Application of Particle Swarm Optimization (PSO) algorithm for Black Powder (BP) source identification in gas pipeline network based on 1-D model

Shi, Jing; Al‐Durra, Ahmed; Matraji, Imad; Al-Wahedi, Khaled; Abou‐Khousa, Mohamed A.

doi:10.2516/ogst/2019016

Cited by 8 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also important to mention that the solution of the proposed LT-based BP sources concentrations estimation approach as well as the other optimization base approaches is unique, and it is valid if and only if the number of measurements devices is equal to the unknown sources [30].…”

Section: ) Bp Estimation Methods Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Black Powder Concentrations in Natural Gas Pipeline Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

Natural gas transmission pipeline networks are facing serious issues related to the solid particles deposition that is called black powder (BP). The creation of BP inside natural gas pipeline is not completely understood, but it mainly results from the pipelines inner walls corrosion, which is a complex chemical reaction between the natural gas and pipelines. Moreover, the exact BP generation time, amount, and location are still vague. This work aims to reduce the effect of BP by estimating BP concentrations in natural gas networks. This will significantly help natural gas pipeline networks operators to plan more effective maintenance schedules. This work takes into account the industrial need for simple, fast, and accurate techniques for estimating BP concentration in natural gas pipeline networks. The proposed BP estimation technique is based on a lookup table (LT) which is constructed from a one-dimensional model of BP deposition and transport. This technique eliminates the need for extensive computational efforts and long estimation times that are necessary for other optimization-based BP estimation techniques. The proposed LTbased BP estimation technique is validated and compared to other BP estimation techniques using simulation studies. The results of the proposed BP estimation technique show that it is promising and has potentials for application in existing and future natural gas networks. INDEX TERMS Black powder (BP), lookup table (LT), natural gas network, optimization techniques.

show abstract

Section: ) Bp Estimation Methods Comparisonmentioning

confidence: 99%

“…Geometric parameters and flow conditions for 15-pipe gas network[30]. Simulated real values of BP concentration at each source.…”

mentioning

confidence: 99%

Assessment of Black Powder Concentrations in Natural Gas Pipeline Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous research works also confirmed the efficiency of using the PSO algorithm and LSTM neural networks in solving complex engineering problems. 34 − 39 The PSO algorithm is based on a simple design and provides strong global search ability and fast convergence and hence is often used to solve various complex optimization problems. 40 , 41 In this paper, we use the PSO algorithm to optimize the training parameters in the LSTM model, accelerate the convergence speed of the model, reduce the training time, and improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

Lou

Cheng

et al. 2022

ACS Omega

View full text Add to dashboard Cite

Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R 2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

show abstract

“…Pipeline networks are the most common means of natural gas transportation. They are thus being constructed on a large scale in recent years [3]. The scale of natural gas pipeline networks is becoming increasingly larger and the topological structure is becoming increasingly more complex [4].…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process

Xiang

Wang

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

show abstract

Application of Particle Swarm Optimization (PSO) algorithm for Black Powder (BP) source identification in gas pipeline network based on 1-D model

Cited by 8 publications

References 24 publications

Assessment of Black Powder Concentrations in Natural Gas Pipeline Networks

Assessment of Black Powder Concentrations in Natural Gas Pipeline Networks

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process

Contact Info

Product

Resources

About