GRA and AHP Analysis of Pneumatic Control Valve Failure in an LNG Plant

Yang, Yuan; Xiao, Chuantao; Yang, Ye

doi:10.1007/s13369-020-05028-x

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The analytic hierarchy process (AHP) decomposes abstract complex problems into various components through decision-making, then sets the factors and problem logic into a target, criterion, and scheme layer with a hierarchical grouping structure. Finally, matrix construction compares the mutual influence relationships between the factors, thus quantifying the problem components and calculating their respective weights in the system [28].…”

Section: Ahp Modelingmentioning

confidence: 99%

Development scheme screening considering reservoir damage based on the AHP model

Yang

Jiang

et al. 2023

SN Appl. Sci.

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The development process of oil and gas fields is affected by engineering and geology. Reservoir damage is significant as a bridge connecting these two disciplines. The life cycle of oil and gas fields can be prolonged by making development plans that consider reservoir damage. Therefore, this study performed experiments and discusses a Cretaceous reservoir development in the Chun17 block of the Chunguang Oilfield. We have carried out five sensitivity experiments on typical cores from the block, and analyzed lithology, physical properties, and crude oil. A mathematical model based on the Analytic Hierarchy Process (AHP) was then established based on the core data.Overall, the reservoir in the study area has ultra-high porosity and high permeability. The reservoir has moderately strong water sensitivity (damage rate is 38 ~ 90%) and moderately strong alkali sensitivity (damage rate is 22 ~ 75%). The crude oil belongs to extra-heavy oil reservoirs (containing 2.43% wax, 0.96% sulfur, 20.25 m% gum, 7.35 m% asphalt, 38.26 m% saturated hydrocarbon, and 27.51 m%). The crude oil is sensitive to temperature changes. Based on the AHP model analysis, the development scheme chooses the third (weight 0.1757) of the conventional analytic results of AHP; the Thermal oil production C1 scheme has the most stable future trend and appears to be the best option. In the next step, using fire flooding technology to develop the reservoir will benefit the most economically. The workflow consisting of "conclusion of reservoir experiment → AHP mathematical modeling → verification of relative permeability of high-temperature oil and water → verification of trial production" is simple and effective. The findings of this study can help to better understand the idea and process of making development plans for sensitive oil and gas fields through reservoir evaluation. Combining experimental data with mathematical modeling can find a balanced combination point in qualitative and quantitative analysis. Whether this combination point is correct can be verified by field engineering practice.

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Section: Ahp Modelingmentioning

confidence: 99%

Development scheme screening considering reservoir damage based on the AHP model

Yang

Jiang

et al. 2023

SN Appl. Sci.

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“…From the perspective of evaluation methods, there are many evaluation methods currently studied, such as AHP [18], Dempster-Shafer (D-S) evidence theory method [19], multiple connection number method [20], cloud barycenter evaluation method, neural network [21,22] etc. Dawson J.M [23] uses AHP to search the complexity of the electromagnetic environment, then establishes a hierarchical structure, and gives specific evaluation criteria.…”

Section: Introductionmentioning

confidence: 99%

Evaluation method for electromagnetic interference complexity of high voltage switch based on feature extraction and GCC‐GRU network

Lu,

Duan,

Cheng

et al. 2024

High Voltage

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When some new‐type sensors are subjected electromagnetic interference (EMI), the traditional standard can no longer serve as the guidance for their EMI protection. Therefore, it is necessary to evaluate the complexity of the new sensing equipment's suffering from electromagnetic interference. A hybrid method of feature extraction and complexity evaluation of Electromagnetic Interference signals based on generalised correntropy criterion (GCC) is proposed. First, a variational mode decomposition algorithm for adaptive extraction of decomposed signals is constructed using GCC, and Generalised S‐transform is used for processing and reorganisation. Then, according to the time‐frequency space model, a new quantitative evaluation criterion interference factor is proposed, which overcomes the shortcomings of the traditional qualitative classification and the inability to solve the cross classification of indicators. Then, the Gated Recurrent Unit is introduced, and the GCC‐GRU evaluation model with GCC as the loss function is derived. The authors conduct EMI tests and measured signal evaluation with Medium Voltage and High Voltage switches, and the results show that the method proposed is correct and effective.

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“…Nevertheless, flow control valves are often mandated to operate in a variety of environmental conditions, which can include extreme factors like high temperatures, high pressures, corrosive media, and hazardous explosive zones [1]. In these challenging environments, control valves face issues such as leakage and viscosity effects, which can lead to unforeseeable production failures and safety hazards [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

Liu,

Zhao,

Zhang

2024

Actuators

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In the process industrial systems, flow control valves are deemed vital components that ensure the system’s safe operation. Hence, detecting faults in control valves is of significant importance. However, the stable operating conditions of flow control valves are prone to change, resulting in a decreased effectiveness of the conventional fault detection method. In this paper, an online fault detection approach considering the variable operating conditions of flow control valves is proposed. This approach is based on residual analysis, combining LightGBM online model with Seasonal and Trend decomposition using Loess (STL). LightGBM is a tree-based machine learning algorithm. In the proposed method, an online LightGBM is employed to establish and continuously update a flow prediction model for control valves, ensuring model accuracy during changes in operational conditions. Subsequently, STL decomposition is applied to the model’s residuals to capture the trend of residual changes, which is then transformed into a Health Index (HI) for evaluating the health level of the flow control valves. Finally, fault occurrences are detected based on the magnitude of the HI. We validate this approach using both simulated and real factory data. The experimental results demonstrate that the proposed method can promptly reflect the occurrence of faults through the HI.

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GRA and AHP Analysis of Pneumatic Control Valve Failure in an LNG Plant

Cited by 5 publications

References 20 publications

Development scheme screening considering reservoir damage based on the AHP model

Development scheme screening considering reservoir damage based on the AHP model

Evaluation method for electromagnetic interference complexity of high voltage switch based on feature extraction and GCC‐GRU network

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

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