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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....
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... In [5]- [7], state estimation methods based on optimization for the pressure and flow changes at the inlet and outlet of the pipeline caused by the large consumption and increase of natural gas were presented. Apart from the state estimation based on the optimization, some research focus on the linearization of natural gas flow equations using finite difference method and finite element method [8]- [10]. Thereinto, a cascade control algorithm monitoring the pressure of natural gas pipeline on the basis of state space model was established in [8]. ...
... Apart from the state estimation based on the optimization, some research focus on the linearization of natural gas flow equations using finite difference method and finite element method [8]- [10]. Thereinto, a cascade control algorithm monitoring the pressure of natural gas pipeline on the basis of state space model was established in [8]. In [9], a general modeling method of gas transmission pipeline network based on transfer function was developed. ...
The large-scale coverage of natural gas makes the composition structure and operation mode of natural gas network more complex, higher requirements are put forward for the effectiveness and accuracy of state estimation. The existing methods for state estimation of natural gas network with noise are all modeled after processing the data with noise, leading to the real data being distorted to a certain extent. With that in mind, a data-driven method is presented in this paper. While solving the problem of state estimation for natural gas network with measurement noise in the input data, filtering and denoising are unnecessary during state estimation, retaining the complete information of real data. It avoids destruction of real data induced by separating noise from measured data owing to different methods and intensities of noise processing. According to the gas flow characteristic equation of natural gas system, the original problem is converted into a weighted low-rank approximation problem, the search space is shrunk to an orthogonal complement space. The selection of initial values is not merely unrestricted but there will be no accumulation and transmission of iteration error. The effectiveness of the proposed method is demonstrated through simulating 10-node natural gas network. Compared with the Newton’s method, the data-driven method has superior performance, the RMSE achieves 0.2268 and the MAPE achieves 1.63%.
... Access to energy depends a lot on the construction of energy infrastructures. For instance, the utilization of water conservancy resources requires the construction of reservoirs and hydropower stations [1]; the exploitation of natural gas or geothermal resources involves the construction of risers and pipelines [2,3]; the utilization of wind resources depends on the installation of wind turbines [4][5][6][7], etc. Hence, the discipline of geotechnics has been closely related to energy science. ...
... 2. Incapable of modeling the multi-phase nature of the soil. 3. Incapable of modeling the soil plug inside the pipe pile. ...
... Incapable of modeling the multi-phase nature of the soil. 3. ...
The dynamic viscoelastic theory of soil–pile interaction dominates the initial impedance calculation during the pile dynamic design and analysis. Further, it provides a firm theoretical ground for the wave propagation simulation, which could be the basis of seismic analysis and some geotechnical testing approaches. This review traces the development history and key findings of viscoelastic soil–pile interaction theory and expounds on the advantages and limitations of various theoretical advances in terms of dynamic design and wave propagation modeling. The review consists of three sub-divisions, which are the longitudinal, horizontal, and torsional viscoelastic soil-pile theories. The development and implement of multi-phase soil constitutive equations, multi-dimensional soil–pile interaction modeling methods, pile–soil–pile mutual interactions in pile groups, and the fluid–structure interaction problems in offshore piles are especially remarked and concluded. Finally, the shortcomings and deficiencies of the present development are pointed out with a view to addressing them in the future.
... In the gas industry, the exact measurement of the quantity of gas passing within a pipe cross-section per unit time has supreme significance (Wen et al., 2019a). Furthermore, measurement and control of natural gas distribution essentially require a Supervisory Control and E-mail address: onur.dogan@bakircay.edu.tr. ...
We have still consumed natural gas as a restricted source of energy in our daily life. Moreover, the consumption of natural gas energy will continue to increase by the year. Although many studies have focused on electrical energy consumption, natural gas is another significant energy source that can be examined. Since companies consume much more gas, their gas consumption data are examined in this study. The study contributes to the literature by applying intuitionistic fuzzy c-means (IFCM) methodology to the natural gas industry. The main motivation and advantage of the methodology is twofold. Because of its fuzziness, one data point can be assigned into more than one cluster, similar to real-world cases. Because of its intuitionistic side, it considers membership, non-membership and hesitant degrees. These two strengths of IFCM improves the clustering accuracy. IFCM clustering was used to arrange the companies with respect to the consumption amount to increase the understandability because 1049 companies' consumption data were collected. A calendar view was developed to visualize the consumption amounts in the clusters. The changes in consumption amounts were presented in different weather temperatures. Whereas some clustered companies were directly affected by temperature changes, others were not affected. The companies in the clusters were analyzed with respect to two main criteria: regularity and complexity. The findings showed while high levels in routine are related to manufacturing companies, high complexity level is an indicator of being active in the service industry.
Several specific low-bitrate coding strategies are examined through their effect on linear–quadratic (LQ) control performance. By limiting the subject to these methods, we are able to identify principles underlying coding for control, a subject of significant recent interest but few tangible, i.e., implementable, results. In particular, we consider coding the quantized output signal deploying period-two codes of differing delay-versus-accuracy tradeoff. The quantification of coding performance is via the LQ control cost over a long but finite horizon up to a specified average
escape time
. Infinite-horizon stabilization is replaced by a mean-time-before-failure property, which is a new viewpoint but allows contact with uncoded controllers. The feedback control system comprises the coder–decoder in the path between the output and the state estimator, which is followed by linear state-variable feedback, as is optimal in the memoryless case. The static quantizer is treated as the functional composition of an infinitely long linear staircase function and saturation. This permits the analysis to subdivide into estimator computations, seemingly independent of the control performance criterion, and an escape time evaluation, which ties the control back into the choice of quantizer saturation bound. An example is studied, which illustrates the role of the control objective in determining the efficacy of coding using these schemes. The results mesh well with those observed in signal coding. However, the introduction of a realization-based escape time is a novelty departing significantly from mean square computations and from adaptive techniques.
Natural gas has been recognized as a promising energy supply for modern society due to its relatively less air pollution in consumption, while pipeline transportation is preferred especially for long-distance transmissions. A simplified pipeline control scenario is proposed in this paper to deeply accelerate the management and decision process in pipeline dispatch, in which a direct relevance between compressor operations and the inlet flux at certain stations is established as the main dispatch logic. A deep neural network is designed with specific input and output features for this scenario and the hyper-parameters are carefully tuned for a better adaptability of this problem. The realistic operation data of two pipelines have been obtained and prepared for learning and testing. The proposed algorithm with the optimized network structure is proved to be effective and reliable in predicting the pipeline operation status, under both the normal operation conditions and abnormal situations. The successful definition of ”ghost compressors” make this algorithm to be the first self-adaptive deep learning algorithm to assist natural gas pipeline intelligent control.
In this paper, we present two new Lyapunov-based observers for the decentralized multiphase flow measurement that are based on the interconnections between the two subsystems to precisely estimate the states of the multiphase flow at the gas refinery. Because the system is composed of two interconnected subsystems, the states of the condensate and gas subsystems were separately estimated using the differential mean value theorem (DMVT), the sliding mode observer (SMOU), and the HYSYS® simulator (Hyprotech, Ltd., Calgary, Alberta, Canada) by considering the relationship between two subsystems, designing an observer, and converting the conditions to linear matrix inequality (LMI). Using the HYSYS simulator with the real process data, we found that both the observers are capable of estimating the states with some differences in performance, and the drift flux model (DFM) is sufficient for states estimation of the multiphase flow entering the gas refinery.
