Simulation of natural gas quality distribution for pipeline systems

Chaczykowski, Maciej; Zarodkiewicz, Paweł

doi:10.1016/j.energy.2017.06.020

Cited by 57 publications

(12 citation statements)

References 41 publications

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“…One of the options is disconnecting individual compressor stations. As shown in [18][19][20], depending on the quantity and serial numbers of the working stations, it is possible to achieve the required performance of the gas transmission system. At the same time, given the low efficiency of gas-pumping units with gas turbine drives, disconnecting individual CSs can be the most effective method of controlling performance from the energetic point of view.…”

Section: Ability To Regulate Pressure By Disconnecting Individual Commentioning

confidence: 99%

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Mathematical Modeling of Unsteady Gas Transmission System Operating Conditions under Insufficient Loading

et al. 2019

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Under insufficient loading of a main gas transmission system, high-amplitude fluctuations of pressure may occur in it. A mathematical model is proposed to estimate the amplitude of pressure fluctuations in a gas pipeline along its length. It has been revealed that the shutdown of compressor stations along the gas pipeline route has a significant impact on the parameters of the unsteady transient operating conditions. The possibility of minimizing oscillation processes by disconnecting compressor stations is substantiated for the “Soyuz” main gas pipeline.

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Section: Ability To Regulate Pressure By Disconnecting Individual Commentioning

confidence: 99%

“…To estimate the duration of the unsteady process, it is necessary to construct the dependence of fluctuations over the period of the mass flow of gas as the most inertial characteristic in the initial or final section of the gas pipeline [16,19,20].…”

Section: Ability To Regulate Pressure By Disconnecting Individual Commentioning

confidence: 99%

Mathematical Modeling of Unsteady Gas Transmission System Operating Conditions under Insufficient Loading

et al. 2019

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“…new LNG terminals, unconventional gas sources and decentralized green fuel injections (hydrogen, substitute natural gas). Furthermore, the growing LNG markets and unconventional gas markets lead to increasing gas quality variations in the grids (Chaczykowski et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Simulation Data Acquisition and Control for LNG Custody Transfer Based on ISO 8943

Arman

Mulyono

Falahuddin

et al. 2021

IJATR

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Liquefied Natural Gas (LNG) is a complex mixture of low-molecular-weight hydrocarbons with nitrogen as a principal inert impurity. Nowadays, the instrumentation control tools are needed in LNG Commercialization to ensure the LNG is safely transported, while minimizing the energy losses across the network. This research was a preliminary design that used LNG custody transfer control tools referred the ISO 8943:2007 standard. The temperature was measured by thermocouple (Rosemount transmitter 3144 series), while the pressure was measured using pressure transmitter (Rosemount transmitter 3051 series). The simulated signal temperature was done by temperature control box, while the simulated pressure was represented by WIKA pressure calibrator. Data from transmitter was sent to the LabVolt 9063 series that has data acquisition for Software Development Kit (SDK) enabled. Previously, the LabVolt 9063 did not recognized the signal from the transmitter. The SDK software bridged the LabVolt hardware with the LabView software. In the simulated system, temperature alarm will be triggered when the temperature value exceeds 20oC. For simulated pressure, the alarm will respond if pressure value exceeds 30 psi. From the results of this research, the communication between hardware and software worked properly. The data acquisition system was stable during the data collection stage, processing and displaying. In addition, the alarm indicator for both temperature and pressure was met within the threshold value.

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“…3. From Table 1, it can also be noted that most studies considered small pipelines, i.e., within a length equal to or lesser than 10 km besides, gas pipelines usually have higher lengths [35,36]. 4.…”

Section: Introductionmentioning

confidence: 99%

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

et al. 2020

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Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be concluded that the proposed methodology possesses a high potential to avoid false alarms and detect small leaks under transient conditions. In the future, the current methodology may be extended to locate and estimate the leakage point and size.

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Simulation of natural gas quality distribution for pipeline systems

Cited by 57 publications

References 41 publications

Mathematical Modeling of Unsteady Gas Transmission System Operating Conditions under Insufficient Loading

Mathematical Modeling of Unsteady Gas Transmission System Operating Conditions under Insufficient Loading

Simulation Data Acquisition and Control for LNG Custody Transfer Based on ISO 8943

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

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