In addition to the validation of the feasibility of our approach, risk uncertainty is proven to be less instructive for identifying critical regions than the severity of failure consequences.INDEX TERMS Critical region, critical industry infrastructure, Poisson point process, risk assessment, vulnerability analysis.
As a high-performance switching power supply, DC power supply is widely used in the micro grid involving new energy generation. In this paper, for the strongly nonlinear BUCK circuit (switching power supply), the cerebellar model articulation controller (CMAC) and PID compound control algorithm is proposed to achieve nonlinear system control. By using MATLAB/Simulink simulation study, results show that the system has better steady-state and dynamic performance.
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