On steady state modelling for optimization of natural gas pipeline networks

Marfatia, Zaid; Li, Xiang

doi:10.1016/j.ces.2022.117636

Cited by 8 publications

(1 citation statement)

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“…In the reference (Xiong et al, 2022), the opposition-based learning strategy and the adaptive T-distribution mutation operator were introduced to overcome the DELM effect's effect on the random input weight in each ELM-AE and improve the quantitative prediction performance. The reference (Marfatia and Li. 2022) simplified the commonly used model in the FCMP.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport

Feng¹,

Yan²,

Yang³

et al. 2022

Front. Energy Res.

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A new deep-learning-based surrogate model is developed and applied for predicting dynamic temperature, pressure, gas rate, oil rate, and water rate with different boundary conditions in pipeline flow. The surrogate model is based on the multilayer perceptron (MLP), batch normalization and Parametric Rectified Linear Unit techniques. In training, the loss function for data mismatch is considered to optimize the model parameters with means absolute error (MAE). In addition, we also use the dynamic weights, calculated by the input data value, to increase the contribution of smaller inputs and avoid errors caused by large values eating small values in total loss. Finally, the surrogate model is applied to simulate a complex pipeline flow in the eastern part of the South China Sea. We use flow and pressure boundary as the input data in the numerical experiment. A total of 215690 high-fidelity training simulations are performed in the offline stage with commercial software LeadFlow, in which 172552 simulation runs are used for training the surrogate model, which takes about 240 min on an RTX2060 graphics processing unit. Then the trained model is used to provide pipeline flow forecasts under various boundary conduction. As a result, it is consistent with those obtained from the high-fidelity simulations (e.g., the media of relative error for temperature is 0.56%, pressure is 0.79%, the gas rate is 1.02%, and oil rate is 1.85%, and water is 0.80%, respectively). The online computations from our surrogate model, about 0.008 s per run, achieve speedups of over 1,250 relative to the high-fidelity simulations, about 10 s per run. Overall, this model provides reliable and fast predictions of the dynamic flow along the pipeline.

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Section: Introductionmentioning

confidence: 99%