scholarly journals The Cascade Control of Natural Gas Pipeline Systems

2019 ◽  
Vol 9 (3) ◽  
pp. 481
Author(s):  
Kai Wen ◽  
Jing Gong ◽  
Yan Wu

With the boost of natural gas consumption, an automatic gas pipeline scheduling method is required to replace the dispatchers in decision making. Since the state space model is the fundamental work of modern control theory, it is possible that the classical controller synthesis method can be used for the complicated gas pipeline controller design. In this paper, a cascade control algorithm is proposed based on the state space model that is used for the transient flow simulation of the natural gas pipelines. A linear quadratic regulator is designed following the classical optimal control theory. Finally, the transient process with different control methods shows the effectiveness of the cascade control using information of the entire pipeline. According to the hardware configuration of natural gas pipelines, automatic scheduling process is ready to deploy as one step to the intelligent natural gas pipelines.

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.


Author(s):  
Aleksandar Tomic ◽  
Shahani Kariyawasam

A lethality zone due to an ignited natural gas release is often used to characterize the consequences of a pipeline rupture. A 1% lethality zone defines a zone where the lethality to a human is greater than or equal to 1%. The boundary of the zone is defined by the distance (from the point of rupture) at which the probability of lethality is 1%. Currently in the gas pipeline industry, the most detailed and validated method for calculating this zone is embodied in the PIPESAFE software. PIPESAFE is a software tool developed by a joint industry group for undertaking quantitative risk assessments of natural gas pipelines. PIPESAFE consequence models have been verified in laboratory experiments, full scale tests, and actual failures, and have been extensively used over the past 10–15 years for quantitative risk calculations. The primary advantage of using PIPESAFE is it allows for accurate estimation of the likelihood of lethality inside the impacted zone (i.e. receptors such as structures closer to the failure are subject to appropriately higher lethality percentages). Potential Impact Radius (PIR) is defined as the zone in which the extent of property damage and serious or fatal injury would be expected to be significant. It corresponds to the 1% lethality zone for a natural gas pipeline of a certain diameter and pressure when thermal radiation and exposure are taken into account. PIR is one of the two methods used to identify HCAs in US (49 CFR 192.903). Since PIR is a widely used parameter and given that it can be interpreted to delineate a 1% lethality zone, it is important to understand how PIR compares to the more accurate estimation of the lethality zones for different diameters and operating pressures. In previous internal studies, it was found that PIR, when compared to the more detailed measures of the 1% lethality zone, could be highly conservative. This conservatism could be beneficial from a safety perspective, however it is adding additional costs and reducing the efficiency of the integrity management process. Therefore, the goal of this study is to determine when PIR is overly conservative and to determine a way to address this conservatism. In order to assess its accuracy, PIR was compared to a more accurate measure of the 1% lethality zone, calculated by PIPESAFE, for a range of different operating pressures and line diameters. Upon comparison of the distances calculated through the application of PIR and PIPESAFE, it was observed that for large diameters pipelines the distances calculated by PIR are slightly conservative, and that this conservativeness increases exponentially for smaller diameter lines. The explanation for the conservatism of the PIR for small diameter pipelines is the higher wall friction forces per volume transported in smaller diameter lines. When these higher friction forces are not accounted for it leads to overestimation of the effective outflow rate (a product of the initial flow rate and the decay factor) which subsequently leads to the overestimation of the impact radius. Since the effective outflow rate is a function of both line pressure and diameter, a simple relationship is proposed to make the decay factor a function of these two variables to correct the excess conservatism for small diameter pipelines.


1994 ◽  
Vol 20 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Siddhartha Chib ◽  
Ram C. Tiwari

2010 ◽  
Vol 40-41 ◽  
pp. 27-33 ◽  
Author(s):  
Yi Hui Lin ◽  
Hai Bo Zhang

The method of state space model fitting is carried out by using the linear relation of the variable of the differential equations and separating the steady process and instant process to eliminate the steady errors course by instant errors. The improved fitting method is without solving the linear differential equations or using any iterative methods. The coefficient of the state space model can be solve simply using matrix operation under the premise of high accuracy, so it has a higher computational efficiency than former least square method. And this method can also be used with other fitting method. Finally, to illustrate the validity and accuracy of the improved method, a small perturbation state space model of a certain turboshaft engine model has been established by this method, and the simulation result between state space model and nonlinear model was also compared. Also, the state space model could be applied to fault diagnosis and control system design for aeroengines.


2009 ◽  
Vol 10 (2) ◽  
pp. 117-138 ◽  
Author(s):  
Wai-Yuan Tan ◽  
Weiming Ke ◽  
G. Webb

We develop a state space model documenting Gompertz behaviour of tumour growth. The state space model consists of two sub-models: a stochastic system model that is an extension of the deterministic model proposed by Gyllenberg and Webb (1991), and an observation model that is a statistical model based on data for the total number of tumour cells over time. In the stochastic system model we derive through stochastic equations the probability distributions of the numbers of different types of tumour cells. Combining with the statistic model, we use these distribution results to develop a generalized Bayesian method and a Gibbs sampling procedure to estimate the unknown parameters and to predict the state variables (number of tumour cells). We apply these models and methods to real data and to computer simulated data to illustrate the usefulness of the models, the methods, and the procedures.


Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1445
Author(s):  
Rodi Lykou ◽  
George Tsaklidis

Observational errors of Particle Filtering are studied over the case of a state-space model with a linear observation equation. In this study, the observational errors are estimated prior to the upcoming observations. This action is added to the basic algorithm of the filter as a new step for the acquisition of the state estimations. This intervention is useful in the presence of missing data problems mainly, as well as sample tracking for impoverishment issues. It applies theory of Homogeneous and Non-Homogeneous closed Markov Systems to the study of particle distribution over the state domain and, thus, lays the foundations for the employment of stochastic control against impoverishment. A simulating example is quoted to demonstrate the effectiveness of the proposed method in comparison with existing ones, showing that the proposed method is able to combine satisfactory precision of results with a low computational cost and provide an example to achieve impoverishment prediction and tracking.


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